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Category: Informational Y-G. Hong Category: Informational Y-G. Hong
ISSN: 2070-1721 Daejeon University ISSN: 2070-1721 Daejeon University
X. de Foy X. de Foy
InterDigital Communications, LLC InterDigital Communications, LLC
M. Kovatsch M. Kovatsch
Huawei Technologies Duesseldorf GmbH Huawei Technologies Duesseldorf GmbH
E. Schooler E. Schooler
University of Oxford University of Oxford
D. Kutscher D. Kutscher
HKUST(GZ) HKUST(GZ)
March 2024 April 2024
Internet of Things (IoT) Edge Challenges and Functions Internet of Things (IoT) Edge Challenges and Functions
Abstract Abstract
Many Internet of Things (IoT) applications have requirements that Many Internet of Things (IoT) applications have requirements that
cannot be satisfied by centralized cloud-based systems (i.e., cloud cannot be satisfied by centralized cloud-based systems (i.e., cloud
computing). These include time sensitivity, data volume, computing). These include time sensitivity, data volume,
connectivity cost, operation in the face of intermittent services, connectivity cost, operation in the face of intermittent services,
privacy, and security. As a result, IoT is driving the Internet privacy, and security. As a result, IoT is driving the Internet
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energy consumption, security, and privacy [Lin]. Some, less- energy consumption, security, and privacy [Lin]. Some, less-
resource-constrained Things, can generate a voluminous amount of resource-constrained Things, can generate a voluminous amount of
data. This range of factors led to IoT designs that integrate Things data. This range of factors led to IoT designs that integrate Things
into larger distributed systems, for example, edge or cloud computing into larger distributed systems, for example, edge or cloud computing
systems. systems.
2.2. Cloud Computing 2.2. Cloud Computing
Cloud computing has been defined in [NIST]: Cloud computing has been defined in [NIST]:
| cloud computing is a model for enabling ubiquitous, convenient, | Cloud computing is a model for enabling ubiquitous, convenient,
| on-demand network access to a shared pool of configurable | on-demand network access to a shared pool of configurable
| computing resources (e.g., networks, servers, storage, | computing resources (e.g., networks, servers, storage,
| applications, and services) that can be rapidly provisioned and | applications, and services) that can be rapidly provisioned and
| released with minimal management effort or service provider | released with minimal management effort or service provider
| interaction. | interaction.
The low cost and massive availability of storage and processing power The low cost and massive availability of storage and processing power
enabled the realization of another computing model in which enabled the realization of another computing model in which
virtualized resources can be leased in an on-demand fashion and virtualized resources can be leased in an on-demand fashion and
provided as general utilities. Platform-as-a-Service (PaaS) and provided as general utilities. Platform-as-a-Service (PaaS) and
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reduce the cost of failure through preliminary measures. In the reduce the cost of failure through preliminary measures. In the
existing manufacturing field, production facilities are manually existing manufacturing field, production facilities are manually
run according to a program entered in advance; however, edge run according to a program entered in advance; however, edge
computing in a smart factory enables tailoring solutions by computing in a smart factory enables tailoring solutions by
analyzing data at each production facility and machine level. analyzing data at each production facility and machine level.
Digital twins [Jones] of IoT devices have been jointly used with Digital twins [Jones] of IoT devices have been jointly used with
edge computing in industrial IoT scenarios [Chen]. edge computing in industrial IoT scenarios [Chen].
*Smart Grid* *Smart Grid*
In future smart-city scenarios, the smart grid will be critical in In future smart-city scenarios, the smart grid will be critical in
ensuring highly available/efficient energy control in city-wide ensuring highly available and efficient energy control in city-
electricity management [Mehmood]. Edge computing is expected to wide electricity management [Mehmood]. Edge computing is expected
play a significant role in these systems to improve the to play a significant role in these systems to improve the
transmission efficiency of electricity, to react to and restore transmission efficiency of electricity, to react to and restore
power after a disturbance, to reduce operation costs, and to reuse power after a disturbance, to reduce operation costs, and to reuse
energy effectively since these operations involve local decision- energy effectively since these operations involve local decision-
making. In addition, edge computing can help monitor power making. In addition, edge computing can help monitor power
generation and power demand and make local electrical energy generation and power demand and make local electrical energy
storage decisions in smart grid systems. storage decisions in smart grid systems.
*Smart Agriculture* *Smart Agriculture*
Smart agriculture integrates information and communication Smart agriculture integrates information and communication
technologies with farming technology. Intelligent farms use IoT technologies with farming technology. Intelligent farms use IoT
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to cloud servers that process data and recommend actions. The use to cloud servers that process data and recommend actions. The use
of edge computing can reduce the volume of back-and-forth data of edge computing can reduce the volume of back-and-forth data
transmissions significantly, resulting in cost and bandwidth transmissions significantly, resulting in cost and bandwidth
savings. Locally generated data can be processed at the edge, and savings. Locally generated data can be processed at the edge, and
local computing and analytics can drive local actions. With edge local computing and analytics can drive local actions. With edge
computing, it is easy for farmers to select large amounts of data computing, it is easy for farmers to select large amounts of data
for processing, and data can be analyzed even in remote areas with for processing, and data can be analyzed even in remote areas with
poor access conditions. Other applications include enabling poor access conditions. Other applications include enabling
dashboarding, for example, to visualize the farm status, as well dashboarding, for example, to visualize the farm status, as well
as enhancing Extended Reality (XR) applications that require edge as enhancing Extended Reality (XR) applications that require edge
audio/video processing. As the number of people working on audio and/or video processing. As the number of people working on
farming has been decreasing over time, increasing automation farming has been decreasing over time, increasing automation
enabled by edge computing can be a driving force for future smart enabled by edge computing can be a driving force for future smart
agriculture [OGrady]. agriculture [OGrady].
*Smart Construction* *Smart Construction*
Safety is critical at construction sites. Every year, many Safety is critical at construction sites. Every year, many
construction workers lose their lives because of falls, construction workers lose their lives because of falls,
collisions, electric shocks, and other accidents [BigRentz]. collisions, electric shocks, and other accidents [BigRentz].
Therefore, solutions have been developed to improve construction Therefore, solutions have been developed to improve construction
site safety, including the real-time identification of workers, site safety, including the real-time identification of workers,
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With edge computing, real-time data is collected, processed, and With edge computing, real-time data is collected, processed, and
analyzed directly at the edge, allowing for the accurate analyzed directly at the edge, allowing for the accurate
monitoring and simulation of physical assets. Moreover, edge monitoring and simulation of physical assets. Moreover, edge
computing effectively minimizes latency, enabling rapid responses computing effectively minimizes latency, enabling rapid responses
to dynamic conditions as computational resources are brought to dynamic conditions as computational resources are brought
closer to the physical object. Running digital twin processing at closer to the physical object. Running digital twin processing at
the edge enables organizations to obtain timely insights and make the edge enables organizations to obtain timely insights and make
informed decisions that maximize efficiency and performance. informed decisions that maximize efficiency and performance.
*Other Use Cases* *Other Use Cases*
Artificial intelligence (AI) / machine learning (ML) systems at Artificial intelligence (AI) and machine learning (ML) systems at
the edge empower real-time analysis, faster decision-making, the edge empower real-time analysis, faster decision-making,
reduced latency, improved operational efficiency, and personalized reduced latency, improved operational efficiency, and personalized
experiences across various industries by bringing AI and ML experiences across various industries by bringing AI and ML
capabilities closer to edge devices. capabilities closer to edge devices.
In addition, oneM2M has studied several IoT edge computing use In addition, oneM2M has studied several IoT edge computing use
cases, which are documented in [oneM2M-TR0001], [oneM2M-TR0018], cases, which are documented in [oneM2M-TR0001], [oneM2M-TR0018],
and [oneM2M-TR0026]. The edge-computing-related requirements and [oneM2M-TR0026]. The edge-computing-related requirements
raised through the analysis of these use cases are captured in raised through the analysis of these use cases are captured in
[oneM2M-TS0002]. [oneM2M-TS0002].
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IoT services integrated with big data and AI enabled by flexible in- IoT services integrated with big data and AI enabled by flexible in-
network computing platforms. Although there are many approaches to network computing platforms. Although there are many approaches to
edge computing, this section lays out an attempt at a general model edge computing, this section lays out an attempt at a general model
and lists associated logical functions. In practice, this model can and lists associated logical functions. In practice, this model can
be mapped to different architectures, such as: be mapped to different architectures, such as:
* A single IoT gateway, or a hierarchy of IoT gateways, typically * A single IoT gateway, or a hierarchy of IoT gateways, typically
connected to the cloud (e.g., to extend the centralized cloud- connected to the cloud (e.g., to extend the centralized cloud-
based management of IoT devices and data to the edge). The IoT based management of IoT devices and data to the edge). The IoT
gateway plays a common role in providing access to a heterogeneous gateway plays a common role in providing access to a heterogeneous
set of IoT devices/sensors, handling IoT data, and delivering IoT set of IoT devices and sensors, handling IoT data, and delivering
data to its final destination in a cloud network. An IoT gateway IoT data to its final destination in a cloud network. An IoT
requires interactions with the cloud; however, it can also operate gateway requires interactions with the cloud; however, it can also
independently in a disconnected mode. operate independently in a disconnected mode.
* A set of distributed computing nodes, for example, embedded in * A set of distributed computing nodes, for example, embedded in
switches, routers, edge cloud servers, or mobile devices. Some switches, routers, edge cloud servers, or mobile devices. Some
IoT devices have sufficient computing capabilities to participate IoT devices have sufficient computing capabilities to participate
in such distributed systems owing to advances in hardware in such distributed systems owing to advances in hardware
technology. In this model, edge computing nodes can collaborate technology. In this model, edge computing nodes can collaborate
to share resources. to share resources.
* A hybrid system involving both IoT gateways and supporting * A hybrid system involving both IoT gateways and supporting
functions in distributed computing nodes. functions in distributed computing nodes.
In the general model described in Figure 1, the edge computing domain In the general model described in Figure 1, the edge computing domain
is interconnected with IoT devices (southbound connectivity), is interconnected with IoT devices (southbound connectivity),
possibly with a remote/cloud network (northbound connectivity), and possibly with a remote (e.g., cloud) network (northbound
with a service operator's system. Edge computing nodes provide connectivity), and with a service operator's system. Edge computing
multiple logical functions or components that may not be present in a nodes provide multiple logical functions or components that may not
given system. They may be implemented in a centralized or be present in a given system. They may be implemented in a
distributed fashion, at the network edge, or through interworking centralized or distributed fashion, at the network edge, or through
between the edge network and remote cloud networks. interworking between the edge network and remote cloud networks.
+---------------------+ +---------------------+
| Remote Network | +---------------+ | Remote Network | +---------------+
|(e.g., cloud network)| | Service | |(e.g., cloud network)| | Service |
+-----------+---------+ | Operator | +-----------+---------+ | Operator |
| +------+--------+ | +------+--------+
| | | |
+--------------+-------------------+-----------+ +--------------+-------------------+-----------+
| Edge Computing Domain | | Edge Computing Domain |
| | | |
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| End | | End | ... |Node/End| | End | | End | ... |Node/End|
|Device 1| |Device 2| ...| |Device n| | |Device 1| |Device 2| ...| |Device n| |
+--------+ +--------+ +--------+ +--------+ +--------+ +--------+
+ - - - - - - - -+ + - - - - - - - -+
Figure 1: Model of IoT Edge Computing Figure 1: Model of IoT Edge Computing
In the distributed model described in Figure 2, the edge computing In the distributed model described in Figure 2, the edge computing
domain is composed of IoT edge gateways and IoT devices that are also domain is composed of IoT edge gateways and IoT devices that are also
used as computing nodes. Edge computing domains are connected to a used as computing nodes. Edge computing domains are connected to a
remote/cloud network and their respective service operator's system. remote (e.g., cloud) network and their respective service operator's
IoT devices/computing nodes provide logical functions, for example, system. The computing nodes provide logical functions, for example,
as part of distributed machine learning or distributed image as part of distributed machine learning or distributed image
processing applications. The processing capabilities in IoT devices processing applications. The processing capabilities in IoT devices
are limited; they require the support of other nodes. In a are limited; they require the support of other nodes. In a
distributed machine learning application, the training process for AI distributed machine learning application, the training process for AI
services can be executed at IoT edge gateways or cloud networks, and services can be executed at IoT edge gateways or cloud networks, and
the prediction (inference) service is executed in the IoT devices. the prediction (inference) service is executed in the IoT devices.
Similarly, in a distributed image processing application, some image Similarly, in a distributed image processing application, some image
processing functions can be executed at the edge or in the cloud. To processing functions can be executed at the edge or in the cloud. To
limit the amount of data to be uploaded to central cloud functions, limit the amount of data to be uploaded to central cloud functions,
IoT edge devices may pre-process data. IoT edge devices may pre-process data.
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* Support for scaling and enabling fault tolerance or self-healing * Support for scaling and enabling fault tolerance or self-healing
[Jeong]. In addition to using a hierarchical organization to cope [Jeong]. In addition to using a hierarchical organization to cope
with scaling, another available and possibly complementary with scaling, another available and possibly complementary
mechanism is multicast [RFC7390] [CORE-GROUPCOMM-BIS]. Other mechanism is multicast [RFC7390] [CORE-GROUPCOMM-BIS]. Other
approaches include relying on blockchains [Ali]. approaches include relying on blockchains [Ali].
* Integration of edge computing with virtualized Radio Access * Integration of edge computing with virtualized Radio Access
Networks (Fog RAN) [SFC-FOG-RAN] and 5G access networks. Networks (Fog RAN) [SFC-FOG-RAN] and 5G access networks.
* Sharing resources in multi-vendor/operator scenarios to optimize * Sharing resources in multi-vendor and multi-operator scenarios to
criteria such as profit [Anglano], resource usage, latency, and optimize criteria such as profit [Anglano], resource usage,
energy consumption. latency, and energy consumption.
* Capacity planning, placement of infrastructure nodes to minimize * Capacity planning, placement of infrastructure nodes to minimize
delay [Fan], cost, energy, etc. delay [Fan], cost, energy, etc.
* Incentives for participation, for example, in peer-to-peer * Incentives for participation, for example, in peer-to-peer
federation schemes. federation schemes.
* Design of federated AI over IoT edge computing systems [Brecko], * Design of federated AI over IoT edge computing systems [Brecko],
for example, for anomaly detection. for example, for anomaly detection.
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and from a network node. [Cloudlets] describes an example of and from a network node. [Cloudlets] describes an example of
offloading computation from an end device to a network node. In offloading computation from an end device to a network node. In
contrast, oneM2M is an example of a system that allows a cloud-based 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 IoT platform to transfer resources and tasks to a target edge node
[oneM2M-TR0052]. Once transferred, the edge node can directly [oneM2M-TR0052]. Once transferred, the edge node can directly
support IoT devices that it serves with the service offloaded by the support IoT devices that it serves with the service offloaded by the
cloud (e.g., group management, location management, etc.). cloud (e.g., group management, location management, etc.).
QoS can be provided in some systems through the combination of QoS can be provided in some systems through the combination of
network QoS (e.g., traffic engineering or wireless resource network QoS (e.g., traffic engineering or wireless resource
scheduling) and compute/storage resource allocations. For example, scheduling) and compute and storage resource allocations. For
in some systems, a bandwidth manager service can be exposed to enable example, in some systems, a bandwidth manager service can be exposed
allocation of the bandwidth to/from an edge computing application to enable allocation of the bandwidth to or from an edge computing
instance. application instance.
In-network computation can leverage the underlying services provided In-network computation can leverage the underlying services provided
using data generated by IoT devices and access networks. Such using data generated by IoT devices and access networks. Such
services include IoT device location, radio network information, services include IoT device location, radio network information,
bandwidth management, and congestion management (e.g., the congestion bandwidth management, and congestion management (e.g., the congestion
management feature of oneM2M [oneM2M-TR0052]). management feature of oneM2M [oneM2M-TR0052]).
Related challenges include: Related challenges include:
* Computation placement: in a centralized or distributed/peer-to- * Computation placement: in a centralized or distributed (e.g.,
peer manner, selecting an appropriate compute device. The peer-to-peer) manner, selecting an appropriate compute device.
selection is based on available resources, location of data input The selection is based on available resources, location of data
and data sinks, compute node properties, etc. with varying goals. input and data sinks, compute node properties, etc. with varying
These goals include end-to-end latency, privacy, high goals. These goals include end-to-end latency, privacy, high
availability, energy conservation, or network efficiency (for availability, energy conservation, or network efficiency (for
example, using load-balancing techniques to avoid congestion). example, using load-balancing techniques to avoid congestion).
* Onboarding code on a platform or computing device and invoking * Onboarding code on a platform or computing device and invoking
remote code execution, possibly as part of a distributed remote code execution, possibly as part of a distributed
programming model and with respect to similar concerns of latency, programming model and with respect to similar concerns of latency,
privacy, etc. For example, offloading can be included in a privacy, etc. For example, offloading can be included in a
vehicular scenario [Grewe]. These operations should deal with vehicular scenario [Grewe]. These operations should deal with
heterogeneous compute nodes [Schafer] and may also support end heterogeneous compute nodes [Schafer] and may also support end
devices, including IoT devices, as compute nodes [Larrea]. devices, including IoT devices, as compute nodes [Larrea].
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system. system.
Related challenges include: Related challenges include:
* Cache and data placement: using cache positioning and data * Cache and data placement: using cache positioning and data
placement strategies to minimize data retrieval delay [Liu] and placement strategies to minimize data retrieval delay [Liu] and
energy consumption. Caches may be positioned in the access- energy consumption. Caches may be positioned in the access-
network infrastructure or on end devices. network infrastructure or on end devices.
* Maintaining consistency, freshness, reliability, and privacy of * Maintaining consistency, freshness, reliability, and privacy of
stored/cached data in systems that are distributed, constrained, data stored or cached in systems that are distributed,
and dynamic (e.g., due to node mobility, energy-saving regimes, constrained, and dynamic (e.g., due to node mobility, energy-
and disruptions) and which can have additional data governance saving regimes, and disruptions) and which can have additional
constraints on data storage location. For example, [Mortazavi] data governance constraints on data storage location. For
describes leveraging a hierarchical storage organization. example, [Mortazavi] describes leveraging a hierarchical storage
Freshness-related metrics include the age of information [Yates] organization. Freshness-related metrics include the age of
that captures the timeliness of information received from a sender information [Yates] that captures the timeliness of information
(e.g., an IoT device). received from a sender (e.g., an IoT device).
4.4.3. Communication 4.4.3. Communication
An edge cloud may provide a northbound data plane or management plane An edge cloud may provide a northbound data plane or management plane
interface to a remote network, such as a cloud, home, or enterprise interface to a remote network, such as a cloud, home, or enterprise
network. This interface does not exist in stand-alone (local-only) network. This interface does not exist in stand-alone (local-only)
scenarios. To support such an interface when it exists, an edge scenarios. To support such an interface when it exists, an edge
computing component needs to expose an API, deal with authentication computing component needs to expose an API, deal with authentication
and authorization, and support secure communication. and authorization, and support secure communication.
An edge cloud may provide an API or interface to local or mobile An edge cloud may provide an API or interface to local or mobile
users, for example, to provide access to services and applications or users, for example, to provide access to services and applications or
to manage data published by local/mobile devices. to manage data published by local or mobile devices.
Edge computing nodes communicate with IoT devices over a southbound Edge computing nodes communicate with IoT devices over a southbound
interface, typically for data acquisition and IoT device management. interface, typically for data acquisition and IoT device management.
Communication brokering is a typical function of IoT edge computing Communication brokering is a typical function of IoT edge computing
that facilitates communication with IoT devices, enables clients to that facilitates communication with IoT devices, enables clients to
register as recipients for data from devices, forwards traffic to or register as recipients for data from devices, forwards traffic to or
from IoT devices, enables various data discovery and redistribution from IoT devices, enables various data discovery and redistribution
patterns (for example, north-south with clouds and east-west with patterns (for example, north-south with clouds and east-west with
other edge devices [EDGE-DATA-DISCOVERY-OVERVIEW]). Another related other edge devices [EDGE-DATA-DISCOVERY-OVERVIEW]). Another related
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Section 2.4. While describing the components of individual Section 2.4. While describing the components of individual
applications is out of our scope, some of those applications share applications is out of our scope, some of those applications share
similar functions, such as IoT device management and data management, similar functions, such as IoT device management and data management,
as described below. as described below.
4.5.1. IoT Device Management 4.5.1. IoT Device Management
IoT device management includes managing information regarding IoT IoT device management includes managing information regarding IoT
devices, including their sensors and how to communicate with them. devices, including their sensors and how to communicate with them.
Edge computing addresses the scalability challenges of a large number Edge computing addresses the scalability challenges of a large number
of IoT devices by separating the scalability domain into edge/local of IoT devices by separating the scalability domain into local (e.g.,
networks and remote networks. For example, in the context of the edge) networks and remote networks. For example, in the context of
oneM2M standard, a device management functionality (called "software the oneM2M standard, a device management functionality (called
campaign" in oneM2M) enables the installation, deletion, activation, "software campaign" in oneM2M) enables the installation, deletion,
and deactivation of software functions/services on a potentially activation, and deactivation of software functions and services on a
large number of edge nodes [oneM2M-TR0052]. Using a dashboard or potentially large number of edge nodes [oneM2M-TR0052]. Using a
management software, a service provider issues these requests through dashboard or management software, a service provider issues these
an IoT cloud platform supporting the software campaign functionality. requests through an IoT cloud platform supporting the software
campaign functionality.
The challenges listed in Section 4.3.1 may be applicable to IoT The challenges listed in Section 4.3.1 may be applicable to IoT
device management as well. device management as well.
4.5.2. Data Management and Analytics 4.5.2. Data Management and Analytics
Data storage and processing at the edge are major aspects of IoT edge Data storage and processing at the edge are major aspects of IoT edge
computing, directly addressing the high-level IoT challenges listed computing, directly addressing the high-level IoT challenges listed
in Section 3. Data analysis, for example, through AI/ML tasks in Section 3. Data analysis, for example, through AI/ML tasks
performed at the edge, may benefit from specialized hardware support performed at the edge, may benefit from specialized hardware support
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