Problem Statement of IoT integrated with Edge Computing
draft-hong-iot-edge-computing-01

T2T Research Group                                               J. Hong
Internet-Draft                                                 Y-G. Hong
Intended status: Informational                                      ETRI
Expires: April 25, 2019                                        J-S. Youn
                                                           DONG-EUI Univ
                                                        October 22, 2018

        Problem Statement of IoT integrated with Edge Computing
                    draft-hong-iot-edge-computing-01

Abstract

   This document describes new challenges for IoT services originated
   from the changes in the IoT environment.  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 its use cases.  It
   discusses benefits and challenges of Edge computing, focusing mainly
   on IoT data.  The direction of Edge computing for IoT should be
   discussed in IETF/IRTF.

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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Conventions and Terminology . . . . . . . . . . . . . . . . .   3
   3.  Background  . . . . . . . . . . . . . . . . . . . . . . . . .   3
     3.1.  Internet of Things (IoT)  . . . . . . . . . . . . . . . .   3
     3.2.  IoT with Cloud computing  . . . . . . . . . . . . . . . .   3
     3.3.  Environment changes/Paradigm shift  . . . . . . . . . . .   4
   4.  New challenges of IoT . . . . . . . . . . . . . . . . . . . .   4
     4.1.  Strict Latency  . . . . . . . . . . . . . . . . . . . . .   4
     4.2.  Constrained Network Bandwidth . . . . . . . . . . . . . .   5
     4.3.  Constrained Devices . . . . . . . . . . . . . . . . . . .   5
     4.4.  Uninterrupted Services with Intermittent Connectivity to
           the Cloud . . . . . . . . . . . . . . . . . . . . . . . .   5
     4.5.  Privacy and Security  . . . . . . . . . . . . . . . . . .   5
   5.  IoT integrated with Edge Computing  . . . . . . . . . . . . .   5
     5.1.  IoT Data in Edge Computing  . . . . . . . . . . . . . . .   5
       5.1.1.  Data Storage  . . . . . . . . . . . . . . . . . . . .   6
       5.1.2.  Data Processing . . . . . . . . . . . . . . . . . . .   6
       5.1.3.  Data Analyzing  . . . . . . . . . . . . . . . . . . .   7
     5.2.  IoT Device Management in Edge Computing . . . . . . . . .   7
     5.3.  Edge Computing in IoT . . . . . . . . . . . . . . . . . .   7
   6.  Use Cases of Edge Computing in IoT  . . . . . . . . . . . . .   8
     6.1.  Smart Constructions . . . . . . . . . . . . . . . . . . .   8
     6.2.  Smart Grid  . . . . . . . . . . . . . . . . . . . . . . .   9
     6.3.  Smart Water System  . . . . . . . . . . . . . . . . . . .   9
     6.4.  Smart Buildings . . . . . . . . . . . . . . . . . . . . .   9
     6.5.  Smart Cities  . . . . . . . . . . . . . . . . . . . . . .   9
     6.6.  Connected Vehicles  . . . . . . . . . . . . . . . . . . .  10
   7.  Security Considerations . . . . . . . . . . . . . . . . . . .  10
   8.  References  . . . . . . . . . . . . . . . . . . . . . . . . .  10
     8.1.  Normative References  . . . . . . . . . . . . . . . . . .  10
     8.2.  Informative References  . . . . . . . . . . . . . . . . .  10
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  11

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.

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

   Thus, in this document, we describe new challenges for emerging IoT
   services such as strict latency, constrained network bandwidth,
   constrained devices, uninterrupted services with intermittent
   connectivity, privacy and security due to the IoT environmental
   changes.

   In order to address those new challenges for IoT services, the
   integration of Edge computing and IoT has been emerged as a promising
   solution.  In this document, we thus describe the concept of IoT
   integrated with Edge computing as well as its use cases to discuss
   the benefits and challenges of Edge computing mainly focused on IoT
   data.  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].

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].

3.2.  IoT with Cloud computing

   IoT is generally characterized by real world small things that are
   widely distributed but have limited storage and processing power.  On
   the other hand, Cloud computing is an emerging technology which has
   virtually unlimited capacity in terms of storage and processing
   power.  Thus, the IoT with Cloud computing has been recognized as an
   efficient way to overcome those IoT issues [Botta].

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   The integration of IoT with Cloud computing brings many advantages
   such as flexibility, efficiency, and capability to store and use IoT
   data since Cloud computing has been a mature technology used to
   provide computing services or IoT data storage over the Internet.

3.3.  Environment changes/Paradigm shift

   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 networks and many applications will be deployed on the edge
   netwoks to consume these IoT data.  Some of the applications may have
   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.

   Cisco Systems predicts 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 networks to
   the cloud data center may not be an efficient way anymore to process
   vast amounts of data.

   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 networks [Shi].

4.  New challenges of IoT

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

4.1.  Strict Latency

   Many industrial control systems, such as manufacturing systems, smart
   grids, oil and gas systems, etc., often require end-to-end latency
   between the sensor and control node remains within a few milliseconds
   and some other IoT applications may require latency below a few tens
   of milliseconds [Weiner].  These requirements for latency are
   difficult to achieve by today's Cloud services.

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4.2.  Constrained Network Bandwidth

   With an exponential rate, IoT data is generated by the massive things
   connected into the Internet [Kelly] and extremely high network
   bandwidth is required to send all the data to the cloud.  Since 90%
   of the IoT data generated by the endpoints will be stored and
   processed locally rather than in the cloud, sending all the IoT data
   to the cloud is often unnecessary.  Or sometimes it is prohibited due
   to regulations and data privacy concerns.

4.3.  Constrained Devices

   Many IoT things such as sensors, data collectors, actuators,
   controllers, cars, drones, etc., have very limited hardware
   resources.  Many constrained IoT things cannot rely solely on their
   limited resources to meet all their computing needs.  It is not
   practical to require everyone to interact directly with the cloud.
   This is because these interactions require resource-intensive
   processing and complex protocols.

4.4.  Uninterrupted Services with Intermittent Connectivity to the Cloud

   Cloud services will have difficulty providing uninterrupted services
   to devices and systems such as vehicles, drones, and oil rigs that
   have intermittent network connectivity to the cloud.

4.5.  Privacy and Security

   When IoT services are deployed at home, personal information can be
   learned from detected usage data.  For example, one can easily guess
   whether a home is empty by reading its electricity or water usage.
   In this case, the way to support services without exposing personal
   information is a challenge.

   When IoT data is sent to the cloud which is the end point in the
   traditional end-to-end communication system, privacy of the data is a
   challenge since it may travel across multiple routers to the cloud.

5.  IoT integrated with Edge Computing

5.1.  IoT Data in Edge Computing

   As described in section 4, new challenges for supporting IoT services
   exist and Edge computing is one of the candidates to satisfy these
   challenges.  The concept of Edge computing is very intuitive.  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

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   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 IoT Edge computing as
   "Distribute the required functions close to users and data".

   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
   draft, we will first concentrate on IoT data's aspect because the
   benefit of Edge computing with IoT data is very big in a use cases.

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
   networks, not to traverse through cloud networks.  Until now, mainly
   IoT data generated IoT things are transferred and accumulated in a
   remote server and to store IoT data in a remote server requires
   expensive cost of 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 networks and the other is stored
   in cloud networks.  The effect of Edge computing is revealed with the
   handling IoT data in edge 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, not
   to read the content or modify them.  Based on end-to-end
   communication, data is acknowledged and proceed at a final
   corresponding node.  This is a typical usage of cloud computing and
   client-server communication.  But, in the IoT environment, some IoT
   data will be transferred to a cloud network and some IoT data will be
   delivered to an edge node/fog node.  The main reason of this
   separation is to provide real-time processing and security
   enhancement.  Although, there are many new technologies to reduce the
   delay time 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.  And even though, there are
   power functions to provide security, the more basic rule is that not
   to expose the privacy data to public networks.  If we separate IoT

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   data into private data and non-private data and keep private data
   within an edge network, not to expose them in a public network, it
   will reduce many weak points of security.

5.1.3.  Data Analyzing

   If it is possible to separate IoT data in edge networks and cloud
   networks, Edge computing can do more functions with IoT data in edge
   networks.  Because Edge computing has the capabilities to handle IoT
   data in edge networks, it is also possible to analyze IoT data to
   provide enhanced IoT services such as intelligence.  To analyze IoT
   data in an edge 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.

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 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/fog 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.

5.3.  Edge Computing in IoT

   At an Edge computing discussion in IETF/IRTF meetings, the motivation
   for IoT Edge computing is describe as follows; [IETF_Edge]

   o  Delay-sensitive

   o  High-volume

   o  Trust-sensitive

   o  (Intermittently) disconnected

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   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 be 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 data storage.
   Thus, the relationship of Edge Computing to Cloud Computing is also
   another challenge of Edge Computing in IoT [ISO_TR].

6.  Use Cases of Edge Computing in IoT

6.1.  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.  To protect lives and property, nowadays, there
   are many trials in a construction area.

   Measurements of noise, vibration, and gas in a construction area are
   recorded on a remote server and reported to an inspector.  Today,
   much of this type of information is collected by a gateway in a
   construction area and transferred to a 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/she 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 a
   construction area 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.

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   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.

6.2.  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.

6.3.  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.

6.4.  Smart Buildings

   [TBA]

6.5.  Smart Cities

   [TBA]

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6.6.  Connected Vehicles

   [TBA]

7.  Security Considerations

   [TBA]

8.  References

8.1.  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>.

8.2.  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.

   [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.

   [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.

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   [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.

Authors' Addresses

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

   Phone: +82 42 860 0926
   Email: jhong@etri.re.kr

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

   Phone: +82 42 860 6557
   Email: yghong@etri.re.kr

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   Joo-Sang Youn
   DONG-EUI University
   176 Eomgwangno Busan_jin_gu
   Busan  614-714
   Korea

   Phone: +82 51 890 1993
   Email: joosang.youn@gmail.com

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