IoT Edge Computing Challenges and Functions
draft-hong-t2trg-iot-edge-computing-01
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draft-hong-t2trg-iot-edge-computing-01
Network Working Group J. Hong
Internet-Draft Y-G. Hong
Intended status: Informational ETRI
Expires: May 7, 2020 X. de Foy
InterDigital Communications, LLC
M. Kovatsch
Huawei Technologies Duesseldorf GmbH
E. Schooler
Intel
D. Kutscher
University of Applied Sciences Emden/Leer
November 04, 2019
IoT Edge Computing Challenges and Functions
draft-hong-t2trg-iot-edge-computing-01
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
emerged as a promising solution. This document describes the concept
of IoT integrated with Edge computing as well as the state-of-the-art
of IoT Edge computing. It also proposes a general model for IoT Edge
computing. The direction of Edge computing for IoT should be
discussed in the IETF/IRTF.
Status of This Memo
This Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
Internet-Drafts are working documents of the Internet Engineering
Task Force (IETF). Note that other groups may also distribute
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material or to cite them other than as "work in progress."
This Internet-Draft will expire on May 7, 2020.
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Copyright Notice
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This document is subject to BCP 78 and the IETF Trust's Legal
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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. Challenges for IoT and Impacts of Edge Computing . . . . . . 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 Edge Computing Model . . . . . . . . . . . . . . . . . . 7
5.1. Gateway Function and Remote Network . . . . . . . . . . . 8
5.2. Edge Computing Domain Management and Manager Role . . . . 9
5.3. Edge Computing Logical Functions . . . . . . . . . . . . 9
5.4. Edge Networking Function and IoT End Devices . . . . . . 9
6. State-of-the-Art of IoT Edge Computing . . . . . . . . . . . 10
6.1. Common aspects of IoT Edge computing service platforms . 10
6.2. Use Cases of IoT Edge Computing . . . . . . . . . . . . . 11
6.2.1. Smart Constructions . . . . . . . . . . . . . . . . . 11
6.2.2. Smart Grid . . . . . . . . . . . . . . . . . . . . . 12
6.2.3. Smart Water System . . . . . . . . . . . . . . . . . 12
7. Security Considerations . . . . . . . . . . . . . . . . . . . 13
8. Acknowledgement . . . . . . . . . . . . . . . . . . . . . . . 13
9. References . . . . . . . . . . . . . . . . . . . . . . . . . 13
9.1. Normative References . . . . . . . . . . . . . . . . . . 13
9.2. Informative References . . . . . . . . . . . . . . . . . 13
Appendix A. Overview of the IoT Edge Computing . . . . . . . . . 16
A.1. Open Source Projects . . . . . . . . . . . . . . . . . . 16
A.1.1. Gateway/CPE Platforms . . . . . . . . . . . . . . . . 16
A.1.2. Edge Cloud Management Platforms . . . . . . . . . . . 17
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A.1.3. Related Projects . . . . . . . . . . . . . . . . . . 18
A.2. Products . . . . . . . . . . . . . . . . . . . . . . . . 18
A.2.1. IoT Gateways . . . . . . . . . . . . . . . . . . . . 18
A.2.2. Edge Cloud Platforms . . . . . . . . . . . . . . . . 19
A.3. Standards Initiatives . . . . . . . . . . . . . . . . . . 19
A.3.1. ETSI Multi-access Edge Computing . . . . . . . . . . 19
A.3.2. Edge Computing Support in 3GPP . . . . . . . . . . . 20
A.3.3. OpenFog Consortium . . . . . . . . . . . . . . . . . 21
A.3.4. Related Standards . . . . . . . . . . . . . . . . . . 21
A.4. Research Projects . . . . . . . . . . . . . . . . . . . . 21
A.4.1. Named Function Networking . . . . . . . . . . . . . . 21
A.4.2. 5G-CORAL . . . . . . . . . . . . . . . . . . . . . . 22
A.4.3. FLAME . . . . . . . . . . . . . . . . . . . . . . . . 23
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 23
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. Challenges for IoT and Impacts of Edge Computing
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. Example of related challenges
include support for IoT device and Edge computing node mobility, as
well as software instance migration.
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
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as the setup of production lines. Hence, the owner of these systems
are generally reluctant to upload related IoT to the cloud.
5. IoT Edge Computing Model
It is expected Edge computing will play an important role to deploy
new IoT services integrated with Big data, AI services. Although
there are lots of approach to Edge computing, this section focus on
common function of Edge computing, therefore draw an IoT Edge
computing model. In this section we discuss a general model that
aims to be applicable to multiple Edge computing architectures, such
as:
o A single IoT gateway, or a hierarchy of IoT gateways, typically
connected to the cloud (e.g., to extend the traditionally cloud-
based management of IoT devices and data to the edge). A common
role of an IoT Gateway is to provide access to an heterogeneous
set of IoT devices/sensors; handle IoT data; and deliver IoT data
to its final destination in a cloud network. Whereas an IoT
gateway needs interactoins with cloud like as conventional Cloud
computing, Edge computing can operate independently.
o A set of distributed computing nodes, e.g. embedded in switches,
routers, edge cloud servers or mobile devices. In the future,
some IoT end devices may have enough computing capabilities to
participate in such distributed systems. In this model, each Edge
computing node can collaborate with each other to share its
resources to others or ask other's resources.
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+---------------------+
| Remote network | +---------------+
|(e.g. cloud network) | | Edge Computing|
+-----------+---------+ | Manager |
| +------+--------+
| |
+----------------------------------------------+
| | | |
| +------------+-------------+ +--+---------+ |
| | Edge gateway function +--+ | |
| | (Northbound) | | | |
| +------------+-------------+ | | |
| | | | |
| +------------+-------------+ | | |
| | Edge computing functions | | | |
| | (on computing nodes) +--+ Edge | |
| | * finding resources | | Computing | |
| | * authentication | | Domain | |
| | * storage/processing | | Management | |
| | * data/device management | | | |
| +------------+-------------+ | | |
| | | | |
| +------------+-------------+ | | |
| | Edge networking function +--+ | |
| | (Southbound) | | | |
| +--------------------------+ +------------+ |
| Edge Computing Domain |
+------+--------------+---------------+--------+
| | |
| | |
+----+---+ +-----+--+ +-----+--+
| End | | End | .... | End |
|Device 1| |Device 2| .... |Device n|
+--------+ +--------+ +--------+
Figure 1: Model of IoT integrated with Edge computing
The Edge computing domains is interconnected with IoT end devices
(southbound connectivity) and possibly with a remote/cloud network
(northbound connectivity). Edge computing nodes provide multiple
logical functions such as finding resources, authentication, storage/
processing and management.
5.1. Gateway Function and Remote Network
A northbound interface is provided by a gateway component to a remote
network, e.g. a cloud, home or enterprise network. These components
may not exist in standalone scenarios, where Edge computing is
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provided locally without a connection to a remote network. The
northbound interface is a data plane interface. Nevertheless, the
remote network may also host an edge cloud manager function.
5.2. Edge Computing Domain Management and Manager Role
Edge computing domain management includes management of resources and
functions in the Edge computing domain. Management of IoT end
devices and IoT data may be included or may be part of the Edge
computing logical functions (OPEN QUESTION). The management function
can provides SaaS, PaaS, IaaS service APIs to an Edge computing
manager. The edge management role may be taken by an entity in the
cloud, but it may also be a local entity, or even be non-existent
(during normal operation) in autonomic systems.
5.3. Edge Computing Logical Functions
Edge computing nodes host logical functions relative to:
o Finding resources, such as compute, storage or data resources;
o Authenticating platforms, end devices, functions, data;
o Providing compute and storage offloading;
o Management, e.g. of IoT end devices and data.
With regard to the high level challenges listed in Section 4, data
storage and processing at the edge is a major aspect of IoT Edge
computing. Data may therefore need to be classified (e.g. in terms
of privacy, importance, validity, etc.). Data analysis such as
performed in AI/ML tasks performed at the edge may benefit from
specialized hardware support on computing nodes. IoT Edge computing
will face detailed challenges in term of, for example,
programmability, naming, data abstraction and service management.
Furthermore, while Edge computing can support IoT services
independently of Cloud computing, it is increasingly connected to
Cloud computing in most IoT systems: thus, the relationship of IoT
Edge Computing to Cloud Computing is another potential challenge
[ISO_TR].
5.4. Edge Networking Function and IoT End Devices
IoT end devices can be sensors, actuators, or more generally IoT
things. Not only the big volume of IoT data but also the massive
number of IoT end devices are the cause of a massive scalability
issue in future IoT environments. To address this challenge Edge
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computing separates the scalability domain into edge/local networks
and remote network.
Edge computing nodes communicate between themselves and with end
devices over an underlying network. There is therefore a need for
the Edge computing domain to directly or indirectly control those
network functions.
6. State-of-the-Art of IoT Edge Computing
6.1. 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.
6.2. Use Cases of IoT Edge Computing
6.2.1. 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.
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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.
6.2.2. Smart Grid
In future smart cities, Smart grids will be critical in ensuring
availability and efficiency for energy saving and control in city-
wide electricity management. Edge computing is expected to play a
significant role in those systems to improve transmission efficiency
of electricity, react and restore for power disturbances, reduce
operation cost, reuse renewable energy effectively, save energy of
electricity for future usage, and so on. In addition, Edge computing
can help monitoring power generation and power demands, and making
electrical energy storage decisions in the Smart grid system.
6.2.3. 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
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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. Security Considerations
T.B.D.
8. Acknowledgement
The authors would like to thank Joo-Sang Youn and Akbak Rahman for
their valuable comments and suggestions on this document.
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>.
9.2. Informative References
[_3GPP.23.501]
3GPP, ., "System Architecture for the 5G System", 3GPP TS
23.501 , 2019,
<http://www.3gpp.org/ftp/Specs/html-info/23501.htm>.
[_5G-CORAL]
Horizon 2020 Programme, ., "5G Convergent Virtualised
Radio Access Network Living at the Edge (5G-CORAL)
Project", Portal , 2019, <http://5g-coral.eu/>.
[_60802] IEC/IEEE, ., "Use Cases IEC/IEEE 60802 V1.3", IEC/IEEE
60802 , 2018, <http://www.ieee802.org/1/files/public/
docs2018/60802-industrial-use-cases-0818-v13.pdf>.
[Ashton] Ashton, K., "That Internet of Things thing", RFID J. vol.
22, no. 7, pp. 97-114 , 2009.
[Botta] Botta, A., Donato, W., Persico, V., and A. Pescape,
"Integration of Cloud computing and Internet of Things: A
survey", Future Gener. Comput. Syst., vol. 56, pp.
684-700 , 2016.
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[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 , 2016.
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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
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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
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-
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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
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
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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);
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]).
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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.
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
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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.
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
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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
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
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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
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
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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
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: ietf@kovatsch.net
Eve Schooler
Intel
2200 Mission College Blvd.
Santa Clara, CA 95054-1537
USA
Email: eve.m.schooler@intel.com
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Dirk Kutscher
University of Applied Sciences Emden/Leer
Constantiaplatz 4
Emden 26723
Germany
Email: ietf@dkutscher.net
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