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IoT Edge Challenges and Functions
draft-irtf-t2trg-iot-edge-10

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This is an older version of an Internet-Draft that was ultimately published as RFC 9556.
Authors Jungha Hong , Yong-Geun Hong , Xavier de Foy , Matthias Kovatsch , Eve Schooler , Dirk Kutscher
Last updated 2024-04-11 (Latest revision 2023-09-15)
Replaces draft-hong-t2trg-iot-edge-computing
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draft-irtf-t2trg-iot-edge-10
Network Working Group                                            J. Hong
Internet-Draft                                                      ETRI
Intended status: Informational                                Y.-G. Hong
Expires: 18 March 2024                                Daejeon University
                                                               X. de Foy
                                        InterDigital Communications, LLC
                                                             M. Kovatsch
                                    Huawei Technologies Duesseldorf GmbH
                                                             E. Schooler
                                                                   Intel
                                                             D. Kutscher
              Hong Kong University of Science and Technology (Guangzhou)
                                                       15 September 2023

                   IoT Edge Challenges and Functions
                      draft-irtf-t2trg-iot-edge-10

Abstract

   Many Internet of Things (IoT) applications have requirements that
   cannot be satisfied by traditional cloud-based systems (i.e., cloud
   computing).  These include time sensitivity, data volume,
   connectivity cost, operation in the face of intermittent services,
   privacy, and security.  As a result, 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 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).

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

   Internet-Drafts are working documents of the Internet Engineering
   Task Force (IETF).  Note that other groups may also distribute
   working documents as Internet-Drafts.  The list of current Internet-
   Drafts is at https://datatracker.ietf.org/drafts/current/.

   Internet-Drafts are draft documents valid for a maximum of six months
   and may be updated, replaced, or obsoleted by other documents at any
   time.  It is inappropriate to use Internet-Drafts as reference
   material or to cite them other than as "work in progress."

   This Internet-Draft will expire on 18 March 2024.

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Copyright Notice

   Copyright (c) 2023 IETF Trust and the persons identified as the
   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/
   license-info) in effect on the date of publication of this document.
   Please review these documents carefully, as they describe your rights
   and restrictions with respect to this document.

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 . . . . . . . .  10
     3.1.  Time Sensitivity  . . . . . . . . . . . . . . . . . . . .  10
     3.2.  Connectivity Cost . . . . . . . . . . . . . . . . . . . .  10
     3.3.  Resilience to Intermittent Services . . . . . . . . . . .  11
     3.4.  Privacy and Security  . . . . . . . . . . . . . . . . . .  11
   4.  IoT Edge Computing Functions  . . . . . . . . . . . . . . . .  11
     4.1.  Overview of IoT Edge Computing Today  . . . . . . . . . .  12
     4.2.  General Model . . . . . . . . . . . . . . . . . . . . . .  14
     4.3.  OAM Components  . . . . . . . . . . . . . . . . . . . . .  17
       4.3.1.  Resource Discovery and Authentication . . . . . . . .  17
       4.3.2.  Edge Organization and Federation  . . . . . . . . . .  18
       4.3.3.  Multi-Tenancy and Isolation . . . . . . . . . . . . .  19
     4.4.  Functional Components . . . . . . . . . . . . . . . . . .  19
       4.4.1.  In-Network Computation  . . . . . . . . . . . . . . .  19
       4.4.2.  Edge Storage and Caching  . . . . . . . . . . . . . .  21
       4.4.3.  Communication . . . . . . . . . . . . . . . . . . . .  21
     4.5.  Application Components  . . . . . . . . . . . . . . . . .  22
       4.5.1.  IoT Device Management . . . . . . . . . . . . . . . .  23
       4.5.2.  Data Management and Analytics . . . . . . . . . . . .  23
     4.6.  Simulation and Emulation Environments . . . . . . . . . .  24
   5.  Security Considerations . . . . . . . . . . . . . . . . . . .  25
   6.  Conclusion  . . . . . . . . . . . . . . . . . . . . . . . . .  25
   7.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  26
   8.  Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .  26
   9.  Informative References  . . . . . . . . . . . . . . . . . . .  26
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  36

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1.  Introduction

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

   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, processed, analyzed, and acted upon close to the data
   sources.  These requirements include time sensitivity, data volume,
   connectivity cost, and resiliency in the presence of intermittent
   connectivity, privacy, and security, which cannot be addressed by
   centralized cloud computing.  A more flexible approach is necessary
   to address these needs effectively.  This involves distributing
   computing (and storage) 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 describes the related
   background, use cases, challenges, system models, and functional
   components.

   Owing to the dynamic nature of the IoT edge computing landscape, this
   document does not list existing projects in this field.  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].

   This document represents the consensus of the Thing-to-Thing Research
   Group (T2TRG).  It has been reviewed extensively by the Research
   Group (RG) members who are actively involved in the research and
   development of the technology covered by this document.  It is not an
   IETF product and is not a standard.

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 (often wireless) networks over which things can send and
   receive information without human intervention.  Recently, the term
   has become more literal by connecting things to the Internet and

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   converging on Internet and Web technologies.

   A Thing is a physical item made available in the IoT, thereby
   enabling digital interaction with the physical world for humans,
   services, and/or other Things ([I-D.irtf-t2trg-rest-iot]).  In this
   document we will use the term "IoT device" to designate the embedded
   system attached to the Thing.

   Resource-constrained Things such as sensors, home appliances and
   wearable devices often have limited storage and processing power,
   which can provide challenges with respect to reliability,
   performance, energy consumption, security, and privacy [Lin].  Some,
   less resource-constrained Things, can generate a voluminous amount of
   data.  This range of factors led IoT designs that integrate Things
   into larger distributed systems, for example edge or cloud computing
   systems.

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".  The 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 and be provided as general utilities.  Platform-as-
   a-Service and cloud computing platforms 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 require very short response
   times, access to local personal data, or generate vast amounts of
   data.  These 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, close to mobile
   devices, sensors, actuators, or machines.  Edge computing happens
   near data sources [Mahadev], as well as close to where decisions are
   made or where interactions with the physical world take place
   ("close" here can refer to a distance which is topological, physical,

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   latency-based, etc.).  It processes both downstream data (originating
   from cloud services) and upstream data (originating from end devices
   or network elements).  The term "fog computing" usually represents
   the notion of multi-tiered edge computing, that is, several layers of
   compute infrastructure between end devices and cloud services.

   An edge device is any computing or networking resource residing
   between 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 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.

   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 Industry IoT Consortium (IIC, now incorporating what was
      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 distributing 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:

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   *  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").

   *  The automation industry defines the edge as the connection point
      between IT and OT (Operational Technology).  Hence, 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 educational scenarios.
   Here, we discuss only a few examples of such use cases, to identify
   differentiating requirements, providing 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.  Even a very small environmental change in
   a smart factory 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 the temperature and humidity in the factory,
   or operating machines based on the real-time collection of the
   operational status of each machine.  However, data requiring highly
   precise analysis, such as machine lifecycle management or accident
   risk prediction, can be transferred to a central data center for
   processing.

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   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 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; however, edge computing in a smart factory enables
   tailoring solutions by analyzing data at each production facility and
   machine level.  Digital twins [Jones] of IoT devices have been
   jointly used with edge computing in industrial IoT scenarios [Chen].

   *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 these systems to improve the transmission
   efficiency of electricity, to react to, and restore power after a
   disturbance, to reduce operation costs, and to reuse energy
   effectively, since these operations involve local decision-making.
   In addition, edge computing can help monitor power generation and
   power demand, and make local electrical energy storage decisions in
   smart grid systems.

   *Smart Agriculture*

   Smart agriculture integrates information and communication
   technologies with farming technology.  Intelligent farms use IoT
   technology to measure and analyze parameters, such as the
   temperature, humidity, sunlight, carbon dioxide, and soil quality, in
   crop cultivation facilities.  Depending on the analysis results,
   control devices are used to set the 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 be easily and inexpensively implemented
   using IoT technology.  Field sensors gather data on field and crop
   condition.  This data is then transmitted to cloud servers that
   process data and recommend actions.  The use of edge computing can
   reduce the volume of back-and-forth data transmissions significantly,
   resulting in cost and bandwidth savings.  Locally generated data can
   be processed at the edge, and local computing and analytics can drive
   local actions.  With edge computing, it is easy for farmers to select
   large amounts of data for processing, and data can be analyzed even
   in remote areas with poor access conditions.  Other applications
   include enabling dashboarding, for example, to visualize the farm

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   status, as well as enhancing Extended Reality (XR) applications that
   require edge audio/video processing.  As the number of people working
   on farming has been decreasing over time, increasing automation
   enabled by edge computing can be a driving force for future smart
   agriculture.

   *Smart Construction*

   Safety is critical at construction sites.  Every year, many
   construction workers lose their lives because of falls, collisions,
   electric shocks, and other accidents.  Therefore, solutions have been
   developed to improve construction site safety, including the real-
   time identification of workers, monitoring of equipment location, and
   predictive accident prevention.  To deploy these solutions, many
   cameras and IoT sensors have been installed on construction sites, to
   measure noise, vibration, gas concentration, etc.  Typically, the
   data generated from these measurements is collected in on-site
   gateways and sent to remote cloud servers 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 because of transmission costs, for example, of video
   streams over a mobile network connection, and because usage fees of
   private cloud services.

   Using edge computing, data generated at 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 reducing transmission costs.  It is also
   possible to locally generate warnings to prevent accidents in real-
   time.

   *Self-Driving Car*

   Edge computing plays a crucial role in safety-focused self-driving
   car systems.  With a multitude of sensors, such as high-resolution
   cameras, radar, LIDAR, sonar sensors, and GPS systems, autonomous
   vehicles generate vast amounts of real-time data.  Local processing
   utilizing edge computing nodes allows for efficient collection and
   analysis of this data to monitor vehicle distances and road
   conditions and respond promptly to unexpected situations.  Roadside
   computing nodes can also be leveraged to offload tasks when
   necessary, for example, when the local processing capacity of the car
   is insufficient because of hardware constraints or a large data
   volume.

   For instance, when the car ahead slows, a self-driving car adjusts
   its speed to maintain a safe distance, or when a roadside signal
   changes, it adapts its behavior accordingly.  In another example,

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   cars equipped with self-parking features utilize local processing to
   analyze sensor data, determine suitable parking spots, and execute
   precise parking maneuvers without relying on external processing or
   connectivity.  It is also possible to use in-cabin cameras coupled
   with 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.

   Edge computing empowers self-driving cars by enabling real-time
   processing, reducing latency, enhancing data privacy, and optimizing
   bandwidth usage.  By leveraging local processing capabilities, self-
   driving cars can make rapid decisions, adapt to changing
   environments, and ensure safer and more efficient autonomous driving
   experiences.

   *Digital Twin*

   A digital twin can simulate different scenarios and predict outcomes
   based on real-time data collected from the physical environment.
   This simulation capability empowers proactive maintenance,
   optimization of operations, and the prediction of potential issues or
   failures.  Decision makers can use digital twins to test and validate
   different strategies, identify inefficiencies, and optimize
   performance.

   With edge computing, real-time data is collected, processed, and
   analyzed directly at the edge, allowing for the accurate monitoring
   and simulation of physical assets.  Moreover, edge computing
   effectively minimizes latency, enabling rapid responses to dynamic
   conditions as computational resources are brought closer to the
   physical object.  Running digital twin processing at the edge enables
   organizations to obtain timely insights and make informed decisions
   that maximize efficiency and performance.

   *Other Use Cases*

   AI/ML systems at the edge empower real-time analysis, faster
   decision-making, reduced latency, improved operational efficiency,
   and personalized experiences across various industries, by bringing
   artificial intelligence and machine learning capabilities closer to
   edge devices.

   In addition, oneM2M has studied several IoT edge computing use cases,
   which are documented in [oneM2M-TR0001], [oneM2M-TR0018] and
   [oneM2M-TR0026].  The 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 the challenges faced by IoT that are
   motivating the adoption of edge computing.  These are distinct from
   the research challenges applicable to IoT edge computing, some of
   which are mentioned in Section 4.

   IoT technology is used with increasingly demanding applications, for
   example, in industrial, automotive and healthcare domains, leading to
   new challenges.  For example, industrial machines such as laser
   cutters produce over 1 terabyte of data 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
   these new challenges [Chiang].

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

3.1.  Time Sensitivity

   Many industrial control systems, such as manufacturing systems, smart
   grids, and oil and gas systems often require stringent end-to-end
   latency between the sensor and control nodes.  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 cloud-based
   solutions; however, this is not the only challenge relative to time
   sensitivity.  Guarantees for bounded latency and jitter ([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 Internet, this challenge is virtually
   impossible to address, without using end-to-end guarantees for
   individual message delivery and continuous data flows.

3.2.  Connectivity Cost

   Some IoT deployments may not face bandwidth constraints when
   uploading data to the Cloud.  5G and Wi-Fi 6 networks both
   theoretically top out at 10 gigabits per second (i.e., 4.5 terabytes
   per hour), allowing to transfer large amounts of uplink data.
   However, the cost of maintaining continuous high-bandwidth
   connectivity for such usage is unjustifiable and impractical for most
   IoT applications.  In some settings, for example, in aeronautical
   communication, higher communication costs reduce the amount of data
   that can be practically uploaded even further.  Minimizing reliance

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   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.

3.3.  Resilience to Intermittent Services

   Many IoT devices, such as sensors, actuators, and controllers, have
   very limited hardware resources and cannot rely solely on their own
   resources to meet their computing and/or storage needs.  They require
   reliable, uninterrupted, or resilient services to augment their
   capabilities to fulfill their application tasks.  This is difficult
   and partly impossible to achieve using cloud services for systems
   such as vehicles, drones, or oil rigs that have intermittent network
   connectivity.  Conversely, a cloud back-end might want to device data
   even if it is 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 have begun to
   provide frameworks that limit the usage of personal data and impose
   strict requirements on data controllers and processors.  Data stored
   indefinitely in the Cloud also increases the risk of data leakage,
   for instance, through attacks on rich targets.

   It is often argues that industrial systems do not provide privacy
   implications, 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 setup of production lines.  Hence, owners
   of these systems are generally reluctant to upload IoT data to the
   Cloud.

   Furthermore, passive observers can perform traffic analysis on
   device-to-cloud paths.  Therefore, hiding traffic patterns associated
   with sensor networks can be another requirement for edge computing.

4.  IoT Edge Computing Functions

   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 a context for IoT edge-computing functions, which are
   listed in Section 4.3, Section 4.4 and Section 4.5.

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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 products, represent a common class of IoT edge-
   computing products, where the gateway provides 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 [RFC7252], MQTT [mqtt5], 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 (using VMs and
   application containers), including IoT gateway software, on servers
   in the mobile network infrastructure (at base stations and
   concentration points), edge data centers (in central offices), and
   regional data centers located near central offices.  End devices are
   envisioned to become computing devices in forward-looking projects,
   but are not commonly used today.

   In addition to 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.  Furthermore, ETSI MEC developed an IoT API
   [ETSI_MEC_33] that enables the deployment of heterogeneous IoT
   platforms and provides a means to configure the various components of
   an IoT system.

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

   Resilience in the IoT can entail the ability to operate autonomously
   in periods of disconnectedness 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 always-on and
   unattended modes, using fault detection and unassisted recovery
   functions.

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   The life cycle management of services and applications on physical
   IoT gateways is generally cloud-based.  Edge cloud management
   platforms and products (such as StarlingX, Akraino Edge Stack, or
   proprietary products from major Cloud providers) adapt cloud
   management technologies (e.g., Kubernetes) to the edge cloud, that
   is, to smaller, distributed computing devices running outside a
   controlled data center.  The service and application life-cycle is
   typically using an NFV-like management and orchestration model.

   The platform typically enables advertising or consuming services
   hosted on the platform (e.g., the Mp1 interface in ETSI MEC supports
   service discovery and communication), and enables communication with
   local and remote endpoints (e.g., message routing function in IoT
   gateways).  The platform is typically extensible to edge applications
   because it can advertise a service that other edge applications can
   consume.  The IoT communication services include protocol
   translation, analytics, and transcoding.  Communication between edge-
   computing devices is enabled in tiered 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
   distributed storage such as that provided by a distributed storage
   platform (e.g., EdgeFS, Ceph), or, in more experimental settings, by
   an ICN network, for example, systems such as Chipmunk [chipmunk] and
   Kua [kua] have been proposed as distributed information-centric
   objects stores.  External storage, for example, 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 that host native
   programs, VMs, or containers.  Stateless computing is supported on
   platforms providing a "serverless computing" service (also known as
   function-as-a-service, e.g., using stateless containers), or on
   systems based on named function networking.

   In many IoT use cases, a typical network usage pattern is a 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.  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 many approaches to
   edge computing, in this section, we attempt to lay out a general
   model and the list associated logical functions.  In practice, this
   model can be mapped 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).  The IoT
      gateway plays a common role in providing access to a heterogeneous
      set of IoT devices/sensors, handling IoT data, and delivering IoT
      data to its final destination in a cloud network.  Whereas an IoT
      gateway requires interactions with the cloud, it can also operate
      independently in a disconnected mode.

   *  A set of distributed computing nodes, for example, embedded in
      switches, routers, edge cloud servers, or mobile devices.  Some
      IoT devices have sufficient computing capabilities to participate
      in such distributed systems owing to advances in hardware
      technology.  In this model, edge-computing nodes can collaborate
      to share resources.

   *  A hybrid system involving both IoT gateways and supporting
      functions in distributed computing nodes.

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

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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                             |
             |   - Communication                            |
             |   - Other Services                           |
             |   - ...                                      |
             |                                              |
             |   Application Components                     |
             |   - IoT 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 devices which are
   also used as computing nodes.  Edge computing domains are connected
   to a remote/cloud network and their respective service operator's
   system.  IoT devices/computing nodes provide logical functions, for
   example as part of distributed machine learning or distributed image

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   processing applications.  The processing capabilities in IoT devices
   are limited; they require the support of other nodes, and in a
   distributed machine learning application, the training process for AI
   services can be executed at IoT edge gateways or cloud networks and
   the prediction (inference) service is executed in the IoT devices.
   In a distributed image processing application, some image processing
   functions can be similarly executed at the edge or in the cloud,
   while preprocessing, which helps limiting the amount of uploaded
   data, is performed by the IoT device.

             +----------------------------------------------+
             |            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

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   In the following, we 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 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 extends beyond the network-related OAM functions
   listed in [RFC6291].  In addition to infrastructure (network,
   storage, and computing resources), edge computing systems can also
   include computing environments (for VMs, software containers,
   functions), IoT devices, data, and code.

   Operation-related functions include performance monitoring for
   service-level agreement measurements, 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.  Discovery during normal operation (e.g., discovery of
   compute or storage nodes by endpoints) is typically not included in
   OAM; however, in this document, we do not address it separately.
   Management covers the monitoring and diagnostics of failures, as well
   as means to minimize their occurrence and take corrective actions.
   This may include software update management and high service
   availability through redundancy and multipath communication.
   Centralized (e.g., SDN) and decentralized management systems can be
   used.  Finally, we arbitrarily chose to address data management as an
   application component, however, in some systems, data management may
   be considered similar to a network management function.

   We further detail a few relevant OAM components.

4.3.1.  Resource Discovery and Authentication

   Discovery and authentication may target platforms and ,
   infrastructure resources, such as computing, networking, and storage,
   as well as other resources such as IoT devices, sensors, data, code
   units, services, applications, and users interacting with the system.
   Broker-based solutions can be used, for example, using an IoT gateway
   as a broker to discover IoT resources.  More decentralized 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

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   this device [RFC7252].  For device authentication, current
   centralized gateway-based systems rely on the installation of a
   secret on IoT devices and computing devices (e.g., a device
   certificate stored in a hardware security module, or a combination of
   code and data stored in a trusted execution environment).

   Related challenges include:

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

   *  Intermittent connectivity to the Internet, removing the need to
      rely on a third-party authority [Echeverria].

   *  Resiliency to failure [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-
   organized, into hierarchies 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.

   Related challenges include:

   *  Support for scaling, and enabling fault-tolerance or self-healing
      [Jeong].  In addition to using a hierarchical organization to cope
      with scaling, another available and possibly complementary
      mechanism is multicast ([RFC7390] [I-D.ietf-core-groupcomm-bis]).
      Other approaches include relying on blockchains [Ali].

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

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

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

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

   *  Design of federated AI over IoT edge computing systems [Brecko],
      for example, for anomaly detection.

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 remotes clouds and can reuse some of their
   ecosystems.  Virtualization function management is largely covered by
   ETSI NFV and MEC standards and recommendations.  Projects such as
   [LFEDGE-EVE] further cover virtualization and its management in
   distributed edge-computing settings.

   Related challenges include:

   *  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, typically for application-layer
   processing, such as 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
   application lifecycles.  Support for in-network computation may vary
   in terms of capability, for example, computing nodes can host virtual
   machines, software containers, software actors, uni-kernels running
   stateful or stateless code, or a rule engine providing an API to
   register actions in response to conditions such as IoT device ID,
   sensor values to check, thresholds, etc.

   Edge offloading includes offloading to and from an IoT device, and to
   and from a network node.  [Cloudlets] offer an example of offloading
   computation from an end device to a network node.  In contrast,
   oneM2M is an example of a system that allows a cloud-based IoT

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   platform to transfer resources and tasks to a target edge node
   [oneM2M-TR0052].  Once transferred, the edge node can directly
   support IoT devices that it serves with the service offloaded by the
   cloud (e.g., group management, location management, etc.).

   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 the bandwidth to/from an edge-computing application
   instance.

   In-network computation can leverage the underlying services, provided
   using data generated by IoT devices and access networks.  Such
   services include IoT device location, radio network information,
   bandwidth management and congestion management (e.g., the congestion
   management feature of oneM2M [oneM2M-TR0052]).

   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
      including end-to-end latency, privacy, high availability, energy
      conservation, or network efficiency, for example, 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.: For example, offloading can be included in a
      vehicular scenario [Grewe].  These operations should deal with
      heterogeneous compute nodes [Schafer], and may 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: for example, for
      application programs [I-D.sarathchandra-coin-appcentres] or
      network programs [I-D.hsingh-coinrg-reqs-p4comp].

   *  Supporting computation across trust domains: for example,
      verifying computation results.

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   *  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, for example, using WebAssembly [Nieke].

   *  Defining, managing, and verifying Service Level Agreements (SLA)
      for edge-computing systems: pricing is a challenging task.

4.4.2.  Edge Storage and Caching

   Local storage or caching enables local data processing (e.g.,
   preprocessing or analysis) as well as delayed data transfer to the
   cloud or delayed physical shipping.  An edge node may offer local
   data storage (in which persistence is subject to retention policies),
   caching, or both.  Caching generally refers 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 the form of a distributed storage systems.

   Related challenges include:

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

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

4.4.3.  Communication

   An edge cloud may provide a northbound data plane or management plane
   interface to a remote network, such as a cloud, home or enterprise
   network.  This interface does not exist in stand-alone (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, and support secure communication.

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   An 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 device management.

   Communication brokering is a typical function of IoT edge computing
   that facilitates communication with IoT devices, enabling clients to
   register as recipients for data from devices, as well as forwarding/
   routing of traffic to or from IoT devices, enabling various data
   discovery and redistribution patterns, for example, 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 the edge-computing domain.  Protocol translation,
   analytics, and video transcoding can also be performed when
   necessary.  Communication brokering may be centralized in some
   systems, for example, using a hub-and-spoke message broker, or
   distributed with message buses, possibly in a layered bus approach.
   Distributed systems can leverage direct communication between end
   devices over device-to-device links.  A broker can ensure
   communication reliability and traceability and, in some cases,
   transaction management.

   Related challenges include:

   *  Defining edge computing abstractions, such as PaaS [Yangui],
      suitable for users and cloud systems to interact with edge
      computing systems and dealing with interoperability issues such as
      data model heterogeneity.

   *  Enabling secure and resilient communication between IoT devices
      and remote cloud, for example, through multipath support.

4.5.  Application Components

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

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4.5.1.  IoT Device Management

   IoT device management includes managing information regarding IoT
   devices, including their sensors, and how to communicate with them.
   Edge computing addresses the scalability challenges of a large number
   of IoT devices by separating the scalability domain into edge/local
   networks and remote networks.  For example, in the context of the
   oneM2M standard, a device management functionality (called "software
   campaign" in oneM2M) enables the installation, deletion, activation,
   and deactivation of software functions/services on a potentially
   large number of edge nodes [oneM2M-TR0052].  Using a dashboard or
   management software, a service provider issues these requests through
   an IoT cloud platform supporting the software campaign functionality.

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

4.5.2.  Data Management and Analytics

   Data storage and processing at the edge are major aspects of IoT edge
   computing, directly addressing the high-level IoT challenges listed
   in Section 3.  Data analysis, for example, through AI/ML tasks
   performed at the edge, may benefit from specialized hardware support
   on the computing nodes.

   Related challenges include:

   *  Addressing concerns regarding resource usage, security, and
      privacy when sharing, processing, discovering, or managing data:
      for example 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), and compressing and encrypting
      data, for example, using homomorphic encryption to directly
      process encrypted data [Stanciu].

   *  Other concerns regarding edge data discovery (e.g., streaming
      data, metadata, and events) include siloization and lack of
      standards 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], for example, event-based,
      including handling naming and data abstractions.

   *  Data integration in an environment that without data
      standardization, or where different sources use different
      ontologies [Farnbauer-Schmidt].

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   *  Addressing concerns such as limited resources, privacy, dynamic,
      and heterogeneous environments to deploy machine learning at the
      edge: for example, making machine learning more lightweight and
      distributed (e.g., enabling distributed inference at the edge),
      supporting shorter training times and simplified models, and
      supporting models that can be compressed for efficient
      communication [Murshed].

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

4.6.  Simulation and Emulation Environments

   IoT Edge Computing introduces new challenges to the simulation and
   emulation tools used by researchers and developers.  A varied set of
   applications, networks, and computing technologies can coexist in a
   distributed system, making 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 and Kubernetes) 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], whereas 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.

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   Digital twins are virtual instances of a physical system (twin) that
   are continually updated with the latter's performance, maintenance,
   and health status data throughout the life cycle of the physical
   system.  [Madni].  In contrast to a traditional emulation or
   simulated environment, digital twins, once generated, are maintained
   in sync by their physical twin, which can be, among many other
   instances, an IoT device, edge device, an edge network.  The benefits
   of digital twins go beyond those of emulation and include accelerated
   business processes, enhanced productivity, and faster innovation with
   reduced costs [I-D.irtf-nmrg-network-digital-twin-arch].

5.  Security Considerations

   Privacy and security are drivers of the adoption of edge computing
   for the IoT (Section 3.4).  As discussed in Section 4.3.1,
   authentication and trust (among computing nodes, management nodes,
   and end devices) can be challenging as scale, mobility, and
   heterogeneity increase.  The sometimes disconnected nature of edge
   resources can avoid reliance on third-party authorities.  Distributed
   edge computing is exposed reliability and denial of service attacks.
   Personal or proprietary IoT data leakage is also a major threat,
   particularly because of the distributed nature of the systems
   (Section 4.5.2).  Furthermore, blockchain-based distributed IoT edge
   computing must be designed for privacy, since public blockchain
   addressing does not guarantee absolute anonymity [Ali].

   However, edge computing also offers 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 perform actions based on sensitive data or to
   anonymize or aggregate data prior to transmission to a remote cloud
   server.  Edge computing communication brokering functions can also be
   used to secure communication between edge and cloud networks.

6.  Conclusion

   IoT edge computing plays an essential role, complementary to the
   cloud, in enabling IoT systems in certain situations.  In this
   document, we presented use cases and listing the core challenges
   faced by IoT that drive the need for IoT edge computing.  The first
   part of this document may therefore help focus future 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 purpose of having a link between
   individual research areas and where they are applicable in an IoT
   edge computing system.

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7.  IANA Considerations

   This document has no IANA actions.

8.  Acknowledgements

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

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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

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   Eve Schooler
   Intel
   2200 Mission College Blvd.
   Santa Clara, CA,  95054-1537
   United States of America
   Email: eve.schooler@gmail.com

   Dirk Kutscher
   Hong Kong University of Science and Technology (Guangzhou)
   No.1 Du Xue Rd
   Guangzhou
   China
   Email: ietf@dkutscher.net

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