T2T Research Group J. Hong
Internet-Draft Y-G. Hong
Intended status: Informational ETRI
Expires: September 12, 2019 J-S. Youn
DONG-EUI Univ
March 11, 2019
Problem Statement of IoT integrated with Edge Computing
draft-hong-iot-edge-computing-02
Abstract
This document describes new challenges such as strict latency,
constrained network bandwidth and devices, intermittent connectivity,
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 its use cases. It also discusses benefits
and challenges of Edge computing. The direction of Edge computing
for IoT should be discussed in the IETF/IRTF.
Status of This Memo
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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 . . . . . . . . . . . . . . . . 4
3.3. IoT Environmental changes . . . . . . . . . . . . . . . . 4
4. New challenges of IoT . . . . . . . . . . . . . . . . . . . . 4
4.1. Strict Latency . . . . . . . . . . . . . . . . . . . . . 5
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 . . . . . . . . . . . . . 6
5.1. IoT Data in Edge Computing . . . . . . . . . . . . . . . 6
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 . . . . . . . . . . . . . . . . . . 8
6. Architecture of IoT integrated with Edge Computing . . . . . 8
7. Use Cases of Edge Computing in IoT . . . . . . . . . . . . . 10
7.1. Smart Constructions . . . . . . . . . . . . . . . . . . . 10
7.2. Smart Grid . . . . . . . . . . . . . . . . . . . . . . . 10
7.3. Smart Water System . . . . . . . . . . . . . . . . . . . 11
7.4. Smart Buildings . . . . . . . . . . . . . . . . . . . . . 11
7.5. Smart Cities . . . . . . . . . . . . . . . . . . . . . . 11
7.6. Connected Vehicles . . . . . . . . . . . . . . . . . . . 11
8. Security Considerations . . . . . . . . . . . . . . . . . . . 11
9. References . . . . . . . . . . . . . . . . . . . . . . . . . 11
9.1. Normative References . . . . . . . . . . . . . . . . . . 11
9.2. Informative References . . . . . . . . . . . . . . . . . 12
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 13
1. Introduction
Nowadays, most IoT services are based on Cloud computing since it can
provide virtually unlimited storage and processing power. The
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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, 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 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 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].
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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 a predominant 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].
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. IoT Environmental changes
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.
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].
4. New challenges of IoT
As 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 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].
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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.
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.
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5. IoT integrated with Edge Computing
5.1. IoT Data in Edge Computing
As described in section 4, new challenges for supporting emerging 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
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".
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.
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
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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.
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.
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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
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].
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. Use Cases of Edge Computing in IoT
7.1. 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. 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 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.
7.2. 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
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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.
7.3. 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.
7.4. Smart Buildings
[TBA]
7.5. Smart Cities
[TBA]
7.6. Connected Vehicles
[TBA]
8. Security Considerations
[TBA]
9. References
9.1. 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>.
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9.2. 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.
[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.
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[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
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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