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T2T Research Group                                               J. Hong
Internet-Draft                                                 Y-G. Hong
Intended status: Informational                                      ETRI
Expires: January 9, 2020                                       X. de Foy
                                             InterDigital Communications
                                                             M. Kovatsch
                                    Huawei Technologies Duesseldorf GmbH
                                                             E. Schooler
                                                                   Intel
                                                             D. Kutscher
                               University of Applied Sciences Emden/Leer
                                                           July 08, 2019


        Problem Statement of IoT integrated with Edge Computing
                 draft-hong-t2trg-iot-edge-computing-00

Abstract

   This document describes new challenges such as strict latency, uplink
   cost, uninterrupted services, privacy and security, for IoT services
   originated from the IoT environmental changes.  In order to address
   those new challenges, the integration of Edge computing and IoT has
   been emerged as a promising solution.  This document discribes the
   concept of IoT integrated with Edge computing as well as the state-
   of-the-art of IoT Edge computing.  It also proposes an architecture
   of IoT Edge computing.  The direction of Edge computing for IoT
   should be discussed in the IETF/IRTF.

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
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   Drafts is at https://datatracker.ietf.org/drafts/current/.

   Internet-Drafts are draft documents valid for a maximum of six months
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   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 January 9, 2020.






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Copyright Notice

   Copyright (c) 2019 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
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   include Simplified BSD License text as described in Section 4.e of
   the Trust Legal Provisions and are provided without warranty as
   described in the Simplified BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  Conventions and Terminology . . . . . . . . . . . . . . . . .   3
   3.  Background  . . . . . . . . . . . . . . . . . . . . . . . . .   4
     3.1.  Internet of Things (IoT)  . . . . . . . . . . . . . . . .   4
     3.2.  Cloud computing . . . . . . . . . . . . . . . . . . . . .   4
     3.3.  Edge computing  . . . . . . . . . . . . . . . . . . . . .   5
   4.  New challenges of IoT . . . . . . . . . . . . . . . . . . . .   5
     4.1.  Strict Latency and Jitter . . . . . . . . . . . . . . . .   5
     4.2.  Uplink Cost . . . . . . . . . . . . . . . . . . . . . . .   6
     4.3.  Uninterrupted Services  . . . . . . . . . . . . . . . . .   6
     4.4.  Privacy and Security  . . . . . . . . . . . . . . . . . .   6
   5.  IoT integrated with Edge Computing  . . . . . . . . . . . . .   7
     5.1.  IoT Data in Edge Computing  . . . . . . . . . . . . . . .   7
       5.1.1.  Data Storage  . . . . . . . . . . . . . . . . . . . .   8
       5.1.2.  Data Processing . . . . . . . . . . . . . . . . . . .   8
       5.1.3.  Data Analyzing  . . . . . . . . . . . . . . . . . . .   8
     5.2.  IoT Device Management in Edge Computing . . . . . . . . .   9
   6.  Architecture of IoT integrated with Edge Computing  . . . . .   9
   7.  State-of-the-art of IoT Edge Computing  . . . . . . . . . . .  11
     7.1.  Common aspects of IoT edge computing service platforms  .  11
     7.2.  Use Cases of IoT Edge Computing . . . . . . . . . . . . .  12
   8.  Security Considerations . . . . . . . . . . . . . . . . . . .  14
   9.  Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .  14
   10. References  . . . . . . . . . . . . . . . . . . . . . . . . .  14
     10.1.  Normative References . . . . . . . . . . . . . . . . . .  14
     10.2.  Informative References . . . . . . . . . . . . . . . . .  14
   Appendix A.  Overview of the IoT Edge Computing . . . . . . . . .  17
     A.1.  Open Source Projects  . . . . . . . . . . . . . . . . . .  17
       A.1.1.  Gateway/CPE Platforms . . . . . . . . . . . . . . . .  17
       A.1.2.  Edge Cloud Management Platforms . . . . . . . . . . .  18
       A.1.3.  Related Projects  . . . . . . . . . . . . . . . . . .  19



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     A.2.  Products  . . . . . . . . . . . . . . . . . . . . . . . .  19
       A.2.1.  IoT Gateways  . . . . . . . . . . . . . . . . . . . .  19
       A.2.2.  Edge Cloud Platforms  . . . . . . . . . . . . . . . .  20
     A.3.  Standards Initiatives . . . . . . . . . . . . . . . . . .  20
       A.3.1.  ETSI Multi-access Edge Computing  . . . . . . . . . .  20
       A.3.2.  Edge Computing Support in 3GPP  . . . . . . . . . . .  21
       A.3.3.  OpenFog Consortium  . . . . . . . . . . . . . . . . .  22
       A.3.4.  Related Standards . . . . . . . . . . . . . . . . . .  22
     A.4.  Research Projects . . . . . . . . . . . . . . . . . . . .  22
       A.4.1.  Named Function Networking . . . . . . . . . . . . . .  22
       A.4.2.  5G-CORAL  . . . . . . . . . . . . . . . . . . . . . .  23
       A.4.3.  FLAME . . . . . . . . . . . . . . . . . . . . . . . .  23
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  24

1.  Introduction

   Nowadays, most IoT services are based on Cloud computing since it can
   provide virtually unlimited storage and processing power.  The
   integration of IoT with Cloud computing brings many advantages such
   as flexibility, efficiency, and ability to store and use data.

   However, the IoT environment is changing in such a way that vast
   amounts of data are created at edge/local networks and about a half
   of data is stored, processed, analyzed and acted upon close to the
   data producer.  Thus, emerging IoT services introduce new challenges
   that cannot be addressed by today's centralized Cloud computing
   models alone.

   In this document, we describe new challenges for emerging IoT
   services such as strict latency, uplink cost, uninterrupted services,
   privacy and security due to the IoT environmental changes.

   In order to address those new challenges for IoT services, the
   integration of Edge computing with IoT has been emerged as a
   promising solution.  In this document, we describe the concept of IoT
   integrated with Edge computing as well as the state-of-the-art of IoT
   Edge computing and propose an architecture of IoT Edge computing.
   The purpose of this document is to bring up the issues of Edge
   computing for IoT services in IETF/IRTF.

2.  Conventions and Terminology

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
   document are to be interpreted as described in [RFC2119].






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3.  Background

3.1.  Internet of Things (IoT)

   Since the phrase 'Internet of Things (IoT)' was coined by Kevin
   Ashton in 1999 working on Radio-frequency identification (RFID)
   technology at the Auto-ID Center of the Massachusetts Institute of
   Technology (MIT) [Ashton], the concept of IoT has been that things
   connected to the Internet can send and receive information collected
   by sensors without human intervention, where things are various
   embedded systems such as home appliances, mobile equipment, wearable
   devices, etc.  IoT has become one of the notable innovations playing
   an important role in our daily lives [Lin].  IoT is generally
   characterized by real world small things that are widely distributed
   but have limited storage and processing power, which involve concerns
   regarding reliability, performance, security, and privacy.

3.2.  Cloud computing

   Cloud computing have 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".  Cloud computing has been a predominant
   technology which has virtually unlimited capacity in terms of storage
   and processing power.  The availability of virtually unlimited
   storage and processing capabilities at low cost enabled the
   realization of a new 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].

   Now with IoT, we will reach the era of post-Clouds where
   unprecedented volume and variety of data will be generated by things
   at edge/local networks and many applications will be deployed on the
   edge netwoks to consume these IoT data.  Some of the applications may
   need very short response times, some may contain personal data, and
   others may generate vast amounts of data.  Today's Cloud based
   service models are not suitable for these applications.

   It is predicted that by 2019, 45% of the data created in IoT will be
   stored, processed, analyzed and acted close to, or at the edge of the
   network and about 50 billion devices will connect to the Internet by
   2020 [Evans].  So, moving all data from edge/local networks to the
   cloud data center may not be an efficient way anymore to process vast
   amounts of data.



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   In Cloud computing, users traditionally only consumed IoT data
   through Cloud services.  Now, however, users are also producing IoT
   data with their mobile devices.  This change requires more
   functionality at edge/local networks [Shi].

3.3.  Edge computing

   Edge computing is a new paradigm in which substantial computing and
   storage resources are placed at the Internet's edge in close
   proximity to mobile devices or sensors so that computing happens near
   data sources [Mahadev].  It works on both downstream data on behalf
   of cloud services and upstream data on behalf of IoT services.  An
   edge device is any computing or networking resource residing between
   data sources and cloud-based datacenters.  In Edge computing, the end
   device not only consumes data but also produces 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].

   The definition of Edge computing from ISO is '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].  And
   the similar concept of Fog computing from Open Fog Consortium is '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 "Distribute
   the required functions close to users and data".

4.  New challenges of IoT

   As the IoT is maturing, systems are converging, deployments are
   growing, and IoT technology is used with more and more demanding
   applications such as industrial, automotive, or healthcare.  This
   leads to new challenges for the IoT.  In particular, the amount of
   data created at the edge is expected to be vast.  Industrial machines
   such as laser cutters already produce over 1 terabyte per hour, the
   same applies for 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].

4.1.  Strict Latency and Jitter

   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



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   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].  An important
   aspect for real-time communications is not only the latency, but also
   guarantees for jitter.  This means control packets need to arrive
   with as little variation as possible with a strict deadline.  Given
   the best-effort characteristics of the Internet, this challenge is
   virtually impossible to address with a pure cloud model, when also
   taking the further challenges into account.

4.2.  Uplink Cost

   Many 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 terabyte 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.

4.3.  Uninterrupted 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 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.

4.4.  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 classified, as one might be able to infer trade secrets such
   as the setup of production lines.  Hence, the owner of these systems
   are generally reluctant to upload related IoT to the cloud.




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5.  IoT integrated with Edge Computing

   As described in section 4, there are new challenges for supporting
   emerging IoT services and Edge computing is one of the candidates to
   satisfy these challenges.  The motivation for IoT Edge computing was
   discussed at an Edge computing discussion in IETF/IRTF meetings as
   follows: [IETF_Edge]

   o  Delay-sensitive

   o  High-volume

   o  Trust-sensitive

   o  (Intermittently) disconnected

   o  Energy-challenged

   o  Costly to transmit

   As we described at previous sections, the above motivation for IoT
   Edge computing could directly be benefits of Edge computing in the
   IoT environment.  The above motivation for IoT Edge computing is
   mainly related to IoT data and other motivation for IoT Edge
   computing can exist as other aspects of networking and communication.

   In spite of its benefits, Edge computing in IoT services has
   challenges such as programmability, naming, data abstraction, service
   management, privacy and security and optimization metrics.

   Edge computing can support IoT services independently of Cloud
   computing.  However, Edge computing is increasingly connected to
   Cloud computing in most IoT systems for processing and storaging
   data.  Thus, the relationship of Edge Computing to Cloud Computing is
   also another challenge of Edge Computing in IoT [ISO_TR].

5.1.  IoT Data in Edge Computing

   As an aspect of IoT, Edge computing can provide many capabilities for
   IoT services because IoT systems are based on sensors and actuator
   devices in edge area and IoT data generated from sensors and actuator
   devices are gathered through a gateway [ISO_TR].  Besides on IoT
   data, other functions such as computing, control and network
   functions are also very remarkable to support IoT services.  In this
   document, we will first concentrate on IoT data's aspect since the
   benefit of Edge computing with IoT data is very big in use cases.





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5.1.1.  Data Storage

   As tremendous IoT sensors, IoT actuators, and IoT devices are
   connected to the Internet, IoT data volume from these things are
   expected to increase explosively.  And it is expected that much of
   this high volume of IoT data is produced and/or consumed within edge/
   local networks, not to traverse through cloud networks.  Until now,
   most IoT data generated by IoT things is transferred and accumulated
   in a remote server and storage of IoT data in a remote server is
   expensive in transmission and storage.  To mitigate the cost of
   transmission and storage, it is required to divide IoT data into two
   types of data; one is stored in edge/local networks and the other is
   stored in cloud networks.  The effect of Edge computing is revealed
   with the handling IoT data in edge/local networks.

5.1.2.  Data Processing

   Until now, most network equipment such as routers, gateways, and
   switches just forward data delivered from other network devices
   without reading or modifying the content.  In end-to-end
   communication, data is acknowledged and proceed at a final
   corresponding node.  This is a typical usage of cloud computing and a
   client-server communication.  But, in the IoT environment, some IoT
   data will be transferred to a cloud network and some will be
   delivered to an edge node.  The main reason of this separation is to
   provide real-time processing and security enhancement in IoT.
   Although there are many new technologies to reduce the delay and
   transmission time, it is not easy to guarantee real-time processing.
   The typical use case of this requirement is industrial Internet and
   smart factory.  Even though there are also several solutions to
   provide security in IoT, the more basic rule is not to expose the
   privacy data to public networks.  If we separate IoT data into
   private and non-private data, and keep private data within an edge/
   local network not to expose them in a public network, the security
   and privacy in IoT cna be addressed by the separation.

5.1.3.  Data Analyzing

   If it is possible to separate IoT data in edge/local networks and
   cloud networks, Edge computing can do more functions with IoT data in
   edge/local networks.  Because Edge computing has the capabilities to
   handle IoT data in edge/local networks, it is also possible to
   analyze IoT data to provide enhanced IoT services such as
   intelligence.  To analyze IoT data in an edge/local network, it is
   required to have comparatively processing performance and this
   requirement is not obstacle to deploy Edge computing due to the
   development of H/W and S/W.




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5.2.  IoT Device Management in Edge Computing

   If we consider new challenges of IoT services, not only the big
   volume of IoT data but also the massive number of IoT things can be a
   critical problem.  Even though, we acknowledge this future problem,
   the Internet architecture originally has the capability of
   scalability and it will mitigate scalability issue in the IoT
   environment.  But, we cannot estimate the number of IoT things in the
   future and we cannot guarantee the Internet architecture still
   sustain the scalability issue in the IoT environment.  Edge computing
   will separate the scalability domain into edge/local networks and
   outside network (e.g., cloud networks) and this separation of
   scalability domain can provide more efficient way to tackle the
   massive number of IoT things.

   Because Edge computing can handle IoT data in an edge area and store
   the IoT data in an edge node, and proceed IoT data if it is needed,
   it can also separate the management domain into two parts.  Edge
   Computing can concentrate on management of IoT things in an edge area
   and cooperate with the management of other outside networks.

6.  Architecture of IoT integrated with Edge Computing

   When we consider the implementation and deployment of Edge computing,
   it can be mainly referred to an IoT Gateway.  The role of an IoT
   Gateway is to provide multiple accesses to the heterogeneous IoT
   devices/sensors, handling IoT data and delivering the IoT data to the
   final destinations such as cloud networks.  Similar to an IoT
   Gateway, an Edge computing architecture as an edge computing node
   provides downside connectivity to IoT sensors and devices (southbound
   connectivity) and upside connectivity to cloud networks (northbound
   connectivity).  Also, the architecture provides the function of data
   storage.  Beside these functions, the Edge computing architecture
   should provide the computing functions, such as data processing, data
   analyzing, and additional function of intelligence.
















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                           +---------------------------+
                           |                           |
                           |       Cloud networks      |
                           |                           |
                           +------------+--------------+
                                        |
                                        |
                 +----------------------+-----------------------+
                 |                      |                       |
                 |      +---------------+---------------+       |
                 |      |                               |       |
                 |      |    Edge gateway function      |       |
                 |      |         (Northbound)          |       |
                 |      |                               |       |
                 |      +---------------+---------------+       |
                 |                      |                       |
                 |      +---------------+---------------+       |
                 |      |                               |       |
                 |      |    Edge computing function    |       |
                 |      |     (Storage, Processing,     |       |
                 |      |     Analyzing, Intelligence)  |       |
                 |      |                               |       |
                 |      +---------------+---------------+       |
                 |                      |                       |
                 |      +---------------+---------------+       |
                 |      |                               |       |
                 |      |    Edge networking function   |       |
                 |      |         (Southbound)          |       |
                 |      |                               |       |
                 |      +-------------------------------+       |
                 |                                              |
                 |              Edge computing node             |
                 +-----+-------+------+-------+-------+-------+-+
                       |       |      |       |       |       |
                       |       |      |       |       |       |
                   +---+----+  |  +---+----+  |   +---+----+  |
                   |Sensor 1|  |  |Sensor 2| .|.. |Sensor n|  |
                   +--------+  |  +--------+  |   +--------+  |
                               |              |               |
                               |              |               |
                          +----+---+    +-----+--+      +-----+--+
                          |Device 1|    |Device 2| .... |Device n|
                          +--------+    +--------+      +--------+


       Figure 1: Architecture of IoT integrated with Edge computing





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   It is expected that the Edge computing architecture will play an
   important role to deploy new IoT services with integration to big
   data and AI services.

7.  State-of-the-art of IoT Edge Computing

7.1.  Common aspects of IoT edge computing service platforms

   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 Appendix A.  Common aspects of IoT edge
   computing service platforms are summarized here:

   Computing devices:  IoT gateways (Appendix A.2.1, Appendix A.1.1)
      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, while the
      gateway communicates with the distant cloud using typically HTTP
      and WebSocket.

      Virtualization platforms enable the deployment of virtual edge
      computing functions, including IoT gateway software, on servers in
      the mobile network infrastructure (at base station and
      concentration points), in edge datacenters (in central offices) or
      regional datacenters located near central offices.

      End devices as computing devices are envisioned in fog
      architecture and research projects, but are not commonly used as
      such today.

   Service models:  Physical or virtual IoT gateways can host
      application programs built using an SDK.

      Edge cloud system operators host their customers' applications VMs
      or containers on servers located in or near access networks.
      These application have access to edge service APIs.  For example,
      mobile network services include radio network information,
      location, bandwidth management.

      In a cloud-like service model, service providers consume low-level
      edge platform APIs and offer high-level APIs to their own
      customers' applications.  This cloud-like model can be offered as
      an edge cloud service, or as an hybrid cloud service covering edge
      and distant cloud.





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   Management:  Life cycle management of services and applications on
      physical IoT gateways is often cloud-based.  Edge cloud management
      platforms and products (Appendix A.1.2, Appendix A.2.2) adapt
      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 a NFV-like management and orchestration model.

   Communication services:  The platform typically includes services to
      advertise or consume APIs, and enables communicating with local
      and remote endpoints.  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.

   Storage models:  An edge cloud platform may enable pass-through
      without storage, local storage (e.g. on IoT gateways).  Some edge
      cloud platforms use a distributed form of storage, e.g. an ICN
      network or a distributed storage platform.  External storage, e.g.
      on databases in distant or local IT cloud, is typically used for
      filtered data deemed worthy of long term storage, or in some cases
      for all data, for example when required for regulatory reasons.

   Computing models:  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.

   Network traffic patterns:  Network traffic is typically high volume
      uplink with throttling by edge computing devices (or deferred to
      off-peak hours or using physical shipping); and downlink for
      control and software updates.

7.2.  Use Cases of IoT Edge Computing

   Smart Constructions:  In traditional construction domain, there are
      many heavy equipment and machineries and dangerous elements.  Even
      though human pay attention to risk elements, it is not easy to
      avoid them.  If some accidents are happened in a construction
      site, it causes a loss of lives and property.  Thus, there have
      been many trials in a construction area to protect lives and
      property.  Measurements of noise, vibration, and gas in a
      construction area are recorded on a remote server and reported to
      an inspector.  Today, data produced bu such measurements is
      collected by a gateway in a construction area and transferred to a



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      remote server.  This incurs transmission cost, e.g. over a LTE
      connection, and storage cost, e.g. when using Amazon Web Services.
      When an inspector wants to investigate some accidents, he checks
      the information stored in a server.  If we deploy Edge computing
      in a construction area, the sensor data can be processed and
      analyzed in a gateway located within or near a construction area.
      And with the help of a statistical analysis or machine learning
      technologies, we can predict future accidents in advance and this
      prediction can be used as an alarm in a construction area and a
      notification to an inspector.  To determine the exact cause of
      some accident, not only sensor data but also audio and video data
      are transferred to a remote server or cloud networks.  In this
      case, the data volume of audio and video is quite big and the cost
      of transmission can be a problem.  If Edge computing can predict
      the time of accident, it can reduce the data volume of
      transmission; in general period, it can transmit the audio and
      video data with a low resolution/degree and in emergent period, it
      transmits the audio and video data with a high resolution/degree.
      By adjusting the resolution/degree of audio and video data, it can
      reduce transmission cost significantly.

   Smart Grid:  In future smart cities, Smart grids will be critical in
      ensuring availability and efficiency for energy saving and control
      in city-wide electricity management.  Edge computing is expected
      to play a significant role in those systems to improve
      transmission efficiency of electricity, react and restore for
      power disturbances, reduce operation cost, reuse renewable energy
      effectively, save energy of electricity for future usage, and so
      on.  In addition, Edge computing can help monitoring power
      generation and power demands, and making electrical energy storage
      decisions in the Smart grid system.

   Smart Water System:  The Water system is one of the most important
      aspects for building smart city.  Effective use of water, and
      cost-effective and environment-friendly treatment of water are
      critical for water control and management.  This can be
      facilitated by Edge computing in Smart water systems, to help
      monitor water consumption, transportation, prediction of future
      water use, and so on.  For example, water harvesting and ground
      water monitoring will be supported from Edge computing.  Also, a
      Smart water system is able to analyze collected information
      related to water control and management, control the reduction of
      water losses and improve the city water system through Edge
      computing.

   Smart Buildings:  [TBA]

   Smart Cities:  [TBA]



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   Connected Vehicles:  [TBA]

8.  Security Considerations

   [TBA]

9.  Acknowledgements

   The authors would like to thank Joo-Sang Youn and Akbak Rahman for
   their valuable comments and suggestions on this document.

10.  References

10.1.  Normative References

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119,
              DOI 10.17487/RFC2119, March 1997,
              <https://www.rfc-editor.org/info/rfc2119>.

10.2.  Informative References

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

   [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, Oct. 2017.

   [NIST]     Mell, P. and T. Grance, "The NIST definition of Cloud
              computing", Natl. Inst. Stand. Technol 53 (6), pp. 50,
              2009.

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

   [Evans]    Evans, D., "The Internet of Things: How the next evolution
              of the Internet is changing everything", CISCO White
              Paper vol. 1, pp. 1-11, 2011.

   [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, Oct. 2016.




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

   [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, Dec. 2016.

   [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.

   [Kelly]    Kelly, R., "Internet of Things Data to Top 1.6 Zettabytes
              by 2022",
              https://campustechnology.com/articles/2015/04/15/internet-
              of-thingsdata-to-top-1-6-zettabytes-by-2020.aspx , April
              2016.

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

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

   [IETF_Edge]
              Kutscher, D. and E. Schooler, "IoT Edge Computing
              Discussion @ IETF-98", slides-99-t2trg-edge-computing-
              summary-of-chicago-discussion-and-ideas-for-next-
              steps-00 , Mar. 2017.

   [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_MEC_02]
              ETSI, "Multi-access Edge Computing (MEC); Phase 2: Use
              Cases and Requirements", ETSI GS 002, 2016,
              <https://www.etsi.org/deliver/etsi_gs/
              MEC/001_099/002/02.01.01_60/gs_MEC002v020101p.pdf>.

   [_3GPP.23.501]
              3GPP, "System Architecture for the 5G System", 3GPP
              TS 23.501, 2019,
              <http://www.3gpp.org/ftp/Specs/html-info/23501.htm>.




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   [ETSI_MEC_WP_28]
              ETSI, "MEC in 5G networks", White Paper , June 2018,
              <https://www.etsi.org/images/files/ETSIWhitePapers/
              etsi_wp28_mec_in_5G_FINAL.pdf>.

   [Linux_Foundation_Edge]
              Linux Foundation, "Linux Foundation Edge", Portal , 2019,
              <https://www.lfedge.org/>.

   [StarlingX]
              OpenStack Foundation, "StarlingX", Portal , 2019,
              <https://www.starlingx.io/>.

   [Sifalakis]
              Sifalakis, M., Kohler, B., Scherb, C., and C. Tschudin,
              "An Information Centric Network for Computing the
              Distribution of Computations", Proceedings of the 1st
              international conference on Information-centric
              networking INC '14, 2014.

   [FLAME]    Horizon 2020 Programme, "FLAME Project", Portal , 2019,
              <https://www.ict-flame.eu/>.

   [POINT]    Horizon 2020 Programme, "IP Over ICN - the better IP
              (POINT) Project", Portal , 2019,
              <https://www.point-h2020.eu/>.

   [_5G-CORAL]
              Horizon 2020 Programme, "5G Convergent Virtualised Radio
              Access Network Living at the Edge (5G-CORAL) Project",
              Portal , 2019, <http://5g-coral.eu/>.

   [OpenEdgeComputing]
              "Open Edge Computing", Portal , 2019,
              <http://openedgecomputing.org/>.

   [IEEE-1934]
              IEEE, "FOG - Fog Computing and Networking Architecture
              Framework", Portal , 2019,
              <https://standards.ieee.org/standard/1934-2018.html>.

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






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   [_60802]   IEEE 802, "Use Cases IEC/IEEE 60802 V1.3", IEC/IEEE
              60802 , September 2018,
              <http://www.ieee802.org/1/files/public/
              docs2018/60802-industrial-use-cases-0818-v13.pdf>.

   [ENERGY]   Beckel, C., Sadamori, L., Staake, T., and S. Santini,
              "Revealing Household Characteristics from Smart Meter
              Data", Energy vol. 78, pp. 397-410, December 2014,
              <https://www.vs.inf.ethz.ch/publ/papers/
              beckel-2014-energy.pdf>.

Appendix A.  Overview of the IoT Edge Computing

   This list of initiatives, projects and products aim to provide an
   overview of the IoT Edge Computing.  Our goal is to be representative
   rather than exhaustive.  Please help us complete this overview by
   communicating with us about entries we have missed.

A.1.  Open Source Projects

A.1.1.  Gateway/CPE Platforms

   EdgeX Foundry, Home Edge, Edge Virtualization Engine are Linux
   Foundation projects ([Linux_Foundation_Edge]) aiming to provide a
   platform for edge computing devices.  Such an open source platform
   can, for example, host proprietary programs currently run on IoT
   gateway products (Appendix A.2).  EdgeX Foundry develops an edge
   computing framework running on the IoT gateway.  Home Edge develops
   an edge computing framework especially dedicated to home computing
   devices, controlling home appliances, sensors, etc., and enabling AI
   applications, especially distributed and parallel machine learning.
   The Edge Virtualization Engine (EVE) project develops a
   virtualization platform (for VMs and containers) designed to run
   outside of the datacenter, in an edge network; EVE is deployed on
   bare-metal hardware.

   Computing devices:  Hardware support for EdgeX and EVE is similar:
      they support x86 and ARM-based computing devices; A typical target
      can be a Linux Raspberry Pi with 1GB RAM, 64bit CPU, 32GB storage.

   Service platform:  EdgeX uses a micro-service architecture.  Micro-
      services on the gateway are connected together, and to outside
      applications, through REST, or messaging technologies such as
      MQTT, AMQP and 0MQ.  The gateway can communicate with external
      backend applications or other gateways (north-south in tiered
      deployments or east-west in more distributed deployments).
      Gateway-device communication can use a wide range of IoT
      protocols.  "Export services" enable on-gateway and off-gateway



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      clients to register as recipient for data from devices.  Core
      services are microservices that deal with persisting data from
      devices or alternatively "streaming" device data through, without
      persistence (core data service); managing information about the
      IoT devices, including their sensors, how to communicate with
      them, etc. (metadata service); and actual communication with IoT
      devices, on behalf of other on-gateway or off-gateway services
      (command service).  A rule engine provides an API to register
      actions in response to conditions typically including an IoT
      device ID, sensor values to check, thresholds, etc.  The
      scheduling micro service deals with organizing the removal of data
      persisted on the gateway.  Alerts and notifications microservice
      can be used to dispatch alert/notifications from internal or
      external sources to interested consumers including backend
      servers, or human operators through email or SMS.

   Edge cloud applications:  Target applications for EdgeX include
      industrial IoT (e.g.  IoT sensor data and actuator control mixed
      with augmented reality application for technicians).  Home Edge
      focuses on smart home use cases, including using AI lifestyle and
      safety applications.

A.1.2.  Edge Cloud Management Platforms

   This set of open-source projects setup and manage clouds of
   individual edge computing devices.  StarlingX ([StarlingX]) extends
   OpenStack to provide virtualization platform management for edge
   clouds, which are distributed (in the range of 100 compute devices),
   secure and highly available.  Akraino Edge Stack, another project
   from the Linux Fundation Edge [Linux_Foundation_Edge], has a wider
   scope of developing a management platform adapted for the edge (e.g.,
   covering 1000 plus locations), aiming for zero-touch provisioning,
   and zero-touch lifecycle management.

   Computing devices:  Compute devices are typically Linux-based
      application servers or more constrained devices.

   Service platform:  StarlingX adds new management services to
      OpenStack by leveraging building blocks such as Ceph for
      distributed storage, Kubernetes for orchestration.  The new
      services are for management of configuration (enabling auto-
      discovery and configuration), faults, hosts (enabling host failure
      detection and auto-recovery), services (providing high
      availability through service redundancy and multi-path
      communication) and software (enabling updates).

   Edge cloud applications:  An edge computing platform may support a
      wide range of use cases.  E.g., autonomous vehicles, industrial



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      automation and robotics, cloud RAN, metering and monitoring,
      mobile HD video, content delivery, healthcare imaging and
      diagnostics, caching and surveillance, augmented/virtual reality,
      small cell services for high density locations (stadiums),
      universal CPE applications, retail.

A.1.3.  Related Projects

   Open Edge Computing ([OpenEdgeComputing]) is an initiative from
   universities, manufacturers, infrastructure providers and operators,
   enabling efficiently offloading cloudlets (VMs) to the edge.
   Computing devices are typically powerful, well-connected servers
   located in mobile networks (e.g. collocated with base stations or
   aggregation sites).  The service platform is built on top of
   OpenStack++, an extension of OpenStack to support cloudlets.  This
   project is mentioned here as a related project because of its edge
   computing focus, and potential for some IoT use cases.  Nevertheless,
   its primary use cases are typically non-IoT related, such as
   offloading processing-intensive applications from a mobile device to
   the edge.

A.2.  Products

A.2.1.  IoT Gateways

   Multiple products are marketed as IoT gateways (Amazon Greengrass,
   Microsoft Azure IoT Edge, Google Cloud IoT Core, and gateway
   solutions from Bosh and Siemens).  They are typically composed of a
   software frameworks that can run on a wide range of IoT gateway
   hardware devices to provide local support for cloud services, as well
   as some other local IoT gateway features such as relaying
   communication and caching content.  Remote cloud is both used for
   management of the IoT gateways, and for hosting customer application
   components.  Some IoT gateway products (Amazon Snowball) have a
   primary purpose of storing edge data on premises, to enable
   physically moving this data into the cloud without incurring digital
   data transfer cost.

   Computing devices:  Typical computing devices run Linux, Windows or a
      Real-Time OS over an ARM or x86 architecture.  The level of
      service support on the computing device can range from low-level
      packages giving maximum control to embedded developers, to high-
      level SDKs.  Typical requirements can start at 1GHz and 128MB RAM,
      e.g. ranging from Raspberry Pi to a server-level appliance.

   Service platform:  IoT gateways can provide a range of service
      including: running stateless functions; routing messages between
      connected IoT devices (using a wide range of IoT protocols);



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      caching data; enabling some form of synchronization between IoT
      devices; authenticating and encrypting device data.  Association
      between IoT devices and gateway based can require a device
      certificate.

   Edge cloud applications:  Pre-processing of IoT data for later
      processing in the Cloud is a major driver.  Use cases include
      industrial automation, farming, etc.

A.2.2.  Edge Cloud Platforms

   Services such as MobileEdgeX provide a platform for application
   developers to deploy software (e.g. as software containers) on edge
   networks.

   Computing devices:  Bare metal and virtual servers provided by mobile
      network operators are used as computing devices.

   Service platform:  The service platform provides end device location
      service, using GPS data obtained from platform software deployed
      in end devices, correlated with location information obtained from
      the mobile network.  The service platform manages the deployment
      of application instances (containers) on servers close to end
      devices, using a declarative specification of optimal location
      from the application provider.

   Edge cloud applications:  Use cases include autonomous mobility,
      asset management, AI-based systems (e.g. quality inspection,
      assistance systems, safety and security cameras) and privacy-
      preserving video processing.  There are also non-IoT use cases
      such as augmented reality and gaming.

A.3.  Standards Initiatives

A.3.1.  ETSI Multi-access Edge Computing

   The ETSI MEC industry standardization group develops specifications
   that enable efficient and seamless integration of applications from
   vendors, service providers, and 3rd parties across multi-vendor MEC
   platforms ([ETSI_MEC_03]).  Basic principles followed include:
   leveraging NFV infrastructure; being compliant with 3GPP systems;
   focusing on orchestration, MEC services, applications and platforms.
   Phase 1 (2015-2016) focused on basic platform services.  Phase 2
   (2017-2019) focuses on: supporting non-3GPP radio access
   technologies, especially WiFi; supporting a distributed, multi-
   operator and multi-vendor architecture; supporting non-VM based
   virtualization such as containers and PaaS.




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   Computing devices:  Computing devices are typically application
      servers, attached to an eNodeB or at a higher level of aggregation
      point, and provide service to end users.

   Service platform:  The mobile edge platform offers an environment
      where the mobile edge applications can discover, advertise,
      consume and offer mobile edge services.  The platform can provide
      certain native services such as radio network information,
      location, bandwidth management etc.  The platform manager is
      responsible for managing the life cycle of applications including
      informing the mobile edge orchestrator of relevant application
      related events, managing the application rules and requirements
      including service authorizations, traffic rules, DNS
      configuration.

   Edge cloud applications:  Some of the use cases for MEC
      ([ETSI_MEC_02]) are IoT-related, including: security and safety
      (face recognition and monitoring), sensor data monitoring, active
      device location (e.g., crowd management), low latency vehicle-to-
      infrastructure and vehicle-to-vehicle (V2X, e.g., hazard
      warnings), video production and delivery, camera as a service.

A.3.2.  Edge Computing Support in 3GPP

   The 3GPP standards organization included edge computing support in 5G
   [_3GPP.23.501].  Integration of MEC and 5G systems has been studied
   in ETSI as well [ETSI_MEC_WP_28].

   Computing devices:  From 3GPP standpoint, a mobile device may access
      any computing device located in a local data network, i.e. traffic
      is steered towards the local data network where the computing
      device is located.

   Service platform:  An external party may influence steering, QoS and
      charging of traffic towards the computing device.  Session and
      service continuity can ensure that edge service is maintained when
      a client device moves.  The network supports multiple-anchor
      connections, which makes it possible to connect a client device to
      both a local and a remote data network.  The client device can be
      made aware of the availability of a local area data network, based
      on its location.

   Edge cloud applications:  Edge cloud applications in 3GPP can help
      support the major use cases envisioned for 5G, including massive
      IoT and V2X.






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A.3.3.  OpenFog Consortium

   The OpenFog Consortium (now part of the Industrial Internet
   Consortium) aims to standardize industrial IoT, fog and edge
   computing.  It produced a reference architecture for the Fog
   ([OpenFog]), which has been published as IEEE standard P1934 in 2018.

   Computing devices:  Fog nodes include computational, networking,
      storage and acceleration elements.  This includes nodes collocated
      with sensors and actuators, roadside or mobile nodes involved in
      V2X connectivity.  Fog nodes should be programmable and may
      support multi-tenancy.  Fog computing devices must employ a
      hardware-based immutable root of trust, i.e. a trusted hardware
      component which receives control at power-on.

   Service platform:  The service platform is structured around
      "pillars" including: security end-to-end, scalability by adding
      internal components or adding more fog nodes, openness in term of
      discovery of/by other nodes and networks, autonomy from
      centralized clouds (for discovery, orchestration and management,
      security and operation) and hierarchical organization of fog
      nodes.

   Edge cloud applications:  Major use cases include smart cars and
      traffic control, visual security and surveillance, smart cities.

A.3.4.  Related Standards

   The IEEE Fog Computing and Networking Architecture Framework Working
   Group [IEEE-1934] published the OpenFog architecture as an IEEE
   document, and plan to do further work on taxonomy, architecture
   framework, and compliance guidelines.

A.4.  Research Projects

A.4.1.  Named Function Networking

   Named Function Networking ([Sifalakis]) is a research project that
   aims to extend ICN concepts (especially named data networking) to
   have the network orchestrate computation.  Interests are sent for a
   combination of function and argument names, instead of using the
   content name in NDN.

   Computing devices:  NFN-capable switches are collocated with
      computing devices.

   Service platform:  NFN enables accessing static data and dynamic
      computation results in one data-oriented framework, thus



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      benefiting from usual ICN features such as data authenticity and
      caching, as well as enabling the network to perform various
      optimizations, e.g. moving data, code or both closer to
      requesters.  NFN also enables secure access to individual elements
      within Named Data Objects, e.g. for filtering or aggregation.

   Edge cloud applications:  Use cases include some form of MapReduce
      operations and service chaining.  NDN, on which NFN is based, has
      been studied in the context of IoT, where it can provide local
      trust management and rendezvous service.

A.4.2.  5G-CORAL

   The 5G-CORAL project ([_5G-CORAL]) aims to enable convergence of
   access across multiple RATs using Fog computing, using for this
   purpose an Edge and Fog Computing System (EFS).

   Computing devices:  Computing devices used in 5G-CORAL include cloud
      and central data center servers, edge data center servers, and
      fixed or mobile "Fog Computing Devices", which can be computing
      devices located in vehicles or factories, e.g.  IoT gateways,
      mobile phones, cyber-physical devices, etc.

   Service platform:  5G-CORAL architecture is based on an integrated
      virtualized edge and fog computing system (EFS), that aims to be
      flexible, scalable and interoperable with other domains including
      transport (fronthaul, backhaul), core and clouds.  An
      Orchestration and Control System (OCS) enables automatic discovery
      of heterogeneous, multiple-owner resources, and federate them into
      a unified hosting environment.  OCS monitors resource usage to
      guarantee service levels.  Finally, OCS also includes
      orchestration and life cycle functions, including live migration
      and scaling.  Applications (user and third-party) both inside and
      outside the EFS subscribe to EFS services through APIs, with
      emphasis on IoT and cyber-physical functionalities.

   Edge cloud applications:  EFS-hosted services include analytics
      obtained from IoT gateways (e.g.  LORA or eNodeB gateways),
      context information services from RATs, transport (fronthaul and
      backhaul) and core networks.  EFS-hosted functions include network
      performance acceleration functions, virtualized C-RAN functions
      for access nodes and possible end user devices.

A.4.3.  FLAME

   The FLAME project ([FLAME]) aims to improve performance of
   interactive media systems while keeping infrastructure costs low.  It
   builds over virtualization technologies such as XOS, OpenStack and



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   ONOS/ODL to offer a programmable media service platform.  FLAME
   leverages IP-over-ICN technology developed through earlier projects
   including POINT ([POINT]).

   Computing devices:  The FLAME platform provides a service layer on
      top of an infrastructure platform, which can include cloud servers
      as well as computing devices collocated with WiFi access points.

   Service platform:  The FLAME platform can be seen as an edge + cloud
      computing platform with a use case focus on media dissemination,
      although the basic platform can be suitable for micro-services in
      general.  The computing platform is comprised of: computing
      devices, an infrastructure platform (XOS, OpenStack, ONOS/ODL),
      NFV-MANO components (orchestrator, virtual infrastructure manager)
      and FLAME platform core services (PCE, network access point,
      surrogate manager).

   Edge cloud applications:  IoT use cases include public safety, such
      as supporting body-worn camera for police and social workers.  As
      opposed to other multi-media applications that are also envisioned
      (pre-processing, user reporting, curation...), where a typical
      goal is to curate content early at the edge, to reduce expected
      high data volume, public safety use cases are typically about
      implementing triggers at the edge: everything needs to be kept
      anyway, to be available in case of an audit.  Content is stored
      offline during off peak-hours delivery.  For privacy and data
      volume concerns, triggers for, e.g., alerting police, cannot be
      performed in the cloud and should be performed as close to the
      data source as possible.

Authors' Addresses

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

   Email: jhong@etri.re.kr


   Yong-Geun Hong
   ETRI
   218 Gajeong-ro, Yuseung-Gu
   Daejeon  34129
   Korea

   Email: yghong@etri.re.kr



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   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
   Munich  80992
   Germany

   Email: matthias.kovatsch@huawei.com


   Eve Schooler
   Intel

   Email: eve.m.schooler@intel.com


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

   Email: ietf@dkutscher.net




















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