< draft-hong-iot-edge-computing-01.txt   draft-hong-iot-edge-computing-02.txt >
T2T Research Group J. Hong T2T Research Group J. Hong
Internet-Draft Y-G. Hong Internet-Draft Y-G. Hong
Intended status: Informational ETRI Intended status: Informational ETRI
Expires: April 25, 2019 J-S. Youn Expires: September 12, 2019 J-S. Youn
DONG-EUI Univ DONG-EUI Univ
October 22, 2018 March 11, 2019
Problem Statement of IoT integrated with Edge Computing Problem Statement of IoT integrated with Edge Computing
draft-hong-iot-edge-computing-01 draft-hong-iot-edge-computing-02
Abstract Abstract
This document describes new challenges for IoT services originated This document describes new challenges such as strict latency,
from the changes in the IoT environment. In order to address those constrained network bandwidth and devices, intermittent connectivity,
new challenges, the integration of Edge computing and IoT has been privacy and security, for IoT services originated from the IoT
emerged as a promising solution. This document discribes the concept environmental changes. In order to address those new challenges, the
of IoT integrated with Edge computing as well as its use cases. It integration of Edge computing and IoT has been emerged as a promising
discusses benefits and challenges of Edge computing, focusing mainly solution. This document discribes the concept of IoT integrated with
on IoT data. The direction of Edge computing for IoT should be Edge computing as well as its use cases. It also discusses benefits
discussed in IETF/IRTF. and challenges of Edge computing. The direction of Edge computing
for IoT should be discussed in the IETF/IRTF.
Status of This Memo Status of This Memo
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Table of Contents Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2 1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Conventions and Terminology . . . . . . . . . . . . . . . . . 3 2. Conventions and Terminology . . . . . . . . . . . . . . . . . 3
3. Background . . . . . . . . . . . . . . . . . . . . . . . . . 3 3. Background . . . . . . . . . . . . . . . . . . . . . . . . . 3
3.1. Internet of Things (IoT) . . . . . . . . . . . . . . . . 3 3.1. Internet of Things (IoT) . . . . . . . . . . . . . . . . 3
3.2. IoT with Cloud computing . . . . . . . . . . . . . . . . 3 3.2. IoT with Cloud computing . . . . . . . . . . . . . . . . 4
3.3. Environment changes/Paradigm shift . . . . . . . . . . . 4 3.3. IoT Environmental changes . . . . . . . . . . . . . . . . 4
4. New challenges of IoT . . . . . . . . . . . . . . . . . . . . 4 4. New challenges of IoT . . . . . . . . . . . . . . . . . . . . 4
4.1. Strict Latency . . . . . . . . . . . . . . . . . . . . . 4 4.1. Strict Latency . . . . . . . . . . . . . . . . . . . . . 5
4.2. Constrained Network Bandwidth . . . . . . . . . . . . . . 5 4.2. Constrained Network Bandwidth . . . . . . . . . . . . . . 5
4.3. Constrained Devices . . . . . . . . . . . . . . . . . . . 5 4.3. Constrained Devices . . . . . . . . . . . . . . . . . . . 5
4.4. Uninterrupted Services with Intermittent Connectivity to 4.4. Uninterrupted Services with Intermittent Connectivity to
the Cloud . . . . . . . . . . . . . . . . . . . . . . . . 5 the Cloud . . . . . . . . . . . . . . . . . . . . . . . . 5
4.5. Privacy and Security . . . . . . . . . . . . . . . . . . 5 4.5. Privacy and Security . . . . . . . . . . . . . . . . . . 5
5. IoT integrated with Edge Computing . . . . . . . . . . . . . 5 5. IoT integrated with Edge Computing . . . . . . . . . . . . . 6
5.1. IoT Data in Edge Computing . . . . . . . . . . . . . . . 5 5.1. IoT Data in Edge Computing . . . . . . . . . . . . . . . 6
5.1.1. Data Storage . . . . . . . . . . . . . . . . . . . . 6 5.1.1. Data Storage . . . . . . . . . . . . . . . . . . . . 6
5.1.2. Data Processing . . . . . . . . . . . . . . . . . . . 6 5.1.2. Data Processing . . . . . . . . . . . . . . . . . . . 6
5.1.3. Data Analyzing . . . . . . . . . . . . . . . . . . . 7 5.1.3. Data Analyzing . . . . . . . . . . . . . . . . . . . 7
5.2. IoT Device Management in Edge Computing . . . . . . . . . 7 5.2. IoT Device Management in Edge Computing . . . . . . . . . 7
5.3. Edge Computing in IoT . . . . . . . . . . . . . . . . . . 7 5.3. Edge Computing in IoT . . . . . . . . . . . . . . . . . . 8
6. Use Cases of Edge Computing in IoT . . . . . . . . . . . . . 8 6. Architecture of IoT integrated with Edge Computing . . . . . 8
6.1. Smart Constructions . . . . . . . . . . . . . . . . . . . 8 7. Use Cases of Edge Computing in IoT . . . . . . . . . . . . . 10
6.2. Smart Grid . . . . . . . . . . . . . . . . . . . . . . . 9 7.1. Smart Constructions . . . . . . . . . . . . . . . . . . . 10
6.3. Smart Water System . . . . . . . . . . . . . . . . . . . 9 7.2. Smart Grid . . . . . . . . . . . . . . . . . . . . . . . 10
6.4. Smart Buildings . . . . . . . . . . . . . . . . . . . . . 9 7.3. Smart Water System . . . . . . . . . . . . . . . . . . . 11
6.5. Smart Cities . . . . . . . . . . . . . . . . . . . . . . 9 7.4. Smart Buildings . . . . . . . . . . . . . . . . . . . . . 11
6.6. Connected Vehicles . . . . . . . . . . . . . . . . . . . 10 7.5. Smart Cities . . . . . . . . . . . . . . . . . . . . . . 11
7. Security Considerations . . . . . . . . . . . . . . . . . . . 10 7.6. Connected Vehicles . . . . . . . . . . . . . . . . . . . 11
8. References . . . . . . . . . . . . . . . . . . . . . . . . . 10 8. Security Considerations . . . . . . . . . . . . . . . . . . . 11
8.1. Normative References . . . . . . . . . . . . . . . . . . 10 9. References . . . . . . . . . . . . . . . . . . . . . . . . . 11
8.2. Informative References . . . . . . . . . . . . . . . . . 10 9.1. Normative References . . . . . . . . . . . . . . . . . . 11
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 11 9.2. Informative References . . . . . . . . . . . . . . . . . 12
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 13
1. Introduction 1. Introduction
Nowadays, most IoT services are based on Cloud computing since it can Nowadays, most IoT services are based on Cloud computing since it can
provide virtually unlimited storage and processing power. The provide virtually unlimited storage and processing power. The
integration of IoT with Cloud computing brings many advantages such integration of IoT with Cloud computing brings many advantages such
as flexibility, efficiency, and ability to store and use data. as flexibility, efficiency, and ability to store and use data.
However, the IoT environment is changing in such a way that vast 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 amounts of data are created at edge/local networks and about a half
is stored, processed, analyzed and acted upon close to the data of data is stored, processed, analyzed and acted upon close to the
producer. Emerging IoT services introduce new challenges that cannot data producer. Thus, emerging IoT services introduce new challenges
be addressed by today's centralized Cloud computing models alone. that cannot be addressed by today's centralized Cloud computing
models alone.
Thus, in this document, we describe new challenges for emerging IoT In this document, we describe new challenges for emerging IoT
services such as strict latency, constrained network bandwidth, services such as strict latency, constrained network bandwidth,
constrained devices, uninterrupted services with intermittent constrained devices, uninterrupted services with intermittent
connectivity, privacy and security due to the IoT environmental connectivity, privacy and security due to the IoT environmental
changes. changes.
In order to address those new challenges for IoT services, the In order to address those new challenges for IoT services, the
integration of Edge computing and IoT has been emerged as a promising integration of Edge computing with IoT has been emerged as a
solution. In this document, we thus describe the concept of IoT promising solution. In this document, we describe the concept of IoT
integrated with Edge computing as well as its use cases to discuss integrated with Edge computing as well as its use cases to discuss
the benefits and challenges of Edge computing mainly focused on IoT 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 data. The purpose of this document is to bring up the issues of Edge
computing for IoT services in IETF/IRTF. computing for IoT services in IETF/IRTF.
2. Conventions and Terminology 2. Conventions and Terminology
The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT", The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
document are to be interpreted as described in [RFC2119]. document are to be interpreted as described in [RFC2119].
skipping to change at page 3, line 49 skipping to change at page 4, line 9
connected to the Internet can send and receive information collected connected to the Internet can send and receive information collected
by sensors without human intervention, where things are various by sensors without human intervention, where things are various
embedded systems such as home appliances, mobile equipment, wearable embedded systems such as home appliances, mobile equipment, wearable
devices, etc. IoT has become one of the notable innovations playing devices, etc. IoT has become one of the notable innovations playing
an important role in our daily lives [Lin]. an important role in our daily lives [Lin].
3.2. IoT with Cloud computing 3.2. IoT with Cloud computing
IoT is generally characterized by real world small things that are IoT is generally characterized by real world small things that are
widely distributed but have limited storage and processing power. On widely distributed but have limited storage and processing power. On
the other hand, Cloud computing is an emerging technology which has the other hand, Cloud computing is a predominant technology which has
virtually unlimited capacity in terms of storage and processing virtually unlimited capacity in terms of storage and processing
power. Thus, the IoT with Cloud computing has been recognized as an power. Thus, the IoT with Cloud computing has been recognized as an
efficient way to overcome those IoT issues [Botta]. efficient way to overcome those IoT issues [Botta].
The integration of IoT with Cloud computing brings many advantages The integration of IoT with Cloud computing brings many advantages
such as flexibility, efficiency, and capability to store and use IoT such as flexibility, efficiency, and capability to store and use IoT
data since Cloud computing has been a mature technology used to data since Cloud computing has been a mature technology used to
provide computing services or IoT data storage over the Internet. provide computing services or IoT data storage over the Internet.
3.3. Environment changes/Paradigm shift 3.3. IoT Environmental changes
Now with IoT, we will reach the era of post-Clouds where Now with IoT, we will reach the era of post-Clouds where
unprecedented volume and variety of data will be generated by things unprecedented volume and variety of data will be generated by things
at edge networks and many applications will be deployed on the edge at edge/local networks and many applications will be deployed on the
netwoks to consume these IoT data. Some of the applications may have edge netwoks to consume these IoT data. Some of the applications may
very short response times, some may contain personal data, and others need very short response times, some may contain personal data, and
may generate vast amounts of data. Today's Cloud based service others may generate vast amounts of data. Today's Cloud based
models are not suitable for these applications. service models are not suitable for these applications.
Cisco Systems predicts that by 2019, 45% of the data created in IoT It is predicted that by 2019, 45% of the data created in IoT will be
will be stored, processed, analyzed and acted close to, or at the stored, processed, analyzed and acted close to, or at the edge of the
edge of the network and about 50 billion devices will connect to the network and about 50 billion devices will connect to the Internet by
Internet by 2020 [Evans]. So, moving all data from edge networks to 2020 [Evans]. So, moving all data from edge/local networks to the
the cloud data center may not be an efficient way anymore to process cloud data center may not be an efficient way anymore to process vast
vast amounts of data. amounts of data.
In Cloud computing, users traditionally only consumed IoT data In Cloud computing, users traditionally only consumed IoT data
through Cloud services. Now, however, users are also producing IoT through Cloud services. Now, however, users are also producing IoT
data with their mobile devices. This change requires more data with their mobile devices. This change requires more
functionality at edge networks [Shi]. functionality at edge/local networks [Shi].
4. New challenges of IoT 4. New challenges of IoT
As the IoT environment is changing in such a way that vast amounts of 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 data are created at edge/local networks and about a half of IoT data
stored, processed, analyzed and acted close to the IoT data producer, is stored, processed, analyzed and acted close to the IoT data
the emerging IoT services introduce new challenges that cannot be producer, the emerging IoT services introduce new challenges that
addressed by today's centralized Cloud computing models alone cannot be addressed by today's centralized Cloud computing models
[Chiang]. alone [Chiang].
4.1. Strict Latency 4.1. Strict Latency
Many industrial control systems, such as manufacturing systems, smart Many industrial control systems, such as manufacturing systems, smart
grids, oil and gas systems, etc., often require end-to-end latency grids, oil and gas systems, etc., often require end-to-end latency
between the sensor and control node remains within a few milliseconds between the sensor and control node remains within a few milliseconds
and some other IoT applications may require latency below a few tens and some other IoT applications may require latency below a few tens
of milliseconds [Weiner]. These requirements for latency are of milliseconds [Weiner]. These requirements for latency are
difficult to achieve by today's Cloud services. difficult to achieve by today's Cloud services.
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information is a challenge. information is a challenge.
When IoT data is sent to the cloud which is the end point in the 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 traditional end-to-end communication system, privacy of the data is a
challenge since it may travel across multiple routers to the cloud. challenge since it may travel across multiple routers to the cloud.
5. IoT integrated with Edge Computing 5. IoT integrated with Edge Computing
5.1. IoT Data in Edge Computing 5.1. IoT Data in Edge Computing
As described in section 4, new challenges for supporting IoT services As described in section 4, new challenges for supporting emerging IoT
exist and Edge computing is one of the candidates to satisfy these services exist and Edge computing is one of the candidates to satisfy
challenges. The concept of Edge computing is very intuitive. The these challenges. The concept of Edge computing is very intuitive.
definition of Edge computing from ISO is 'Form of distributed The definition of Edge computing from ISO is 'Form of distributed
computing in which significant processing and data storage takes computing in which significant processing and data storage takes
place on nodes which are at the edge of the network' [ISO_TR]. And 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 the similar concept of Fog computing from Open Fog Consortium is 'A
horizontal, system-level architecture that distributes computing, horizontal, system-level architecture that distributes computing,
storage, control and networking functions closer to the users along a storage, control and networking functions closer to the users along a
cloud-to-thing continuum' [OpenFog]. Based on these definitions, we cloud-to-thing continuum' [OpenFog]. Based on these definitions, we
can summarize a general philosophy of IoT Edge computing as can summarize a general philosophy of Edge computing as "Distribute
"Distribute the required functions close to users and data". the required functions close to users and data".
As an aspect of IoT, Edge computing can provide many capabilities for As an aspect of IoT, Edge computing can provide many capabilities for
IoT services because IoT systems are based on sensors and actuator IoT services because IoT systems are based on sensors and actuator
devices in edge area and IoT data generated from 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 devices are gathered through a gateway [ISO_TR]. Besides on IoT
data, other functions such as computing, control and network data, other functions such as computing, control and network
functions are also very remarkable to support IoT services. In this functions are also very remarkable to support IoT services. In this
draft, we will first concentrate on IoT data's aspect because the document, we will first concentrate on IoT data's aspect since the
benefit of Edge computing with IoT data is very big in a use cases. benefit of Edge computing with IoT data is very big in use cases.
5.1.1. Data Storage 5.1.1. Data Storage
As tremendous IoT sensors, IoT actuators, and IoT devices are As tremendous IoT sensors, IoT actuators, and IoT devices are
connected to the Internet, IoT data volume from these things are connected to the Internet, IoT data volume from these things are
expected to increase explosively. And it is expected that much of expected to increase explosively. And it is expected that much of
this high volume of IoT data is produced and/or consumed within edge this high volume of IoT data is produced and/or consumed within edge/
networks, not to traverse through cloud networks. Until now, mainly local networks, not to traverse through cloud networks. Until now,
IoT data generated IoT things are transferred and accumulated in a most IoT data generated by IoT things is transferred and accumulated
remote server and to store IoT data in a remote server requires in a remote server and storage of IoT data in a remote server is
expensive cost of transmission and storage. To mitigate the cost of expensive in transmission and storage. To mitigate the cost of
transmission and storage, it is required to divide IoT data into two 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 types of data; one is stored in edge/local networks and the other is
in cloud networks. The effect of Edge computing is revealed with the stored in cloud networks. The effect of Edge computing is revealed
handling IoT data in edge networks. with the handling IoT data in edge/local networks.
5.1.2. Data Processing 5.1.2. Data Processing
Until now, most network equipment such as routers, gateways, and Until now, most network equipment such as routers, gateways, and
switches just forward data delivered from other network devices, not switches just forward data delivered from other network devices
to read the content or modify them. Based on end-to-end without reading or modifying the content. In end-to-end
communication, data is acknowledged and proceed at a final communication, data is acknowledged and proceed at a final
corresponding node. This is a typical usage of cloud computing and corresponding node. This is a typical usage of cloud computing and a
client-server communication. But, in the IoT environment, some IoT client-server communication. But, in the IoT environment, some IoT
data will be transferred to a cloud network and some IoT data will be data will be transferred to a cloud network and some will be
delivered to an edge node/fog node. The main reason of this delivered to an edge node. The main reason of this separation is to
separation is to provide real-time processing and security provide real-time processing and security enhancement in IoT.
enhancement. Although, there are many new technologies to reduce the Although there are many new technologies to reduce the delay and
delay time and transmission time, it is not easy to guarantee real- transmission time, it is not easy to guarantee real-time processing.
time processing. The typical use case of this requirement is The typical use case of this requirement is industrial Internet and
Industrial Internet and smart factory. And even though, there are smart factory. Even though there are also several solutions to
power functions to provide security, the more basic rule is that not provide security in IoT, the more basic rule is not to expose the
to expose the privacy data to public networks. If we separate IoT privacy data to public networks. If we separate IoT data into
data into private data and non-private data and keep private data private and non-private data, and keep private data within an edge/
within an edge network, not to expose them in a public network, it local network not to expose them in a public network, the security
will reduce many weak points of security. and privacy in IoT cna be addressed by the separation.
5.1.3. Data Analyzing 5.1.3. Data Analyzing
If it is possible to separate IoT data in edge networks and cloud If it is possible to separate IoT data in edge/local networks and
networks, Edge computing can do more functions with IoT data in edge cloud networks, Edge computing can do more functions with IoT data in
networks. Because Edge computing has the capabilities to handle IoT edge/local networks. Because Edge computing has the capabilities to
data in edge networks, it is also possible to analyze IoT data to handle IoT data in edge/local networks, it is also possible to
provide enhanced IoT services such as intelligence. To analyze IoT analyze IoT data to provide enhanced IoT services such as
data in an edge network, it is required to have comparatively intelligence. To analyze IoT data in an edge/local network, it is
processing performance and this requirement is not obstacle to deploy required to have comparatively processing performance and this
Edge computing due to the development of H/W and S/W. 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 5.2. IoT Device Management in Edge Computing
If we consider new challenges of IoT services, not only the big 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 volume of IoT data but also the massive number of IoT things can be a
critical problem. Even though, we acknowledge this future problem, critical problem. Even though, we acknowledge this future problem,
the Internet architecture originally has the capability of the Internet architecture originally has the capability of
scalability and it will mitigate scalability issue in the IoT scalability and it will mitigate scalability issue in the IoT
environment. But, we cannot estimate the number of IoT things in the environment. But, we cannot estimate the number of IoT things in the
future and we cannot guarantee the Internet architecture still future and we cannot guarantee the Internet architecture still
sustain the scalability issue in the IoT environment. Edge computing sustain the scalability issue in the IoT environment. Edge computing
will separate the scalability domain into edge networks and outside will separate the scalability domain into edge/local networks and
network (e.g., cloud networks) and this separation of scalability outside network (e.g., cloud networks) and this separation of
domain can provide more efficient way to tackle the massive number of scalability domain can provide more efficient way to tackle the
IoT things. massive number of IoT things.
Because Edge computing can handle IoT data in an edge area and store 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 the IoT data in an edge node, and proceed IoT data if it is needed,
needed, it can also separate the management domain into two parts. it can also separate the management domain into two parts. Edge
Edge Computing can concentrate on management of IoT things in an edge Computing can concentrate on management of IoT things in an edge area
area and cooperate with the management of other outside networks. and cooperate with the management of other outside networks.
5.3. Edge Computing in IoT 5.3. Edge Computing in IoT
At an Edge computing discussion in IETF/IRTF meetings, the motivation At an Edge computing discussion in IETF/IRTF meetings, the motivation
for IoT Edge computing is describe as follows; [IETF_Edge] for IoT Edge computing is describe as follows; [IETF_Edge]
o Delay-sensitive o Delay-sensitive
o High-volume o High-volume
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At an Edge computing discussion in IETF/IRTF meetings, the motivation At an Edge computing discussion in IETF/IRTF meetings, the motivation
for IoT Edge computing is describe as follows; [IETF_Edge] for IoT Edge computing is describe as follows; [IETF_Edge]
o Delay-sensitive o Delay-sensitive
o High-volume o High-volume
o Trust-sensitive o Trust-sensitive
o (Intermittently) disconnected o (Intermittently) disconnected
o Energy-challenged o Energy-challenged
o Costly to transmit o Costly to transmit
As we described at previous sections, the above motivation for IoT As we described at previous sections, the above motivation for IoT
Edge computing could directly be benefits of Edge computing in the Edge computing could directly be benefits of Edge computing in the
IoT environment. The above motivation for IoT Edge computing is IoT environment. The above motivation for IoT Edge computing is
mainly related to IoT data and other motivation for IoT Edge mainly related to IoT data and other motivation for IoT Edge
computing can be exist as other aspects of networking and computing can exist as other aspects of networking and communication.
communication.
In spite of its benefits, Edge computing in IoT services has In spite of its benefits, Edge computing in IoT services has
challenges such as programmability, naming, data abstraction, service challenges such as programmability, naming, data abstraction, service
management, privacy and security and optimization metrics. management, privacy and security and optimization metrics.
Edge computing can support IoT services independently of Cloud Edge computing can support IoT services independently of Cloud
computing. However, Edge computing is increasingly connected to computing. However, Edge computing is increasingly connected to
Cloud computing in most IoT systems for processing and data storage. Cloud computing in most IoT systems for processing and storaging
Thus, the relationship of Edge Computing to Cloud Computing is also data. Thus, the relationship of Edge Computing to Cloud Computing is
another challenge of Edge Computing in IoT [ISO_TR]. also another challenge of Edge Computing in IoT [ISO_TR].
6. Use Cases of Edge Computing in IoT 6. Architecture of IoT integrated with Edge Computing
6.1. Smart Constructions 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.
+---------------------------+
| |
| 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
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. Use Cases of Edge Computing in IoT
7.1. Smart Constructions
In traditional construction domain, there are many heavy equipment In traditional construction domain, there are many heavy equipment
and machineries and dangerous elements. Even though human pay and machineries and dangerous elements. Even though human pay
attention to risk elements, it is not easy to avoid them. If some 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 accidents are happened in a construction site, it causes a loss of
lives and property. To protect lives and property, nowadays, there lives and property. Thus, there have been many trials in a
are many trials in a construction area. construction area to protect lives and property.
Measurements of noise, vibration, and gas in a construction area are Measurements of noise, vibration, and gas in a construction area are
recorded on a remote server and reported to an inspector. Today, recorded on a remote server and reported to an inspector. Today,
much of this type of information is collected by a gateway in a data produced bu such measurements is collected by a gateway in a
construction area and transferred to a remote server. This incurs construction area and transferred to a remote server. This incurs
transmission cost, e.g. over a LTE connection, and storage cost, e.g. transmission cost, e.g. over a LTE connection, and storage cost, e.g.
when using Amazon Web Services. When an inspector wants to when using Amazon Web Services. When an inspector wants to
investigate some accidents, he/she checks the information stored in a investigate some accidents, he checks the information stored in a
server. server.
If we deploy Edge computing in a construction area, the sensor data If we deploy Edge computing in a construction area, the sensor data
can be processed and analyzed in a gateway located within a can be processed and analyzed in a gateway located within or near a
construction area or near a construction area. And with the help of construction area. And with the help of a statistical analysis or
a statistical analysis or machine learning technologies, we can machine learning technologies, we can predict future accidents in
predict future accidents in advance and this prediction can be used advance and this prediction can be used as an alarm in a construction
as an alarm in a construction area and a notification to an area and a notification to an inspector.
inspector.
To determine the exact cause of some accident, not only sensor data 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 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 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 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 computing can predict the time of accident, it can reduce the data
volume of transmission; in general period, it can transmit the audio volume of transmission; in general period, it can transmit the audio
and video data with a low resolution/degree and in emergent period, and video data with a low resolution/degree and in emergent period,
it transmits the audio and video data with a high resolution/degree. it transmits the audio and video data with a high resolution/degree.
By adjusting the resolution/degree of audio and video data, it can By adjusting the resolution/degree of audio and video data, it can
reduce transmission cost significantly. reduce transmission cost significantly.
6.2. Smart Grid 7.2. Smart Grid
In future smart cities, Smart grids will be critical in ensuring In future smart cities, Smart grids will be critical in ensuring
availability and efficiency for energy saving and control in city- availability and efficiency for energy saving and control in city-
wide electricity management. Edge computing is expected to play a wide electricity management. Edge computing is expected to play a
significant role in those systems to improve transmission efficiency significant role in those systems to improve transmission efficiency
of electricity, react and restore for power disturbances, reduce of electricity, react and restore for power disturbances, reduce
operation cost, reuse renewable energy effectively, save energy of operation cost, reuse renewable energy effectively, save energy of
electricity for future usage, and so on. In addition, Edge computing electricity for future usage, and so on. In addition, Edge computing
can help monitoring power generation and power demands, and making can help monitoring power generation and power demands, and making
electrical energy storage decisions in the Smart grid system. electrical energy storage decisions in the Smart grid system.
6.3. Smart Water System 7.3. Smart Water System
The Water system is one of the most important aspects for building The Water system is one of the most important aspects for building
smart city. Effective use of water, and cost-effective and smart city. Effective use of water, and cost-effective and
environment-friendly treatment of water are critical for water environment-friendly treatment of water are critical for water
control and management. This can be facilitated by Edge computing in control and management. This can be facilitated by Edge computing in
Smart water systems, to help monitor water consumption, Smart water systems, to help monitor water consumption,
transportation, prediction of future water use, and so on. For transportation, prediction of future water use, and so on. For
example, water harvesting and ground water monitoring will be example, water harvesting and ground water monitoring will be
supported from Edge computing. Also, a Smart water system is able to supported from Edge computing. Also, a Smart water system is able to
analyze collected information related to water control and analyze collected information related to water control and
management, control the reduction of water losses and improve the management, control the reduction of water losses and improve the
city water system through Edge computing. city water system through Edge computing.
6.4. Smart Buildings 7.4. Smart Buildings
[TBA] [TBA]
6.5. Smart Cities 7.5. Smart Cities
[TBA] [TBA]
6.6. Connected Vehicles 7.6. Connected Vehicles
[TBA] [TBA]
7. Security Considerations 8. Security Considerations
[TBA] [TBA]
8. References 9. References
8.1. Normative References 9.1. Normative References
[RFC2119] Bradner, S., "Key words for use in RFCs to Indicate [RFC2119] Bradner, S., "Key words for use in RFCs to Indicate
Requirement Levels", BCP 14, RFC 2119, Requirement Levels", BCP 14, RFC 2119,
DOI 10.17487/RFC2119, March 1997, DOI 10.17487/RFC2119, March 1997,
<https://www.rfc-editor.org/info/rfc2119>. <https://www.rfc-editor.org/info/rfc2119>.
8.2. Informative References 9.2. Informative References
[Ashton] Ashton, K., "That Internet of Things thing", RFID J. vol. [Ashton] Ashton, K., "That Internet of Things thing", RFID J. vol.
22, no. 7, pp. 97-114, 2009. 22, no. 7, pp. 97-114, 2009.
[Lin] Lin, J., Yu, W., Zhang, N., Yang, X., Zhang, H., and W. [Lin] Lin, J., Yu, W., Zhang, N., Yang, X., Zhang, H., and W.
Zhao, "A survey on Internet of Things: Architecture, Zhao, "A survey on Internet of Things: Architecture,
enabling technologies, security and privacy, and enabling technologies, security and privacy, and
applications", IEEE Internet of Things J. vol. 4, no. 5, applications", IEEE Internet of Things J. vol. 4, no. 5,
pp. 1125-1142, Oct. 2017. pp. 1125-1142, Oct. 2017.
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