wdiff rfc9556.alt-original rfc9556.txt

Network Working Group

Internet Research Task Force (IRTF) J. Hong
Internet-Draft
Request for Comments: 9556 ETRI
Intended status:
Category: Informational Y.-G. Y-G. Hong
Expires: 18 March 2024
ISSN: 2070-1721 Daejeon University
X. de Foy
InterDigital Communications, LLC
M. Kovatsch
Huawei Technologies Duesseldorf GmbH
E. Schooler
Intel
University of Oxford
D. Kutscher
Hong Kong University
HKUST(GZ)
March 2024

Internet of Science and Technology (Guangzhou)
15 September 2023

IoT Things (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 centralized 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 edge and its challenges. It presents a general
model and major components of the IoT Edge edge to provide a common basis
for future discussions in the T2TRG Thing-to-Thing Research Group (T2TRG)
and other IRTF and IETF groups. This document is a product of the
IRTF Thing-to-Thing Research Group
(T2TRG). T2TRG.

Status of This Memo

This Internet-Draft document is submitted in full conformance with not an Internet Standards Track specification; it is
published for informational purposes.

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This Internet-Draft will expire on 18 March 2024.
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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 toward 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
Acknowledgements
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 36

1. Introduction

Currently,

At the time of writing, many IoT services leverage cloud computing platforms,
platforms because they provide virtually unlimited storage and
processing power. The reliance of IoT on back-end cloud computing
provides additional advantages advantages, such as scalability and efficiency. Today's
At the time of writing, 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 document 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, field based on a limited review
of standards, research, and open-source and proprietary products in [I-D.defoy-t2trg-iot-edge-computing-background].
[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) research
group 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 while working on Radio-Frequency Identification (RFID)
technology [Ashton], the concept of IoT has evolved. It now At the time of
writing, it 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 converging on Internet and Web web technologies.

A Thing "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]). [REST-IOT]. In this
document document, we will
use the term "IoT device" to designate the embedded system attached
to the Thing.

Resource-constrained Things Things, such as sensors, home appliances appliances, and
wearable devices devices, often have limited storage and processing power,
which can provide create challenges with respect to reliability, performance,
energy consumption, security, and privacy [Lin]. Some,
less less-
resource-constrained Things, can generate a voluminous amount of
data. This range of factors led to IoT designs that integrate Things
into larger distributed systems, for example example, edge or cloud computing
systems.

2.2. Cloud Computing

Cloud computing has been defined in [NIST]: "cloud

| 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".
| interaction.

The low cost and massive availability of storage and processing power
enabled the realization of another computing model, model in which
virtualized resources can be leased in an on-demand fashion and be
provided as general utilities. Platform-as-
a-Service Platform-as-a-Service (PaaS) and
cloud computing platforms widely adopted this paradigm for delivering
services over the Internet, gaining both economical and technical
benefits [Botta].

Today,

At the time of writing, 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 that require very short
response times, require 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 "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], [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 that is topological, physical,
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
cloud but also handle computing tasks including processing, storage, storing,
caching, and load balancing on data sent to and from the Cloud 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) Standards Developing Organizations (SDOs) 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 (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 models, although, in practice, these
approaches are not incompatible and may integrate with each other:

* The telecommunication industry tends to use a model where edge
computing services are deployed over a Network Function
Virtualization (NFV) infrastructure, at aggregation points 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). Operational Technology (OT). Hence, edge computing
sometimes refers to applying IT solutions to OT problems, such as
analytics, more flexible 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, cases to identify
differentiating requirements, providing references to other use
cases.

*Smart Factory*
As part of the 4th industrial revolution, Fourth Industrial Revolution, smart factories run real-
time
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, 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 life-cycle
management or accident risk prediction, can be transferred to a
central data center for processing.

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 smart-city scenarios, the Smart Grid 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, to and restore power after a
disturbance, to reduce operation costs, and to reuse energy
effectively,
effectively since these operations involve local decision-making.
In addition, edge computing can help monitor power generation and
power demand, 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 devices, such as smartphones.

In existing farms, simple systems systems, such as management according to
temperature and humidity 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 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 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, 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 costs (for example, of video streams over a mobile
network connection, 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- 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, radars, Light Detection and Ranging (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,
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 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
Artificial intelligence (AI) / machine learning (ML) systems at
the edge empower real-time analysis, faster decision-making,
reduced latency, improved operational efficiency, and personalized
experiences across various industries, industries by bringing
artificial intelligence AI and machine learning ML
capabilities closer to edge devices.

In addition, oneM2M has studied several IoT edge computing use
cases, which are documented in [oneM2M-TR0001], [oneM2M-TR0018] [oneM2M-TR0018],
and [oneM2M-TR0026]. The edge computing related edge-computing-related requirements
raised through the analysis of these use cases are captured in
[oneM2M-TS0002].

3. IoT Challenges Leading Towards toward Edge Computing

This section describes the challenges faced by the 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, applications in
domains such as industrial, automotive automotive, and healthcare domains, leading healthcare, which leads
to new challenges. For example, industrial machines machines, such as laser
cutters
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

Often, many industrial control systems, such as manufacturing
systems, smart grids, and oil and gas systems often systems, require stringent end-to-end 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]. [IEC_IEEE_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 ([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, 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. cloud. Theoretically, both 5G and Wi-Fi 6
networks both
theoretically top out at 10 gigabits per second (i.e., 4.5 terabytes per
hour), allowing to the transfer of 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 Therefore, minimizing
reliance on high-bandwidth connectivity is therefore a requirement, requirement; this can be
done, for example, by processing data at the edge and deriving
summarized or actionable insights that can be transmitted to the Cloud.
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 backend 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 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 cloud also increases the risk of data leakage,
for instance, through attacks on rich targets.

It is often argues argued 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 secrets, such as the setup of production lines. Hence, owners
of these systems are generally reluctant to upload IoT data to the
Cloud.
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), 4.1) and then define a general system model (Section 4.2).
This provides a context for IoT edge-computing edge computing functions, which are
listed in Section Sections 4.3, Section 4.4 4.4, and Section 4.5.

4.1. Overview of IoT Edge Computing Today

This section provides an overview of today's the current (at the time of
writing) IoT edge computing field based on a limited review of
standards, research, and open-source and proprietary products in
[I-D.defoy-t2trg-iot-edge-computing-background].
[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- 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 a Constrained Application Protocol (CoAP)
[RFC7252], MQTT [mqtt5], Message Queuing Telemetry Transport (MQTT) [MQTT5], and
many specialized IoT protocols (such as OPC UA Open Platform Communications
Unified Architecture (OPC UA) and DDS Data Distribution Service (DDS) in
the Industrial 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 Virtual
Machines (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, forward-
looking projects but are not commonly used today. at the time of writing.

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 interoperability, and promote reuse in
the IoT ecosystem. Furthermore, ETSI MEC Multi-access Edge Computing
(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 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.

The life cycle life-cycle management of services and applications on physical
IoT gateways is generally cloud-based. cloud based. Edge cloud management
platforms and products (such as StarlingX, Akraino Edge Stack, or
proprietary products from major Cloud 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 Typically, the service and application life-cycle life
cycle is
typically using an NFV-like management and orchestration model.

The platform typically generally 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 usually 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- 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), EdgeFS and Ceph) or, in more experimental settings,
by an ICN Information-Centric Networking (ICN) network, for example,
systems such as Chipmunk [chipmunk] [Chipmunk] and Kua [kua] [Kua] have been proposed
as distributed information-centric objects stores. External storage,
for example, on databases in a distant or local IT cloud, is
typically used for filtered data deemed worthy of long-term storage, although storage;
although, in some cases cases, it may be for all data, for example example, when
required for regulatory reasons.

Stateful computing is supported the default on platforms that host native
programs, most systems, VMs, or and
containers. Stateless computing is supported on platforms providing
a "serverless computing" service (also known as
function-as-a-service, function-as-
a-service, e.g., using stateless containers), containers) or on systems based on
named function networking.

In many IoT use cases, a typical network usage pattern is a high high-
volume uplink with some form of traffic reduction enabled by
processing over edge-computing 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). Content Delivery Networks (CDNs)).

4.2. General Model

Edge computing is expected to play an important role in deploying new
IoT services integrated with Big Data 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 section lays out an attempt at a general model
and the list lists 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 centralized 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 An IoT gateway
requires interactions with the cloud, cloud; however, 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 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 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.

Figure 1: Model of IoT Edge Computing

In the distributed model described in Figure 2, the edge-computing edge computing
domain is composed of IoT edge gateways and IoT devices which that 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 example,
as part of distributed machine learning or distributed image
processing applications. The processing capabilities in IoT devices
are limited; they require the support of other nodes, and in nodes. In a
distributed machine learning application, the training process for AI
services can be executed at IoT edge gateways or cloud networks networks, and
the prediction (inference) service is executed in the IoT devices.
In
Similarly, 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 cloud. To
limit the amount of data to be uploaded
data, is performed by the to central cloud functions,
IoT device. edge devices may pre-process data.

Figure 2: Example: Example of Machine Learning over a Distributed IoT Edge
Computing System

In the following, we enumerate major edge computing domain
components. They Here, they are here loosely organized into OAM (Operations, Operations,
Administration, and Maintenance), functional, Maintenance (OAM); 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, coauthors,
participants of T2TRG meetings, and some comprehensive reviews of the
field ([Yousefpour], [Zhang2], and [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, and
functions), IoT devices, data, and code.

Operation-related functions include performance monitoring for
service-level agreement
Service Level Agreement (SLA) measurements, fault management management, and
provisioning for links, nodes, compute and storage resources,
platforms, and services. Administration covers network/compute/
storage resources, platforms platform and services service 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) Software-Defined Networking (SDN)) and
decentralized management systems can be used. Finally, we
arbitrarily chose to address data management as an application component,
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 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 In a
broker-based system, an IoT gateway can act as a broker to discover
IoT resources. More decentralized solutions can also be used in
replacement of or complement, in complement to the broker-based solutions; for
example, CoAP enables multicast discovery of an IoT device, device and CoAP
service discovery enables obtaining one to obtain a list of resources made
available by 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, 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 denial-of-service attacks, and
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, organized or self-
organized, 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, scaling and enabling fault-tolerance 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]). [RFC7390] [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] [SFC-FOG-RAN] and 5G access networks.

* Sharing resources in multi-vendor/operator scenarios, 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.

* 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
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 remote 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 edge
computing settings.

Related challenges include:

* Adapting cloud management platforms to the edge, edge to account for its
distributed nature, e.g., heterogeneity, need for customization, and
limited resources (for example, using Conflict-free Replicated
Data Types (CRDT) [Jeffery], heterogeneity and customization, e.g.,
using (CRDTs) [Jeffery] or intent-based management mechanisms [Cao], and limited
resources.
[Cao]).

* 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, and 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. life cycles. Support for in-network
computation may vary in terms of capability, capability; for example, computing
nodes can host virtual machines, software containers, software
actors, uni-kernels unikernels running stateful or stateless code, or a rule
engine providing an API to register actions in response to conditions such
(such as an IoT device ID, sensor values to check, thresholds, etc. etc.).

Edge offloading includes offloading to and from an IoT device, device and to
and from a network node. [Cloudlets] offer describes 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 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 edge computing application
instance.

In-network computation can leverage the underlying services, services provided
using data generated by IoT devices and access networks. Such
services include IoT device location, radio network information,
bandwidth management management, and congestion management (e.g., the congestion
management feature of oneM2M [oneM2M-TR0052]).

Related challenges include:

* (Computation placement) Selecting, Computation placement: in a centralized or
distributed/peer-to-peer distributed/peer-to-
peer manner, selecting an appropriate compute device device. The
selection is based on available resources, location of data input
and data sinks, compute node properties, etc., and etc. with varying goals.
These goals
including include end-to-end latency, privacy, high
availability, energy conservation, or network efficiency, for efficiency (for
example, using load-
balancing load-balancing techniques to avoid congestion. congestion).

* Onboarding code on a platform or computing device, device and invoking
remote code execution, possibly as part of a distributed
programming model and with respect to similar concerns of latency,
privacy, etc.: etc. For example, offloading can be included in a
vehicular scenario [Grewe]. These operations should deal with
heterogeneous compute nodes [Schafer], [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 code. For example, for
application programs [I-D.sarathchandra-coin-appcentres] [COIN-APPCENTRES] or network programs [I-D.hsingh-coinrg-reqs-p4comp].
[REQS-P4COMP].

* Supporting computation across trust domains: for domains. For example,
verifying computation results.

* Support for Supporting computation mobility: relocating an instance from one
compute node to another, 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) SLAs for edge-computing systems: 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 Generally, "caching" refers to temporary storage
to improve performance without persistence guarantees. An edge-caching edge-
caching component manages data persistence, 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.
system.

Related challenges include:

* (Cache Cache and data placement) Using placement: using cache positioning and data
placement strategies to minimize data retrieval delay [Liu] and
energy consumption. Caches may be positioned in the access 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 due to end devices node mobility, energy-saving regimes,
and computing nodes churn
or mobility), disruptions) and which can have additional data governance
constraints on data storage location. For example, [Mortazavi]
leverages
describes leveraging 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 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.

An edge cloud may provide an API or interface to local or mobile
users, for example, to provide access to services and applications, applications or
to manage data published by local/mobile devices.

Edge-computing

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 enables clients to
register as recipients for data from devices, as well as forwarding/
routing of forwards traffic to or
from IoT devices, enabling enables various data discovery and redistribution patterns, for
patterns (for example, north-south with
clouds, clouds and east-west with
other edge devices
[I-D.mcbride-edge-data-discovery-overview]. [EDGE-DATA-DISCOVERY-OVERVIEW]). Another related
aspect is dispatching alerts and notifications to interested
consumers both inside and outside the edge-computing 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, 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 issues, such
as
data model data-model heterogeneity.

* Enabling secure and resilient communication between IoT devices
and a 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.

4.5.1. IoT Device Management

IoT device management includes managing information regarding IoT
devices, including their sensors, 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

The challenges listed in Section 4.3.1 may be applicable to IoT devices
device 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 example, presenting data in views composed of an aggregation
of related data [Zhang]; [Zhang], protecting data communication between
authenticated peers [Basudan], classifying data (e.g., in terms of
privacy, importance, and 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].
[EDGE-DATA-DISCOVERY-OVERVIEW].

* Data-driven programming models [Renart], for example, event-based, those that
are event based, including handling naming and data abstractions.

* Data integration in an environment that without data
standardization, standardization or
where different sources use different ontologies
[Farnbauer-Schmidt].

* Addressing concerns such as limited resources, privacy, dynamic, and
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 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 conditions, such as variable delays, throughput
throughput, and mobility events. To gain in scale, emulated and
simulated systems can be used together in hybrid federation-based
approaches
[PseudoDynamicTesting], [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], [ETSI_Sandbox]
and open source open-source simulators and emulators ([AdvantEDGE] emulator and
tools cited in [SimulatingFog]). EdgeNet [Senel] is a globally
distributed edge cloud for Internet researchers, using which uses nodes
contributed by
institutions, institutions and which is based on Docker for
containerization and Kubernetes for deployment and node management.

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.
system [Madni]. In contrast to a traditional an 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, or 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]. [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 to reliability and denial of service denial-of-service
attacks.
Personal A 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 listed the core challenges faced
by the IoT that drive the need for IoT edge computing. The Therefore,
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.

7. IANA Considerations

This document has no IANA actions.

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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 Bormann, and Ari Keränen for
their valuable comments and suggestions on this document.

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

Eve Schooler
Intel
2200 Mission College Blvd.
Santa Clara, CA, 95054-1537
United States
University of America Oxford
Parks Road
Oxford
OX1 3PJ
United Kingdom
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