IEEE Systems, Man and Cybernetics Magazine - October 2022 - 34

Special Section Editorial
by Gaurav Dhiman , Atulya Nagar ,
and Seifedine Kadry
Explainable Artificial
Intelligence for
the Social Internet
of Things
Analysis and Modeling Using Collaborative Technologies
N
ext -generat ion explainable
artificial intelligence
(XAI) is rapidly combining
machine intelligence and human
intelligence to generate social intelligence,
which supports the rising
interactions between social spaces.
With transformative advances in science
and society, a new challenge
emerges when attempting to understand
the basic mechanisms and
principles of the evolving multidimensional
reality. Next-generation
wireless communication, such as the
Internet of Things (IoT), will play
a vital role in supporting complex
wireless interconnectivity [1]. Social
IoT (SIoT) systems promise to enable
ubiquitous connectivity among users
by merging human social behaviors
with physical IoT devices.
This vast variety and variability,
combined with the pervasive expansion
of the IoT, presents new difficulties
for academia, business, and standards
organizations to handle. Adding
more semantics to smart objects, on
the other hand, can enable them to
undertake previously inconceivable
tasks and activities [2]. The social interaction
of SIoT objects contributes
a large volume of data to be processed
and used by various applications in the
areas of smart cities, smart homes,
Digital Object Identifier 10.1109/MSMC.2022.3198845
Date of current version: 20 October 2022
34
smart grids, and smart factories to
satisfy human needs, interests, and
objectives, such as social vehicular
ad hoc networks, social connected
health, SIoT-based recommendation
services, traffic services, policing, energy
management, and so on.
Such a dynamic landscape, with
billions of social communities of objects
and devices, necessitates the development
of new models, theories,
and approaches to
interaction and collaboration,
which
could be established
by drawing on the
experience that people
have gained in
the social networking
domain over the
past few years [3].
Given these opportunities,
this special
section invited novel
research and practical
papers that improve
SIoT systems
using XAI methods in terms of architecture,
technology, and applications. This
special section on " Explainable Artificial
Intelligence for the Social Internet
of Things: Analysis and Modeling Using
Collaborative Technologies " of IEEE
Systems, Man, & Cybernetics Magazine
attracted three outstanding research
articles discussing new research results
covering a wide range of the SIoT.
IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE October 2022
Sugandh et al. provide a thorough
analysis of the use of merging blockchain
and the IoT in the creation of
smart applications for smart agriculture.
The study is broken into three
parts. The first section contains a thorough
survey and bibliometric study of
blockchain in agriculture. Then, the
authors present the issues that Indian
farmers confront as well as the study
aims and research questions. Finally,
the study develops a novel blockchain
model that has the potential to
be used as a substantial solution to
important issues in IoT-based smart
agricultural systems. The study also
assesses and extensively addresses
the primary roles and strengths of the
most prevalent blockchain platforms
used in managing various subsectors
in smart agriculture, such as crops,
livestock grazing, and the food supply
chain, among others.
Social IoT systems
promise to enable
ubiquitous
connectivity
among users by
merging human
social behaviors
with physical
IoT devices.
Alotaibi and Yadav
solve concerns by
developing a social
network information
transmission model
that incorporates
XAI and is compatible
with a normal communication
connection.
They propose a
method of information
transfer known
as local greedy, which
assists in the preservation
of user privacy.
Its influence serves as
a buffer between the competing goals
of privacy protection and information
dissemination. To address the enumeration
problem of seed set selection, an
incremental strategy for creating seed
sets with minimal time overhead is
described; a local influence subgraph
method for computing nodes is also proposed
to efficiently evaluate the influence
of seed set propagation. The group
https://orcid.org/0000-0002-6343-5197 https://orcid.org/0000-0001-5549-6435 https://orcid.org/0000-0002-1939-4842
IEEE Systems, Man and Cybernetics Magazine - October 2022

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