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<front> <front>
<title abbrev="IoT Edge Computing">IoT Edge Challenges and Functions</title> <title abbrev="IoT Edge Computing">Internet of Things (IoT) Edge Challenges
<seriesInfo name="Internet-Draft" value="draft-irtf-t2trg-iot-edge-10"/> and Functions</title>
<seriesInfo name="RFC" value="9556"/>
<author initials="J." surname="Hong" fullname="Jungha Hong"> <author initials="J." surname="Hong" fullname="Jungha Hong">
<organization>ETRI</organization> <organization>ETRI</organization>
<address> <address>
<postal> <postal>
<street>218 Gajeong-ro, Yuseung-Gu</street> <street>218 Gajeong-ro, Yuseung-Gu</street>
<city>Daejeon</city> <city>Daejeon</city>
<code>34129</code> <code>34129</code>
<country>Republic of Korea</country> <country>Republic of Korea</country>
</postal> </postal>
<email>jhong@etri.re.kr</email> <email>jhong@etri.re.kr</email>
</address> </address>
</author> </author>
<author initials="Y.-G." surname="Hong" fullname="Yong-Geun Hong"> <author initials="Y-G." surname="Hong" fullname="Yong-Geun Hong">
<organization>Daejeon University</organization> <organization>Daejeon University</organization>
<address> <address>
<postal> <postal>
<street>62 Daehak-ro, Dong-gu</street> <street>62 Daehak-ro, Dong-gu</street>
<city>Daejeon</city> <city>Daejeon</city>
<code>300716</code> <code>300716</code>
<country>Republic of Korea</country> <country>Republic of Korea</country>
</postal> </postal>
<email>yonggeun.hong@gmail.com</email> <email>yonggeun.hong@gmail.com</email>
</address> </address>
skipping to change at line 69 skipping to change at line 66
<postal> <postal>
<street>Riesstr. 25 C // 3.OG</street> <street>Riesstr. 25 C // 3.OG</street>
<city>Munich</city> <city>Munich</city>
<code>80992</code> <code>80992</code>
<country>Germany</country> <country>Germany</country>
</postal> </postal>
<email>ietf@kovatsch.net</email> <email>ietf@kovatsch.net</email>
</address> </address>
</author> </author>
<author initials="E." surname="Schooler" fullname="Eve Schooler"> <author initials="E." surname="Schooler" fullname="Eve Schooler">
<organization>Intel</organization> <organization>University of Oxford</organization>
<address> <address>
<postal> <postal>
<street>2200 Mission College Blvd.</street> <street>Parks Road</street>
<city>Santa Clara, CA</city> <city>Oxford</city>
<code>95054-1537</code> <code>OX1 3PJ</code>
<country>USA</country> <country>United Kingdom</country>
</postal> </postal>
<email>eve.schooler@gmail.com</email> <email>eve.schooler@gmail.com</email>
</address> </address>
</author> </author>
<author initials="D." surname="Kutscher" fullname="Dirk Kutscher"> <author initials="D." surname="Kutscher" fullname="Dirk Kutscher">
<organization>Hong Kong University of Science and Technology (Guangzhou)</ organization> <organization abbrev="HKUST(GZ)">Hong Kong University of Science and Techn ology (Guangzhou)</organization>
<address> <address>
<postal> <postal>
<street>No.1 Du Xue Rd</street> <street>No.1 Du Xue Rd</street>
<city>Guangzhou</city> <city>Guangzhou</city>
<country>China</country> <country>China</country>
</postal> </postal>
<email>ietf@dkutscher.net</email> <email>ietf@dkutscher.net</email>
</address> </address>
</author> </author>
<date year="2023" month="September" day="15"/> <date year="2024" month="April"/>
<area>T2TRG</area> <workgroup>Thing-to-Thing</workgroup>
<keyword>in-network computing</keyword>
<keyword>in-network caching</keyword>
<keyword>in-network storage</keyword>
<abstract> <abstract>
<?line 879?>
<t>Many Internet of Things (IoT) applications have requirements that cannot be s atisfied by traditional cloud-based systems (i.e., cloud computing). These inclu de time sensitivity, data volume, connectivity cost, operation in the face of in termittent services, privacy, and security. As a result, IoT is driving the Inte rnet toward edge computing. This document outlines the requirements of the emerg ing IoT Edge and its challenges. It presents a general model and major component s of the IoT Edge to provide a common basis for future discussions in the T2TRG and other IRTF and IETF groups. This document is a product of the IRTF Thing-to- Thing Research Group (T2TRG).</t> <t>Many Internet of Things (IoT) applications have requirements that cannot be s atisfied by centralized cloud-based systems (i.e., cloud computing). These inclu de time sensitivity, data volume, connectivity cost, operation in the face of in termittent services, privacy, and security. As a result, IoT is driving the Inte rnet toward edge computing. This document outlines the requirements of the emerg ing IoT edge and its challenges. It presents a general model and major component s of the IoT edge to provide a common basis for future discussions in the Thing- to-Thing Research Group (T2TRG) and other IRTF and IETF groups. This document is a product of the IRTF T2TRG.</t>
</abstract> </abstract>
</front> </front>
<middle> <middle>
<?line 883?> <section anchor="introduction">
<section anchor="introduction">
<name>Introduction</name> <name>Introduction</name>
<t>Currently, many IoT services leverage cloud computing platforms, becaus <t>At the time of writing, many IoT services leverage cloud computing plat
e they provide virtually unlimited storage and processing power. The reliance of forms because they provide virtually unlimited storage and processing power. The
IoT on back-end cloud computing provides additional advantages such as scalabil reliance of IoT on back-end cloud computing provides additional advantages, suc
ity and efficiency. Today's IoT systems are fairly static with respect to integ h as scalability and efficiency. At the time of writing, IoT systems are fairly
rating and supporting computation. It is not that there is no computation, but static with respect to integrating and supporting computation. It is not that
that systems are often limited to static configurations (edge gateways and cloud there is no computation, but that systems are often limited to static configurat
services).</t> ions (edge gateways and cloud services).</t>
<t>However, IoT devices generate large amounts of data at the edges of the <t>However, IoT devices generate large amounts of data at the edges of the
network. To meet IoT use case requirements, data is increasingly being stored, network. To meet IoT use case requirements, data is increasingly being stored,
processed, analyzed, and acted upon close to the data sources. These requirement processed, analyzed, and acted upon close to the data sources. These requirement
s include time sensitivity, data volume, connectivity cost, and resiliency in th s include time sensitivity, data volume, connectivity cost, and resiliency in th
e presence of intermittent connectivity, privacy, and security, which cannot be e presence of intermittent connectivity, privacy, and security, which cannot be
addressed by centralized cloud computing. A more flexible approach is necessary addressed by centralized cloud computing. A more flexible approach is necessary
to address these needs effectively. This involves distributing computing (and st to address these needs effectively. This involves distributing computing (and st
orage) and seamlessly integrating it into the edge-cloud continuum. We refer to orage) and seamlessly integrating it into the edge-cloud continuum. We refer to
this integration of edge computing and IoT as "IoT edge computing". This draft d this integration of edge computing and IoT as "IoT edge computing". This documen
escribes the related background, use cases, challenges, system models, and funct t describes the related background, use cases, challenges, system models, and fu
ional components.</t> nctional components.</t>
<t>Owing to the dynamic nature of the IoT edge computing landscape, this d <t>Owing to the dynamic nature of the IoT edge computing landscape, this d
ocument does not list existing projects in this field. <xref target="sec-overvie ocument does not list existing projects in this field. <xref target="sec-overvie
w"/> presents a high-level overview of the field, based on a limited review of s w"/> presents a high-level overview of the field based on a limited review of st
tandards, research, open-source and proprietary products in <xref target="I-D.de andards, research, and open-source and proprietary products in <xref target="I-D
foy-t2trg-iot-edge-computing-background"/>.</t> .defoy-t2trg-iot-edge-computing-background"/>.</t>
<t>This document represents the consensus of the Thing-to-Thing Research G <t>This document represents the consensus of the Thing-to-Thing Research G
roup (T2TRG). It has been reviewed extensively by the Research Group (RG) member roup (T2TRG). It has been reviewed extensively by the research group members who
s who are actively involved in the research and development of the technology co are actively involved in the research and development of the technology covered
vered by this document. It is not an IETF product and is not a standard.</t> by this document. It is not an IETF product and is not a standard.</t>
</section> </section>
<section anchor="background"> <section anchor="background">
<name>Background</name> <name>Background</name>
<section anchor="internet-of-things-iot"> <section anchor="internet-of-things-iot">
<name>Internet of Things (IoT)</name> <name>Internet of Things (IoT)</name>
<t>Since the term "Internet of Things" (IoT) was coined by Kevin Ashton <t>Since the term "Internet of Things" was coined by Kevin Ashton in 199
in 1999 working on Radio-Frequency Identification (RFID) technology <xref target 9 while working on Radio-Frequency Identification (RFID) technology <xref target
="Ashton"/>, the concept of IoT has evolved. It now reflects a vision of connect ="Ashton"/>, the concept of IoT has evolved. At the time of writing, it reflects
ing the physical world to the virtual world of computers using (often wireless) a vision of connecting the physical world to the virtual world of computers usi
networks over which things can send and receive information without human interv ng (often wireless) networks over which things can send and receive information
ention. Recently, the term has become more literal by connecting things to the without human intervention. Recently, the term has become more literal by conne
Internet and converging on Internet and Web technologies.</t> cting things to the Internet and converging on Internet and web technologies.</t
<t>A Thing is a physical item made available in the IoT, thereby enablin >
g digital interaction with the physical world for humans, services, and/or other <t>A "Thing" is a physical item made available in the IoT, thereby enabl
Things (<xref target="I-D.irtf-t2trg-rest-iot"/>). In this document we will use ing digital interaction with the physical world for humans, services, and/or oth
the term "IoT device" to designate the embedded system attached to the Thing.</ er Things <xref target="I-D.irtf-t2trg-rest-iot"/>. In this document, we will us
t> e the term "IoT device" to designate the embedded system attached to the Thing.<
<t>Resource-constrained Things such as sensors, home appliances and wear /t>
able devices often have limited storage and processing power, which can provide <t>Resource-constrained Things, such as sensors, home appliances, and we
challenges with respect to reliability, performance, energy consumption, securit arable devices, often have limited storage and processing power, which can creat
y, and privacy <xref target="Lin"/>. Some, less resource-constrained Things, can e challenges with respect to reliability, performance, energy consumption, secur
generate a voluminous amount of data. This range of factors led IoT designs tha ity, and privacy <xref target="Lin"/>. Some, less-resource-constrained Things, c
t integrate Things into larger distributed systems, for example edge or cloud co an generate a voluminous amount of data. This range of factors led to IoT design
mputing systems.</t> s that integrate Things into larger distributed systems, for example, edge or cl
oud computing systems.</t>
</section> </section>
<section anchor="cloud-computing"> <section anchor="cloud-computing">
<name>Cloud Computing</name> <name>Cloud Computing</name>
<t>Cloud computing has been defined in <xref target="NIST"/>: "cloud com <t>Cloud computing has been defined in <xref target="NIST"/>:</t>
puting is a model for enabling ubiquitous, convenient, on-demand network access <blockquote>Cloud computing is a model for enabling ubiquitous, convenien
to a shared pool of configurable computing resources (e.g., networks, servers, s t, on-demand network access to a shared pool of configurable computing resources
torage, applications, and services) that can be rapidly provisioned and released (e.g., networks, servers, storage, applications, and services) that can be rapi
with minimal management effort or service provider interaction". The low cost dly provisioned and released with minimal management effort or service provider
and massive availability of storage and processing power enabled the realization interaction.</blockquote>
of another computing model, in which virtualized resources can be leased in an <t>The low cost and massive availability of storage and processing power
on-demand fashion and be provided as general utilities. Platform-as-a-Service an enabled the realization of another computing model in which virtualized resource
d cloud computing platforms widely adopted this paradigm for delivering services s can be leased in an on-demand fashion and provided as general utilities. Platf
over the Internet, gaining both economical and technical benefits <xref target= orm-as-a-Service (PaaS) and cloud computing platforms widely adopted this paradi
"Botta"/>.</t> gm for delivering services over the Internet, gaining both economical and techni
<t>Today, an unprecedented volume and variety of data is generated by Th cal benefits <xref target="Botta"/>.</t>
ings, and applications deployed at the network edge consume this data. In this <t>At the time of writing, an unprecedented volume and variety of data i
context, cloud-based service models are not suitable for some classes of applica s generated by Things, and applications deployed at the network edge consume thi
tions which require very short response times, access to local personal data, or s data. In this context, cloud-based service models are not suitable for some c
generate vast amounts of data. These applications may instead leverage edge co lasses of applications that require very short response times, require access to
mputing.</t> local personal data, or generate vast amounts of data. These applications may
instead leverage edge computing.</t>
</section> </section>
<section anchor="edge-computing"> <section anchor="edge-computing">
<name>Edge Computing</name> <name>Edge Computing</name>
<t>Edge computing, also referred to as fog computing in some settings, i <t>Edge computing, also referred to as "fog computing" in some settings,
s a new paradigm in which substantial computing and storage resources are placed is a new paradigm in which substantial computing and storage resources are plac
at the edge of the Internet, close to mobile devices, sensors, actuators, or ma ed at the edge of the Internet, close to mobile devices, sensors, actuators, or
chines. Edge computing happens near data sources <xref target="Mahadev"/>, as w machines. Edge computing happens near data sources <xref target="Mahadev"/> as
ell as close to where decisions are made or where interactions with the physical well as close to where decisions are made or where interactions with the physica
world take place ("close" here can refer to a distance which is topological, ph l world take place ("close" here can refer to a distance that is topological, ph
ysical, latency-based, etc.). It processes both downstream data (originating fr ysical, latency-based, etc.). It processes both downstream data (originating fr
om cloud services) and upstream data (originating from end devices or network el om cloud services) and upstream data (originating from end devices or network el
ements). The term "fog computing" usually represents the notion of multi-tiered ements). The term "fog computing" usually represents the notion of multi-tiered
edge computing, that is, several layers of compute infrastructure between end d edge computing, that is, several layers of compute infrastructure between end d
evices and cloud services.</t> evices and cloud services.</t>
<t>An edge device is any computing or networking resource residing betwe <t>An edge device is any computing or networking resource residing betwe
en end-device data sources and cloud-based data centers. In edge computing, end en end-device data sources and cloud-based data centers. In edge computing, end
devices consume and produce data. At the network edge, devices not only request devices consume and produce data. At the network edge, devices not only request
services and information from the Cloud but also handle computing tasks includi services and information from the cloud but also handle computing tasks includi
ng processing, storage, caching, and load balancing on data sent to and from the ng processing, storing, caching, and load balancing on data sent to and from the
Cloud <xref target="Shi"/>. This does not preclude end devices from hosting co cloud <xref target="Shi"/>. This does not preclude end devices from hosting co
mputation themselves, when possible, independently or as part of a distributed e mputation themselves, when possible, independently or as part of a distributed e
dge computing platform.</t> dge computing platform.</t>
<t>Several standards developing organization (SDO) and industry forums h <t>Several Standards Developing Organizations (SDOs) and industry forums
ave provided definitions of edge and fog computing:</t> have provided definitions of edge and fog computing:</t>
<ul spacing="normal"> <ul spacing="normal">
<li>ISO defines edge computing as a "form of distributed computing in which significant processing and data storage takes place on nodes which are at the edge of the network" <xref target="ISO_TR"/>.</li> <li>ISO defines edge computing as a "form of distributed computing in which significant processing and data storage takes place on nodes which are at the edge of the network" <xref target="ISO_TR"/>.</li>
<li>ETSI defines multi-access edge computing as a "system which provid es an IT service environment and cloud-computing capabilities at the edge of an access network which contains one or more type of access technology, and in clos e proximity to its users" <xref target="ETSI_MEC_01"/>.</li> <li>ETSI defines multi-access edge computing as a "system which provid es an IT service environment and cloud-computing capabilities at the edge of an access network which contains one or more type of access technology, and in clos e proximity to its users" <xref target="ETSI_MEC_01"/>.</li>
<li>The Industry IoT Consortium (IIC, now incorporating what was forme rly OpenFog) defines fog computing as "a horizontal, system-level architecture t hat distributes computing, storage, control and networking functions closer to t he users along a cloud-to-thing continuum" <xref target="OpenFog"/>.</li> <li>The Industry IoT Consortium (IIC) (now incorporating what was form erly OpenFog) defines fog computing as "a horizontal, system-level architecture that distributes computing, storage, control and networking functions closer to the users along a cloud-to-thing continuum" <xref target="OpenFog"/>.</li>
</ul> </ul>
<t>Based on these definitions, we can summarize a general philosophy of edge computing as distributing the required functions close to users and data, w hile the difference to classic local systems is the usage of management and orch estration features adopted from cloud computing.</t> <t>Based on these definitions, we can summarize a general philosophy of edge computing as distributing the required functions close to users and data, w hile the difference to classic local systems is the usage of management and orch estration features adopted from cloud computing.</t>
<t>Actors from various industries approach edge computing using differen t terms and reference models although, in practice, these approaches are not inc ompatible and may integrate with each other:</t> <t>Actors from various industries approach edge computing using differen t terms and reference models, although, in practice, these approaches are not in compatible and may integrate with each other:</t>
<ul spacing="normal"> <ul spacing="normal">
<li>The telecommunication industry tends to use a model where edge com puting services are deployed over Network Function Virtualization (NFV) infrastr ucture, at aggregation points or in proximity to the user equipment (e.g., gNode Bs) <xref target="ETSI_MEC_03"/>.</li> <li>The telecommunication industry tends to use a model where edge com puting services are deployed over a Network Function Virtualization (NFV) infras tructure, at aggregation points, or in proximity to the user equipment (e.g., gN odeBs) <xref target="ETSI_MEC_03"/>.</li>
<li>Enterprise and campus solutions often interpret edge computing as an "edge cloud", that is, a smaller data center directly connected to the local network (often referred to as "on-premise").</li> <li>Enterprise and campus solutions often interpret edge computing as an "edge cloud", that is, a smaller data center directly connected to the local network (often referred to as "on-premise").</li>
<li>The automation industry defines the edge as the connection point b etween IT and OT (Operational Technology). Hence, edge computing sometimes refer s to applying IT solutions to OT problems, such as analytics, more flexible user interfaces, or simply having more computing power than an automation controller .</li> <li>The automation industry defines the edge as the connection point b etween IT and Operational Technology (OT). Hence, edge computing sometimes refer s to applying IT solutions to OT problems, such as analytics, more-flexible user interfaces, or simply having more computing power than an automation controller .</li>
</ul> </ul>
</section> </section>
<section anchor="sec-uc"> <section anchor="sec-uc">
<name>Examples of IoT Edge Computing Use Cases</name> <name>Examples of IoT Edge Computing Use Cases</name>
<t>IoT edge computing can be used in home, industry, grid, healthcare, c <t>IoT edge computing can be used in home, industry, grid, healthcare, c
ity, transportation, agriculture, and/or educational scenarios. Here, we discuss ity, transportation, agriculture, and/or educational scenarios. Here, we discuss
only a few examples of such use cases, to identify differentiating requirements only a few examples of such use cases to identify differentiating requirements,
, providing references to other use cases.</t> providing references to other use cases.</t>
<t><strong>Smart Factory</strong></t> <dl newline="true" spacing="normal">
<t>As part of the 4th industrial revolution, smart factories run real-ti <dt><strong>Smart Factory</strong></dt>
me processes based on IT technologies, such as artificial intelligence and big d <dd><t>As part of the Fourth Industrial Revolution, smart factories run
ata. Even a very small environmental change in a smart factory can lead to a sit real-time processes based on IT technologies, such as artificial intelligence an
uation in which production efficiency decreases or product quality problems occu d big data. Even a very small environmental change in a smart factory can lead t
r. Therefore, simple but time-sensitive processing can be performed at the edge, o a situation in which production efficiency decreases or product quality proble
for example, controlling the temperature and humidity in the factory, or operat ms occur. Therefore, simple but time-sensitive processing can be performed at th
ing machines based on the real-time collection of the operational status of each e edge, for example, controlling the temperature and humidity in the factory or
machine. However, data requiring highly precise analysis, such as machine lifec operating machines based on the real-time collection of the operational status o
ycle management or accident risk prediction, can be transferred to a central dat f each machine. However, data requiring highly precise analysis, such as machine
a center for processing.</t> life-cycle management or accident risk prediction, can be transferred to a cent
<t>The use of edge computing in a smart factory can reduce the cost of n ral data center for processing.</t>
etwork and storage resources by reducing the communication load to the central d <t>The use of edge computing in a smart factory <xref target="Argungu"/>
ata center or server. It is also possible to improve process efficiency and faci can reduce the cost of network and storage resources by reducing the communicat
lity asset productivity through real-time prediction of failures and to reduce t ion load to the central data center or server. It is also possible to improve pr
he cost of failure through preliminary measures. In the existing manufacturing f ocess efficiency and facility asset productivity through real-time prediction of
ield, production facilities are manually run according to a program entered in a failures and to reduce the cost of failure through preliminary measures. In the
dvance; however, edge computing in a smart factory enables tailoring solutions b existing manufacturing field, production facilities are manually run according
y analyzing data at each production facility and machine level. Digital twins <x to a program entered in advance; however, edge computing in a smart factory enab
ref target="Jones"/> of IoT devices have been jointly used with edge computing i les tailoring solutions by analyzing data at each production facility and machin
n industrial IoT scenarios <xref target="Chen"/>.</t> e level. Digital twins <xref target="Jones"/> of IoT devices have been jointly u
<t><strong>Smart Grid</strong></t> sed with edge computing in industrial IoT scenarios <xref target="Chen"/>.</t></
<t>In future smart city scenarios, the Smart Grid will be critical in en dd>
suring highly available/efficient energy control in city-wide electricity manage <dt><strong>Smart Grid</strong></dt>
ment. Edge computing is expected to play a significant role in these systems to <dd><t>In future smart-city scenarios, the smart grid will be critical i
improve the transmission efficiency of electricity, to react to, and restore po n ensuring highly available and efficient energy control in city-wide electricit
wer after a disturbance, to reduce operation costs, and to reuse energy effectiv y management <xref target="Mehmood"/>. Edge computing is expected to play a sig
ely, since these operations involve local decision-making. In addition, edge com nificant role in these systems to improve the transmission efficiency of electri
puting can help monitor power generation and power demand, and make local electr city, to react to and restore power after a disturbance, to reduce operation cos
ical energy storage decisions in smart grid systems.</t> ts, and to reuse energy effectively since these operations involve local decisio
<t><strong>Smart Agriculture</strong></t> n-making. In addition, edge computing can help monitor power generation and powe
<t>Smart agriculture integrates information and communication technologi r demand and make local electrical energy storage decisions in smart grid system
es with farming technology. Intelligent farms use IoT technology to measure and s.</t></dd>
analyze parameters, such as the temperature, humidity, sunlight, carbon dioxide, <dt><strong>Smart Agriculture</strong></dt>
and soil quality, in crop cultivation facilities. Depending on the analysis res <dd><t>Smart agriculture integrates information and communication techno
ults, control devices are used to set the environmental parameters to an appropr logies with farming technology. Intelligent farms use IoT technology to measure
iate state. Remote management is also possible through mobile devices such as sm and analyze parameters, such as the temperature, humidity, sunlight, carbon diox
artphones.</t> ide, and soil quality, in crop cultivation facilities. Depending on the analysis
<t>In existing farms, simple systems such as management according to tem results, control devices are used to set the environmental parameters to an app
perature and humidity can be easily and inexpensively implemented using IoT tech ropriate state. Remote management is also possible through mobile devices, such
nology. Field sensors gather data on field and crop condition. This data is then as smartphones.</t>
transmitted to cloud servers that process data and recommend actions. The use o <t>In existing farms, simple systems, such as management according to te
f edge computing can reduce the volume of back-and-forth data transmissions sign mperature and humidity, can be easily and inexpensively implemented using IoT te
ificantly, resulting in cost and bandwidth savings. Locally generated data can b chnology <xref target="Tanveer"/>. Field sensors gather data on field and crop c
e processed at the edge, and local computing and analytics can drive local actio ondition. This data is then transmitted to cloud servers that process data and r
ns. With edge computing, it is easy for farmers to select large amounts of data ecommend actions. The use of edge computing can reduce the volume of back-and-fo
for processing, and data can be analyzed even in remote areas with poor access c rth data transmissions significantly, resulting in cost and bandwidth savings. L
onditions. Other applications include enabling dashboarding, for example, to vis ocally generated data can be processed at the edge, and local computing and anal
ualize the farm status, as well as enhancing Extended Reality (XR) applications ytics can drive local actions. With edge computing, it is easy for farmers to se
that require edge audio/video processing. As the number of people working on far lect large amounts of data for processing, and data can be analyzed even in remo
ming has been decreasing over time, increasing automation enabled by edge comput te areas with poor access conditions. Other applications include enabling dashbo
ing can be a driving force for future smart agriculture.</t> arding, for example, to visualize the farm status, as well as enhancing Extended
<t><strong>Smart Construction</strong></t> Reality (XR) applications that require edge audio and/or video processing. As t
<t>Safety is critical at construction sites. Every year, many constructi he number of people working on farming has been decreasing over time, increasing
on workers lose their lives because of falls, collisions, electric shocks, and o automation enabled by edge computing can be a driving force for future smart ag
ther accidents. Therefore, solutions have been developed to improve constructio riculture <xref target="OGrady"/>.</t></dd>
n site safety, including the real-time identification of workers, monitoring of <dt><strong>Smart Construction</strong></dt>
equipment location, and predictive accident prevention. To deploy these solution <dd><t>Safety is critical at construction sites. Every year, many constr
s, many cameras and IoT sensors have been installed on construction sites, to me uction workers lose their lives because of falls, collisions, electric shocks, a
asure noise, vibration, gas concentration, etc. Typically, the data generated fr nd other accidents <xref target="BigRentz"/>. Therefore, solutions have been de
om these measurements is collected in on-site gateways and sent to remote cloud veloped to improve construction site safety, including the real-time identificat
servers for storage and analysis. Thus, an inspector can check the information s ion of workers, monitoring of equipment location, and predictive accident preven
tored on the cloud server to investigate an incident. However, this approach can tion. To deploy these solutions, many cameras and IoT sensors have been installe
be expensive because of transmission costs, for example, of video streams over d on construction sites to measure noise, vibration, gas concentration, etc. Typ
a mobile network connection, and because usage fees of private cloud services.</ ically, the data generated from these measurements is collected in on-site gatew
t> ays and sent to remote cloud servers for storage and analysis. Thus, an inspecto
<t>Using edge computing, data generated at the construction site can be r can check the information stored on the cloud server to investigate an inciden
processed and analyzed on an edge server located within or near the site. Only t t. However, this approach can be expensive because of transmission costs (for ex
he result of this processing needs to be transferred to a cloud server, thus red ample, of video streams over a mobile network connection) and because usage fees
ucing transmission costs. It is also possible to locally generate warnings to pr of private cloud services.</t>
event accidents in real-time.</t> <t>Using edge computing <xref target="Yue"/>, data generated at the cons
<t><strong>Self-Driving Car</strong></t> truction site can be processed and analyzed on an edge server located within or
<t>Edge computing plays a crucial role in safety-focused self-driving ca near the site. Only the result of this processing needs to be transferred to a c
r systems. With a multitude of sensors, such as high-resolution cameras, radar, loud server, thus reducing transmission costs. It is also possible to locally ge
LIDAR, sonar sensors, and GPS systems, autonomous vehicles generate vast amounts nerate warnings to prevent accidents in real time.</t></dd>
of real-time data. Local processing utilizing edge computing nodes allows for e <dt><strong>Self-Driving Car</strong></dt>
fficient collection and analysis of this data to monitor vehicle distances and r <dd><t>Edge computing plays a crucial role in safety-focused self-drivin
oad conditions and respond promptly to unexpected situations. Roadside computing g car systems <xref target="Badjie"/>. With a multitude of sensors, such as high
nodes can also be leveraged to offload tasks when necessary, for example, when -resolution cameras, radars, Light Detection and Ranging (LiDAR) systems, sonar
the local processing capacity of the car is insufficient because of hardware con sensors, and GPS systems, autonomous vehicles generate vast amounts of real-time
straints or a large data volume.</t> data. Local processing utilizing edge computing nodes allows for efficient coll
ection and analysis of this data to monitor vehicle distances and road condition
s and respond promptly to unexpected situations. Roadside computing nodes can al
so be leveraged to offload tasks when necessary, for example, when the local pro
cessing capacity of the car is insufficient because of hardware constraints or a
large data volume.</t>
<t>For instance, when the car ahead slows, a self-driving car adjusts it s speed to maintain a safe distance, or when a roadside signal changes, it adapt s its behavior accordingly. In another example, cars equipped with self-parking features utilize local processing to analyze sensor data, determine suitable par king spots, and execute precise parking maneuvers without relying on external pr ocessing or connectivity. It is also possible to use in-cabin cameras coupled wi th local processing to monitor the driver's attention level and detect signs of drowsiness or distraction. The system can issue warnings or implement preventive measures to ensure driver safety.</t> <t>For instance, when the car ahead slows, a self-driving car adjusts it s speed to maintain a safe distance, or when a roadside signal changes, it adapt s its behavior accordingly. In another example, cars equipped with self-parking features utilize local processing to analyze sensor data, determine suitable par king spots, and execute precise parking maneuvers without relying on external pr ocessing or connectivity. It is also possible to use in-cabin cameras coupled wi th local processing to monitor the driver's attention level and detect signs of drowsiness or distraction. The system can issue warnings or implement preventive measures to ensure driver safety.</t>
<t>Edge computing empowers self-driving cars by enabling real-time proce <t>Edge computing empowers self-driving cars by enabling real-time proce
ssing, reducing latency, enhancing data privacy, and optimizing bandwidth usage. ssing, reducing latency, enhancing data privacy, and optimizing bandwidth usage.
By leveraging local processing capabilities, self-driving cars can make rapid d By leveraging local processing capabilities, self-driving cars can make rapid d
ecisions, adapt to changing environments, and ensure safer and more efficient au ecisions, adapt to changing environments, and ensure safer and more efficient au
tonomous driving experiences.</t> tonomous driving experiences.</t></dd>
<t><strong>Digital Twin</strong></t> <dt><strong>Digital Twin</strong></dt>
<t>A digital twin can simulate different scenarios and predict outcomes <dd><t>A digital twin can simulate different scenarios and predict outco
based on real-time data collected from the physical environment. This simulation mes based on real-time data collected from the physical environment. This simula
capability empowers proactive maintenance, optimization of operations, and the tion capability empowers proactive maintenance, optimization of operations, and
prediction of potential issues or failures. Decision makers can use digital twin the prediction of potential issues or failures. Decision makers can use digital
s to test and validate different strategies, identify inefficiencies, and optimi twins to test and validate different strategies, identify inefficiencies, and op
ze performance.</t> timize performance <xref target="CertMagic"/>.</t>
<t>With edge computing, real-time data is collected, processed, and anal <t>With edge computing, real-time data is collected, processed, and anal
yzed directly at the edge, allowing for the accurate monitoring and simulation o yzed directly at the edge, allowing for the accurate monitoring and simulation o
f physical assets. Moreover, edge computing effectively minimizes latency, enabl f physical assets. Moreover, edge computing effectively minimizes latency, enabl
ing rapid responses to dynamic conditions as computational resources are brought ing rapid responses to dynamic conditions as computational resources are brought
closer to the physical object. Running digital twin processing at the edge enab closer to the physical object. Running digital twin processing at the edge enab
les organizations to obtain timely insights and make informed decisions that max les organizations to obtain timely insights and make informed decisions that max
imize efficiency and performance.</t> imize efficiency and performance.</t></dd>
<t><strong>Other Use Cases</strong></t> <dt><strong>Other Use Cases</strong></dt>
<t>AI/ML systems at the edge empower real-time analysis, faster decision <dd><t>Artificial intelligence (AI) and machine learning (ML) systems at
-making, reduced latency, improved operational efficiency, and personalized expe the edge empower real-time analysis, faster decision-making, reduced latency, i
riences across various industries, by bringing artificial intelligence and machi mproved operational efficiency, and personalized experiences across various indu
ne learning capabilities closer to edge devices.</t> stries by bringing AI and ML capabilities closer to edge devices.</t>
<t>In addition, oneM2M has studied several IoT edge computing use cases, <t>In addition, oneM2M has studied several IoT edge computing use cases,
which are documented in <xref target="oneM2M-TR0001"/>, <xref target="oneM2M-TR which are documented in <xref target="oneM2M-TR0001"/>, <xref target="oneM2M-TR
0018"/> and <xref target="oneM2M-TR0026"/>. The edge computing related requireme 0018"/>, and <xref target="oneM2M-TR0026"/>. The edge-computing-related requirem
nts raised through the analysis of these use cases are captured in <xref target= ents raised through the analysis of these use cases are captured in <xref target
"oneM2M-TS0002"/>.</t> ="oneM2M-TS0002"/>.</t></dd>
</dl>
</section> </section>
</section> </section>
<section anchor="sec-challenges"> <section anchor="sec-challenges">
<name>IoT Challenges Leading Towards Edge Computing</name> <name>IoT Challenges Leading toward Edge Computing</name>
<t>This section describes the challenges faced by IoT that are motivating
the adoption of edge computing. These are distinct from the research challenges <t>This section describes the challenges faced by the IoT that are motivat
applicable to IoT edge computing, some of which are mentioned in <xref target="s ing the adoption of edge computing. These are distinct from the research challen
ec-functions"/>.</t> ges applicable to IoT edge computing, some of which are mentioned in <xref targe
<t>IoT technology is used with increasingly demanding applications, for ex t="sec-functions"/>.</t>
ample, in industrial, automotive and healthcare domains, leading to new challeng <t>IoT technology is used with increasingly demanding applications in
es. For example, industrial machines such as laser cutters produce over 1 terab domains such as industrial, automotive, and healthcare, which leads
yte of data per hour, and similar amounts can be generated in autonomous cars <x to new challenges. For example, industrial machines, such as laser cutters,
ref target="NVIDIA"/>. 90% of IoT data is expected to be stored, processed, ana produce over 1 terabyte of data per hour, and similar amounts can be generated i
lyzed, and acted upon close to the source <xref target="Kelly"/>, as cloud compu n autonomous cars <xref target="NVIDIA"/>. 90% of IoT data is expected to be st
ting models alone cannot address these new challenges <xref target="Chiang"/>.</ ored, processed, analyzed, and acted upon close to the source <xref target="Kell
t> y"/>, as cloud computing models alone cannot address these new challenges <xref
target="Chiang"/>.</t>
<t>Below, we discuss IoT use case requirements that are moving cloud capab ilities to be more proximate, distributed, and disaggregated.</t> <t>Below, we discuss IoT use case requirements that are moving cloud capab ilities to be more proximate, distributed, and disaggregated.</t>
<section anchor="time-sensitivity"> <section anchor="time-sensitivity">
<name>Time Sensitivity</name> <name>Time Sensitivity</name>
<t>Many industrial control systems, such as manufacturing systems, smart grids, and oil and gas systems often require stringent end-to-end latency betwe en the sensor and control nodes. While some IoT applications may require latenc y below a few tens of milliseconds <xref target="Weiner"/>, industrial robots an d motion control systems have use cases for cycle times in the order of microsec onds <xref target="_60802"/>. In some cases, speed-of-light limitations may sim ply prevent a cloud-based solutions; however, this is not the only challenge rel ative to time sensitivity. Guarantees for bounded latency and jitter (<xref tar get="RFC8578"/> section 7) are also important for industrial IoT applications. This means that control packets must arrive with as little variation as possible and within a strict deadline. Given the best-effort characteristics of the Int ernet, this challenge is virtually impossible to address, without using end-to-e nd guarantees for individual message delivery and continuous data flows.</t> <t>Often, many industrial control systems, such as manufacturing systems , smart grids, and oil and gas systems, require stringent end-to-end latency bet ween the sensor and control nodes. While some IoT applications may require late ncy below a few tens of milliseconds <xref target="Weiner"/>, industrial robots and motion control systems have use cases for cycle times in the order of micros econds <xref target="IEC_IEEE_60802"/>. In some cases, speed-of-light limitatio ns may simply prevent cloud-based solutions; however, this is not the only chall enge relative to time sensitivity. Guarantees for bounded latency and jitter (< xref target="RFC8578" sectionFormat="comma" section="7"/>) are also important fo r industrial IoT applications. This means that control packets must arrive with as little variation as possible and within a strict deadline. Given the best-e ffort characteristics of the Internet, this challenge is virtually impossible to address without using end-to-end guarantees for individual message delivery and continuous data flows.</t>
</section> </section>
<section anchor="connectivity-cost"> <section anchor="connectivity-cost">
<name>Connectivity Cost</name> <name>Connectivity Cost</name>
<t>Some IoT deployments may not face bandwidth constraints when uploadin g data to the Cloud. 5G and Wi-Fi 6 networks both theoretically top out at 10 g igabits per second (i.e., 4.5 terabytes per hour), allowing to transfer large am ounts of uplink data. However, the cost of maintaining continuous high-bandwidt h connectivity for such usage is unjustifiable and impractical for most IoT appl ications. In some settings, for example, in aeronautical communication, higher communication costs reduce the amount of data that can be practically uploaded e ven further. Minimizing reliance on high-bandwidth connectivity is therefore a requirement, for example, by processing data at the edge and deriving summarized or actionable insights that can be transmitted to the Cloud.</t> <t>Some IoT deployments may not face bandwidth constraints when uploadin g data to the cloud. Theoretically, both 5G and Wi-Fi 6 networks top out at 10 gigabits per second (i.e., 4.5 terabytes per hour), allowing the transfer of lar ge amounts of uplink data. However, the cost of maintaining continuous high-ban dwidth connectivity for such usage is unjustifiable and impractical for most IoT applications. In some settings, for example, in aeronautical communication, hi gher communication costs reduce the amount of data that can be practically uploa ded even further. Therefore, minimizing reliance on high-bandwidth connectivity is a requirement; this can be done, for example, by processing data at the edge and deriving summarized or actionable insights that can be transmitted to the c loud.</t>
</section> </section>
<section anchor="resilience-to-intermittent-services"> <section anchor="resilience-to-intermittent-services">
<name>Resilience to Intermittent Services</name> <name>Resilience to Intermittent Services</name>
<t>Many IoT devices, such as sensors, actuators, and controllers, have v <t>Many IoT devices, such as sensors, actuators, and controllers, have v
ery limited hardware resources and cannot rely solely on their own resources to ery limited hardware resources and cannot rely solely on their own resources to
meet their computing and/or storage needs. They require reliable, uninterrupted meet their computing and/or storage needs. They require reliable, uninterrupted
, or resilient services to augment their capabilities to fulfill their applicati , or resilient services to augment their capabilities to fulfill their applicati
on tasks. This is difficult and partly impossible to achieve using cloud servic on tasks. This is difficult and partly impossible to achieve using cloud servic
es for systems such as vehicles, drones, or oil rigs that have intermittent netw es for systems such as vehicles, drones, or oil rigs that have intermittent netw
ork connectivity. Conversely, a cloud back-end might want to device data even i ork connectivity. Conversely, a cloud backend might want to access device data
f it is currently asleep.</t> even if the device is currently asleep.</t>
</section> </section>
<section anchor="sec-priv"> <section anchor="sec-priv">
<name>Privacy and Security</name> <name>Privacy and Security</name>
<t>When IoT services are deployed at home, personal information can be l <t>When IoT services are deployed at home, personal information can be l
earned from detected usage data. For example, one can extract information about earned from detected usage data. For example, one can extract information about
employment, family status, age, and income by analyzing smart-meter data <xref employment, family status, age, and income by analyzing smart meter data <xref
target="ENERGY"/>. Policy makers have begun to provide frameworks that limit th target="ENERGY"/>. Policy makers have begun to provide frameworks that limit th
e usage of personal data and impose strict requirements on data controllers and e usage of personal data and impose strict requirements on data controllers and
processors. Data stored indefinitely in the Cloud also increases the risk of da processors. Data stored indefinitely in the cloud also increases the risk of da
ta leakage, for instance, through attacks on rich targets.</t> ta leakage, for instance, through attacks on rich targets.</t>
<t>It is often argues that industrial systems do not provide privacy imp <t>It is often argued that industrial systems do not provide privacy imp
lications, as no personal data is gathered. However, data from such systems is lications, as no personal data is gathered. However, data from such systems is
often highly sensitive, as one might be able to infer trade secrets such as the often highly sensitive, as one might be able to infer trade secrets, such as the
setup of production lines. Hence, owners of these systems are generally relucta setup of production lines. Hence, owners of these systems are generally reluct
nt to upload IoT data to the Cloud.</t> ant to upload IoT data to the cloud.</t>
<t>Furthermore, passive observers can perform traffic analysis on device -to-cloud paths. Therefore, hiding traffic patterns associated with sensor netw orks can be another requirement for edge computing.</t> <t>Furthermore, passive observers can perform traffic analysis on device -to-cloud paths. Therefore, hiding traffic patterns associated with sensor netw orks can be another requirement for edge computing.</t>
</section> </section>
</section> </section>
<section anchor="sec-functions"> <section anchor="sec-functions">
<name>IoT Edge Computing Functions</name> <name>IoT Edge Computing Functions</name>
<t>We first look at the current state of IoT edge computing (<xref target= "sec-overview"/>), and then define a general system model (<xref target="sec-mod el"/>). This provides a context for IoT edge-computing functions, which are list ed in <xref target="sec-components-oam"/>, <xref target="sec-components-function al"/> and <xref target="sec-components-app"/>.</t> <t>We first look at the current state of IoT edge computing (<xref target= "sec-overview"/>) and then define a general system model (<xref target="sec-mode l"/>). This provides a context for IoT edge computing functions, which are liste d in Sections <xref target="sec-components-oam" format="counter"/>, <xref target ="sec-components-functional" format="counter"/>, and <xref target="sec-component s-app" format="counter"/>.</t>
<section anchor="sec-overview"> <section anchor="sec-overview">
<name>Overview of IoT Edge Computing Today</name> <name>Overview of IoT Edge Computing</name>
<t>This section provides an overview of today's IoT edge computing field <t>This section provides an overview of the current (at the time of writ
based on a limited review of standards, research, open-source and proprietary p ing) IoT edge computing field based on a limited review of standards, research,
roducts in <xref target="I-D.defoy-t2trg-iot-edge-computing-background"/>.</t> and open-source and proprietary products in <xref target="I-D.defoy-t2trg-iot-ed
<t>IoT gateways, both open-source (such as EdgeX Foundry or Home Edge) a ge-computing-background"/>.</t>
nd proprietary products, represent a common class of IoT edge-computing products <t>IoT gateways, both open-source (such as EdgeX Foundry or Home Edge) a
, where the gateway provides a local service on customer premises and is remotel nd proprietary products, represent a common class of IoT edge computing products
y managed through a cloud service. IoT communication protocols are typically use , where the gateway provides a local service on customer premises and is remotel
d between IoT devices and the gateway, including CoAP <xref target="RFC7252"/>, y managed through a cloud service. IoT communication protocols are typically use
MQTT <xref target="mqtt5"/>, and many specialized IoT protocols (such as OPC UA d between IoT devices and the gateway, including a Constrained Application Proto
and DDS in the Industrial IoT space), while the gateway communicates with the di col (CoAP) <xref target="RFC7252"/>, Message Queuing Telemetry Transport (MQTT)
stant cloud typically using HTTPS. Virtualization platforms enable the deploymen <xref target="MQTT5"/>, and many specialized IoT protocols (such as Open Platfo
t of virtual edge computing functions (using VMs and application containers), in rm Communications Unified Architecture (OPC UA) and Data Distribution Service (D
cluding IoT gateway software, on servers in the mobile network infrastructure (a DS) in the industrial IoT space), while the gateway communicates with the distan
t base stations and concentration points), edge data centers (in central offices t cloud typically using HTTPS. Virtualization platforms enable the deployment of
), and regional data centers located near central offices. End devices are envis virtual edge computing functions (using Virtual Machines (VMs) and application
ioned to become computing devices in forward-looking projects, but are not commo containers), including IoT gateway software, on servers in the mobile network in
nly used today.</t> frastructure (at base stations and concentration points), edge data centers (in
<t>In addition to open-source and proprietary solutions, a horizontal Io central offices), and regional data centers located near central offices. End de
T service layer is standardized by the oneM2M standards body to reduce fragmenta vices are envisioned to become computing devices in forward-looking projects but
tion, increase interoperability and promote reuse in the IoT ecosystem. Furtherm are not commonly used at the time of writing.</t>
ore, ETSI MEC developed an IoT API <xref target="ETSI_MEC_33"/> that enables the <t>In addition to open-source and proprietary solutions, a horizontal Io
deployment of heterogeneous IoT platforms and provides a means to configure the T service layer is standardized by the oneM2M standards body to reduce fragmenta
various components of an IoT system.</t> tion, increase interoperability, and promote reuse in the IoT ecosystem. Further
<t>Physical or virtual IoT gateways can host application programs that a more, ETSI Multi-access Edge Computing (MEC) developed an IoT API <xref target="
re typically built using an SDK to access local services through a programmatic ETSI_MEC_33"/> that enables the deployment of heterogeneous IoT platforms and pr
API. Edge cloud system operators host their customers' application VMs or conta ovides a means to configure the various components of an IoT system.</t>
iners on servers located in or near access networks that can implement local edg <t>Physical or virtual IoT gateways can host application programs that a
e services. For example, mobile networks can provide edge services for radio-net re typically built using an SDK to access local services through a programmatic
work information, location, and bandwidth management.</t> API. Edge cloud system operators host their customers' application VMs or conta
iners on servers located in or near access networks that can implement local edg
e services. For example, mobile networks can provide edge services for radio net
work information, location, and bandwidth management.</t>
<t>Resilience in the IoT can entail the ability to operate autonomously in periods of disconnectedness to preserve the integrity and safety of the contr olled system, possibly in a degraded mode. IoT devices and gateways are often ex pected to operate in always-on and unattended modes, using fault detection and u nassisted recovery functions.</t> <t>Resilience in the IoT can entail the ability to operate autonomously in periods of disconnectedness to preserve the integrity and safety of the contr olled system, possibly in a degraded mode. IoT devices and gateways are often ex pected to operate in always-on and unattended modes, using fault detection and u nassisted recovery functions.</t>
<t>The life cycle management of services and applications on physical Io <t>The life-cycle management of services and applications on physical Io
T gateways is generally cloud-based. Edge cloud management platforms and produc T gateways is generally cloud based. Edge cloud management platforms and produc
ts (such as StarlingX, Akraino Edge Stack, or proprietary products from major Cl ts (such as StarlingX, Akraino Edge Stack, or proprietary products from major cl
oud providers) adapt cloud management technologies (e.g., Kubernetes) to the edg oud providers) adapt cloud management technologies (e.g., Kubernetes) to the edg
e cloud, that is, to smaller, distributed computing devices running outside a co e cloud, that is, to smaller, distributed computing devices running outside a co
ntrolled data center. The service and application life-cycle is typically using ntrolled data center. Typically, the service and application life cycle is usin
an NFV-like management and orchestration model.</t> g an NFV-like management and orchestration model.</t>
<t>The platform typically enables advertising or consuming services host <t> The platform generally enables advertising or consuming services
ed on the platform (e.g., the Mp1 interface in ETSI MEC supports service discove hosted on the platform (e.g., the Mp1 interface in ETSI MEC supports
ry and communication), and enables communication with local and remote endpoints service discovery and communication), and enables communication with
(e.g., message routing function in IoT gateways). The platform is typically ex local and remote endpoints (e.g., message routing function in IoT
tensible to edge applications because it can advertise a service that other edge gateways). The platform is usually extensible to edge applications
applications can consume. The IoT communication services include protocol trans because it can advertise a service that other edge applications can
lation, analytics, and transcoding. Communication between edge-computing device consume. The IoT communication services include protocol translation, analyt
s is enabled in tiered or distributed deployments.</t> ics, and transcoding. Communication between edge computing devices is enabled i
<t>An edge cloud platform may enable pass-through without storage or loc n tiered or distributed deployments.</t>
al storage (e.g., on IoT gateways). Some edge cloud platforms use distributed st <t>An edge cloud platform may enable pass-through without storage or loc
orage such as that provided by a distributed storage platform (e.g., EdgeFS, Cep al storage (e.g., on IoT gateways). Some edge cloud platforms use distributed st
h), or, in more experimental settings, by an ICN network, for example, systems s orage such as that provided by a distributed storage platform (e.g., EdgeFS and
uch as Chipmunk <xref target="chipmunk"/> and Kua <xref target="kua"/> have been Ceph) or, in more experimental settings, by an Information-Centric Networking (I
proposed as distributed information-centric objects stores. External storage, CN) network, for example, systems such as Chipmunk <xref target="Chipmunk"/> and
for example, on databases in distant or local IT cloud, is typically used for fi Kua <xref target="Kua"/> have been proposed as distributed information-centric
ltered data deemed worthy of long-term storage, although in some cases it may be objects stores. External storage, for example, on databases in a distant or loc
for all data, for example when required for regulatory reasons.</t> al IT cloud, is typically used for filtered data deemed worthy of long-term stor
<t>Stateful computing is supported on platforms that host native program age; although, in some cases, it may be for all data, for example, when required
s, VMs, or containers. Stateless computing is supported on platforms providing a for regulatory reasons.</t>
"serverless computing" service (also known as function-as-a-service, e.g., usin <t>Stateful computing is the default on most systems, VMs, and container
g stateless containers), or on systems based on named function networking.</t> s. Stateless computing is supported on platforms providing a "serverless computi
<t>In many IoT use cases, a typical network usage pattern is a high volu ng" service (also known as function-as-a-service, e.g., using stateless containe
me uplink with some form of traffic reduction enabled by processing over edge-co rs) or on systems based on named function networking.</t>
mputing devices. Alternatives to traffic reduction include deferred transmission <t>In many IoT use cases, a typical network usage pattern is a high-volu
(to off-peak hours or using physical shipping). Downlink traffic includes appli me uplink with some form of traffic reduction enabled by processing over edge co
cation control and software updates. Downlink-heavy traffic patterns are not exc mputing devices. Alternatives to traffic reduction include deferred transmission
luded but are more often associated with non-IoT usage (e.g., video CDNs).</t> (to off-peak hours or using physical shipping). Downlink traffic includes appli
cation control and software updates. Downlink-heavy traffic patterns are not exc
luded but are more often associated with non-IoT usage (e.g., video Content Deli
very Networks (CDNs)).</t>
</section> </section>
<section anchor="sec-model"> <section anchor="sec-model">
<name>General Model</name> <name>General Model</name>
<t>Edge computing is expected to play an important role in deploying new <t>Edge computing is expected to play an important role in deploying new
IoT services integrated with Big Data and AI enabled by flexible in-network com IoT services integrated with big data and AI enabled by flexible in-network com
puting platforms. Although there are many approaches to edge computing, in this puting platforms. Although there are many approaches to
section, we attempt to lay out a general model and the list associated logical f edge computing, this section lays out an attempt at a general
unctions. In practice, this model can be mapped to different architectures, such model and lists associated logical functions. In practice, this model can be
as:</t> mapped to different architectures, such as:</t>
<ul spacing="normal"> <ul spacing="normal">
<li>A single IoT gateway, or a hierarchy of IoT gateways, typically co <li>A single IoT gateway, or a hierarchy of IoT gateways, typically co
nnected to the cloud (e.g., to extend the traditional cloud-based management of nnected to the cloud (e.g., to extend the centralized cloud-based management of
IoT devices and data to the edge). The IoT gateway plays a common role in provid IoT devices and data to the edge). The IoT gateway plays a common role in provid
ing access to a heterogeneous set of IoT devices/sensors, handling IoT data, and ing access to a heterogeneous set of IoT devices and sensors, handling IoT data,
delivering IoT data to its final destination in a cloud network. Whereas an IoT and delivering IoT data to its final destination in a cloud network. An IoT ga
gateway requires interactions with the cloud, it can also operate independently teway requires interactions with the cloud; however, it can also operate indepen
in a disconnected mode.</li> dently in a disconnected mode.</li>
<li>A set of distributed computing nodes, for example, embedded in swi <li>A set of distributed computing nodes, for example, embedded in swi
tches, routers, edge cloud servers, or mobile devices. Some IoT devices have suf tches, routers, edge cloud servers, or mobile devices. Some IoT devices have suf
ficient computing capabilities to participate in such distributed systems owing ficient computing capabilities to participate in such distributed systems owing
to advances in hardware technology. In this model, edge-computing nodes can coll to advances in hardware technology. In this model, edge computing nodes can coll
aborate to share resources.</li> aborate to share resources.</li>
<li>A hybrid system involving both IoT gateways and supporting functio ns in distributed computing nodes.</li> <li>A hybrid system involving both IoT gateways and supporting functio ns in distributed computing nodes.</li>
</ul> </ul>
<t>In the general model described in <xref target="rl-fig1"/>, the edge <t>In the general model described in <xref target="rl-fig1"/>, the edge
computing domain is interconnected with IoT devices (southbound connectivity), p computing domain is interconnected with IoT devices (southbound connectivity), p
ossibly with a remote/cloud network (northbound connectivity), and with a servic ossibly with a remote (e.g., cloud) network (northbound connectivity), and with
e operator's system. Edge-computing nodes provide multiple logical functions or a service operator's system. Edge computing nodes provide multiple logical func
components that may not be present in a given system. They may be implemented i tions or components that may not be present in a given system. They may be imple
n a centralized or distributed fashion, at the network edge, or through interwor mented in a centralized or distributed fashion, at the network edge, or through
king between the edge network and remote cloud networks.</t> interworking between the edge network and remote cloud networks.</t>
<figure anchor="rl-fig1"> <figure anchor="rl-fig1">
<name>Model of IoT Edge Computing</name> <name>Model of IoT Edge Computing</name>
<artset> <artset>
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<g class="text"> <g class="text">
<text x="84" y="52">Remote</text> <text x="84" y="52">Remote</text>
<text x="144" y="52">network</text> <text x="144" y="52">Network</text>
<text x="60" y="68">(e.g.,</text> <text x="60" y="68">(e.g.,</text>
<text x="112" y="68">cloud</text> <text x="112" y="68">cloud</text>
<text x="172" y="68">network)</text> <text x="172" y="68">network)</text>
<text x="288" y="68">Service</text> <text x="288" y="68">Service</text>
<text x="292" y="84">Operator</text> <text x="292" y="84">Operator</text>
<text x="124" y="148">Edge</text> <text x="124" y="148">Edge</text>
<text x="184" y="148">Computing</text> <text x="184" y="148">Computing</text>
<text x="252" y="148">Domain</text> <text x="252" y="148">Domain</text>
<text x="48" y="180">One</text> <text x="48" y="180">One</text>
<text x="76" y="180">or</text> <text x="76" y="180">or</text>
<text x="108" y="180">more</text> <text x="108" y="180">more</text>
<text x="168" y="180">Computing</text> <text x="168" y="180">computing</text>
<text x="232" y="180">Nodes</text> <text x="232" y="180">nodes</text>
<text x="52" y="196">(IoT</text> <text x="52" y="196">(IoT</text>
<text x="108" y="196">gateway,</text> <text x="108" y="196">gateway,</text>
<text x="160" y="196">end</text> <text x="160" y="196">end</text>
<text x="212" y="196">devices,</text> <text x="212" y="196">devices,</text>
<text x="288" y="196">switches,</text> <text x="288" y="196">switches,</text>
<text x="68" y="212">routers,</text> <text x="68" y="212">routers,</text>
<text x="168" y="212">mini/micro-data</text> <text x="168" y="212">mini/micro-data</text>
<text x="268" y="212">centers,</text> <text x="268" y="212">centers,</text>
<text x="328" y="212">etc.)</text> <text x="328" y="212">etc.)</text>
<text x="48" y="244">OAM</text> <text x="48" y="244">OAM</text>
skipping to change at line 328 skipping to change at line 343
<text x="40" y="500">-</text> <text x="40" y="500">-</text>
<text x="64" y="500">...</text> <text x="64" y="500">...</text>
<text x="264" y="532">-</text> <text x="264" y="532">-</text>
<text x="280" y="532">-</text> <text x="280" y="532">-</text>
<text x="296" y="532">-</text> <text x="296" y="532">-</text>
<text x="344" y="532">-</text> <text x="344" y="532">-</text>
<text x="360" y="532">-</text> <text x="360" y="532">-</text>
<text x="376" y="532">-</text> <text x="376" y="532">-</text>
<text x="248" y="548">|</text> <text x="248" y="548">|</text>
<text x="248" y="580">|</text> <text x="248" y="580">|</text>
<text x="304" y="580">compute</text> <text x="304" y="580">Compute</text>
<text x="384" y="580">|</text> <text x="384" y="580">|</text>
<text x="56" y="596">End</text> <text x="56" y="596">End</text>
<text x="168" y="596">End</text> <text x="168" y="596">End</text>
<text x="232" y="596">...</text> <text x="232" y="596">...</text>
<text x="308" y="596">node/end</text> <text x="308" y="596">Node/End</text>
<text x="52" y="612">Device</text> <text x="52" y="612">Device</text>
<text x="88" y="612">1</text> <text x="88" y="612">1</text>
<text x="164" y="612">Device</text> <text x="164" y="612">Device</text>
<text x="200" y="612">2</text> <text x="200" y="612">2</text>
<text x="236" y="612">...|</text> <text x="236" y="612">...|</text>
<text x="300" y="612">device</text> <text x="300" y="612">Device</text>
<text x="336" y="612">n</text> <text x="336" y="612">n</text>
<text x="384" y="612">|</text> <text x="384" y="612">|</text>
<text x="248" y="644">+</text> <text x="248" y="644">+</text>
<text x="264" y="644">-</text> <text x="264" y="644">-</text>
<text x="280" y="644">-</text> <text x="280" y="644">-</text>
<text x="296" y="644">-</text> <text x="296" y="644">-</text>
<text x="312" y="644">-</text> <text x="312" y="644">-</text>
<text x="328" y="644">-</text> <text x="328" y="644">-</text>
<text x="344" y="644">-</text> <text x="344" y="644">-</text>
<text x="360" y="644">-</text> <text x="360" y="644">-</text>
<text x="380" y="644">-+</text> <text x="380" y="644">-+</text>
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<artwork type="ascii-art" align="center"><![CDATA[ <artwork type="ascii-art" align="center"><![CDATA[
+---------------------+ +---------------------+
| Remote network | +---------------+ | Remote Network | +---------------+
|(e.g., cloud network)| | Service | |(e.g., cloud network)| | Service |
+-----------+---------+ | Operator | +-----------+---------+ | Operator |
| +------+--------+ | +------+--------+
| | | |
+--------------+-------------------+-----------+ +--------------+-------------------+-----------+
| Edge Computing Domain | | Edge Computing Domain |
| | | |
| One or more Computing Nodes | | One or more computing nodes |
| (IoT gateway, end devices, switches, | | (IoT gateway, end devices, switches, |
| routers, mini/micro-data centers, etc.) | | routers, mini/micro-data centers, etc.) |
| | | |
| OAM Components | | OAM Components |
| - Resource Discovery and Authentication | | - Resource Discovery and Authentication |
| - Edge Organization and Federation | | - Edge Organization and Federation |
| - Multi-Tenancy and Isolation | | - Multi-Tenancy and Isolation |
| - ... | | - ... |
| | | |
| Functional Components | | Functional Components |
skipping to change at line 389 skipping to change at line 404
| - ... | | - ... |
| | | |
| Application Components | | Application Components |
| - IoT Devices Management | | - IoT Devices Management |
| - Data Management and Analytics | | - Data Management and Analytics |
| - ... | | - ... |
| | | |
+------+--------------+-------- - - - -+- - - -+ +------+--------------+-------- - - - -+- - - -+
| | | | | | | | | |
| | +-----+--+ | | +-----+--+
+----+---+ +-----+--+ | |compute | | +----+---+ +-----+--+ | |Compute | |
| End | | End | ... |node/end| | End | | End | ... |Node/End|
|Device 1| |Device 2| ...| |device n| | |Device 1| |Device 2| ...| |Device n| |
+--------+ +--------+ +--------+ +--------+ +--------+ +--------+
+ - - - - - - - -+ + - - - - - - - -+
]]></artwork> ]]></artwork>
</artset> </artset>
</figure> </figure>
<t>In the distributed model described in <xref target="rl-fig2"/>, the e <t>In the distributed model described in <xref target="rl-fig2"/>, the e
dge-computing domain is composed of IoT edge gateways and IoT devices which are dge computing domain is composed of IoT edge gateways and IoT devices that are a
also used as computing nodes. Edge computing domains are connected to a remote/ lso used as computing nodes. Edge computing domains are connected to a remote (
cloud network and their respective service operator's system. IoT devices/comput e.g., cloud) network and their respective service operator's system. The computi
ing nodes provide logical functions, for example as part of distributed machine ng nodes provide logical functions, for example, as part of distributed machine
learning or distributed image processing applications. The processing capabiliti learning or distributed image processing applications. The processing capabiliti
es in IoT devices are limited; they require the support of other nodes, and in a es in IoT devices are limited; they require the support of other nodes. In a di
distributed machine learning application, the training process for AI services stributed machine learning application, the training process for AI services can
can be executed at IoT edge gateways or cloud networks and the prediction (infer be executed at IoT edge gateways or cloud networks, and the prediction (inferen
ence) service is executed in the IoT devices. In a distributed image processing ce) service is executed in the IoT devices. Similarly, in a distributed image p
application, some image processing functions can be similarly executed at the ed rocessing application, some image processing
ge or in the cloud, while preprocessing, which helps limiting the amount of uplo functions can be executed at the edge or in the cloud. To limit the amount of da
aded data, is performed by the IoT device.</t> ta to be uploaded to central cloud functions, IoT edge devices may pre-process d
ata.</t>
<figure anchor="rl-fig2"> <figure anchor="rl-fig2">
<name>Example: Machine Learning over a Distributed IoT Edge Computing System</name> <name>Example of Machine Learning over a Distributed IoT Edge Computin g System</name>
<artset> <artset>
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<g class="text"> <g class="text">
<text x="124" y="52">Edge</text> <text x="124" y="52">Edge</text>
<text x="184" y="52">Computing</text> <text x="184" y="52">Computing</text>
<text x="252" y="52">Domain</text> <text x="252" y="52">Domain</text>
<text x="56" y="100">Compute</text> <text x="56" y="100">Compute</text>
<text x="168" y="100">Compute</text> <text x="168" y="100">Compute</text>
<text x="312" y="100">Compute</text> <text x="312" y="100">Compute</text>
<text x="60" y="116">node/End</text> <text x="60" y="116">Node/End</text>
<text x="172" y="116">node/End</text> <text x="172" y="116">Node/End</text>
<text x="244" y="116">....</text> <text x="244" y="116">....</text>
<text x="316" y="116">node/End</text> <text x="316" y="116">Node/End</text>
<text x="52" y="132">device</text> <text x="52" y="132">Device</text>
<text x="88" y="132">1</text> <text x="88" y="132">1</text>
<text x="164" y="132">device</text> <text x="164" y="132">Device</text>
<text x="200" y="132">2</text> <text x="200" y="132">2</text>
<text x="244" y="132">....</text> <text x="244" y="132">....</text>
<text x="308" y="132">device</text> <text x="308" y="132">Device</text>
<text x="344" y="132">m</text> <text x="344" y="132">m</text>
<text x="136" y="196">IoT</text> <text x="136" y="196">IoT</text>
<text x="172" y="196">Edge</text> <text x="172" y="196">Edge</text>
<text x="224" y="196">Gateway</text> <text x="224" y="196">Gateway</text>
<text x="84" y="292">Remote</text> <text x="84" y="292">Remote</text>
<text x="144" y="292">network</text> <text x="144" y="292">Network</text>
<text x="288" y="292">Service</text> <text x="288" y="292">Service</text>
<text x="60" y="308">(e.g.,</text> <text x="60" y="308">(e.g.,</text>
<text x="112" y="308">cloud</text> <text x="112" y="308">cloud</text>
<text x="172" y="308">network)</text> <text x="172" y="308">network)</text>
<text x="296" y="308">Operator(s)</text> <text x="296" y="308">Operator(s)</text>
<text x="136" y="404">IoT</text> <text x="136" y="404">IoT</text>
<text x="172" y="404">Edge</text> <text x="172" y="404">Edge</text>
<text x="224" y="404">Gateway</text> <text x="224" y="404">Gateway</text>
<text x="56" y="468">Compute</text> <text x="56" y="468">Compute</text>
<text x="168" y="468">Compute</text> <text x="168" y="468">Compute</text>
<text x="312" y="468">Compute</text> <text x="312" y="468">Compute</text>
<text x="60" y="484">node/End</text> <text x="60" y="484">Node/End</text>
<text x="172" y="484">node/End</text> <text x="172" y="484">Node/End</text>
<text x="244" y="484">....</text> <text x="244" y="484">....</text>
<text x="316" y="484">node/End</text> <text x="316" y="484">Node/End</text>
<text x="52" y="500">device</text> <text x="52" y="500">Device</text>
<text x="88" y="500">1</text> <text x="88" y="500">1</text>
<text x="164" y="500">device</text> <text x="164" y="500">Device</text>
<text x="200" y="500">2</text> <text x="200" y="500">2</text>
<text x="244" y="500">....</text> <text x="244" y="500">....</text>
<text x="308" y="500">device</text> <text x="308" y="500">Device</text>
<text x="344" y="500">n</text> <text x="344" y="500">n</text>
<text x="124" y="548">Edge</text> <text x="124" y="548">Edge</text>
<text x="184" y="548">Computing</text> <text x="184" y="548">Computing</text>
<text x="252" y="548">Domain</text> <text x="252" y="548">Domain</text>
</g> </g>
</svg> </svg>
</artwork> </artwork>
<artwork type="ascii-art" align="center"><![CDATA[ <artwork type="ascii-art" align="center"><![CDATA[
+----------------------------------------------+ +----------------------------------------------+
| Edge Computing Domain | | Edge Computing Domain |
| | | |
| +--------+ +--------+ +--------+ | | +--------+ +--------+ +--------+ |
| |Compute | |Compute | |Compute | | | |Compute | |Compute | |Compute | |
| |node/End| |node/End| .... |node/End| | | |Node/End| |Node/End| .... |Node/End| |
| |device 1| |device 2| .... |device m| | | |Device 1| |Device 2| .... |Device m| |
| +----+---+ +----+---+ +----+---+ | | +----+---+ +----+---+ +----+---+ |
| | | | | | | | | |
| +---+-------------+-----------------+--+ | | +---+-------------+-----------------+--+ |
| | IoT Edge Gateway | | | | IoT Edge Gateway | |
| +-----------+-------------------+------+ | | +-----------+-------------------+------+ |
| | | | | | | |
+--------------+-------------------+-----------+ +--------------+-------------------+-----------+
| | | |
+-----------+---------+ +------+-------+ +-----------+---------+ +------+-------+
| Remote network | | Service | | Remote Network | | Service |
|(e.g., cloud network)| | Operator(s) | |(e.g., cloud network)| | Operator(s) |
+-----------+---------+ +------+-------+ +-----------+---------+ +------+-------+
| | | |
+--------------+-------------------+-----------+ +--------------+-------------------+-----------+
| | | | | | | |
| +-----------+-------------------+------+ | | +-----------+-------------------+------+ |
| | IoT Edge Gateway | | | | IoT Edge Gateway | |
| +---+-------------+-----------------+--+ | | +---+-------------+-----------------+--+ |
| | | | | | | | | |
| +----+---+ +----+---+ +----+---+ | | +----+---+ +----+---+ +----+---+ |
| |Compute | |Compute | |Compute | | | |Compute | |Compute | |Compute | |
| |node/End| |node/End| .... |node/End| | | |Node/End| |Node/End| .... |Node/End| |
| |device 1| |device 2| .... |device n| | | |Device 1| |Device 2| .... |Device n| |
| +--------+ +--------+ +--------+ | | +--------+ +--------+ +--------+ |
| | | |
| Edge Computing Domain | | Edge Computing Domain |
+----------------------------------------------+ +----------------------------------------------+
]]></artwork> ]]></artwork>
</artset> </artset>
</figure> </figure>
<t>In the following, we enumerate major edge computing domain components . They are here loosely organized into OAM (Operations, Administration, and Main tenance), functional, and application components, with the understanding that th e distinction between these classes may not always be clear, depending on actual system architectures. Some representative research challenges are associated wi th those functions. We used input from co-authors, IRTF attendees, and some comp rehensive reviews of the field (<xref target="Yousefpour"/>, <xref target="Zhang 2"/>, <xref target="Khan"/>).</t> <t>In the following, we enumerate major edge computing domain components . Here, they are loosely organized into Operations, Administration, and Maintena nce (OAM); functional; and application components, with the understanding that t he distinction between these classes may not always be clear, depending on actua l system architectures. Some representative research challenges are associated w ith those functions. We used input from coauthors, participants of T2TRG meetin gs, and some comprehensive reviews of the field (<xref target="Yousefpour"/>, <x ref target="Zhang2"/>, and <xref target="Khan"/>).</t>
</section> </section>
<section anchor="sec-components-oam"> <section anchor="sec-components-oam">
<name>OAM Components</name> <name>OAM Components</name>
<t>Edge computing OAM extends beyond the network-related OAM functions l <t>Edge computing OAM extends beyond the network-related OAM functions l
isted in <xref target="RFC6291"/>. In addition to infrastructure (network, stora isted in <xref target="RFC6291"/>. In addition to infrastructure (network, stora
ge, and computing resources), edge computing systems can also include computing ge, and computing resources), edge computing systems can also include computing
environments (for VMs, software containers, functions), IoT devices, data, and c environments (for VMs, software containers, and functions), IoT devices, data, a
ode.</t> nd code.</t>
<t>Operation-related functions include performance monitoring for servic <t>Operation-related functions include performance monitoring for Servic
e-level agreement measurements, fault management and provisioning for links, nod e Level Agreement (SLA) measurements, fault management, and provisioning for lin
es, compute and storage resources, platforms, and services. Administration cover ks, nodes, compute and storage resources, platforms, and services. Administratio
s network/compute/storage resources, platforms and services discovery, configura n covers network/compute/storage resources, platform and service discovery, conf
tion, and planning. Discovery during normal operation (e.g., discovery of comput iguration, and planning. Discovery during normal operation (e.g., discovery of c
e or storage nodes by endpoints) is typically not included in OAM; however, in t ompute or storage nodes by endpoints) is typically not included in OAM; however,
his document, we do not address it separately. Management covers the monitoring in this document, we do not address it separately. Management covers the monito
and diagnostics of failures, as well as means to minimize their occurrence and t ring and diagnostics of failures, as well as means to minimize their occurrence
ake corrective actions. This may include software update management and high ser and take corrective actions. This may include software update management and hig
vice availability through redundancy and multipath communication. Centralized (e h service availability through redundancy and multipath communication. Centraliz
.g., SDN) and decentralized management systems can be used. Finally, we arbitrar ed (e.g., Software-Defined Networking (SDN)) and decentralized management system
ily chose to address data management as an application component, however, in so s can be used. Finally, we arbitrarily chose to address data management as an ap
me systems, data management may be considered similar to a network management fu plication component; however, in some systems, data management may be considered
nction.</t> similar to a network management function.</t>
<t>We further detail a few relevant OAM components.</t> <t>We further detail a few relevant OAM components.</t>
<section anchor="sec-dis-auth"> <section anchor="sec-dis-auth">
<name>Resource Discovery and Authentication</name> <name>Resource Discovery and Authentication</name>
<t>Discovery and authentication may target platforms and , infrastruct ure resources, such as computing, networking, and storage, as well as other reso urces such as IoT devices, sensors, data, code units, services, applications, an d users interacting with the system. Broker-based solutions can be used, for exa mple, using an IoT gateway as a broker to discover IoT resources. More decentral ized solutions can also be used in replacement or complement, for example, CoAP enables multicast discovery of an IoT device, and CoAP service discovery enables obtaining a list of resources made available by this device <xref target="RFC72 52"/>. For device authentication, current centralized gateway-based systems rely on the installation of a secret on IoT devices and computing devices (e.g., a d evice certificate stored in a hardware security module, or a combination of code and data stored in a trusted execution environment).</t> <t>Discovery and authentication may target platforms and infrastructur e resources, such as computing, networking, and storage, as well as other resour ces, such as IoT devices, sensors, data, code units, services, applications, and users interacting with the system. In a broker-based system, an IoT gateway can act as a broker to discover IoT resources. More decentralized solutions can als o be used in replacement of or in complement to the broker-based solutions; for example, CoAP enables multicast discovery of an IoT device and CoAP service disc overy enables one to obtain a list of resources made available by this device <x ref target="RFC7252"/>. For device authentication, current centralized gateway-b ased systems rely on the installation of a secret on IoT devices and computing d evices (e.g., a device certificate stored in a hardware security module or a com bination of code and data stored in a trusted execution environment).</t>
<t>Related challenges include:</t> <t>Related challenges include:</t>
<ul spacing="normal"> <ul spacing="normal">
<li>Discovery, authentication, and trust establishment between IoT d evices, compute nodes, and platforms, with regard to concerns such as mobility, heterogeneous devices and networks, scale, multiple trust domains, constrained d evices, anonymity, and traceability.</li> <li>Discovery, authentication, and trust establishment between IoT d evices, compute nodes, and platforms, with regard to concerns such as mobility, heterogeneous devices and networks, scale, multiple trust domains, constrained d evices, anonymity, and traceability.</li>
<li>Intermittent connectivity to the Internet, removing the need to rely on a third-party authority <xref target="Echeverria"/>.</li> <li>Intermittent connectivity to the Internet, removing the need to rely on a third-party authority <xref target="Echeverria"/>.</li>
<li>Resiliency to failure <xref target="Harchol"/>, denial of servic e attacks, easier physical access for attackers.</li> <li>Resiliency to failure <xref target="Harchol"/>, denial-of-servic e attacks, and easier physical access for attackers.</li>
</ul> </ul>
</section> </section>
<section anchor="edge-organization-and-federation"> <section anchor="edge-organization-and-federation">
<name>Edge Organization and Federation</name> <name>Edge Organization and Federation</name>
<t>In a distributed system context, once edge devices have discovered and authenticated each other, they can be organized, or self-organized, into hie rarchies or clusters. The organizational structure may range from centralized to peer-to-peer, or it may be closely tied to other systems. Such groups can also form federations with other edges or with remote clouds.</t> <t>In a distributed system context, once edge devices have discovered and authenticated each other, they can be organized or self-organized into hiera rchies or clusters. The organizational structure may range from centralized to p eer-to-peer, or it may be closely tied to other systems. Such groups can also fo rm federations with other edges or with remote clouds.</t>
<t>Related challenges include:</t> <t>Related challenges include:</t>
<ul spacing="normal"> <ul spacing="normal">
<li>Support for scaling, and enabling fault-tolerance or self-healin g <xref target="Jeong"/>. In addition to using a hierarchical organization to co pe with scaling, another available and possibly complementary mechanism is multi cast (<xref target="RFC7390"/> <xref target="I-D.ietf-core-groupcomm-bis"/>). Ot her approaches include relying on blockchains <xref target="Ali"/>.</li> <li>Support for scaling and enabling fault tolerance or self-healing <xref target="Jeong"/>. In addition to using a hierarchical organization to cop e with scaling, another available and possibly complementary mechanism is multic ast <xref target="RFC7390"/> <xref target="I-D.ietf-core-groupcomm-bis"/>. Other approaches include relying on blockchains <xref target="Ali"/>.</li>
<li>Integration of edge computing with virtualized Radio Access Netw orks (Fog RAN) <xref target="I-D.bernardos-sfc-fog-ran"/> and 5G access networks .</li> <li>Integration of edge computing with virtualized Radio Access Netw orks (Fog RAN) <xref target="I-D.bernardos-sfc-fog-ran"/> and 5G access networks .</li>
<li>Sharing resources in multi-vendor/operator scenarios, to optimiz e criteria such as profit <xref target="Anglano"/>, resource usage, latency, and energy consumption.</li> <li>Sharing resources in multi-vendor and multi-operator scenarios t o optimize criteria such as profit <xref target="Anglano"/>, resource usage, lat ency, and energy consumption.</li>
<li>Capacity planning, placement of infrastructure nodes to minimize delay <xref target="Fan"/>, cost, energy, etc.</li> <li>Capacity planning, placement of infrastructure nodes to minimize delay <xref target="Fan"/>, cost, energy, etc.</li>
<li>Incentives for participation, for example, in peer-to-peer feder ation schemes.</li> <li>Incentives for participation, for example, in peer-to-peer feder ation schemes.</li>
<li>Design of federated AI over IoT edge computing systems <xref tar get="Brecko"/>, for example, for anomaly detection.</li> <li>Design of federated AI over IoT edge computing systems <xref tar get="Brecko"/>, for example, for anomaly detection.</li>
</ul> </ul>
</section> </section>
<section anchor="multi-tenancy-and-isolation"> <section anchor="multi-tenancy-and-isolation">
<name>Multi-Tenancy and Isolation</name> <name>Multi-Tenancy and Isolation</name>
<t>Some IoT edge computing systems make use of virtualized (compute, s torage and networking) resources to address the need for secure multi-tenancy at the edge. This leads to "edge clouds" that share properties with remotes clouds and can reuse some of their ecosystems. Virtualization function management is l argely covered by ETSI NFV and MEC standards and recommendations. Projects such as <xref target="LFEDGE-EVE"/> further cover virtualization and its management i n distributed edge-computing settings.</t> <t>Some IoT edge computing systems make use of virtualized (compute, s torage, and networking) resources to address the need for secure multi-tenancy a t the edge. This leads to "edge clouds" that share properties with remote clouds and can reuse some of their ecosystems. Virtualization function management is l argely covered by ETSI NFV and MEC standards and recommendations. Projects such as <xref target="LFEDGE-EVE"/> further cover virtualization and its management i n distributed edge computing settings.</t>
<t>Related challenges include:</t> <t>Related challenges include:</t>
<ul spacing="normal"> <ul spacing="normal">
<li>Adapting cloud management platforms to the edge, to account for its distributed nature, e.g., using Conflict-free Replicated Data Types (CRDT) < xref target="Jeffery"/>, heterogeneity and customization, e.g., using intent-bas ed management mechanisms <xref target="Cao"/>, and limited resources.</li> <li>Adapting cloud management platforms to the edge to account for its d istributed nature, heterogeneity, need for customization, and limited resources (for example, using Conflict-free Replicated Data Types (CRDTs) <xref target="Je ffery"/> or intent-based management mechanisms <xref target="Cao"/>).</li>
<li>Minimizing virtual function instantiation time and resource usag e.</li> <li>Minimizing virtual function instantiation time and resource usag e.</li>
</ul> </ul>
</section> </section>
</section> </section>
<section anchor="sec-components-functional"> <section anchor="sec-components-functional">
<name>Functional Components</name> <name>Functional Components</name>
<section anchor="in-network-computation"> <section anchor="in-network-computation">
<name>In-Network Computation</name> <name>In-Network Computation</name>
<t>A core function of IoT edge computing is to enable local computatio <t>A core function of IoT edge computing is to enable local computatio
n on a node at the network edge, typically for application-layer processing, suc n on a node at the network edge, typically for application-layer processing, suc
h as processing input data from sensors, making local decisions, preprocessing d h as processing input data from sensors, making local decisions, preprocessing d
ata, offloading computation on behalf of a device, service, or user. Related fun ata, and offloading computation on behalf of a device, service, or user. Related
ctions include orchestrating computation (in a centralized or distributed manner functions include orchestrating computation (in a centralized or distributed ma
) and managing application lifecycles. Support for in-network computation may va nner) and managing application life cycles. Support for in-network computation m
ry in terms of capability, for example, computing nodes can host virtual machine ay vary in terms of capability; for example, computing nodes can host virtual ma
s, software containers, software actors, uni-kernels running stateful or statele chines, software containers, software actors, unikernels running stateful or sta
ss code, or a rule engine providing an API to register actions in response to co teless code, or a rule engine providing an API to register actions in response t
nditions such as IoT device ID, sensor values to check, thresholds, etc.</t> o conditions (such as an IoT device ID, sensor values to check, thresholds, etc.
<t>Edge offloading includes offloading to and from an IoT device, and ).</t>
to and from a network node. <xref target="Cloudlets"/> offer an example of offlo <t>Edge offloading includes offloading to and from an IoT device and t
ading computation from an end device to a network node. In contrast, oneM2M is a o and from a network node. <xref target="Cloudlets"/> describes an example of of
n example of a system that allows a cloud-based IoT platform to transfer resourc floading computation from an end device to a network node. In contrast, oneM2M i
es and tasks to a target edge node <xref target="oneM2M-TR0052"/>. Once transfer s an example of a system that allows a cloud-based IoT platform to transfer reso
red, the edge node can directly support IoT devices that it serves with the serv urces and tasks to a target edge node <xref target="oneM2M-TR0052"/>. Once trans
ice offloaded by the cloud (e.g., group management, location management, etc.).< ferred, the edge node can directly support IoT devices that it serves with the s
/t> ervice offloaded by the cloud (e.g., group management, location management, etc.
<t>QoS can be provided in some systems through the combination of netw ).</t>
ork QoS (e.g., traffic engineering or wireless resource scheduling) and compute/ <t>QoS can be provided in some systems through the combination of netw
storage resource allocations. For example, in some systems, a bandwidth manager ork QoS (e.g., traffic engineering or wireless resource scheduling) and compute
service can be exposed to enable allocation of the bandwidth to/from an edge-com and storage resource allocations. For example, in some systems, a bandwidth mana
puting application instance.</t> ger service can be exposed to enable allocation of the bandwidth to or from an e
<t>In-network computation can leverage the underlying services, provid dge computing application instance.</t>
ed using data generated by IoT devices and access networks. Such services includ <t>In-network computation can leverage the underlying services provide
e IoT device location, radio network information, bandwidth management and conge d using data generated by IoT devices and access networks. Such services include
stion management (e.g., the congestion management feature of oneM2M <xref target IoT device location, radio network information, bandwidth management, and conge
="oneM2M-TR0052"/>).</t> stion management (e.g., the congestion management feature of oneM2M <xref target
="oneM2M-TR0052"/>).</t>
<t>Related challenges include:</t> <t>Related challenges include:</t>
<ul spacing="normal"> <ul spacing="normal">
<li>(Computation placement) Selecting, in a centralized or distribut <li>Computation placement: in a centralized or
ed/peer-to-peer manner, an appropriate compute device based on available resourc distributed (e.g., peer-to-peer) manner, selecting an appropriate compute
es, location of data input and data sinks, compute node properties, etc., and wi device. The selection is based on available resources, location of
th varying goals including end-to-end latency, privacy, high availability, energ data input and data sinks, compute node properties, etc. with
y conservation, or network efficiency, for example, using load-balancing techniq varying goals. These goals include end-to-end latency, privacy, high
ues to avoid congestion.</li> availability, energy conservation, or network efficiency (for
<li>Onboarding code on a platform or computing device, and invoking example, using load-balancing techniques to avoid congestion).</li>
remote code execution, possibly as part of a distributed programming model and w <li>Onboarding code on a platform or computing device and invoking r
ith respect to similar concerns of latency, privacy, etc.: For example, offloadi emote code execution, possibly as part of a distributed programming model and wi
ng can be included in a vehicular scenario <xref target="Grewe"/>. These operati th respect to similar concerns of latency, privacy, etc. For example, offloading
ons should deal with heterogeneous compute nodes <xref target="Schafer"/>, and m can be included in a vehicular scenario <xref target="Grewe"/>. These operation
ay also support end devices, including IoT devices, as compute nodes <xref targe s should deal with heterogeneous compute nodes <xref target="Schafer"/> and may
t="Larrea"/>.</li> also support end devices, including IoT devices, as compute nodes <xref target="
Larrea"/>.</li>
<li>Adapting Quality of Results (QoR) for applications where a perfe ct result is not necessary <xref target="Li"/>.</li> <li>Adapting Quality of Results (QoR) for applications where a perfe ct result is not necessary <xref target="Li"/>.</li>
<li>Assisted or automatic partitioning of code: for example, for app <li>Assisted or automatic partitioning of code. For example, for app
lication programs <xref target="I-D.sarathchandra-coin-appcentres"/> or network lication programs <xref target="I-D.sarathchandra-coin-appcentres"/> or network
programs <xref target="I-D.hsingh-coinrg-reqs-p4comp"/>.</li> programs <xref target="I-D.hsingh-coinrg-reqs-p4comp"/>.</li>
<li>Supporting computation across trust domains: for example, verify <li>Supporting computation across trust domains. For example, verify
ing computation results.</li> ing computation results.</li>
<li>Support for computation mobility: relocating an instance from on <li>Supporting computation mobility: relocating an instance from one
e compute node to another, while maintaining a given service level; session cont compute node to another while maintaining a given service level; session contin
inuity when communicating with end devices that are mobile, possibly at high spe uity when communicating with end devices that are mobile, possibly at high speed
ed (e.g., in vehicular scenarios); defining lightweight execution environments f (e.g., in vehicular scenarios); defining lightweight execution environments for
or secure code mobility, for example, using WebAssembly <xref target="Nieke"/>.< secure code mobility, for example, using WebAssembly <xref target="Nieke"/>.</l
/li> i>
<li>Defining, managing, and verifying Service Level Agreements (SLA) <li>Defining, managing, and verifying SLAs for edge computing system
for edge-computing systems: pricing is a challenging task.</li> s; pricing is a challenging task.</li>
</ul> </ul>
</section> </section>
<section anchor="edge-storage-and-caching"> <section anchor="edge-storage-and-caching">
<name>Edge Storage and Caching</name> <name>Edge Storage and Caching</name>
<t>Local storage or caching enables local data processing (e.g., prepr ocessing or analysis) as well as delayed data transfer to the cloud or delayed p hysical shipping. An edge node may offer local data storage (in which persisten ce is subject to retention policies), caching, or both. Caching generally refer s to temporary storage to improve performance without persistence guarantees. A n edge-caching component manages data persistence, for example, it schedules the removal of data when it is no longer needed. Other related aspects include the authentication and encryption of data. Edge storage and caching can take the f orm of a distributed storage systems.</t> <t>Local storage or caching enables local data processing (e.g., prepr ocessing or analysis) as well as delayed data transfer to the cloud or delayed p hysical shipping. An edge node may offer local data storage (in which persisten ce is subject to retention policies), caching, or both. Generally, "caching" re fers to temporary storage to improve performance without persistence guarantees. An edge-caching component manages data persistence; for example, it schedules the removal of data when it is no longer needed. Other related aspects include the authentication and encryption of data. Edge storage and caching can take th e form of a distributed storage system.</t>
<t>Related challenges include:</t> <t>Related challenges include:</t>
<ul spacing="normal"> <ul spacing="normal">
<li>(Cache and data placement) Using cache positioning and data plac <li>Cache and data placement: using cache positioning and data place
ement strategies to minimize data retrieval delay <xref target="Liu"/> and energ ment strategies to minimize data retrieval delay <xref target="Liu"/> and energy
y consumption. Caches may be positioned in the access network infrastructure or consumption. Caches may be positioned in the access-network infrastructure or o
on end devices.</li> n end devices.</li>
<li>Maintaining consistency, freshness, reliability, and privacy of <li>Maintaining consistency, freshness, reliability, and privacy of
stored/cached data in systems that are distributed, constrained, and dynamic (e. data stored or cached in systems that are distributed, constrained, and dynamic
g., owing to end devices and computing nodes churn or mobility), and which can h (e.g., due to node mobility, energy-saving regimes, and disruptions) and which c
ave additional data governance constraints on data storage location. For example an have additional data governance constraints on data storage location. For exa
, <xref target="Mortazavi"/> leverages a hierarchical storage organization. Fres mple, <xref target="Mortazavi"/> describes leveraging a hierarchical storage org
hness-related metrics include the age of information <xref target="Yates"/> that anization. Freshness-related metrics include the age of information <xref target
captures the timeliness of information received from a sender (e.g., an IoT dev ="Yates"/> that captures the timeliness of information received from a sender (e
ice).</li> .g., an IoT device).</li>
</ul> </ul>
</section> </section>
<section anchor="communication"> <section anchor="communication">
<name>Communication</name> <name>Communication</name>
<t>An edge cloud may provide a northbound data plane or management pla <t>An edge cloud may provide a northbound data plane or management pla
ne interface to a remote network, such as a cloud, home or enterprise network. T ne interface to a remote network, such as a cloud, home, or enterprise network.
his interface does not exist in stand-alone (local-only) scenarios. To support s This interface does not exist in stand-alone (local-only) scenarios. To support
uch an interface when it exists, an edge computing component needs to expose an such an interface when it exists, an edge computing component needs to expose an
API, deal with authentication and authorization, and support secure communicatio API, deal with authentication and authorization, and support secure communicati
n.</t> on.</t>
<t>An edge cloud may provide an API or interface to local or mobile us <t>An edge cloud may provide an API or interface to local or mobile us
ers, for example, to provide access to services and applications, or to manage d ers, for example, to provide access to services and applications or to manage da
ata published by local/mobile devices.</t> ta published by local or mobile devices.</t>
<t>Edge-computing nodes communicate with IoT devices over a southbound <t>Edge computing nodes communicate with IoT devices over a southbound
interface, typically for data acquisition and IoT device management.</t> interface, typically for data acquisition and IoT device management.</t>
<t>Communication brokering is a typical function of IoT edge computing <t>Communication brokering is a typical function of IoT edge computing
that facilitates communication with IoT devices, enabling clients to register a that facilitates communication with IoT devices, enables clients to
s recipients for data from devices, as well as forwarding/routing of traffic to register as recipients for data from devices, forwards
or from IoT devices, enabling various data discovery and redistribution patterns traffic to or from IoT devices, enables various data discovery and
, for example, north-south with clouds, east-west with other edge devices <xref redistribution patterns (for example, north-south with clouds and
target="I-D.mcbride-edge-data-discovery-overview"/>. Another related aspect is east-west with other edge devices <xref target="I-D.mcbride-edge-data-discovery-
dispatching alerts and notifications to interested consumers both inside and out overview"/>). Another related aspect is dispatching alerts and notifications to
side the edge-computing domain. Protocol translation, analytics, and video tran interested consumers both inside and outside the edge computing domain. Protoc
scoding can also be performed when necessary. Communication brokering may be cen ol translation, analytics, and video transcoding can also be performed when nece
tralized in some systems, for example, using a hub-and-spoke message broker, or ssary. Communication brokering may be centralized in some systems, for example,
distributed with message buses, possibly in a layered bus approach. Distributed using a hub-and-spoke message broker or distributed with message buses, possibly
systems can leverage direct communication between end devices over device-to-de in a layered bus approach. Distributed systems can leverage direct communicati
vice links. A broker can ensure communication reliability and traceability and, on between end devices over device-to-device links. A broker can ensure communi
in some cases, transaction management.</t> cation reliability and traceability and, in some cases, transaction management.<
/t>
<t>Related challenges include:</t> <t>Related challenges include:</t>
<ul spacing="normal"> <ul spacing="normal">
<li>Defining edge computing abstractions, such as PaaS <xref target= <li>Defining edge computing abstractions, such as PaaS <xref target=
"Yangui"/>, suitable for users and cloud systems to interact with edge computing "Yangui"/>, suitable for users and cloud systems to interact with edge computing
systems and dealing with interoperability issues such as data model heterogenei systems and dealing with interoperability issues, such as data-model heterogene
ty.</li> ity.</li>
<li>Enabling secure and resilient communication between IoT devices <li>Enabling secure and resilient communication between IoT devices
and remote cloud, for example, through multipath support.</li> and a remote cloud, for example, through multipath support.</li>
</ul> </ul>
</section> </section>
</section> </section>
<section anchor="sec-components-app"> <section anchor="sec-components-app">
<name>Application Components</name> <name>Application Components</name>
<t>IoT edge computing can host applications, such as those mentioned in <xref target="sec-uc"/>. While describing the components of individual applicati ons is out of our scope, some of those applications share similar functions, suc h as IoT device management and data management, as described below.</t> <t>IoT edge computing can host applications, such as those mentioned in <xref target="sec-uc"/>. While describing the components of individual applicati ons is out of our scope, some of those applications share similar functions, suc h as IoT device management and data management, as described below.</t>
<section anchor="iot-device-management"> <section anchor="iot-device-management">
<name>IoT Device Management</name> <name>IoT Device Management</name>
<t>IoT device management includes managing information regarding IoT d <t>IoT device management includes managing information regarding IoT d
evices, including their sensors, and how to communicate with them. Edge computin evices, including their sensors and how to communicate with them. Edge computing
g addresses the scalability challenges of a large number of IoT devices by separ addresses the scalability challenges of a large number of IoT devices by separa
ating the scalability domain into edge/local networks and remote networks. For e ting the scalability domain into local (e.g., edge) networks and remote networks
xample, in the context of the oneM2M standard, a device management functionality . For example, in the context of the oneM2M standard, a device management functi
(called "software campaign" in oneM2M) enables the installation, deletion, acti onality (called "software campaign" in oneM2M) enables the installation, deletio
vation, and deactivation of software functions/services on a potentially large n n, activation, and deactivation of software functions and services on a potentia
umber of edge nodes <xref target="oneM2M-TR0052"/>. Using a dashboard or managem lly large number of edge nodes <xref target="oneM2M-TR0052"/>. Using a dashboard
ent software, a service provider issues these requests through an IoT cloud plat or management software, a service provider issues these requests through an IoT
form supporting the software campaign functionality.</t> cloud platform supporting the software campaign functionality.</t>
<t>Challenges listed in <xref target="sec-dis-auth"/> may be applicabl <t>The challenges listed in <xref target="sec-dis-auth"/> may be appli
e to IoT devices management as well.</t> cable to IoT device management as well.</t>
</section> </section>
<section anchor="sec-data"> <section anchor="sec-data">
<name>Data Management and Analytics</name> <name>Data Management and Analytics</name>
<t>Data storage and processing at the edge are major aspects of IoT ed ge computing, directly addressing the high-level IoT challenges listed in <xref target="sec-challenges"/>. Data analysis, for example, through AI/ML tasks perfo rmed at the edge, may benefit from specialized hardware support on the computing nodes.</t> <t>Data storage and processing at the edge are major aspects of IoT ed ge computing, directly addressing the high-level IoT challenges listed in <xref target="sec-challenges"/>. Data analysis, for example, through AI/ML tasks perfo rmed at the edge, may benefit from specialized hardware support on the computing nodes.</t>
<t>Related challenges include:</t> <t>Related challenges include:</t>
<ul spacing="normal"> <ul spacing="normal">
<li>Addressing concerns regarding resource usage, security, and priv acy when sharing, processing, discovering, or managing data: for example present ing data in views composed of an aggregation of related data <xref target="Zhang "/>; protecting data communication between authenticated peers <xref target="Bas udan"/>, classifying data (e.g., in terms of privacy, importance, validity), and compressing and encrypting data, for example, using homomorphic encryption to d irectly process encrypted data <xref target="Stanciu"/>.</li> <li>Addressing concerns regarding resource usage, security, and priv acy when sharing, processing, discovering, or managing data: for example, presen ting data in views composed of an aggregation of related data <xref target="Zhan g"/>, protecting data communication between authenticated peers <xref target="Ba sudan"/>, classifying data (e.g., in terms of privacy, importance, and validity) , and compressing and encrypting data, for example, using homomorphic encryption to directly process encrypted data <xref target="Stanciu"/>.</li>
<li>Other concerns regarding edge data discovery (e.g., streaming da ta, metadata, and events) include siloization and lack of standards in edge envi ronments that can be dynamic (e.g., vehicular networks) and heterogeneous <xref target="I-D.mcbride-edge-data-discovery-overview"/>.</li> <li>Other concerns regarding edge data discovery (e.g., streaming da ta, metadata, and events) include siloization and lack of standards in edge envi ronments that can be dynamic (e.g., vehicular networks) and heterogeneous <xref target="I-D.mcbride-edge-data-discovery-overview"/>.</li>
<li>Data-driven programming models <xref target="Renart"/>, for exam <li>Data-driven programming models <xref target="Renart"/>, for exam
ple, event-based, including handling naming and data abstractions.</li> ple, those that are event based, including handling naming and data abstractions
<li>Data integration in an environment that without data standardiza .</li>
tion, or where different sources use different ontologies <xref target="Farnbaue <li>Data integration in an environment without data standardization
r-Schmidt"/>.</li> or where different sources use different ontologies <xref target="Farnbauer-Schm
<li>Addressing concerns such as limited resources, privacy, dynamic, idt"/>.</li>
and heterogeneous environments to deploy machine learning at the edge: for exam <li>Addressing concerns such as limited resources, privacy, and dyna
ple, making machine learning more lightweight and distributed (e.g., enabling di mic and heterogeneous environments to deploy machine learning at the edge: for e
stributed inference at the edge), supporting shorter training times and simplifi xample, making machine learning more lightweight and distributed (e.g., enabling
ed models, and supporting models that can be compressed for efficient communicat distributed inference at the edge), supporting shorter training times and simpl
ion <xref target="Murshed"/>.</li> ified models, and supporting models that can be compressed for efficient communi
cation <xref target="Murshed"/>.</li>
<li>Although edge computing can support IoT services independently o f cloud computing, it can also be connected to cloud computing. Thus, the relati onship between IoT edge computing and cloud computing, with regard to data manag ement, is another potential challenge <xref target="ISO_TR"/>.</li> <li>Although edge computing can support IoT services independently o f cloud computing, it can also be connected to cloud computing. Thus, the relati onship between IoT edge computing and cloud computing, with regard to data manag ement, is another potential challenge <xref target="ISO_TR"/>.</li>
</ul> </ul>
</section> </section>
</section> </section>
<section anchor="simulation-and-emulation-environments"> <section anchor="simulation-and-emulation-environments">
<name>Simulation and Emulation Environments</name> <name>Simulation and Emulation Environments</name>
<t>IoT Edge Computing introduces new challenges to the simulation and em <t>IoT edge computing introduces new challenges to the simulation and em
ulation tools used by researchers and developers. A varied set of applications, ulation tools used by researchers and developers. A varied set of applications,
networks, and computing technologies can coexist in a distributed system, making networks, and computing technologies can coexist in a distributed system, making
modeling difficult. Scale, mobility, and resource management are additional cha modeling difficult. Scale, mobility, and resource management are additional cha
llenges <xref target="SimulatingFog"/>.</t> llenges <xref target="SimulatingFog"/>.</t>
<t>Tools include simulators, where simplified application logic runs on <t>Tools include simulators, where simplified application logic runs on
top of a fog network model, and emulators, where actual applications can be depl top of a fog network model, and emulators, where actual applications can be depl
oyed, typically in software containers, over a cloud infrastructure (e.g., Docke oyed, typically in software containers, over a cloud infrastructure (e.g., Docke
r and Kubernetes) running over a network emulating network edge conditions such r and Kubernetes) running over a network emulating network edge conditions, such
as variable delays, throughput and mobility events. To gain in scale, emulated a as variable delays, throughput, and mobility events. To gain in scale, emulate
nd simulated systems can be used together in hybrid federation-based approaches d and simulated systems can be used together in hybrid federation-based approach
<xref target="PseudoDynamicTesting"/>, whereas to gain in realism, physical devi es <xref target="PseudoDynamicTesting"/>; whereas to gain in realism, physical d
ces can be interconnected with emulated systems. Examples of related work and pl evices can be interconnected with emulated systems. Examples of related work and
atforms include the publicly accessible MEC sandbox work recently initiated in E platforms include the publicly accessible MEC sandbox work recently initiated i
TSI <xref target="ETSI_Sandbox"/>, and open source simulators and emulators (<xr n ETSI <xref target="ETSI_Sandbox"/> and open-source simulators and emulators (<
ef target="AdvantEDGE"/> emulator and tools cited in <xref target="SimulatingFog xref target="AdvantEDGE"/> emulator and tools cited in <xref target="SimulatingF
"/>). EdgeNet <xref target="Senel"/> is a globally distributed edge cloud for In og"/>). EdgeNet <xref target="Senel"/> is a globally distributed edge cloud for
ternet researchers, using nodes contributed by institutions, and based on Docker Internet researchers, which uses nodes contributed by institutions and which is
for containerization and Kubernetes for deployment and node management.</t> based on Docker for containerization and Kubernetes for deployment and node mana
<t>Digital twins are virtual instances of a physical system (twin) that gement.</t>
are continually updated with the latter's performance, maintenance, and health s <t>Digital twins are virtual instances of a physical system (twin) that
tatus data throughout the life cycle of the physical system. <xref target="Madn are continually updated with the latter's performance, maintenance, and health s
i"/>. In contrast to a traditional emulation or simulated environment, digital t tatus data throughout the life cycle of the physical system <xref target="Madni"
wins, once generated, are maintained in sync by their physical twin, which can b />. In contrast to an emulation or simulated environment, digital twins, once ge
e, among many other instances, an IoT device, edge device, an edge network. The nerated, are maintained in sync by their physical twin, which can be, among many
benefits of digital twins go beyond those of emulation and include accelerated b other instances, an IoT device, edge device, or an edge network. The benefits o
usiness processes, enhanced productivity, and faster innovation with reduced cos f digital twins go beyond those of emulation and include accelerated business pr
ts <xref target="I-D.irtf-nmrg-network-digital-twin-arch"/>.</t> ocesses, enhanced productivity, and faster innovation with reduced costs <xref t
arget="I-D.irtf-nmrg-network-digital-twin-arch"/>.</t>
</section> </section>
</section> </section>
<section anchor="security-considerations"> <section anchor="security-considerations">
<name>Security Considerations</name> <name>Security Considerations</name>
<t>Privacy and security are drivers of the adoption of edge computing for the IoT (<xref target="sec-priv"/>). As discussed in <xref target="sec-dis-auth" />, authentication and trust (among computing nodes, management nodes, and end d evices) can be challenging as scale, mobility, and heterogeneity increase. The s ometimes disconnected nature of edge resources can avoid reliance on third-party authorities. Distributed edge computing is exposed reliability and denial of se rvice attacks. Personal or proprietary IoT data leakage is also a major threat, particularly because of the distributed nature of the systems (<xref target="sec -data"/>). Furthermore, blockchain-based distributed IoT edge computing must be designed for privacy, since public blockchain addressing does not guarantee abso lute anonymity <xref target="Ali"/>.</t> <t>Privacy and security are drivers of the adoption of edge computing for the IoT (<xref target="sec-priv"/>). As discussed in <xref target="sec-dis-auth" />, authentication and trust (among computing nodes, management nodes, and end d evices) can be challenging as scale, mobility, and heterogeneity increase. The s ometimes disconnected nature of edge resources can avoid reliance on third-party authorities. Distributed edge computing is exposed to reliability and denial-of -service attacks. A personal or proprietary IoT data leakage is also a major thr eat, particularly because of the distributed nature of the systems (<xref target ="sec-data"/>). Furthermore, blockchain-based distributed IoT edge computing mus t be designed for privacy, since public blockchain addressing does not guarantee absolute anonymity <xref target="Ali"/>.</t>
<t>However, edge computing also offers solutions in the security space: ma intaining privacy by computing sensitive data closer to data generators is a maj or use case for IoT edge computing. An edge cloud can be used to perform action s based on sensitive data or to anonymize or aggregate data prior to transmissio n to a remote cloud server. Edge computing communication brokering functions can also be used to secure communication between edge and cloud networks.</t> <t>However, edge computing also offers solutions in the security space: ma intaining privacy by computing sensitive data closer to data generators is a maj or use case for IoT edge computing. An edge cloud can be used to perform action s based on sensitive data or to anonymize or aggregate data prior to transmissio n to a remote cloud server. Edge computing communication brokering functions can also be used to secure communication between edge and cloud networks.</t>
</section> </section>
<section anchor="conclusion"> <section anchor="conclusion">
<name>Conclusion</name> <name>Conclusion</name>
<t>IoT edge computing plays an essential role, complementary to the cloud, in enabling IoT systems in certain situations. In this document, we presented u se cases and listing the core challenges faced by IoT that drive the need for Io T edge computing. The first part of this document may therefore help focus futur e research efforts on the aspects of IoT edge computing where it is most useful. The second part of this document presents a general system model and structured overview of the associated research challenges and related work. The structure, based on the system model, is not meant to be restrictive and exists for the pu rpose of having a link between individual research areas and where they are appl icable in an IoT edge computing system.</t> <t>IoT edge computing plays an essential role, complementary to the cloud, in enabling IoT systems in certain situations. In this document, we presented u se cases and listed the core challenges faced by the IoT that drive the need for IoT edge computing. Therefore, the first part of this document may help focus future research efforts on the aspects of IoT edge computing where it is most us eful. The second part of this document presents a general system model and struc tured overview of the associated research challenges and related work. The struc ture, based on the system model, is not meant to be restrictive and exists for t he purpose of having a link between individual research areas and where they are applicable in an IoT edge computing system.</t>
</section> </section>
<section anchor="iana-considerations"> <section anchor="iana-considerations">
<name>IANA Considerations</name> <name>IANA Considerations</name>
<t>This document has no IANA actions.</t> <t>This document has no IANA actions.</t>
</section> </section>
<section anchor="acknowledgements">
<name>Acknowledgements</name>
<t>The authors would like to thank Joo-Sang Youn, Akbar Rahman, Michel Roy
, Robert Gazda, Rute Sofia, Thomas Fossati, Chonggang Wang, <contact fullname="M
arie-José Montpetit"/>, Carlos J. Bernardos, Milan Milenkovic, Dale Seed, JaeSeu
ng Song, Roberto Morabito, Carsten Bormann and <contact fullname="Ari Keränen"/>
for their valuable comments and suggestions on this document.</t>
</section>
</middle> </middle>
<back> <back>
<displayreference target="I-D.mcbride-edge-data-discovery-overview" to="EDGE-DAT
A-DISCOVERY-OVERVIEW"/>
<displayreference target="I-D.irtf-t2trg-rest-iot" to="REST-IOT"/>
<displayreference target="I-D.bernardos-sfc-fog-ran" to="SFC-FOG-RAN"/>
<displayreference target="I-D.ietf-core-groupcomm-bis" to="CORE-GROUPCOMM-BIS"/>
<displayreference target="I-D.sarathchandra-coin-appcentres" to="COIN-APPCENTRES
"/>
<displayreference target="I-D.defoy-t2trg-iot-edge-computing-background" to="EDG
E-COMPUTING-BACKGROUND"/>
<displayreference target="I-D.irtf-nmrg-network-digital-twin-arch" to="NETWORK-D
IGITAL-TWIN-ARCH"/>
<displayreference target="I-D.hsingh-coinrg-reqs-p4comp" to="REQS-P4COMP"/>
<references> <references>
<name>Informative References</name> <name>Informative References</name>
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<front>
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<organization>Futurewei</organization>
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it becomes increasingly dispersed throughout the network. There
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will need to be developed to support distributed data discovery at
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</t> <xi:include href="https://datatracker.ietf.org/doc/bibxml3/reference.I-D.mcbride
</abstract> -edge-data-discovery-overview.xml"/>
</front>
<seriesInfo name="Internet-Draft" value="draft-mcbride-edge-data-discove <xi:include href="https://bib.ietf.org/public/rfc/bibxml/reference.RFC.6291.xml"
ry-overview-05"/> />
</reference> <xi:include href="https://bib.ietf.org/public/rfc/bibxml/reference.RFC.8578.xml"
<reference anchor="RFC6291"> />
<front>
<title>Guidelines for the Use of the "OAM" Acronym in the IETF</title> <xi:include href="https://datatracker.ietf.org/doc/bibxml3/reference.I-D.irtf-t2
<author fullname="L. Andersson" initials="L." surname="Andersson"/> trg-rest-iot.xml"/>
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"/>
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l-understood. Looking at the acronym a bit more closely reveals a set of recurri
ng problems that are revisited time and again.</t>
<t>This document provides a definition of the acronym "OAM" (Operati
ons, Administration, and Maintenance) for use in all future IETF documents that
refer to OAM. There are other definitions and acronyms that will be discussed wh
ile exploring the definition of the constituent parts of the "OAM" term. This me
mo documents an Internet Best Current Practice.</t>
</abstract>
</front>
<seriesInfo name="BCP" value="161"/>
<seriesInfo name="RFC" value="6291"/>
<seriesInfo name="DOI" value="10.17487/RFC6291"/>
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<title>Deterministic Networking Use Cases</title>
<author fullname="E. Grossman" initials="E." role="editor" surname="Gr
ossman"/>
<date month="May" year="2019"/>
<abstract>
<t>This document presents use cases for diverse industries that have
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otably in their network topologies and specific desired behavior, providing as a
group broad industry context for Deterministic Networking (DetNet). For each us
e case, this document will identify the use case, identify representative soluti
ons used today, and describe potential improvements that DetNet can enable.</t>
</abstract>
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<seriesInfo name="RFC" value="8578"/>
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e>
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<organization>Siemens</organization>
</author>
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skipping to change at line 805 skipping to change at line 755
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<title>Edge Computing: Vision and Challenges</title> <title>Edge Computing: Vision and Challenges</title>
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skipping to change at line 1454 skipping to change at line 1416
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<reference anchor="RFC7390">
<front>
<title>Group Communication for the Constrained Application Protocol (C
oAP)</title>
<author fullname="A. Rahman" initials="A." role="editor" surname="Rahm
an"/>
<author fullname="E. Dijk" initials="E." role="editor" surname="Dijk"/
>
<date month="October" year="2014"/>
<abstract>
<t>The Constrained Application Protocol (CoAP) is a specialized web
transfer protocol for constrained devices and constrained networks. It is antici
pated that constrained devices will often naturally operate in groups (e.g., in
a building automation scenario, all lights in a given room may need to be switch
ed on/off as a group). This specification defines how CoAP should be used in a g
roup communication context. An approach for using CoAP on top of IP multicast is
detailed based on existing CoAP functionality as well as new features introduce
d in this specification. Also, various use cases and corresponding protocol flow
s are provided to illustrate important concepts. Finally, guidance is provided f
or deployment in various network topologies.</t>
</abstract>
</front>
<seriesInfo name="RFC" value="7390"/>
<seriesInfo name="DOI" value="10.17487/RFC7390"/>
</reference>
<reference anchor="I-D.ietf-core-groupcomm-bis">
<front>
<title>Group Communication for the Constrained Application Protocol (C
oAP)</title>
<author fullname="Esko Dijk" initials="E." surname="Dijk">
<organization>IoTconsultancy.nl</organization>
</author>
<author fullname="Chonggang Wang" initials="C." surname="Wang">
<organization>InterDigital</organization>
</author>
<author fullname="Marco Tiloca" initials="M." surname="Tiloca">
<organization>RISE AB</organization>
</author>
<date day="10" month="July" year="2023"/>
<abstract>
<t> This document specifies the use of the Constrained Application
Protocol (CoAP) for group communication, including the use of UDP/IP
multicast as the default underlying data transport. Both unsecured
and secured CoAP group communication are specified. Security is
achieved by use of the Group Object Security for Constrained RESTful
Environments (Group OSCORE) protocol. The target application area of
this specification is any group communication use cases that involve
resource-constrained devices or networks that support CoAP. This
document replaces RFC 7390, while it updates RFC 7252 and RFC 7641.
</t> <xi:include href="https://bib.ietf.org/public/rfc/bibxml/reference.RFC.7390.xml"
</abstract> />
</front>
<seriesInfo name="Internet-Draft" value="draft-ietf-core-groupcomm-bis-0 <xi:include href="https://datatracker.ietf.org/doc/bibxml3/reference.I-D.ietf-co
9"/> re-groupcomm-bis.xml"/>
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<organization/> <organization/>
</author> </author>
<author initials="C." surname="Murphy" fullname="Christopher Murphy"> <author initials="C." surname="Murphy" fullname="Christopher Murphy">
<organization/> <organization/>
</author> </author>
<author initials="D." surname="Hou" fullname="Daqing Hou"> <author initials="D." surname="Hou" fullname="Daqing Hou">
skipping to change at line 1520 skipping to change at line 1445
</author> </author>
<author initials="N." surname="Khan" fullname="Nazar Khan"> <author initials="N." surname="Khan" fullname="Nazar Khan">
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<organization/> <organization/>
</author> </author>
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<organization/> <organization/>
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<seriesInfo name="ACM Computing Surveys" value="vol. 54, no. 8, pp. 1-37 "/> <refcontent>ACM Computing Surveys, vol. 54, no. 8, pp. 1-37</refcontent>
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<reference anchor="I-D.sarathchandra-coin-appcentres">
<front>
<title>In-Network Computing for App-Centric Micro-Services</title>
<author fullname="Dirk Trossen" initials="D." surname="Trossen">
<organization>Huawei</organization>
</author>
<author fullname="Chathura Sarathchandra" initials="C." surname="Sarat
hchandra">
<organization>InterDigital Inc.</organization>
</author>
<author fullname="Michael Boniface" initials="M." surname="Boniface">
<organization>University of Southampton</organization>
</author>
<date day="26" month="January" year="2021"/>
<abstract>
<t> The application-centric deployment of 'Internet' services has
increased over the past ten years with many millions of applications
providing user-centric services, executed on increasingly more
powerful smartphones that are supported by Internet-based cloud
services in distributed data centres, the latter mainly provided by
large scale players such as Google, Amazon and alike. This draft
outlines a vision for evolving those data centres towards executing
app-centric micro-services; we dub this evolved data centre as an
AppCentre. Complemented with the proliferation of such AppCentres at
the edge of the network, they will allow for such micro-services to
be distributed across many places of execution, including mobile
terminals themselves, while specific micro-service chains equal
today's applications in existing smartphones.
We outline the key enabling technologies that needs to be provided <xi:include href="https://datatracker.ietf.org/doc/bibxml3/reference.I-D.sarathc
for such evolution to be realized, including references to ongoing handra-coin-appcentres.xml"/>
standardization efforts in key areas.
</t> <xi:include href="https://datatracker.ietf.org/doc/bibxml3/reference.I-D.defoy-t
</abstract> 2trg-iot-edge-computing-background.xml"/>
</front>
<seriesInfo name="Internet-Draft" value="draft-sarathchandra-coin-appcen
tres-04"/>
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<front>
<title>IoT Edge Computing: Initiatives, Projects and Products</title>
<author fullname="Xavier de Foy" initials="X." surname="de Foy">
<organization>InterDigital Communications</organization>
</author>
<author fullname="Jungha Hong" initials="J." surname="Hong">
<organization>ETRI</organization>
</author>
<author fullname="Yong-Geun Hong" initials="Y." surname="Hong">
<organization>ETRI</organization>
</author>
<author fullname="Matthias Kovatsch" initials="M." surname="Kovatsch">
<organization>Huawei Technologies Duesseldorf GmbH</organization>
</author>
<author fullname="Eve Schooler" initials="E." surname="Schooler">
<organization>Intel</organization>
</author>
<author fullname="Dirk Kutscher" initials="D." surname="Kutscher">
<organization>University of Applied Sciences Emden/Leer</organizatio
n>
</author>
<date day="25" month="May" year="2020"/>
<abstract>
<t> Many IoT applications have requirements that cannot be met by
the traditional Cloud. As a result, the IoT is driving the Internet
toward Edge computing. This draft reviews initiatives, projects and
products related to IoT Edge Computing.
</t>
</abstract>
</front>
<seriesInfo name="Internet-Draft" value="draft-defoy-t2trg-iot-edge-comp
uting-background-00"/>
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<title>EdgeNet: A Multi-Tenant and Multi-Provider Edge Cloud</title> <title>EdgeNet: A Multi-Tenant and Multi-Provider Edge Cloud</title>
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<author initials="T." surname="Friedman" fullname="Timur Friedman"> <author initials="T." surname="Friedman" fullname="Timur Friedman">
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</author> </author>
<author initials="R." surname="McGeer" fullname="Rick McGeer"> <author initials="R." surname="McGeer" fullname="Rick McGeer">
<organization/> <organization/>
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<date year="2021" month="April"/> <date year="2021" month="April"/>
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<title>Privacy-Preserving Crowd-Monitoring Using Bloom Filters and Hom omorphic Encryption</title> <title>Privacy-Preserving Crowd-Monitoring Using Bloom Filters and Hom omorphic Encryption</title>
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<seriesInfo name="Proceedings of the 4th International Workshop on Edge Systems, Analytics and" value="Networking"/> <refcontent>Proceedings of the 4th International Workshop on Edge System s, Analytics and Networking</refcontent>
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<date year="2021" month="April"/> <date year="2021" month="April"/>
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<seriesInfo name="Proceedings of the 4th International Workshop on Edge Systems, Analytics and" value="Networking"/> <refcontent>Proceedings of the 4th International Workshop on Edge System s, Analytics and Networking</refcontent>
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<seriesInfo name="Proceedings of the 4th International Workshop on Edge Systems, Analytics and" value="Networking"/> <refcontent>Proceedings of the 4th International Workshop on Edge System s, Analytics and Networking</refcontent>
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<author initials="L." surname="Cao" fullname="Lianjie Cao"> <author initials="L." surname="Cao" fullname="Lianjie Cao">
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skipping to change at line 1698 skipping to change at line 1562
<organization/> <organization/>
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<title>The serverkernel operating system</title> <title>The serverkernel operating system</title>
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<refcontent>USENIX, Workshop on Hot Topics in Edge Computing (HotEdge 18 )</refcontent> <refcontent>USENIX Workshop on Hot Topics in Edge Computing (HotEdge 18) </refcontent>
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<title>TR 0001, Use Cases Collection</title> <title>Use Cases Collection</title>
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<refcontent>oneM2M</refcontent> <seriesInfo name="TR" value="0001"/>
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<section anchor="acknowledgements" numbered="false">
<name>Acknowledgements</name>
<t>The authors would like to thank <contact fullname="Joo-Sang Youn"/>, <c
ontact fullname="Akbar Rahman"/>, <contact fullname="Michel Roy"/>, <contact ful
lname="Robert Gazda"/>, <contact fullname="Rute Sofia"/>, <contact fullname="Tho
mas Fossati"/>, <contact fullname="Chonggang Wang"/>, <contact fullname="Marie-J
osé Montpetit"/>, <contact fullname="Carlos J. Bernardos"/>, <contact fullname="
Milan Milenkovic"/>, <contact fullname="Dale Seed"/>, <contact fullname="JaeSeun
g Song"/>, <contact fullname="Roberto Morabito"/>, <contact fullname="Carsten Bo
rmann"/>, and <contact fullname="Ari Keränen"/> for their valuable comments and
suggestions on this document.</t>
</section>
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 End of changes. 279 change blocks. 
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