IoT Edge Challenges and Functions
draft-irtf-t2trg-iot-edge-03

Document Type Active Internet-Draft (t2trg RG)
Authors Jungha Hong  , Yong-Geun Hong  , Xavier de Foy  , Matthias Kovatsch  , Eve Schooler  , Dirk Kutscher 
Last updated 2021-08-18
Replaces draft-hong-t2trg-iot-edge-computing
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Network Working Group                                            J. Hong
Internet-Draft                                                      ETRI
Intended status: Informational                                 Y-G. Hong
Expires: 19 February 2022                             Daejeon University
                                                               X. de Foy
                                        InterDigital Communications, LLC
                                                             M. Kovatsch
                                    Huawei Technologies Duesseldorf GmbH
                                                             E. Schooler
                                                                   Intel
                                                             D. Kutscher
                               University of Applied Sciences Emden/Leer
                                                          18 August 2021

                   IoT Edge Challenges and Functions
                      draft-irtf-t2trg-iot-edge-03

Abstract

   Many IoT applications have requirements that cannot be met by the
   traditional Cloud (aka cloud computing).  These include time
   sensitivity, data volume, connectivity cost, operation in the face of
   intermittent services, privacy, and security.  As a result, the IoT
   is driving the Internet toward Edge computing.  This document
   outlines the requirements of the emerging IoT Edge and its
   challenges.  It presents a general model, and major components of the
   IoT Edge, to provide a common base for future discussions in T2TRG
   and other IRTF and IETF groups.

Status of This Memo

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   This Internet-Draft will expire on 19 February 2022.

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Copyright Notice

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   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
   Provisions Relating to IETF Documents (https://trustee.ietf.org/
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   Please review these documents carefully, as they describe your rights
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   provided without warranty as described in the Simplified BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  Background  . . . . . . . . . . . . . . . . . . . . . . . . .   3
     2.1.  Internet of Things (IoT)  . . . . . . . . . . . . . . . .   3
     2.2.  Cloud Computing . . . . . . . . . . . . . . . . . . . . .   4
     2.3.  Edge Computing  . . . . . . . . . . . . . . . . . . . . .   4
     2.4.  Examples of IoT Edge Computing Use Cases  . . . . . . . .   6
   3.  IoT Challenges Leading Towards Edge Computing . . . . . . . .   9
     3.1.  Time Sensitivity  . . . . . . . . . . . . . . . . . . . .   9
     3.2.  Connectivity Cost . . . . . . . . . . . . . . . . . . . .  10
     3.3.  Resilience to Intermittent Services . . . . . . . . . . .  10
     3.4.  Privacy and Security  . . . . . . . . . . . . . . . . . .  10
   4.  IoT Edge Computing Functions  . . . . . . . . . . . . . . . .  11
     4.1.  Overview of IoT Edge Computing Today  . . . . . . . . . .  11
     4.2.  General Model . . . . . . . . . . . . . . . . . . . . . .  13
     4.3.  OAM Components  . . . . . . . . . . . . . . . . . . . . .  16
       4.3.1.  Resource Discovery and Authentication . . . . . . . .  16
       4.3.2.  Edge Organization and Federation  . . . . . . . . . .  17
       4.3.3.  Multi-Tenancy and Isolation . . . . . . . . . . . . .  17
     4.4.  Functional Components . . . . . . . . . . . . . . . . . .  18
       4.4.1.  In-Network Computation  . . . . . . . . . . . . . . .  18
       4.4.2.  Edge Storage and Caching  . . . . . . . . . . . . . .  19
       4.4.3.  Northbound/Southbound Communication . . . . . . . . .  20
       4.4.4.  Communication Brokering . . . . . . . . . . . . . . .  21
       4.4.5.  Other Services  . . . . . . . . . . . . . . . . . . .  21
     4.5.  Application Components  . . . . . . . . . . . . . . . . .  21
       4.5.1.  IoT End Devices Management  . . . . . . . . . . . . .  22
       4.5.2.  Data Management and Analytics . . . . . . . . . . . .  22
     4.6.  Simulation and Emulation Environments . . . . . . . . . .  23
   5.  Security Considerations . . . . . . . . . . . . . . . . . . .  23
   6.  Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .  24
   7.  Informative References  . . . . . . . . . . . . . . . . . . .  24
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  31

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1.  Introduction

   Currently, many IoT services leverage the Cloud, since it can provide
   virtually unlimited storage and processing power.  The reliance of
   IoT on back-end cloud computing brings additional advantages such as
   flexibility and efficiency.  Today's IoT systems are fairly static
   with respect to integrating and supporting computation.  It's not
   that there is no computation, but systems are often limited to static
   configurations (edge gateways, cloud services).

   However, IoT devices are creating vast amounts of data at the network
   edge.  To meet IoT use case requirements, that data increasingly is
   being stored, processed, analyzed, and acted upon close to the data
   producers.  These requirements include time sensitivity, data volume,
   connectivity cost, resiliency in the face of intermittent
   connectivity, privacy, and security, which cannot be addressed by
   today's centralized cloud computing.  These requirements suggest a
   more flexible way to distribute computing (and storage) and to
   integrate it in the edge-cloud continuum.  We will refer to this
   integration of edge computing and IoT as "IoT edge computing".  Our
   draft describes related background, uses cases, challenges, system
   models, and functional components.

   Due to the dynamic nature of the IoT edge computing landscape, this
   document does not list existing projects in this field.  However,
   Section 4.1 presents a high-level overview of the field, based on a
   limited review of standards, research, open-source and proprietary
   products in [I-D-defoy-t2trg-iot-edge-computing-background].

2.  Background

2.1.  Internet of Things (IoT)

   Since the term "Internet of Things" (IoT) was coined by Kevin Ashton
   in 1999 working on Radio-Frequency Identification (RFID) technology
   [Ashton], the concept of IoT has evolved.  It now reflects a vision
   of connecting the physical world to the virtual world of computers
   using (wireless) networks over which things can send and receive
   information without human intervention.  Recently, the term has
   become more literal by actually connecting things to the Internet and
   converging on Internet and Web technology.

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   Things are usually embedded systems of various kinds, such as home
   appliances, mobile equipment, wearable devices, etc.  Things are
   widely distributed, but typically have limited storage and processing
   power, which raise concerns regarding reliability, performance,
   energy consumption, security, and privacy [Lin].  This limited
   storage and processing power leads to complementing IoT with cloud
   computing.

2.2.  Cloud Computing

   Cloud computing has been defined in [NIST]: "cloud computing is a
   model for enabling ubiquitous, convenient, on-demand network access
   to a shared pool of configurable computing resources (e.g., networks,
   servers, storage, applications, and services) that can be rapidly
   provisioned and released with minimal management effort or service
   provider interaction".  Low cost and massive availability of storage
   and processing power enabled the realization of another computing
   model, in which virtualized resources can be leased in an on-demand
   fashion, being provided as general utilities.  Companies like Amazon,
   Google, Facebook, etc. widely adopted this paradigm for delivering
   services over the Internet, gaining both economical and technical
   benefits [Botta].

   Today, an unprecedented volume and variety of data is generated by
   things, and applications deployed at the network edge consume this
   data.  In this context, cloud-based service models are not suitable
   for some classes of applications, which for example need very short
   response times, access to local personal data, or generate vast
   amounts of data.  Those applications may instead leverage edge
   computing.

2.3.  Edge Computing

   Edge computing, also referred to as fog computing in some settings,
   is a new paradigm in which substantial computing and storage
   resources are placed at the edge of the Internet, that is, close to
   mobile devices, sensors, actuators, or machines.  Edge computing
   happens near data sources [Mahadev], or closer (topologically,
   physically, in terms of latency, etc.) to where decisions or
   interactions with the physical world are happening.  It processes
   both downstream data, e.g. originated from cloud services, and
   upstream data, e.g. originated from end devices or network elements.
   The term fog computing usually represents the notion of a multi-
   tiered edge computing, that is, several layers of compute
   infrastructure between the end devices and cloud services.

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   An edge device is any computing or networking resource residing
   between data sources and cloud-based data centers.  In edge
   computing, end devices not only consume data but also produce data.
   And at the network edge, devices not only request services and
   information from the Cloud, but also handle computing tasks including
   processing, storage, caching, and load balancing on data sent to and
   from the Cloud [Shi].  This does not preclude end devices from
   hosting computation themselves when possible, independently or as
   part of a distributed edge computing platform (this is also referred
   to as Mist Computing).

   Several standards developing organization (SDO) and industry forums
   have provided definitions of edge and fog computing:

   *  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" [ISO_TR].

   *  ETSI defines multi-access edge computing as a "system which
      provides 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 close proximity to its
      users" [ETSI_MEC_01].

   *  The Industrial Internet Consortium (formerly 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" [OpenFog].

   Based on these definitions, we can summarize a general philosophy of
   edge computing as to distribute the required functions close to users
   and data, while the difference to classic local systems is the usage
   of management and orchestration features adopted from cloud
   computing.

   Actors from various industries approach edge computing using
   different terms and reference models, although in practice these
   approaches are not incompatible and may integrate with each other:

   *  The telecommunication industry tends to use a model where edge
      computing services are deployed over Network Function
      Virtualization (NFV) infrastructure, at aggregation points or in
      proximity to the user equipment (e.g., gNodeBs) [ETSI_MEC_03].

   *  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").

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   *  The automation industry defines the edge as the connection point
      between IT from OT (Operational Technology).  Hence, here edge
      computing sometimes refers to applying IT solutions to OT problems
      such as analytics, more flexible user interfaces, or simply having
      more computing power than an automation controller.

2.4.  Examples of IoT Edge Computing Use Cases

   IoT edge computing can be used in home, industry, grid, healthcare,
   city, transportation, agriculture, and/or education scenarios.  We
   discuss here only a few examples of such use cases, to point out
   differentiating requirements.  These examples are followed with
   references to other use cases.

   *Smart Factory*

   As part of the 4th industrial revolution, smart factories run real-
   time processes based on IT technologies such as artificial
   intelligence and big data.  In a smart factory, even a very small
   environmental change can lead to a situation in which production
   efficiency decreases or product quality problems occur.  Therefore,
   simple but time-sensitive processing can be performed at the edge:
   for example, controlling temperature and humidity in the factory, or
   operating machines based on the real-time collection of the
   operational status of each machine.  On the other hand, data
   requiring highly precise analysis, such as machine lifecycle
   management or accident risk prediction, can be transferred to a
   central data center for processing.

   The use of edge computing in a smart factory can reduce the cost of
   network and storage resources by reducing the communication load to
   the central data center or server.  It is also possible to improve
   process efficiency and facility asset productivity through the real-
   time prediction of failures, and to reduce the cost of failure
   through preliminary measures.  In the existing manufacturing field,
   production facilities are manually run according to a program entered
   in advance, but edge computing in a smart factory enables tailoring
   solutions by analyzing data at each production facility and machine
   level.

   *Smart Grid*

   In future smart city scenarios, the Smart Grid will be critical in
   ensuring highly available/efficient energy control in city-wide
   electricity management.  Edge computing is expected to play a
   significant role in those systems to improve transmission efficiency
   of electricity; to react and restore power after a disturbance; to
   reduce operation costs and reuse renewable energy effectively, since

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   these operations involve local decision-making.  In addition, edge
   computing can help to monitor power generation and power demand, and
   making local electrical energy storage decisions in the smart grid
   system.

   *Smart Agriculture*

   Smart agriculture integrates information and communication technology
   with farming technology.  Intelligent farms use IoT technology to
   measure and analyze temperature, humidity, sunlight, carbon dioxide,
   soil, etc. in crop cultivation facilities.  Depending on analysis
   results, control devices are used to set environmental parameters to
   an appropriate state.  Remote management is also possible through
   mobile devices such as smartphones.

   In existing farms, simple systems such as management according to
   temperature and humidity can easily and inexpensively be implemented
   with IoT technology.  Sensors in fields are gathering data on field
   and crop condition.  This data is then transmitted to cloud servers,
   which process data and recommend actions.  Usage of edge computing
   can reduce by a large amount data transmitted up and down the
   network, resulting in saving cost and bandwidth.  Locally generated
   data can be processed at the edge, and local computing and analytics
   can drive local actions.  With edge computing, it is also easy for
   farmers to select large amounts of data for processing, and data can
   be analyzed even in remote areas with poor access conditions.  As the
   number of people working on farming decreases over time, increasing
   automation enabled by edge computing can be a driving force for
   future smart agriculture.

   *Smart Construction*

   Safety is critical on a construction site.  Every year, many
   construction workers lose their lives due to falls, collisions,
   electric shocks, and other accidents.  Therefore, solutions have been
   developed in order to improve construction site safety, including
   real-time identification of workers, monitoring of equipment
   location, and predictive accident prevention.  To deploy these
   solutions, many cameras and IoT sensors were installed on
   construction sites, that measure noise, vibration, gas concentration,
   etc.  Typically, data generated from these measurements has been
   collected in an on-site gateway and sent to a remote cloud server for
   storage and analysis.  Thus, an inspector can check the information
   stored on the cloud server to investigate an incident.  However, this
   approach can be expensive, due to transmission costs, e.g., of video
   streams over an LTE connection, and due to usage fees of private
   cloud services such as Amazon Web Services.

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   Using edge computing, data generated on the construction site can be
   processed and analyzed on an edge server located within or near the
   site.  Only the result of this processing needs to be transferred to
   a cloud server, thus saving transmission costs.  It is also possible
   to locally generate warnings to prevent accident in real-time.

   *Self-Driving Car*

   The self-driving car, with its focus on safety, is a system where
   edge computing has an essential role.  Autonomous vehicles are
   equipped with high-resolution cameras, radars, laser scanners
   (LIDAR), sonar sensors, and GPS systems.  Edge computing nodes
   collect and analyze vast amounts of data generated in real-time by
   these sensors to keep track of distances between vehicles in front,
   surrounding road conditions, vehicle flow, and to quickly respond to
   unexpected situations.  For example, if the speed of the car running
   in front decreases, speed should be adjusted to maintain the distance
   between the cars, and when a roadside signal changes, a self-driving
   car should operate according to the new signal.  If such processing
   is performed in a central data center, network delays or data
   transmission errors can lead to accidents.  Applying edge computing
   can minimize these network delays and data transmission errors,
   thereby improving safety.

   *AR/VR*

   Augmented Reality (AR) and Virtual Reality (VR) are likely to
   strongly influence the Information and Communication Technology (ICT)
   market in the future, since they can support innovative products in
   most other use cases including smart factories, self-driving cars,
   etc.  In AR/VR, due to large amounts of data generated at endpoints
   such as mobile devices and PCs, user immersion can be significantly
   decreased by a latency of only a few hundred milliseconds.
   Therefore, using an edge computing infrastructure built close to
   endpoints can not only reduce the cost and latency of data
   transmission but also maximize user immersion.  For example, in AR
   using edge computing, streaming video can be displayed realistically
   in higher quality, giving users the best possible experience.

   *Other Use Cases*

   Additionally, oneM2M recently studied several use cases related to
   edge computing, including: smart factories, smart transportation, an
   accident notification service, a high-precision road map service, a
   vulnerable road user service and a vehicular data service.  These use
   cases are documented in [oneM2M-TR0001], [oneM2M-TR0018] and
   [oneM2M-TR0026].  Edge computing related requirements raised through
   the analysis of these use cases are captured in [oneM2M-TS0002].

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3.  IoT Challenges Leading Towards Edge Computing

   This section describes challenges met by IoT, that are motivating the
   adoption of edge computing for IoT.  Those are distinct from research
   challenges applicable to IoT edge computing, some of which will be
   mentioned in Section 4.3.

   IoT technology is used with more and more demanding applications,
   e.g. in industrial, automotive or healthcare domains, leading to new
   challenges.  For example, industrial machines such as laser cutters
   already produce over 1 terabyte per hour, and similar amounts can be
   generated in autonomous cars [NVIDIA]. 90% of IoT data is expected to
   be stored, processed, analyzed, and acted upon close to the source
   [Kelly], as cloud computing models alone cannot address the new
   challenges [Chiang].

   Below we discuss IoT use case requirements that are moving cloud
   capabilities to be more proximate and more distributed and
   disaggregated.

3.1.  Time Sensitivity

   Many industrial control systems, such as manufacturing systems, smart
   grids, oil and gas systems, etc., often require stringent end-to-end
   latency between the sensor and control node.  While some IoT
   applications may require latency below a few tens of milliseconds
   [Weiner], industrial robots and motion control systems have use cases
   for cycle times in the order of microseconds [_60802].  In some cases
   speed-of-light limitations may simply prevent a solution based on
   remote cloud, however it is not the only challenge relative to time
   sensitivity.  Guarantees for jitter are also important to those
   industrial IoT applications.  This means control packets need to
   arrive with as little variation as possible and within a strict
   deadline.  Given the best-effort 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.

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3.2.  Connectivity Cost

   Some IoT deployments are not challenged by a constrained network
   bandwidth to the Cloud.  The fifth generation mobile networks (5G)
   and Wi-Fi 6 both theoretically top out at 10 gigabits per second
   (i.e., 4.5 terabytes per hour), which enables high-bandwidth uplinks.
   However, the resulting cost for high-bandwidth connectivity to upload
   all data to the Cloud is unjustifiable and impractical for most IoT
   applications.  In some settings, e.g. in aeronautical communication,
   higher communication costs reduce the amount of data that can be
   practically uploaded even further.

3.3.  Resilience to Intermittent Services

   Many IoT devices such as sensors, data collectors, actuators,
   controllers, etc. have very limited hardware resources and cannot
   rely solely on their limited resources to meet all their computing
   and/or storage needs.  They require reliable, uninterrupted, or
   resilient services to augment their capabilities in order to fulfill
   their application tasks.  This is hard and partly impossible to
   achieve with cloud services for systems such as vehicles, drones, or
   oil rigs that have intermittent network connectivity.  The dual is
   also true, a cloud back-end might want to have a reading of the
   device even if it's currently asleep.

3.4.  Privacy and Security

   When IoT services are deployed at home, personal information can be
   learned from detected usage data.  For example, one can extract
   information about employment, family status, age, and income by
   analyzing smart meter data [ENERGY].  Policy-makers started to
   provide frameworks that limit the usage of personal data and put
   strict requirements on data controllers and processors.  However,
   data stored indefinitely in the Cloud also increases the risk of data
   leakage, for instance, through attacks on rich targets.

   Industrial systems are often argued to not have privacy implications,
   as no personal data is gathered.  Yet data from such systems is often
   highly sensitive, as one might be able to infer trade secrets such as
   the setup of production lines.  Hence, the owners of these systems
   are generally reluctant to upload IoT data to the Cloud.

   Furthermore, passive observers can perform traffic analysis on the
   device-to-cloud path.  Hiding traffic patterns associated with sensor
   networks can therefore be another requirement for edge computing.

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4.  IoT Edge Computing Functions

   In this section, we first look at the current state of IoT edge
   computing Section 4.1, and then define a general system model
   Section 4.2.  This provides context for IoT edge computing functions,
   which are listed in Section 4.3.

4.1.  Overview of IoT Edge Computing Today

   This section provides an overview of today's IoT edge computing
   field, based on a limited review of standards, research, open-source
   and proprietary products in
   [I-D-defoy-t2trg-iot-edge-computing-background].

   IoT gateways, both open-source (such as EdgeX Foundry or Home Edge)
   and proprietary (such as Amazon Greengrass, Microsoft Azure IoT Edge,
   Google Cloud IoT Core, and gateways from Bosh, Siemens), represent a
   common class of IoT edge computing products, where the gateway is
   providing a local service on customer premises and is remotely
   managed through a cloud service.  IoT communication protocols are
   typically used between IoT devices and the gateway, including CoAP,
   MQTT, and many specialized IoT protocols (such as OPC UA and DDS in
   the Industrial IoT space), while the gateway communicates with the
   distant cloud typically using HTTPS.  Virtualization platforms enable
   the deployment of virtual edge computing functions (as VMs,
   application containers, etc.), including IoT gateway software, on
   servers in the mobile network infrastructure (at base stations and
   concentration points), in edge data centers (in central offices) or
   regional data centers located near central offices.  End devices are
   envisioned to become computing devices in forward-looking projects,
   but they are not commonly used as such today.

   Besides open-source and proprietary solutions, a horizontal IoT
   service layer is standardized by the oneM2M standards body, to reduce
   fragmentation, increase interoperability and promote reuse in the IoT
   ecosystem.

   Physical or virtual IoT gateways can host application programs, which
   are typically built using an SDK to access local services through a
   programmatic API.  Edge cloud system operators host their customers'
   applications VMs or containers on servers located in or near access
   networks, which can implement local edge services.  For example,
   mobile networks can provide edge services for radio network
   information, location, and bandwidth management.

   Life cycle management of services and applications on physical IoT
   gateways is often cloud-based.  Edge cloud management platforms and
   products (such as StarlingX, Akraino Edge Stack, Mobile EdgeX) adapt

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   cloud management technologies (e.g., Kubernetes) to the edge cloud,
   i.e., to smaller, distributed computing devices running outside a
   controlled data center.  Services and application life-cycle is
   typically using an NFV-like management and orchestration model.

   Resilience in IoT often entails the ability to operate autonomously
   in periods of disconnectedness in order to preserve the integrity and
   safety of the controlled system, possibly in a degraded mode.  IoT
   devices and gateways are often expected to operate in the always-on
   and unattended mode, using fault detection and unassisted recovery
   functions.

   The platform typically includes services to advertise or consume APIs
   (e.g., Mp1 interface in ETSI MEC supports service discovery and
   communication), and enables communicating with local and remote
   endpoints (e.g., message routing function in IoT gateways).  The
   service platform is typically extensible by edge applications, since
   they can advertise an API that other edge applications can consume.
   IoT communication services include protocols translation, analytics,
   and transcoding.  Communication between edge computing devices is
   enabled in tiered deployments or distributed deployments.

   An edge cloud platform may enable pass-through without storage or
   local storage (e.g., on IoT gateways).  Some edge cloud platforms use
   a distributed form of storage such as an ICN network, e.g., Named
   Function Networking (NFN) nodes can store data in a Named Data
   Networking (NDN) system, or a distributed storage platform (e.g.,
   IPFS, EdgeFS, Ceph).  External storage, e.g., on databases in distant
   or local IT cloud, is typically used for filtered data deemed worthy
   of long-term storage, although in some cases it may be for all data,
   for example when required for regulatory reasons.

   Stateful computing is supported on platforms hosting native programs,
   VMs or containers.  Stateless computing is supported on platforms
   providing a "serverless computing" service (a.k.a. function-as-
   a-service), or on systems based on named function networking.

   In many IoT use cases, a typical network usage pattern is high volume
   uplink with some form of traffic reduction enabled by processing over
   edge computing devices.  Alternatives to traffic reduction include
   deferred transmission (to off-peak hours or using physical shipping).
   Downlink traffic includes application control and software updates.
   Other, downlink-heavy traffic patterns are not excluded but are more
   often associated with non-IoT usage (e.g., video CDNs).

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4.2.  General Model

   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 computing platforms.  Although there are lots of approaches
   to edge computing, we attempt to lay out a general model and list
   associated logical functions in this section.  In practice, this
   model can map to different architectures, such as:

   *  A single IoT gateway, or a hierarchy of IoT gateways, typically
      connected to the cloud (e.g., to extend the traditional cloud-
      based management of IoT devices and data to the edge).  A common
      role of an IoT Gateway is to provide access to a heterogeneous set
      of IoT devices/sensors; handle IoT data; and deliver IoT data to
      its final destination in a cloud network.  Whereas an IoT gateway
      needs interactions with the cloud, it can also operate
      independently in a disconnected mode.

   *  A set of distributed computing nodes, e.g., embedded in switches,
      routers, edge cloud servers, or mobile devices.  Some IoT end
      devices can have enough computing capabilities to participate in
      such distributed systems due to advances in hardware technology.
      In this model, edge computing nodes can collaborate to share their
      resources.

   In the general model described in Figure 1, the edge computing domain
   is interconnected with IoT end devices (southbound connectivity) and
   possibly with a remote/cloud network (northbound connectivity), and
   with a service operator's system.  Edge computing nodes provide
   multiple logical functions, or components, which may not all be
   present in a given system.  They may be implemented in a centralized
   or distributed fashion, at the network edge, or through some
   interworking between edge network and remote cloud network.

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                +---------------------+
                |   Remote network    |  +---------------+
                |(e.g., cloud network)|  |   Service     |
                +-----------+---------+  |   Operator    |
                            |            +------+--------+
                            |                   |
             +--------------+-------------------+-----------+
             |            Edge Computing Domain             |
             |                                              |
             |   One or more Computing Nodes                |
             |   (IoT gateway, end devices, switches,       |
             |   routers, mini/micro-data centers, etc.)    |
             |                                              |
             |   OAM Components                             |
             |   - Resource Discovery and Authentication    |
             |   - Edge Organization and Federation         |
             |   - Multi-Tenancy and Isolation              |
             |   - ...                                      |
             |                                              |
             |   Functional Components                      |
             |   - In-Network Computation                   |
             |   - Edge Caching                             |
             |   - North/South-bound Communication          |
             |   - Communication Brokering                  |
             |   - Other Services                           |
             |   - ...                                      |
             |                                              |
             |   Application Components                     |
             |   - IoT End Devices Management               |
             |   - Data Management and Analytics            |
             |   - ...                                      |
             |                                              |
             +------+--------------+-------- - - - -+- - - -+
                    |              |       |        |       |
                    |              |          +-----+--+
               +----+---+    +-----+--+    |  |compute |    |
               |  End   |    |  End   | ...   |node/end|
               |Device 1|    |Device 2| ...|  |device n|    |
               +--------+    +--------+       +--------+
                                           + - - - - - - - -+

                   Figure 1: Model of IoT Edge Computing

   In the distributed model described in Figure 2, the edge computing
   domain is composed of IoT edge gateways and IoT end devices which are
   also used as computing nodes.  Edge computing domains are connected
   with a remote/cloud network, and with their respective service
   operator's system.  IoT end devices/computing nodes provide logical

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   functions, as part of a distributed machine learning application.
   The processing capabilities in IoT end devices being limited, they
   require the support of other nodes: the training process for AI
   services is executed at IoT edge gateways or cloud networks and the
   prediction (inference) service is executed in the IoT end devices.

             +----------------------------------------------+
             |            Edge Computing Domain             |
             |                                              |
             | +--------+    +--------+        +--------+   |
             | |Compute |    |Compute |        |Compute |   |
             | |node/End|    |node/End|  ....  |node/End|   |
             | |device 1|    |device 2|  ....  |device m|   |
             | +----+---+    +----+---+        +----+---+   |
             |      |             |                 |       |
             |  +---+-------------+-----------------+--+    |
             |  |           IoT Edge Gateway           |    |
             |  +-----------+-------------------+------+    |
             |              |                   |           |
             +--------------+-------------------+-----------+
                            |                   |
                +-----------+---------+  +------+-------+
                |   Remote network    |  |   Service    |
                |(e.g., cloud network)|  |  Operator(s) |
                +-----------+---------+  +------+-------+
                            |                   |
             +--------------+-------------------+-----------+
             |              |                   |           |
             |  +-----------+-------------------+------+    |
             |  |           IoT Edge Gateway           |    |
             |  +---+-------------+-----------------+--+    |
             |      |             |                 |       |
             | +----+---+    +----+---+        +----+---+   |
             | |Compute |    |Compute |        |Compute |   |
             | |node/End|    |node/End|  ....  |node/End|   |
             | |device 1|    |device 2|  ....  |device n|   |
             | +--------+    +--------+        +--------+   |
             |                                              |
             |            Edge Computing Domain             |
             +----------------------------------------------+

      Figure 2: Example: Machine Learning over a Distributed IoT Edge
                              Computing System

   We now attempt to enumerate major edge computing domain components.
   They are here loosely organized into OAM (Operations, Administration,
   and Maintenance), functional and application components, with the
   understanding that the distinction between these classes may not

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   always be clear, depending on actual system architectures.  Some
   representative research challenges are associated with those
   functions.  We used input from co-authors, IRTF attendees, and some
   comprehensive reviews of the field ([Yousefpour], [Zhang2], [Khan]).

4.3.  OAM Components

   Edge computing OAM goes beyond the network-related OAM functions
   listed in [RFC6291].  Besides infrastructure (network, storage, and
   computing resources), edge computing systems can also include
   computing environments (for VMs, software containers, functions), IoT
   end devices, data, and code.

   Operation-related functions include performance monitoring for
   service level agreement measurement; fault management and
   provisioning for links, nodes, compute and storage resources,
   platforms, and services.  Administration covers network/compute/
   storage resources, platforms and services discovery, configuration,
   and planning.  Management covers monitoring and diagnostics of
   failures, as well as means to minimize their occurrence and take
   corrective actions.  This may include software updates management,
   high service availability through redundancy and multipath
   communication.  Centralized (e.g., SDN) and decentralized management
   systems can be used.

   We further detail a few OAM components.

4.3.1.  Resource Discovery and Authentication

   Discovery and authentication may target platforms, infrastructure
   resources, such as compute, network and storage, but also other
   resources such as IoT end devices, sensors, data, code units,
   services, applications, or users interacting with the system.
   Broker-based solutions can be used, e.g. using an IoT gateway as a
   broker to discover IoT resources.  Today, centralized gateway-based
   systems rely, for device authentication, on the installation of a
   secret on IoT end devices and computing devices (e.g., a device
   certificate stored in a hardware security module).

   Related challenges include:

   *  Discovery, authentication, and trust establishment between end
      devices, compute nodes, and platforms, with regard to concerns
      such as mobility, heterogeneity, scale, multiple trust domains,
      constrained devices, anonymity, and traceability

   *  Intermittent connectivity to the Internet, preventing relying on a
      third-party authority [Echeverria]

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   *  Resiliency to failures [Harchol], denial of service attacks,
      easier physical access for attackers

4.3.2.  Edge Organization and Federation

   In a distributed system context, once edge devices have discovered
   and authenticated each other, they can be organized, or self-
   organize, into hierarchies or clusters.  The organization structure
   may range from centralized to peer-to-peer, or it may be closely tied
   with other systems.  Such groups can also form federations with other
   edge or remote clouds.

   Related challenges include:

   *  Support for scaling, and enabling fault-tolerance or self-healing
      [Jeong].  Besides using hierarchical organization to cope with
      scaling, another available and possibly complementary mechanism is
      multicast ([RFC7390] [I-D.ietf-core-oscore-groupcomm])

   *  Integration of edge computing with virtualized Radio Access
      Networks (Fog RAN) [I-D.bernardos-sfc-fog-ran] and with 5G access
      networks

   *  Sharing resources in multi-vendor/operator scenarios, to optimize
      criteria such as profit [Anglano], resource usage, latency, or
      energy consumption

   *  Capacity planning, placement of infrastructure nodes to minimize
      delay [Fan], cost, energy, etc.

   *  Incentives for participation, e.g. in peer-to-peer federation
      schemes

4.3.3.  Multi-Tenancy and Isolation

   Some IoT edge computing systems make use of virtualized (compute,
   storage and networking) resources to address the need for secure
   multi-tenancy at the edge.  This leads to "edge clouds" that share
   properties with the remote Cloud and can reuse some of its ecosystem.
   Virtualization function management is covered to a large extent by
   ETSI NFV and MEC standards activities.  Projects such as [LFEDGE-EVE]
   further cover virtualization and its management into distributed edge
   computing settings.

   Related challenges include:

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   *  Adapting cloud management platforms to the edge, to account for
      its distributed nature, e.g., using Conflict-free Replicated Data
      Types (CRDT) [Jeffery], heterogeneity and customization, e.g.,
      using intent-based management mechanisms [Cao], and limited
      resources.

   *  Minimizing virtual function instantiation time and resource usage

4.4.  Functional Components

4.4.1.  In-Network Computation

   A core function of IoT edge computing is to enable local computation
   on a node at the network edge, e.g. processing input data from
   sensors, making local decisions, preprocessing data, offloading
   computation on behalf of a device, service, or user.  Related
   functions include orchestrating computation (in a centralized or
   distributed manner) and managing applications lifecycle.  Support for
   in-network computation may vary in term of capability, e.g.,
   computing nodes can host virtual machines, software containers,
   software actors or unikernels able to run stateful or stateless code,
   or a rules engine providing an API to register actions in response to
   conditions such as IoT device ID, sensor values to check, thresholds,
   etc.

   For example, edge offloading in the context of oneM2M allows a cloud-
   based IoT platform to transfer relevant resources and tasks to a
   target edge node supporting the service layer functionality
   [oneM2M-TR0052].  Once transferred, the edge node can directly
   support IoT devices it serves with the service offloaded by the
   cloud.  For example, this functionality enables one or more of the
   individual service functions (e.g. group management, location
   management, etc.) defined in the service layer to be offloaded from
   the cloud to one or more edge nodes.

   QoS can be provided in some systems through the combination of
   network QoS (e.g., traffic engineering or wireless resource
   scheduling) and compute/storage resource allocations.  For example,
   in some systems, a bandwidth manager service can be exposed to enable
   allocation of bandwidth to/from an edge computing application
   instance.

   Related challenges include:

   *  (Computation placement) Selecting, in a centralized or
      distributed/peer-to-peer manner, an appropriate compute device
      based on available resources, location of data input and data
      sinks, compute node properties, etc., and with varying goals

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      including for example end-to-end latency, privacy, high
      availability, energy conservation, or network efficiency, e.g.
      using load balancing techniques to avoid congestion

   *  Onboarding code on a platform or computing device, and invoking
      remote code execution, possibly as part of a distributed
      programming model and with respect to similar concerns of latency,
      privacy, etc.  These operations should deal with heterogeneous
      compute nodes [Schafer], and may in some cases also support end
      devices, including IoT devices, as compute nodes [Larrea]

   *  Adapting Quality of Results (QoR) for applications where a perfect
      result is not necessary [Li]

   *  Assisted or automatic partitioning of code
      [I-D.sarathchandra-coin-appcentres]

   *  Supporting computation across trust domains, e.g. verifying
      computation results

   *  Support for computation mobility: relocating an instance from one
      compute node to another, while maintaining a given service level.
      Session continuity when communicating with end devices that are
      mobile, possibly at high speed (e.g. in vehicular scenarios).
      Defining lightweight execution environments for secure code
      mobility, e.g., using WebAssembly [Nieke]

   *  Defining, managing, and verifying Service Level Agreements (SLA)
      for edge computing systems.  Pricing is a related challenge

4.4.2.  Edge Storage and Caching

   Local storage or caching enable local data processing (e.g., pre-
   processing or analysis), as well as delayed data transfer to the
   cloud or delayed physical shipping.  An edge node may offer local
   data storage (where persistence is subject to retention policies),
   caching, or both.  Caching generally refers to temporary storage to
   improve performance with no persistence guarantees.  An edge caching
   component manages data persistence, e.g., it schedules removal of
   data when it is no longer needed.  Other related aspects include
   authenticating and encrypting data.  Edge storage and caching can
   take the form of a distributed storage system.

   Related challenges include

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   *  (Cache and data placement) Using cache positioning and data
      placement strategies to minimize data retrieval delay [Liu],
      energy consumption.  Caches may be positioned in the access
      network infrastructure, or on end devices

   *  Maintaining consistency, freshness, and privacy of stored/cached
      data in systems that are distributed, constrained, and dynamic
      (e.g. due to end devices and computing nodes churn or mobility).
      For example, [Mortazavi] exploits a hierarchical storage
      organization.  Freshness-related metrics include the age of
      information [Yates], that captures the timeliness of information
      from a sender (e.g. an IoT device).

4.4.3.  Northbound/Southbound Communication

   An IoT edge cloud may provide a northbound data plane or management
   plane interface to a remote network, e.g., a cloud, home or
   enterprise network.  This interface does not exist in standalone
   (local-only) scenarios.  To support such an interface when it exists,
   an edge computing component needs to expose an API, deal with
   authentication and authorization, support secure communication.

   An IoT edge cloud may provide an API or interface to local or mobile
   users, for example, to provide access to services and applications,
   or to manage data published by local/mobile devices.

   Edge computing nodes communicate with IoT devices over a southbound
   interface, typically for data acquisition and IoT end device
   management.

   Related challenges include:

   *  Defining edge computing abstractions suitable for users and cloud
      systems to interact with edge computing systems.  In one example,
      this interaction can be based on the PaaS model [Yangui]

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4.4.4.  Communication Brokering

   A typical function of IoT edge computing is to facilitate
   communication with IoT end devices: for example, enable clients to
   register as recipients for data from devices, as well as forwarding/
   routing of traffic to or from IoT end devices, enabling various data
   discovery and redistribution patterns, e.g., north-south with clouds,
   east-west with other edge devices
   [I-D.mcbride-edge-data-discovery-overview].  Another related aspect
   is dispatching alerts and notifications to interested consumers both
   inside and outside of the edge computing domain.  Protocol
   translation, analytics, and transcoding may also be performed when
   necessary.

   Communication brokering may be centralized in some systems, e.g.,
   using a hub-and-spoke message broker, or distributed like with
   message buses, possibly in a layered bus approach.  Distributed
   systems may leverage direct communication between end devices, over
   device-to-device links.  A broker can ensure communication
   reliability, traceability, and in some cases transaction management.

   Related challenges include:

   *  Enabling secure and resilient communication between IoT end
      devices and remote cloud, e.g. through multipath support

4.4.5.  Other Services

   Data generated by IoT devices and associated information obtained
   from the access network may be used to provide high-level services
   such as end device location, radio network information, bandwidth
   management and congestion management (e.g., by the congestion
   management feature of oneM2M [oneM2M-TR0052]).

4.5.  Application Components

   IoT edge computing can host applications such as the ones mentioned
   in Section 2.4.  While describing components of individual
   applications is out of our scope, some of those applications share
   similar functions, such as IoT end device management, data
   management, described below.

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4.5.1.  IoT End Devices Management

   IoT end device management includes managing information about the IoT
   devices, including their sensors, how to communicate with them, etc.
   Edge computing addresses the scalability challenges from the massive
   number of IoT end devices by separating the scalability domain into
   edge/local networks and remote networks.  For example, in the context
   of the oneM2M standard, the software campaign feature enables
   installing, deleting, activating, and deactivating software
   functions/services on a potentially large number of edge nodes
   [oneM2M-TR0052].  Using a dash board or a management software, a
   service provider issues those requests through an IoT cloud platform
   supporting the software campaign functionality.

   Challenges listed in Section 4.3.1 may be applicable to IoT end
   devices management as well.

4.5.2.  Data Management and Analytics

   Data storage and processing at the edge is a major aspect of IoT edge
   computing, directly addressing high-level IoT challenges listed in
   Section 3.  Data analysis such as performed in AI/ML tasks performed
   at the edge may benefit from specialized hardware support on
   computing nodes.

   Related challenges include:

   *  Addressing concerns on resource usage, security, and privacy when
      sharing, processing, discovering, or managing data.  For example
      by presenting data in views composed of an aggregation of related
      data [Zhang]; protecting data communication between authenticated
      peers [Basudan]; classifying data (e.g., in terms of privacy,
      importance, validity, etc.); compressing and encrypting data,
      e.g., using homomorphic encryption to directly process encrypted
      data [Stanciu].

   *  Other concerns on edge data discovery (e.g., streaming data,
      metadata, events) include siloization and lack of standard in edge
      environments that can be dynamic (e.g. vehicular networks) and
      heterogeneous [I-D.mcbride-edge-data-discovery-overview]

   *  Data-driven programming models [Renart], e.g. event-based,
      including handling of naming and data abstractions

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   *  Addressing concerns such as limited resources, privacy, dynamic
      and heterogeneous environment, to deploy machine learning at the
      edge.  For example, making machine learning more lightweight and
      distributed, supporting shorter training time and simplified
      models, and supporting models that can be compressed for efficient
      communication [Murshed]

   *  While edge computing can support IoT services independently of
      cloud computing, it can also be connected to cloud computing.
      Thus, the relationship of IoT edge computing to cloud computing,
      with regard to data management, is another potential challenge
      [ISO_TR]

4.6.  Simulation and Emulation Environments

   IoT Edge Computing brings new challenges to simulation and emulation
   tools used by researchers and developers.  A varied set of
   applications, network, and computing technologies can coexist in a
   distributed system, which makes modeling difficult.  Scale, mobility,
   and resource management are additional challenges [SimulatingFog].

   Tools include simulators, where simplified application logic runs on
   top of a fog network model, and emulators, where actual applications
   can be deployed, typically in software containers, over a cloud
   infrastructure (e.g.  Docker, Kubernetes) itself running over a
   network emulating network edge conditions such as variable delays,
   throughput and mobility events.  To gain in scale, emulated and
   simulated systems can be used together in hybrid federation-based
   approaches [PseudoDynamicTesting], while to gain in realism physical
   devices can be interconnected with emulated systems.  Examples of
   related work and platforms include the publicly accessible MEC
   sandbox work recently initiated in ETSI [ETSI_Sandbox], and open
   source simulators and emulators ([AdvantEDGE] emulator and tools
   cited in [SimulatingFog]).  EdgeNet [Senel] is a globally distributed
   edge cloud for Internet researchers, using nodes contributed by
   institutions, and based on Docker for containerization and Kubernetes
   for deployment and node management.

5.  Security Considerations

   Privacy and security are drivers for the adoption of edge computing
   for IoT (Section 3.4).  As discussed in Section 4.3.1, authentication
   and trust (between computing nodes, management nodes, end devices)
   can be challenging as scale, mobility, and heterogeneity increase.
   The sometimes disconnected nature of edge resources can prevent
   relying on a third-party authority.  Distributed edge computing is
   exposed to issues with reliability and denial of service attacks.
   Personal or proprietary IoT data leakage is also a major threat,

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   especially due to the distributed nature of the systems
   (Section 4.5.2).

   However, edge computing also brings solutions in the security space:
   maintaining privacy by computing sensitive data closer to data
   generators is a major use case for IoT edge computing.  An edge cloud
   can be used to take actions based on sensitive data, or anonymizing,
   aggregating or compressing data prior to transmitting to a remote
   cloud server.  Edge computing communication brokering functions can
   also be used to secure communication between edge and cloud networks.

6.  Acknowledgements

   The authors would like to thank Joo-Sang Youn, Akbar Rahman, Michel
   Roy, Robert Gazda, Rute Sofia, Thomas Fossati, Chonggang Wang, Marie-
   Jose Montpetit, Carlos J.  Bernardos, Milan Milenkovic, Dale Seed and
   JaeSeung Song for their valuable comments and suggestions on this
   document.

7.  Informative References

   [AdvantEDGE]
              "Mobile Edge Emulation Platform", Source Code Repository ,
              2020, <https://github.com/InterDigitalInc/AdvantEDGE>.

   [Anglano]  Anglano, C., Canonico, M., Castagno, P., Guazzone, M., and
              M. Sereno, "A game-theoretic approach to coalition
              formation in fog provider federations", IEEE Third
              International Conference on Fog and Mobile Edge Computing
              (FMEC), pages 123-130 , 2018.

   [Ashton]   Ashton, K., "That Internet of Things thing", RFID J. vol.
              22, no. 7, pp. 97-114 , 2009.

   [Basudan]  Basudan, S., Lin, X., and K. Sankaranarayanan, "A privacy-
              preserving vehicular crowdsensing-based road surface
              condition monitoring system using fog computing", IEEE
              Internet of Things Journal, 4(3):772-782 , 2017.

   [Botta]    Botta, A., Donato, W., Persico, V., and A. Pescape,
              "Integration of Cloud Computing and Internet of Things: A
              survey", Future Gener. Comput. Syst., vol. 56, pp.
              684-700 , 2016.

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   [Cao]      Cao, L., Merican, A., Zad Tootaghaj, D., Ahmed, F.,
              Sharma, P., and V. Saxena, "ECaaS: A Management Framework
              of Edge Container as a Service for Business Workload", 4th
              International Workshop on Edge Systems, Analytics and
              Networking , 2021,
              <https://doi.org/10.1145/3434770.3459741>.

   [Chiang]   Chiang, M. and T. Zhang, "Fog and IoT: An overview of
              research opportunities", IEEE Internet Things J., vol. 3,
              no. 6, pp. 854-864 , 2016.

   [Echeverria]
              Echeverria, S., Klinedinst, D., Williams, K., and G. A
              Lewis, "Establishing trusted identities in disconnected
              edge environments", IEEE/ACM Symposium Edge Computing
              (SEC), pages 51-63. , 2016.

   [ENERGY]   Beckel, C., Sadamori, L., Staake, T., and S. Santini,
              "Revealing Household Characteristics from Smart Meter
              Data", Energy, vol. 78, pp. 397-410 , 2014.

   [ETSI_MEC_01]
              ETSI, ., "Multi-access Edge Computing (MEC); Terminology",
              ETSI GS 001 , 2019, <https://www.etsi.org/deliver/etsi_gs/
              MEC/001_099/001/02.01.01_60/gs_MEC001v020101p.pdf>.

   [ETSI_MEC_03]
              ETSI, ., "Mobile Edge Computing (MEC); Framework and
              Reference Architecture", ETSI GS 003 , 2019,
              <https://www.etsi.org/deliver/etsi_gs/
              MEC/001_099/003/02.01.01_60/gs_MEC003v020101p.pdf>.

   [ETSI_Sandbox]
              "Multi-access Edge Computing (MEC) MEC Sandbox Work Item",
              Portal , 2020,
              <https://portal.etsi.org/webapp/WorkProgram/
              Report_WorkItem.asp?WKI_ID=57671>.

   [Fan]      Fan, Q. and N. Ansari, "Cost aware cloudlet placement for
              big data processing at the edge", IEEE International
              Conference on Communications (ICC), pages 1-6 , 2017.

   [Harchol]  Harchol, Y., Mushtaq, A., McCauley, J., Panda, A., and S.
              Shenker, "Cessna: Resilient edge-computing", Workshop on
              Mobile Edge Communications, pages 1-6. ACM , 2018.

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   [I-D-defoy-t2trg-iot-edge-computing-background]
              de Foy, X., Hong, J., Hong, Y., Kovatsch, M., Schooler,
              E., and D. Kutscher, "Machine learning at the network
              edge: A survey", draft-defoy-t2trg-iot-edge-computing-
              background-00 , 2020, <http://www.ietf.org/internet-
              drafts/draft-defoy-t2trg-iot-edge-computing-background-
              00.txt>.

   [I-D.bernardos-sfc-fog-ran]
              Bernardos, C. J., Rahman, A., and A. Mourad, "Service
              Function Chaining Use Cases in Fog RAN", Work in Progress,
              Internet-Draft, draft-bernardos-sfc-fog-ran-09, 22 March
              2021, <https://www.ietf.org/archive/id/draft-bernardos-
              sfc-fog-ran-09.txt>.

   [I-D.ietf-core-oscore-groupcomm]
              Tiloca, M., Selander, G., Palombini, F., Mattsson, J. P.,
              and J. Park, "Group OSCORE - Secure Group Communication
              for CoAP", Work in Progress, Internet-Draft, draft-ietf-
              core-oscore-groupcomm-12, 12 July 2021,
              <https://www.ietf.org/archive/id/draft-ietf-core-oscore-
              groupcomm-12.txt>.

   [I-D.mcbride-edge-data-discovery-overview]
              McBride, M., Kutscher, D., Schooler, E., Bernardos, C. J.,
              Lopez, D. R., and X. D. Foy, "Edge Data Discovery for
              COIN", Work in Progress, Internet-Draft, draft-mcbride-
              edge-data-discovery-overview-05, 1 November 2020,
              <https://www.ietf.org/archive/id/draft-mcbride-edge-data-
              discovery-overview-05.txt>.

   [I-D.sarathchandra-coin-appcentres]
              Trossen, D., Sarathchandra, C., and M. Boniface, "In-
              Network Computing for App-Centric Micro-Services", Work in
              Progress, Internet-Draft, draft-sarathchandra-coin-
              appcentres-04, 26 January 2021,
              <https://www.ietf.org/archive/id/draft-sarathchandra-coin-
              appcentres-04.txt>.

   [ISO_TR]   "Information Technology - Cloud Computing - Edge Computing
              Landscape", ISO/IEC TR 23188 , 2018.

   [Jeffery]  Jeffery, A., Howard, H., and R. Mortier, "Rearchitecting
              Kubernetes for the Edge", 4th International Workshop on
              Edge Systems, Analytics and Networking , 2021,
              <https://dl.acm.org/doi/10.1145/3434770.3459730>.

Hong, et al.            Expires 19 February 2022               [Page 26]
Internet-Draft             IoT Edge Computing                August 2021

   [Jeong]    Jeong, T., Chung, J., Hong, J.W., and S. Ha, "Towards a
              distributed computing framework for fog", IEEE Fog World
              Congress (FWC), pages 1-6 , 2017.

   [Kelly]    Kelly, R., "Internet of Things Data to Top 1.6 Zettabytes
              by 2022", 2015,
              <https://campustechnology.com/articles/2015/04/15/
              internet-of-things-data-to-top-1-6-zettabytes-by-
              2020.aspx>.

   [Khan]     Khan, L.U., Yaqoob, I., Tran, N.H., Kazmi, S.M.A., Dang,
              T.N., and C.S. Hong, "Edge Computing Enabled Smart Cities:
              A Comprehensive Survey", arXiv:1909.08747 , 2019.

   [Larrea]   Larrea, J. and A. Barbalace, "The serverkernel operating
              system", Third ACM International Workshop on Edge Systems,
              Analytics and Networking , 2020,
              <https://core.ac.uk/reader/327124532>.

   [LFEDGE-EVE]
              Linux Foundation, ., "Project Edge Virtualization Engine
              (EVE)", Portal , 2020,
              <https://www.lfedge.org/projects/eve>.

   [Li]       Li, Y., Chen, Y., Lan, T., and G. Venkataramani, "Mobiqor:
              Pushing the envelope of mobile edge computing via quality-
              of-result optimization", IEEE 37th International
              Conference on Distributed Computing Systems (ICDCS), pages
              1261-1270 , 2017.

   [Lin]      Lin, J., Yu, W., Zhang, N., Yang, X., Zhang, H., and W.
              Zhao, "A survey on Internet of Things: Architecture,
              enabling technologies, security and privacy, and
              applications", IEEE Internet of Things J., vol. 4, no. 5,
              pp. 1125-1142 , 2017.

   [Liu]      Liu, J., Bai, B., Zhang, J., and K.B. Letaief, "Cache
              placement in fog-rans: From centralized to distributed
              algorithms", IEEE Transactions on Wireless Communications,
              16(11):7039-7051 , 2017.

   [Mahadev]  Satyanarayanan, M., "The Emergence of Edge Computing",
              Computer, vol. 50, no. 1, pp. 30-39 , 2017.

   [Mortazavi]
              Hossein Mortazavi, S., Balasubramanian, B., de Lara, E.,
              and S.P. Narayanan, "Toward Session Consistency for the
              Edge", USENIX, Workshop on Hot Topics in Edge Computing

Hong, et al.            Expires 19 February 2022               [Page 27]
Internet-Draft             IoT Edge Computing                August 2021

              (HotEdge 18) , 2018,
              <https://www.usenix.org/conference/hotedge18/presentation/
              mortazavi>.

   [Murshed]  Murshed, M., Murphy, C., Hou, D., Khan, N.,
              Ananthanarayanan, G., and F. Hussain, "Machine learning at
              the network edge: A survey", arXiv:1908.00080 , 2019.

   [Nieke]    Nieke, M., Almstedt, L., and R. Kapitza, "Edgedancer:
              Secure Mobile WebAssembly Services on the Edge", 4th
              International Workshop on Edge Systems, Analytics and
              Networking , 2021,
              <https://doi.org/10.1145/3434770.3459731>.

   [NIST]     Mell, P. and T. Grance, "The NIST definition of Cloud
              Computing", Natl. Inst. Stand. Technol, vol. 53, no. 6, p.
              50 , 2009.

   [NVIDIA]   Grzywaczewski, A., "Training AI for Self-Driving Vehicles:
              the Challenge of Scale", NVIDIA Developer Blog , 2017,
              <https://devblogs.nvidia.com/training-self-driving-
              vehicles-challenge-scale/>.

   [oneM2M-TR0001]
              Mladin, C., "TR 0001, Use Cases Collection", oneM2M ,
              October 2018,
              <https://member.onem2m.org/Application/documentapp/
              downloadLatestRevision/default.aspx?docID=28153>.

   [oneM2M-TR0018]
              Lu, C. and M. Jiang, "TR 0018, Industrial Domain
              Enablement", oneM2M , February 2019,
              <https://member.onem2m.org/Application/documentapp/
              downloadLatestRevision/default.aspx?docID=29334>.

   [oneM2M-TR0026]
              Yamamoto, K., Mladin, C., and V. Kueh, "TR 0026, Vehicular
              Domain Enablement", oneM2M , January 2020,
              <https://member.onem2m.org/Application/documentapp/
              downloadLatestRevision/default.aspx?docID=31410>.

   [oneM2M-TR0052]
              Yamamoto, K. and C. Mladin, "TR 0052, Study on Edge and
              Fog Computing in oneM2M systems", oneM2M , September 2020,
              <https://member.onem2m.org/Application/documentapp/
              downloadLatestRevision/default.aspx?docID=32633>.

Hong, et al.            Expires 19 February 2022               [Page 28]
Internet-Draft             IoT Edge Computing                August 2021

   [oneM2M-TS0002]
              He, S., "TS 0002, Requirements", oneM2M , February 2019,
              <https://member.onem2m.org/Application/documentapp/
              downloadLatestRevision/default.aspx?docID=29274>.

   [OpenFog]  "OpenFog Reference Architecture for Fog Computing",
              OpenFog Consortium , 2017.

   [PseudoDynamicTesting]
              Ficco, M., Esposito, C., Xiang, Y., and F. Palmieri,
              "Pseudo-Dynamic Testing of Realistic Edge-Fog Cloud
              Ecosystems", IEEE Communications Magazine, Nov. 2017 ,
              2017.

   [Renart]   Renart, E.G., Diaz-Montes, J., and M. Parashar, "Data-
              driven stream processing at the edge", IEEE 1st
              International Conference on Fog and Edge Computing
              (ICFEC), pages 31-40 , 2017.

   [RFC6291]  Andersson, L., van Helvoort, H., Bonica, R., Romascanu,
              D., and S. Mansfield, "Guidelines for the Use of the "OAM"
              Acronym in the IETF", BCP 161, RFC 6291,
              DOI 10.17487/RFC6291, June 2011,
              <https://www.rfc-editor.org/info/rfc6291>.

   [RFC7390]  Rahman, A., Ed. and E. Dijk, Ed., "Group Communication for
              the Constrained Application Protocol (CoAP)", RFC 7390,
              DOI 10.17487/RFC7390, October 2014,
              <https://www.rfc-editor.org/info/rfc7390>.

   [Schafer]  Schafer, D., Edinger, J., VanSyckel, S., Paluska, J.M.,
              and C. Becker, "Tasklets: Overcoming Heterogeneity in
              Distributed Computing Systems", IEEE 36th International
              Conference on Distributed Computing Systems Workshops
              (ICDCSW), Nara, pp. 156-161 , 2016.

   [Senel]    Senel, B., Mouchet, M., Cappos, J., Fourmaux, O.,
              Friedman, T., and R. McGeer, "EdgeNet: A Multi-Tenant and
              Multi-Provider Edge Cloud", 4th International Workshop on
              Edge Systems, Analytics and Networking , 2021,
              <https://dl.acm.org/doi/pdf/10.1145/3434770.3459737>.

   [Shi]      Shi, W., Cao, J., Zhang, Q., Li, Y., and L. Xu, "Edge
              computing: vision and challenges", IEEE Internet of Things
              J., vol. 3, no. 5, pp. 637-646 , 2016.

Hong, et al.            Expires 19 February 2022               [Page 29]
Internet-Draft             IoT Edge Computing                August 2021

   [SimulatingFog]
              Svorobej, S. and . al, "Simulating Fog and Edge Computing
              Scenarios: An Overview and Research Challenges", MPDI
              Future Internet 2019 , 2019.

   [Stanciu]  Stanciu, V., van Steen, M., Dobre, C., and A. Peter,
              "Privacy-Preserving Crowd-Monitoring Using Bloom Filters
              and Homomorphic Encryption", 4th International Workshop on
              Edge Systems, Analytics and Networking , 2021,
              <https://dl.acm.org/doi/10.1145/3434770.3459735>.

   [Weiner]   Weiner, M., Jorgovanovic, M., Sahai, A., and B. Nikolie,
              "Design of a low-latency, high-reliability wireless
              communication system for control applications", IEEE Int.
              Conf. Commun. (ICC), Sydney, NSW, Australia, pp.
              3829-3835 , 2014.

   [Yangui]   Yangui, S., Ravindran, P., Bibani, O., H Glitho, R., Ben
              Hadj-Alouane, N., Morrow, M.J., and P.A. Polakos, "A
              platform as-a-service for hybrid cloud/fog environments",
              IEEE International Symposium on Local and Metropolitan
              Area Networks (LANMAN), pages 1-7 , 2016.

   [Yates]    Yates, R.D. and S.K. Kaul, "The Age of Information: Real-
              Time Status Updating by Multiple Sources", IEEE
              Transactions on Information Theory, vol. 65, no. 3, pp.
              1807-1827 , 2019.

   [Yousefpour]
              Yousefpour, A., Fung, C., Nguyen, T., Kadiyala, K.,
              Jalali, F., Niakanlahiji, A., Kong, J., and J.P. Jue, "All
              one needs to know about fog computing and related edge
              computing paradigms: A complete survey", Journal of
              Systems Architecture, vol. 98, pp. 289-330 , 2019.

   [Zhang]    Zhang, Q., Zhang, X., Zhang, Q., Shi, W., and H. Zhong,
              "Firework: Big data sharing and processing in
              collaborative edge environment", Fourth IEEE Workshop on
              Hot Topics in Web Systems and Technologies (HotWeb), pages
              20-25 , 2016.

   [Zhang2]   Zhang, J., Chen, B., Zhao, Y., Cheng, X., and F. Hu, "Data
              Security and Privacy-Preserving in Edge Computing
              Paradigm: Survey and Open Issues", IEEE Access, vol. 6,
              pp. 18209-18237 , 2018.

Hong, et al.            Expires 19 February 2022               [Page 30]
Internet-Draft             IoT Edge Computing                August 2021

   [_60802]   IEC/IEEE, ., "Use Cases IEC/IEEE 60802 V1.3", IEC/IEEE
              60802 , 2018, <http://www.ieee802.org/1/files/public/
              docs2018/60802-industrial-use-cases-0818-v13.pdf>.

Authors' Addresses

   Jungha Hong
   ETRI
   218 Gajeong-ro, Yuseung-Gu
   Daejeon
   34129
   Republic of Korea

   Email: jhong@etri.re.kr

   Yong-Geun Hong
   Daejeon University
   62 Daehak-ro, Dong-gu
   Daejeon
   300716
   Republic of Korea

   Email: yonggeun.hong@gmail.com

   Xavier de Foy
   InterDigital Communications, LLC
   1000 Sherbrooke West
   Montreal  H3A 3G4
   Canada

   Email: xavier.defoy@interdigital.com

   Matthias Kovatsch
   Huawei Technologies Duesseldorf GmbH
   Riesstr. 25 C // 3.OG
   80992 Munich
   Germany

   Email: ietf@kovatsch.net

Hong, et al.            Expires 19 February 2022               [Page 31]
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   Eve Schooler
   Intel
   2200 Mission College Blvd.
   Santa Clara, CA,  95054-1537
   United States of America

   Email: eve.m.schooler@intel.com

   Dirk Kutscher
   University of Applied Sciences Emden/Leer
   Constantiaplatz 4
   26723 Emden
   Germany

   Email: ietf@dkutscher.net

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