IEEE Robotics & Automation Magazine - June 2022 - 11

aerial vehicles (UAVs) and unmanned ground vehicles
(UGVs), that adapt intelligent connectivity support, data
collection, decision making, and blockchain technology to
facilitate autonomous configurability and service provisioning
for IoT networks.
Background on 5G and 6G Networks
The 5G architecture has laid out the foundation for a gradual
movement from base station (BS)-centric to device-centric
networks. Significantly smaller cell deployments using
unused, high-frequency millimeter-wave bands ranging
from 2.4 to 72 GHz are now used to support hundreds of
times more data and capacity. Although 5G is still being
deployed in many parts of the world, it still has great areas of
improvement, especially with the aid of user-centric networks.
IoT devices are no longer just service requesters but
rather can be considered active participants in the 5G architecture.
With their technological advancements, IoT devices
can now participate in processing, storage, and communication.
Such visions may not be realized in the current 5G variant
but will most likely be seen in B5G deployments, such as
6G networks.
Terahertz band communication envisioned for use in 6G
networks will provide full support for real-time traffic
demands for intelligent and excessively high data requirement
applications for highly mobile IoT devices. 6G will go beyond
traditional wireless cellular connectivity for mobile Internet
usage and will support ubiquitous and seamless AI services
for both core and IoT devices on the network. All IoT devices
(e.g., fixed and mobile robots) must be willing to collaborate
and cooperate using full and unrestricted secure connectivity
to achieve the 6G vision, namely: 1) extremely fast downlink
and uplink data rates of up to 10 Tbps, 2)
extremely low latencies in the range of
10-100 microseconds, 3) increased network
capacity of up to 1 Gb/s/m2, and 4)
high energy efficiency for mobile
resource-constrained devices.
IoT devices have greatly advanced
over the years, revolutionizing and
reshaping their role in the technology
industry. The IoT has now progressed to
provide both fixed and mobile devices
with advanced processing, storage, and
communication capabilities as well as
advanced machine learning (ML) and AI
technologies. These intelligent fixed and
moving IoT devices can range from
small ordinary household appliances to
sophisticated moving consumer, commercial,
infrastructure, and industrial
tools [1]. Vehicular and mobile IoT
devices, such as UAVs and UGVs, have
played a significant role in relaying data
and will have a great impact in supporting
B5G networks, such as 6G. Ad hoc
communication with the support of software-defined networking
(SDN) and network function virtualization (NFV)
has enabled moving robotic devices to communicate and
share data without reliance on fixed telecommunication platforms
[2]. Today, IoT devices with predictable mobility characteristics
are serving as roadside units (RSUs) to enable
connectivity to the core network for other moving IoT devices.
Moving platforms have since evolved to include flying ad
hoc networks (FANETs) to support ultrawide data delivery.
Figure 1 depicts an illustrative view of the integration of
intelligent fixed and moving IoT devices within next-generation
networks.
As is the case in many networking and service provisioning
domains, the assurance of adaptive and continuous service
and network availability and diversity cannot be
achieved without reliance on autonomous, self-configurable,
and self-manageable techniques. AI has played and continues
to play a major role in system autonomy. Today, ML
algorithms are providing high accuracy rates in decision
making, thus maximizing reliability. To adapt to scalability
and efficiency, federated learning (FL) was introduced as a
decentralized ML mechanism to adhere to personal data
integrity, security, and privacy. To adhere to the vision of 6G,
autonomy and self-configurability should be adapted to supportive
moving platforms with the aid of AI at both the edge
and IoT devices.
Plug-and-play AI (PnP-AI) was introduced to support the
autonomous detection of the problem type and the features
required [3]. The training data set, labels, evaluations, and
models are chosen and executed without user intervention. It
allows for the selection of ML algorithms based on the problem's
complexity in an effort to minimize runtime and maximize
accuracy. Such a solution, if adapted at the network core
and edge, will allow for optimal configurations to be adapted
based on real-time network and device resource availability.
Furthermore, to enable AI transparency, PnP-AI produces
explainable models for both the problem domain and the
intended consumers. Moving platforms can significantly benefit
from PnP-AI, allowing for the dynamic reconfiguration of
the virtualized resources and communication channels with
the support of SDN and NFV.
Achieving a cooperative behavior between moving IoT
devices for capability and resource sharing purposes cannot
be attained without the assurance of transaction authenticity
and resource integrity. Relying on centralized solutions to
authenticate and ensure secure transaction processing is lacking
in many aspects, such as scalability, management, data
manipulation, nonpersistency of data, single point of failure,
and being prone to cyberattacks. Similarly, a distributed solution
will also have drawbacks in terms of cost, network overhead,
and complexity in terms of data integrity. Blockchain
technology was introduced as an alternative solution to data
management and transaction processing. It is a ledger-based
system that enables decentralized management and maintains
record copies of all transactions performed by the participants.
These copies are securely stored at each participant's
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IEEE Robotics & Automation Magazine - June 2022

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