IoT Edge Challenges and Functions
draft-irtf-t2trg-iot-edge-10
| Document | Type | Active Internet-Draft (t2trg RG) | |
|---|---|---|---|
| Authors | Jungha Hong , Yong-Geun Hong , Xavier de Foy , Matthias Kovatsch , Eve Schooler , Dirk Kutscher | ||
| Last updated | 2023-09-27 (Latest revision 2023-09-15) | ||
| Replaces | draft-hong-t2trg-iot-edge-computing | ||
| RFC stream | Internet Research Task Force (IRTF) | ||
| Intended RFC status | Informational | ||
| Formats | |||
| IETF conflict review | conflict-review-irtf-t2trg-iot-edge | ||
| Additional resources | Mailing list discussion | ||
| Stream | IRTF state | In IESG Review | |
| Consensus boilerplate | Yes | ||
| Document shepherd | Ari Keränen | ||
| Shepherd write-up | Show Last changed 2022-11-06 | ||
| IESG | IESG state | I-D Exists | |
| Telechat date | (None) | ||
| Responsible AD | (None) | ||
| Send notices to | ari.keranen@ericsson.com | ||
| IANA | IANA review state | IANA OK - No Actions Needed |
draft-irtf-t2trg-iot-edge-10
Network Working Group J. Hong
Internet-Draft ETRI
Intended status: Informational Y.-G. Hong
Expires: 18 March 2024 Daejeon University
X. de Foy
InterDigital Communications, LLC
M. Kovatsch
Huawei Technologies Duesseldorf GmbH
E. Schooler
Intel
D. Kutscher
Hong Kong University of Science and Technology (Guangzhou)
15 September 2023
IoT Edge Challenges and Functions
draft-irtf-t2trg-iot-edge-10
Abstract
Many Internet of Things (IoT) applications have requirements that
cannot be satisfied by traditional cloud-based systems (i.e., cloud
computing). These include time sensitivity, data volume,
connectivity cost, operation in the face of intermittent services,
privacy, and security. As a result, IoT is driving the Internet
toward edge computing. This document outlines the requirements of
the emerging IoT Edge and its challenges. It presents a general
model and major components of the IoT Edge to provide a common basis
for future discussions in the T2TRG and other IRTF and IETF groups.
This document is a product of the IRTF Thing-to-Thing Research Group
(T2TRG).
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
working documents as Internet-Drafts. The list of current Internet-
Drafts is at https://datatracker.ietf.org/drafts/current/.
Internet-Drafts are draft documents valid for a maximum of six months
and may be updated, replaced, or obsoleted by other documents at any
time. It is inappropriate to use Internet-Drafts as reference
material or to cite them other than as "work in progress."
This Internet-Draft will expire on 18 March 2024.
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Copyright Notice
Copyright (c) 2023 IETF Trust and the persons identified as the
document authors. All rights reserved.
This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents (https://trustee.ietf.org/
license-info) in effect on the date of publication of this document.
Please review these documents carefully, as they describe your rights
and restrictions with respect to this document.
Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 3
2. Background . . . . . . . . . . . . . . . . . . . . . . . . . 3
2.1. Internet of Things (IoT) . . . . . . . . . . . . . . . . 3
2.2. Cloud Computing . . . . . . . . . . . . . . . . . . . . . 4
2.3. Edge Computing . . . . . . . . . . . . . . . . . . . . . 4
2.4. Examples of IoT Edge Computing Use Cases . . . . . . . . 6
3. IoT Challenges Leading Towards Edge Computing . . . . . . . . 10
3.1. Time Sensitivity . . . . . . . . . . . . . . . . . . . . 10
3.2. Connectivity Cost . . . . . . . . . . . . . . . . . . . . 10
3.3. Resilience to Intermittent Services . . . . . . . . . . . 11
3.4. Privacy and Security . . . . . . . . . . . . . . . . . . 11
4. IoT Edge Computing Functions . . . . . . . . . . . . . . . . 11
4.1. Overview of IoT Edge Computing Today . . . . . . . . . . 12
4.2. General Model . . . . . . . . . . . . . . . . . . . . . . 14
4.3. OAM Components . . . . . . . . . . . . . . . . . . . . . 17
4.3.1. Resource Discovery and Authentication . . . . . . . . 17
4.3.2. Edge Organization and Federation . . . . . . . . . . 18
4.3.3. Multi-Tenancy and Isolation . . . . . . . . . . . . . 19
4.4. Functional Components . . . . . . . . . . . . . . . . . . 19
4.4.1. In-Network Computation . . . . . . . . . . . . . . . 19
4.4.2. Edge Storage and Caching . . . . . . . . . . . . . . 21
4.4.3. Communication . . . . . . . . . . . . . . . . . . . . 21
4.5. Application Components . . . . . . . . . . . . . . . . . 22
4.5.1. IoT Device Management . . . . . . . . . . . . . . . . 23
4.5.2. Data Management and Analytics . . . . . . . . . . . . 23
4.6. Simulation and Emulation Environments . . . . . . . . . . 24
5. Security Considerations . . . . . . . . . . . . . . . . . . . 25
6. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . 25
7. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 26
8. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . 26
9. Informative References . . . . . . . . . . . . . . . . . . . 26
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 36
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1. Introduction
Currently, many IoT services leverage cloud computing platforms,
because they provide virtually unlimited storage and processing
power. The reliance of IoT on back-end cloud computing provides
additional advantages such as scalability and efficiency. Today's
IoT systems are fairly static with respect to integrating and
supporting computation. It is not that there is no computation, but
that systems are often limited to static configurations (edge
gateways and cloud services).
However, IoT devices generate large amounts of data at the edges of
the network. To meet IoT use case requirements, data is increasingly
being stored, processed, analyzed, and acted upon close to the data
sources. These requirements include time sensitivity, data volume,
connectivity cost, and resiliency in the presence of intermittent
connectivity, privacy, and security, which cannot be addressed by
centralized cloud computing. A more flexible approach is necessary
to address these needs effectively. This involves distributing
computing (and storage) and seamlessly integrating it into the edge-
cloud continuum. We refer to this integration of edge computing and
IoT as "IoT edge computing". This draft describes the related
background, use cases, challenges, system models, and functional
components.
Owing to the dynamic nature of the IoT edge computing landscape, this
document does not list existing projects in this field. Section 4.1
presents a high-level overview of the field, based on a limited
review of standards, research, open-source and proprietary products
in [I-D.defoy-t2trg-iot-edge-computing-background].
This document represents the consensus of the Thing-to-Thing Research
Group (T2TRG). It has been reviewed extensively by the Research
Group (RG) members who are actively involved in the research and
development of the technology covered by this document. It is not an
IETF product and is not a standard.
2. Background
2.1. Internet of Things (IoT)
Since the term "Internet of Things" (IoT) was coined by Kevin Ashton
in 1999 working on Radio-Frequency Identification (RFID) technology
[Ashton], the concept of IoT has evolved. It now reflects a vision
of connecting the physical world to the virtual world of computers
using (often wireless) networks over which things can send and
receive information without human intervention. Recently, the term
has become more literal by connecting things to the Internet and
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converging on Internet and Web technologies.
A Thing is a physical item made available in the IoT, thereby
enabling digital interaction with the physical world for humans,
services, and/or other Things ([I-D.irtf-t2trg-rest-iot]). In this
document we will use the term "IoT device" to designate the embedded
system attached to the Thing.
Resource-constrained Things such as sensors, home appliances and
wearable devices often have limited storage and processing power,
which can provide challenges with respect to reliability,
performance, energy consumption, security, and privacy [Lin]. Some,
less resource-constrained Things, can generate a voluminous amount of
data. This range of factors led IoT designs that integrate Things
into larger distributed systems, for example edge or cloud computing
systems.
2.2. Cloud Computing
Cloud computing has 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". The low cost and massive availability of
storage and processing power enabled the realization of another
computing model, in which virtualized resources can be leased in an
on-demand fashion and be provided as general utilities. Platform-as-
a-Service and cloud computing platforms widely adopted this paradigm
for delivering services over the Internet, gaining both economical
and technical benefits [Botta].
Today, an unprecedented volume and variety of data is generated by
Things, and applications deployed at the network edge consume this
data. In this context, cloud-based service models are not suitable
for some classes of applications which require very short response
times, access to local personal data, or generate vast amounts of
data. These applications may instead leverage edge computing.
2.3. Edge Computing
Edge computing, also referred to as fog computing in some settings,
is a new paradigm in which substantial computing and storage
resources are placed at the edge of the Internet, close to mobile
devices, sensors, actuators, or machines. Edge computing happens
near data sources [Mahadev], as well as close to where decisions are
made or where interactions with the physical world take place
("close" here can refer to a distance which is topological, physical,
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latency-based, etc.). It processes both downstream data (originating
from cloud services) and upstream data (originating from end devices
or network elements). The term "fog computing" usually represents
the notion of multi-tiered edge computing, that is, several layers of
compute infrastructure between end devices and cloud services.
An edge device is any computing or networking resource residing
between end-device data sources and cloud-based data centers. In
edge computing, end devices consume and produce data. 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].
This does not preclude end devices from hosting computation
themselves, when possible, independently or as part of a distributed
edge computing platform.
Several standards developing organization (SDO) and industry forums
have provided definitions of edge and fog computing:
* ISO defines edge computing as a "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].
* ETSI defines multi-access edge computing as a "system which
provides an IT service environment and cloud-computing
capabilities at the edge of an access network which contains one
or more type of access technology, and in close proximity to its
users" [ETSI_MEC_01].
* The Industry IoT Consortium (IIC, now incorporating what was
formerly OpenFog) defines fog computing as "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 distributing the required functions close to users
and data, while the difference to classic local systems is the usage
of management and orchestration features adopted from cloud
computing.
Actors from various industries approach edge computing using
different terms and reference models although, in practice, these
approaches are not incompatible and may integrate with each other:
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* The telecommunication industry tends to use a model where edge
computing services are deployed over Network Function
Virtualization (NFV) infrastructure, at aggregation points or in
proximity to the user equipment (e.g., gNodeBs) [ETSI_MEC_03].
* Enterprise and campus solutions often interpret edge computing as
an "edge cloud", that is, a smaller data center directly connected
to the local network (often referred to as "on-premise").
* The automation industry defines the edge as the connection point
between IT and OT (Operational Technology). Hence, edge computing
sometimes refers to applying IT solutions to OT problems, such as
analytics, more flexible user interfaces, or simply having more
computing power than an automation controller.
2.4. Examples of IoT Edge Computing Use Cases
IoT edge computing can be used in home, industry, grid, healthcare,
city, transportation, agriculture, and/or educational scenarios.
Here, we discuss only a few examples of such use cases, to identify
differentiating requirements, providing references to other use
cases.
*Smart Factory*
As part of the 4th industrial revolution, smart factories run real-
time processes based on IT technologies, such as artificial
intelligence and big data. Even a very small environmental change in
a smart factory can lead to a situation in which production
efficiency decreases or product quality problems occur. Therefore,
simple but time-sensitive processing can be performed at the edge,
for example, controlling the temperature and humidity in the factory,
or operating machines based on the real-time collection of the
operational status of each machine. However, data requiring highly
precise analysis, such as machine lifecycle management or accident
risk prediction, can be transferred to a central data center for
processing.
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The use of edge computing in a smart factory can reduce the cost of
network and storage resources by reducing the communication load to
the central data center or server. It is also possible to improve
process efficiency and facility asset productivity through real-time
prediction of failures and to reduce the cost of failure through
preliminary measures. In the existing manufacturing field,
production facilities are manually run according to a program entered
in advance; however, edge computing in a smart factory enables
tailoring solutions by analyzing data at each production facility and
machine level. Digital twins [Jones] of IoT devices have been
jointly used with edge computing in industrial IoT scenarios [Chen].
*Smart Grid*
In future smart city scenarios, the Smart Grid will be critical in
ensuring highly available/efficient energy control in city-wide
electricity management. Edge computing is expected to play a
significant role in these systems to improve the transmission
efficiency of electricity, to react to, and restore power after a
disturbance, to reduce operation costs, and to reuse energy
effectively, since these operations involve local decision-making.
In addition, edge computing can help monitor power generation and
power demand, and make local electrical energy storage decisions in
smart grid systems.
*Smart Agriculture*
Smart agriculture integrates information and communication
technologies with farming technology. Intelligent farms use IoT
technology to measure and analyze parameters, such as the
temperature, humidity, sunlight, carbon dioxide, and soil quality, in
crop cultivation facilities. Depending on the analysis results,
control devices are used to set the environmental parameters to an
appropriate state. Remote management is also possible through mobile
devices such as smartphones.
In existing farms, simple systems such as management according to
temperature and humidity can be easily and inexpensively implemented
using IoT technology. Field sensors gather data on field and crop
condition. This data is then transmitted to cloud servers that
process data and recommend actions. The use of edge computing can
reduce the volume of back-and-forth data transmissions significantly,
resulting in cost and bandwidth savings. Locally generated data can
be processed at the edge, and local computing and analytics can drive
local actions. With edge computing, it is easy for farmers to select
large amounts of data for processing, and data can be analyzed even
in remote areas with poor access conditions. Other applications
include enabling dashboarding, for example, to visualize the farm
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status, as well as enhancing Extended Reality (XR) applications that
require edge audio/video processing. As the number of people working
on farming has been decreasing over time, increasing automation
enabled by edge computing can be a driving force for future smart
agriculture.
*Smart Construction*
Safety is critical at construction sites. Every year, many
construction workers lose their lives because of falls, collisions,
electric shocks, and other accidents. Therefore, solutions have been
developed to improve construction site safety, including the real-
time identification of workers, monitoring of equipment location, and
predictive accident prevention. To deploy these solutions, many
cameras and IoT sensors have been installed on construction sites, to
measure noise, vibration, gas concentration, etc. Typically, the
data generated from these measurements is collected in on-site
gateways and sent to remote cloud servers for storage and analysis.
Thus, an inspector can check the information stored on the cloud
server to investigate an incident. However, this approach can be
expensive because of transmission costs, for example, of video
streams over a mobile network connection, and because usage fees of
private cloud services.
Using edge computing, data generated at the construction site can be
processed and analyzed on an edge server located within or near the
site. Only the result of this processing needs to be transferred to
a cloud server, thus reducing transmission costs. It is also
possible to locally generate warnings to prevent accidents in real-
time.
*Self-Driving Car*
Edge computing plays a crucial role in safety-focused self-driving
car systems. With a multitude of sensors, such as high-resolution
cameras, radar, LIDAR, sonar sensors, and GPS systems, autonomous
vehicles generate vast amounts of real-time data. Local processing
utilizing edge computing nodes allows for efficient collection and
analysis of this data to monitor vehicle distances and road
conditions and respond promptly to unexpected situations. Roadside
computing nodes can also be leveraged to offload tasks when
necessary, for example, when the local processing capacity of the car
is insufficient because of hardware constraints or a large data
volume.
For instance, when the car ahead slows, a self-driving car adjusts
its speed to maintain a safe distance, or when a roadside signal
changes, it adapts its behavior accordingly. In another example,
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cars equipped with self-parking features utilize local processing to
analyze sensor data, determine suitable parking spots, and execute
precise parking maneuvers without relying on external processing or
connectivity. It is also possible to use in-cabin cameras coupled
with local processing to monitor the driver's attention level and
detect signs of drowsiness or distraction. The system can issue
warnings or implement preventive measures to ensure driver safety.
Edge computing empowers self-driving cars by enabling real-time
processing, reducing latency, enhancing data privacy, and optimizing
bandwidth usage. By leveraging local processing capabilities, self-
driving cars can make rapid decisions, adapt to changing
environments, and ensure safer and more efficient autonomous driving
experiences.
*Digital Twin*
A digital twin can simulate different scenarios and predict outcomes
based on real-time data collected from the physical environment.
This simulation capability empowers proactive maintenance,
optimization of operations, and the prediction of potential issues or
failures. Decision makers can use digital twins to test and validate
different strategies, identify inefficiencies, and optimize
performance.
With edge computing, real-time data is collected, processed, and
analyzed directly at the edge, allowing for the accurate monitoring
and simulation of physical assets. Moreover, edge computing
effectively minimizes latency, enabling rapid responses to dynamic
conditions as computational resources are brought closer to the
physical object. Running digital twin processing at the edge enables
organizations to obtain timely insights and make informed decisions
that maximize efficiency and performance.
*Other Use Cases*
AI/ML systems at the edge empower real-time analysis, faster
decision-making, reduced latency, improved operational efficiency,
and personalized experiences across various industries, by bringing
artificial intelligence and machine learning capabilities closer to
edge devices.
In addition, oneM2M has studied several IoT edge computing use cases,
which are documented in [oneM2M-TR0001], [oneM2M-TR0018] and
[oneM2M-TR0026]. The edge computing related requirements raised
through the analysis of these use cases are captured in
[oneM2M-TS0002].
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3. IoT Challenges Leading Towards Edge Computing
This section describes the challenges faced by IoT that are
motivating the adoption of edge computing. These are distinct from
the research challenges applicable to IoT edge computing, some of
which are mentioned in Section 4.
IoT technology is used with increasingly demanding applications, for
example, in industrial, automotive and healthcare domains, leading to
new challenges. For example, industrial machines such as laser
cutters produce over 1 terabyte of data per hour, and similar amounts
can be generated in 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
these new challenges [Chiang].
Below, we discuss IoT use case requirements that are moving cloud
capabilities to be more proximate, distributed, and disaggregated.
3.1. Time Sensitivity
Many industrial control systems, such as manufacturing systems, smart
grids, and oil and gas systems often require stringent end-to-end
latency between the sensor and control nodes. While some IoT
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]. In some
cases, speed-of-light limitations may simply prevent a cloud-based
solutions; however, this is not the only challenge relative to time
sensitivity. Guarantees for bounded latency and jitter ([RFC8578]
section 7) are also important for industrial IoT applications. This
means that control packets must arrive with as little variation as
possible and within a strict deadline. Given the best-effort
characteristics of the Internet, this challenge is virtually
impossible to address, without using end-to-end guarantees for
individual message delivery and continuous data flows.
3.2. Connectivity Cost
Some IoT deployments may not face bandwidth constraints when
uploading data to the Cloud. 5G and Wi-Fi 6 networks both
theoretically top out at 10 gigabits per second (i.e., 4.5 terabytes
per hour), allowing to transfer large amounts of uplink data.
However, the cost of maintaining continuous high-bandwidth
connectivity for such usage is unjustifiable and impractical for most
IoT applications. In some settings, for example, in aeronautical
communication, higher communication costs reduce the amount of data
that can be practically uploaded even further. Minimizing reliance
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on high-bandwidth connectivity is therefore a requirement, for
example, by processing data at the edge and deriving summarized or
actionable insights that can be transmitted to the Cloud.
3.3. Resilience to Intermittent Services
Many IoT devices, such as sensors, actuators, and controllers, have
very limited hardware resources and cannot rely solely on their own
resources to meet their computing and/or storage needs. They require
reliable, uninterrupted, or resilient services to augment their
capabilities to fulfill their application tasks. This is difficult
and partly impossible to achieve using cloud services for systems
such as vehicles, drones, or oil rigs that have intermittent network
connectivity. Conversely, a cloud back-end might want to device data
even if it is currently asleep.
3.4. 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 have begun to
provide frameworks that limit the usage of personal data and impose
strict requirements on data controllers and processors. Data stored
indefinitely in the Cloud also increases the risk of data leakage,
for instance, through attacks on rich targets.
It is often argues that industrial systems do not provide privacy
implications, as no personal data is gathered. However, data from
such systems is often highly sensitive, as one might be able to infer
trade secrets such as the setup of production lines. Hence, owners
of these systems are generally reluctant to upload IoT data to the
Cloud.
Furthermore, passive observers can perform traffic analysis on
device-to-cloud paths. Therefore, hiding traffic patterns associated
with sensor networks can be another requirement for edge computing.
4. IoT Edge Computing Functions
We first look at the current state of IoT edge computing
(Section 4.1), and then define a general system model (Section 4.2).
This provides a context for IoT edge-computing functions, which are
listed in Section 4.3, Section 4.4 and Section 4.5.
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4.1. Overview of IoT Edge Computing Today
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
[I-D.defoy-t2trg-iot-edge-computing-background].
IoT gateways, both open-source (such as EdgeX Foundry or Home Edge)
and proprietary products, represent a common class of IoT edge-
computing products, where the gateway provides 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 [RFC7252], MQTT [mqtt5], and many
specialized IoT protocols (such as OPC UA and DDS in the Industrial
IoT space), while the gateway communicates with the distant cloud
typically using HTTPS. Virtualization platforms enable the
deployment of virtual edge computing functions (using VMs and
application containers), including IoT gateway software, on servers
in the mobile network infrastructure (at base stations and
concentration points), edge data centers (in central offices), and
regional data centers located near central offices. End devices are
envisioned to become computing devices in forward-looking projects,
but are not commonly used today.
In addition to open-source and proprietary solutions, a horizontal
IoT service layer is standardized by the oneM2M standards body to
reduce fragmentation, increase interoperability and promote reuse in
the IoT ecosystem. Furthermore, ETSI MEC developed an IoT API
[ETSI_MEC_33] that enables the deployment of heterogeneous IoT
platforms and provides a means to configure the various components of
an IoT system.
Physical or virtual IoT gateways can host application programs that
are typically built using an SDK to access local services through a
programmatic API. Edge cloud system operators host their customers'
application VMs or containers on servers located in or near access
networks that can implement local edge services. For example, mobile
networks can provide edge services for radio-network information,
location, and bandwidth management.
Resilience in the IoT can entail the ability to operate autonomously
in periods of disconnectedness to preserve the integrity and safety
of the controlled system, possibly in a degraded mode. IoT devices
and gateways are often expected to operate in always-on and
unattended modes, using fault detection and unassisted recovery
functions.
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The life cycle management of services and applications on physical
IoT gateways is generally cloud-based. Edge cloud management
platforms and products (such as StarlingX, Akraino Edge Stack, or
proprietary products from major Cloud providers) adapt cloud
management technologies (e.g., Kubernetes) to the edge cloud, that
is, to smaller, distributed computing devices running outside a
controlled data center. The service and application life-cycle is
typically using an NFV-like management and orchestration model.
The platform typically enables advertising or consuming services
hosted on the platform (e.g., the Mp1 interface in ETSI MEC supports
service discovery and communication), and enables communication with
local and remote endpoints (e.g., message routing function in IoT
gateways). The platform is typically extensible to edge applications
because it can advertise a service that other edge applications can
consume. The IoT communication services include protocol
translation, analytics, and transcoding. Communication between edge-
computing devices is enabled in tiered or distributed deployments.
An edge cloud platform may enable pass-through without storage or
local storage (e.g., on IoT gateways). Some edge cloud platforms use
distributed storage such as that provided by a distributed storage
platform (e.g., EdgeFS, Ceph), or, in more experimental settings, by
an ICN network, for example, systems such as Chipmunk [chipmunk] and
Kua [kua] have been proposed as distributed information-centric
objects stores. External storage, for example, on databases in
distant or local IT cloud, is typically used for filtered data deemed
worthy of long-term storage, although in some cases it may be for all
data, for example when required for regulatory reasons.
Stateful computing is supported on platforms that host native
programs, VMs, or containers. Stateless computing is supported on
platforms providing a "serverless computing" service (also known as
function-as-a-service, e.g., using stateless containers), or on
systems based on named function networking.
In many IoT use cases, a typical network usage pattern is a high
volume uplink with some form of traffic reduction enabled by
processing over edge-computing devices. Alternatives to traffic
reduction include deferred transmission (to off-peak hours or using
physical shipping). Downlink traffic includes application control
and software updates. Downlink-heavy traffic patterns are not
excluded but are more often associated with non-IoT usage (e.g.,
video CDNs).
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4.2. General Model
Edge computing is expected to play an important role in deploying new
IoT services integrated with Big Data and AI enabled by flexible in-
network computing platforms. Although there are many approaches to
edge computing, in this section, we attempt to lay out a general
model and the list associated logical functions. In practice, this
model can be mapped to different architectures, such as:
* A single IoT gateway, or a hierarchy of IoT gateways, typically
connected to the cloud (e.g., to extend the traditional cloud-
based management of IoT devices and data to the edge). The IoT
gateway plays a common role in providing access to a heterogeneous
set of IoT devices/sensors, handling IoT data, and delivering IoT
data to its final destination in a cloud network. Whereas an IoT
gateway requires interactions with the cloud, it can also operate
independently in a disconnected mode.
* A set of distributed computing nodes, for example, embedded in
switches, routers, edge cloud servers, or mobile devices. Some
IoT devices have sufficient computing capabilities to participate
in such distributed systems owing to advances in hardware
technology. In this model, edge-computing nodes can collaborate
to share resources.
* A hybrid system involving both IoT gateways and supporting
functions in distributed computing nodes.
In the general model described in Figure 1, the edge computing domain
is interconnected with IoT devices (southbound connectivity),
possibly with a remote/cloud network (northbound connectivity), and
with a service operator's system. Edge-computing nodes provide
multiple logical functions or components that may not be present in a
given system. They may be implemented in a centralized or
distributed fashion, at the network edge, or through interworking
between the edge network and remote cloud networks.
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+---------------------+
| Remote network | +---------------+
|(e.g., cloud network)| | Service |
+-----------+---------+ | Operator |
| +------+--------+
| |
+--------------+-------------------+-----------+
| Edge Computing Domain |
| |
| One or more Computing Nodes |
| (IoT gateway, end devices, switches, |
| routers, mini/micro-data centers, etc.) |
| |
| OAM Components |
| - Resource Discovery and Authentication |
| - Edge Organization and Federation |
| - Multi-Tenancy and Isolation |
| - ... |
| |
| Functional Components |
| - In-Network Computation |
| - Edge Caching |
| - Communication |
| - Other Services |
| - ... |
| |
| Application Components |
| - IoT Devices Management |
| - Data Management and Analytics |
| - ... |
| |
+------+--------------+-------- - - - -+- - - -+
| | | | |
| | +-----+--+
+----+---+ +-----+--+ | |compute | |
| End | | End | ... |node/end|
|Device 1| |Device 2| ...| |device n| |
+--------+ +--------+ +--------+
+ - - - - - - - -+
Figure 1: Model of IoT Edge Computing
In the distributed model described in Figure 2, the edge-computing
domain is composed of IoT edge gateways and IoT devices which are
also used as computing nodes. Edge computing domains are connected
to a remote/cloud network and their respective service operator's
system. IoT devices/computing nodes provide logical functions, for
example as part of distributed machine learning or distributed image
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processing applications. The processing capabilities in IoT devices
are limited; they require the support of other nodes, and in a
distributed machine learning application, the training process for AI
services can be executed at IoT edge gateways or cloud networks and
the prediction (inference) service is executed in the IoT devices.
In a distributed image processing application, some image processing
functions can be similarly executed at the edge or in the cloud,
while preprocessing, which helps limiting the amount of uploaded
data, is performed by the IoT device.
+----------------------------------------------+
| Edge Computing Domain |
| |
| +--------+ +--------+ +--------+ |
| |Compute | |Compute | |Compute | |
| |node/End| |node/End| .... |node/End| |
| |device 1| |device 2| .... |device m| |
| +----+---+ +----+---+ +----+---+ |
| | | | |
| +---+-------------+-----------------+--+ |
| | IoT Edge Gateway | |
| +-----------+-------------------+------+ |
| | | |
+--------------+-------------------+-----------+
| |
+-----------+---------+ +------+-------+
| Remote network | | Service |
|(e.g., cloud network)| | Operator(s) |
+-----------+---------+ +------+-------+
| |
+--------------+-------------------+-----------+
| | | |
| +-----------+-------------------+------+ |
| | IoT Edge Gateway | |
| +---+-------------+-----------------+--+ |
| | | | |
| +----+---+ +----+---+ +----+---+ |
| |Compute | |Compute | |Compute | |
| |node/End| |node/End| .... |node/End| |
| |device 1| |device 2| .... |device n| |
| +--------+ +--------+ +--------+ |
| |
| Edge Computing Domain |
+----------------------------------------------+
Figure 2: Example: Machine Learning over a Distributed IoT Edge
Computing System
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In the following, we enumerate major edge computing domain
components. They are here loosely organized into OAM (Operations,
Administration, and Maintenance), functional, and application
components, with the understanding that the distinction between these
classes may not always be clear, depending on actual system
architectures. Some representative research challenges are
associated with those functions. We used input from co-authors, IRTF
attendees, and some comprehensive reviews of the field ([Yousefpour],
[Zhang2], [Khan]).
4.3. OAM Components
Edge computing OAM extends beyond the network-related OAM functions
listed in [RFC6291]. In addition to infrastructure (network,
storage, and computing resources), edge computing systems can also
include computing environments (for VMs, software containers,
functions), IoT devices, data, and code.
Operation-related functions include performance monitoring for
service-level agreement measurements, fault management and
provisioning for links, nodes, compute and storage resources,
platforms, and services. Administration covers network/compute/
storage resources, platforms and services discovery, configuration,
and planning. Discovery during normal operation (e.g., discovery of
compute or storage nodes by endpoints) is typically not included in
OAM; however, in this document, we do not address it separately.
Management covers the monitoring and diagnostics of failures, as well
as means to minimize their occurrence and take corrective actions.
This may include software update management and high service
availability through redundancy and multipath communication.
Centralized (e.g., SDN) and decentralized management systems can be
used. Finally, we arbitrarily chose to address data management as an
application component, however, in some systems, data management may
be considered similar to a network management function.
We further detail a few relevant OAM components.
4.3.1. Resource Discovery and Authentication
Discovery and authentication may target platforms and ,
infrastructure resources, such as computing, networking, and storage,
as well as other resources such as IoT devices, sensors, data, code
units, services, applications, and users interacting with the system.
Broker-based solutions can be used, for example, using an IoT gateway
as a broker to discover IoT resources. More decentralized solutions
can also be used in replacement or complement, for example, CoAP
enables multicast discovery of an IoT device, and CoAP service
discovery enables obtaining a list of resources made available by
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this device [RFC7252]. For device authentication, current
centralized gateway-based systems rely on the installation of a
secret on IoT devices and computing devices (e.g., a device
certificate stored in a hardware security module, or a combination of
code and data stored in a trusted execution environment).
Related challenges include:
* Discovery, authentication, and trust establishment between IoT
devices, compute nodes, and platforms, with regard to concerns
such as mobility, heterogeneous devices and networks, scale,
multiple trust domains, constrained devices, anonymity, and
traceability.
* Intermittent connectivity to the Internet, removing the need to
rely on a third-party authority [Echeverria].
* Resiliency to failure [Harchol], denial of service attacks, easier
physical access for attackers.
4.3.2. Edge Organization and Federation
In a distributed system context, once edge devices have discovered
and authenticated each other, they can be organized, or self-
organized, into hierarchies or clusters. The organizational
structure may range from centralized to peer-to-peer, or it may be
closely tied to other systems. Such groups can also form federations
with other edges or with remote clouds.
Related challenges include:
* Support for scaling, and enabling fault-tolerance or self-healing
[Jeong]. In addition to using a hierarchical organization to cope
with scaling, another available and possibly complementary
mechanism is multicast ([RFC7390] [I-D.ietf-core-groupcomm-bis]).
Other approaches include relying on blockchains [Ali].
* Integration of edge computing with virtualized Radio Access
Networks (Fog RAN) [I-D.bernardos-sfc-fog-ran] and 5G access
networks.
* Sharing resources in multi-vendor/operator scenarios, to optimize
criteria such as profit [Anglano], resource usage, latency, and
energy consumption.
* Capacity planning, placement of infrastructure nodes to minimize
delay [Fan], cost, energy, etc.
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* Incentives for participation, for example, in peer-to-peer
federation schemes.
* Design of federated AI over IoT edge computing systems [Brecko],
for example, for anomaly detection.
4.3.3. Multi-Tenancy and Isolation
Some IoT edge computing systems make use of virtualized (compute,
storage and networking) resources to address the need for secure
multi-tenancy at the edge. This leads to "edge clouds" that share
properties with remotes clouds and can reuse some of their
ecosystems. Virtualization function management is largely covered by
ETSI NFV and MEC standards and recommendations. Projects such as
[LFEDGE-EVE] further cover virtualization and its management in
distributed edge-computing settings.
Related challenges include:
* Adapting cloud management platforms to the edge, to account for
its distributed nature, e.g., using Conflict-free Replicated Data
Types (CRDT) [Jeffery], heterogeneity and customization, e.g.,
using intent-based management mechanisms [Cao], and limited
resources.
* Minimizing virtual function instantiation time and resource usage.
4.4. Functional Components
4.4.1. In-Network Computation
A core function of IoT edge computing is to enable local computation
on a node at the network edge, typically for application-layer
processing, such as processing input data from sensors, making local
decisions, preprocessing data, offloading computation on behalf of a
device, service, or user. Related functions include orchestrating
computation (in a centralized or distributed manner) and managing
application lifecycles. Support for in-network computation may vary
in terms of capability, for example, computing nodes can host virtual
machines, software containers, software actors, uni-kernels running
stateful or stateless code, or a rule engine providing an API to
register actions in response to conditions such as IoT device ID,
sensor values to check, thresholds, etc.
Edge offloading includes offloading to and from an IoT device, and to
and from a network node. [Cloudlets] offer an example of offloading
computation from an end device to a network node. In contrast,
oneM2M is an example of a system that allows a cloud-based IoT
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platform to transfer resources and tasks to a target edge node
[oneM2M-TR0052]. Once transferred, the edge node can directly
support IoT devices that it serves with the service offloaded by the
cloud (e.g., group management, location management, etc.).
QoS can be provided in some systems through the combination of
network QoS (e.g., traffic engineering or wireless resource
scheduling) and compute/storage resource allocations. For example,
in some systems, a bandwidth manager service can be exposed to enable
allocation of the bandwidth to/from an edge-computing application
instance.
In-network computation can leverage the underlying services, provided
using data generated by IoT devices and access networks. Such
services include IoT device location, radio network information,
bandwidth management and congestion management (e.g., the congestion
management feature of oneM2M [oneM2M-TR0052]).
Related challenges include:
* (Computation placement) Selecting, in a centralized or
distributed/peer-to-peer manner, an appropriate compute device
based on available resources, location of data input and data
sinks, compute node properties, etc., and with varying goals
including end-to-end latency, privacy, high availability, energy
conservation, or network efficiency, for example, using load-
balancing techniques to avoid congestion.
* Onboarding code on a platform or computing device, and invoking
remote code execution, possibly as part of a distributed
programming model and with respect to similar concerns of latency,
privacy, etc.: For example, offloading can be included in a
vehicular scenario [Grewe]. These operations should deal with
heterogeneous compute nodes [Schafer], and may also support end
devices, including IoT devices, as compute nodes [Larrea].
* Adapting Quality of Results (QoR) for applications where a perfect
result is not necessary [Li].
* Assisted or automatic partitioning of code: for example, for
application programs [I-D.sarathchandra-coin-appcentres] or
network programs [I-D.hsingh-coinrg-reqs-p4comp].
* Supporting computation across trust domains: for example,
verifying computation results.
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* Support for computation mobility: relocating an instance from one
compute node to another, while maintaining a given service level;
session continuity when communicating with end devices that are
mobile, possibly at high speed (e.g., in vehicular scenarios);
defining lightweight execution environments for secure code
mobility, for example, using WebAssembly [Nieke].
* Defining, managing, and verifying Service Level Agreements (SLA)
for edge-computing systems: pricing is a challenging task.
4.4.2. Edge Storage and Caching
Local storage or caching enables local data processing (e.g.,
preprocessing or analysis) as well as delayed data transfer to the
cloud or delayed physical shipping. An edge node may offer local
data storage (in which persistence is subject to retention policies),
caching, or both. Caching generally refers to temporary storage to
improve performance without persistence guarantees. An edge-caching
component manages data persistence, for example, it schedules the
removal of data when it is no longer needed. Other related aspects
include the authentication and encryption of data. Edge storage and
caching can take the form of a distributed storage systems.
Related challenges include:
* (Cache and data placement) Using cache positioning and data
placement strategies to minimize data retrieval delay [Liu] and
energy consumption. Caches may be positioned in the access
network infrastructure or on end devices.
* Maintaining consistency, freshness, reliability, and privacy of
stored/cached data in systems that are distributed, constrained,
and dynamic (e.g., owing to end devices and computing nodes churn
or mobility), and which can have additional data governance
constraints on data storage location. For example, [Mortazavi]
leverages a hierarchical storage organization. Freshness-related
metrics include the age of information [Yates] that captures the
timeliness of information received from a sender (e.g., an IoT
device).
4.4.3. Communication
An edge cloud may provide a northbound data plane or management plane
interface to a remote network, such as a cloud, home or enterprise
network. This interface does not exist in stand-alone (local-only)
scenarios. To support such an interface when it exists, an edge
computing component needs to expose an API, deal with authentication
and authorization, and support secure communication.
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An edge cloud may provide an API or interface to local or mobile
users, for example, to provide access to services and applications,
or to manage data published by local/mobile devices.
Edge-computing nodes communicate with IoT devices over a southbound
interface, typically for data acquisition and IoT device management.
Communication brokering is a typical function of IoT edge computing
that facilitates communication with IoT devices, enabling clients to
register as recipients for data from devices, as well as forwarding/
routing of traffic to or from IoT devices, enabling various data
discovery and redistribution patterns, for example, north-south with
clouds, east-west with other edge devices
[I-D.mcbride-edge-data-discovery-overview]. Another related aspect
is dispatching alerts and notifications to interested consumers both
inside and outside the edge-computing domain. Protocol translation,
analytics, and video transcoding can also be performed when
necessary. Communication brokering may be centralized in some
systems, for example, using a hub-and-spoke message broker, or
distributed with message buses, possibly in a layered bus approach.
Distributed systems can leverage direct communication between end
devices over device-to-device links. A broker can ensure
communication reliability and traceability and, in some cases,
transaction management.
Related challenges include:
* Defining edge computing abstractions, such as PaaS [Yangui],
suitable for users and cloud systems to interact with edge
computing systems and dealing with interoperability issues such as
data model heterogeneity.
* Enabling secure and resilient communication between IoT devices
and remote cloud, for example, through multipath support.
4.5. Application Components
IoT edge computing can host applications, such as those mentioned in
Section 2.4. While describing the components of individual
applications is out of our scope, some of those applications share
similar functions, such as IoT device management and data management,
as described below.
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4.5.1. IoT Device Management
IoT device management includes managing information regarding IoT
devices, including their sensors, and how to communicate with them.
Edge computing addresses the scalability challenges of a large number
of IoT devices by separating the scalability domain into edge/local
networks and remote networks. For example, in the context of the
oneM2M standard, a device management functionality (called "software
campaign" in oneM2M) enables the installation, deletion, activation,
and deactivation of software functions/services on a potentially
large number of edge nodes [oneM2M-TR0052]. Using a dashboard or
management software, a service provider issues these requests through
an IoT cloud platform supporting the software campaign functionality.
Challenges listed in Section 4.3.1 may be applicable to IoT devices
management as well.
4.5.2. Data Management and Analytics
Data storage and processing at the edge are major aspects of IoT edge
computing, directly addressing the high-level IoT challenges listed
in Section 3. Data analysis, for example, through AI/ML tasks
performed at the edge, may benefit from specialized hardware support
on the computing nodes.
Related challenges include:
* Addressing concerns regarding resource usage, security, and
privacy when sharing, processing, discovering, or managing data:
for example presenting data in views composed of an aggregation of
related data [Zhang]; protecting data communication between
authenticated peers [Basudan], classifying data (e.g., in terms of
privacy, importance, validity), and compressing and encrypting
data, for example, using homomorphic encryption to directly
process encrypted data [Stanciu].
* Other concerns regarding edge data discovery (e.g., streaming
data, metadata, and events) include siloization and lack of
standards in edge environments that can be dynamic (e.g.,
vehicular networks) and heterogeneous
[I-D.mcbride-edge-data-discovery-overview].
* Data-driven programming models [Renart], for example, event-based,
including handling naming and data abstractions.
* Data integration in an environment that without data
standardization, or where different sources use different
ontologies [Farnbauer-Schmidt].
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* Addressing concerns such as limited resources, privacy, dynamic,
and heterogeneous environments to deploy machine learning at the
edge: for example, making machine learning more lightweight and
distributed (e.g., enabling distributed inference at the edge),
supporting shorter training times and simplified models, and
supporting models that can be compressed for efficient
communication [Murshed].
* Although edge computing can support IoT services independently of
cloud computing, it can also be connected to cloud computing.
Thus, the relationship between IoT edge computing and cloud
computing, with regard to data management, is another potential
challenge [ISO_TR].
4.6. Simulation and Emulation Environments
IoT Edge Computing introduces new challenges to the simulation and
emulation tools used by researchers and developers. A varied set of
applications, networks, and computing technologies can coexist in a
distributed system, making modeling difficult. Scale, mobility, and
resource management are additional challenges [SimulatingFog].
Tools include simulators, where simplified application logic runs on
top of a fog network model, and emulators, where actual applications
can be deployed, typically in software containers, over a cloud
infrastructure (e.g., Docker and Kubernetes) running over a network
emulating network edge conditions such as variable delays, throughput
and mobility events. To gain in scale, emulated and simulated
systems can be used together in hybrid federation-based approaches
[PseudoDynamicTesting], whereas to gain in realism, physical devices
can be interconnected with emulated systems. Examples of related
work and platforms include the publicly accessible MEC sandbox work
recently initiated in ETSI [ETSI_Sandbox], and open source simulators
and emulators ([AdvantEDGE] emulator and tools cited in
[SimulatingFog]). EdgeNet [Senel] is a globally distributed edge
cloud for Internet researchers, using nodes contributed by
institutions, and based on Docker for containerization and Kubernetes
for deployment and node management.
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Digital twins are virtual instances of a physical system (twin) that
are continually updated with the latter's performance, maintenance,
and health status data throughout the life cycle of the physical
system. [Madni]. In contrast to a traditional emulation or
simulated environment, digital twins, once generated, are maintained
in sync by their physical twin, which can be, among many other
instances, an IoT device, edge device, an edge network. The benefits
of digital twins go beyond those of emulation and include accelerated
business processes, enhanced productivity, and faster innovation with
reduced costs [I-D.irtf-nmrg-network-digital-twin-arch].
5. Security Considerations
Privacy and security are drivers of the adoption of edge computing
for the IoT (Section 3.4). As discussed in Section 4.3.1,
authentication and trust (among computing nodes, management nodes,
and end devices) can be challenging as scale, mobility, and
heterogeneity increase. The sometimes disconnected nature of edge
resources can avoid reliance on third-party authorities. Distributed
edge computing is exposed reliability and denial of service attacks.
Personal or proprietary IoT data leakage is also a major threat,
particularly because of the distributed nature of the systems
(Section 4.5.2). Furthermore, blockchain-based distributed IoT edge
computing must be designed for privacy, since public blockchain
addressing does not guarantee absolute anonymity [Ali].
However, edge computing also offers solutions in the security space:
maintaining privacy by computing sensitive data closer to data
generators is a major use case for IoT edge computing. An edge cloud
can be used to perform actions based on sensitive data or to
anonymize or aggregate data prior to transmission to a remote cloud
server. Edge computing communication brokering functions can also be
used to secure communication between edge and cloud networks.
6. Conclusion
IoT edge computing plays an essential role, complementary to the
cloud, in enabling IoT systems in certain situations. In this
document, we presented use cases and listing the core challenges
faced by IoT that drive the need for IoT edge computing. The first
part of this document may therefore help focus future research
efforts on the aspects of IoT edge computing where it is most useful.
The second part of this document presents a general system model and
structured overview of the associated research challenges and related
work. The structure, based on the system model, is not meant to be
restrictive and exists for the purpose of having a link between
individual research areas and where they are applicable in an IoT
edge computing system.
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7. IANA Considerations
This document has no IANA actions.
8. Acknowledgements
The authors would like to thank Joo-Sang Youn, Akbar Rahman, Michel
Roy, Robert Gazda, Rute Sofia, Thomas Fossati, Chonggang Wang, Marie-
José Montpetit, Carlos J. Bernardos, Milan Milenkovic, Dale Seed,
JaeSeung Song, Roberto Morabito, Carsten Bormann and Ari Keränen for
their valuable comments and suggestions on this document.
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Authors' Addresses
Jungha Hong
ETRI
218 Gajeong-ro, Yuseung-Gu
Daejeon
34129
Republic of Korea
Email: jhong@etri.re.kr
Yong-Geun Hong
Daejeon University
62 Daehak-ro, Dong-gu
Daejeon
300716
Republic of Korea
Email: yonggeun.hong@gmail.com
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
80992 Munich
Germany
Email: ietf@kovatsch.net
Hong, et al. Expires 18 March 2024 [Page 36]
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Eve Schooler
Intel
2200 Mission College Blvd.
Santa Clara, CA, 95054-1537
United States of America
Email: eve.schooler@gmail.com
Dirk Kutscher
Hong Kong University of Science and Technology (Guangzhou)
No.1 Du Xue Rd
Guangzhou
China
Email: ietf@dkutscher.net
Hong, et al. Expires 18 March 2024 [Page 37]