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
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This Internet-Draft will expire on January 9, 2020.
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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.
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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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