Semantic IoT: The Key to Realizing IoT
Value
Hemanta Kumar Palo
Abstract The virtual representation and integration of the internet with the physical
objects, devices or things have been growing exponentially in recent years. This
has motivated the community to design and develop new Internet of Things (IoT)
platforms to cater, capture, access, store, share, and communicate data for information
retrieval and intelligent applications. However, the associated dynamism, resourceconstrain, cost and the nature of the IoT warrants special design obligations for
its effectiveness in the days ahead, hence pose a challenge to the community. The
understanding of web data from machines according to the subject of terminology in
different fields is a complex task. It opens up new challenges to researchers as such
an effort mandates the provision of semantically structured, appropriate information
sources in this information age. The advent of numerous smart devices, operators, and
IoT service providers subject to time-consuming and complex operations, inadequate
research and innovations give rise to design complexity. For efficient functioning and
effective implementation of the domain requires the inclusion of semantics and the
desired interoperability among these factors. This motivates the authors to review
and emphasizes a few of the emerging trends of the semantic technology impacting
the IoT. Particularly, the work focuses on different aspects as information modeling,
ontology design, machine learning, network tools, security policy and processing of
semantic data—and discuss the issues and challenges in the current scenario.
Keywords Internet of things · Semantic IoT · Interoperability · Ontology ·
Semantic web
H. K. Palo (B)
Department of ECE, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan
(Deemed To Be University), Bhubaneswar, Odisha, India
e-mail: hemantapalo@soa.ac.in
© Springer Nature Switzerland AG 2021
R. Pandey et al. (eds.), Semantic IoT: Theory and Applications, Studies in Computational
Intelligence 941, https://doi.org/10.1007/978-3-030-64619-6_4
81
Value
Hemanta Kumar Palo
Abstract The virtual representation and integration of the internet with the physical
objects, devices or things have been growing exponentially in recent years. This
has motivated the community to design and develop new Internet of Things (IoT)
platforms to cater, capture, access, store, share, and communicate data for information
retrieval and intelligent applications. However, the associated dynamism, resourceconstrain, cost and the nature of the IoT warrants special design obligations for
its effectiveness in the days ahead, hence pose a challenge to the community. The
understanding of web data from machines according to the subject of terminology in
different fields is a complex task. It opens up new challenges to researchers as such
an effort mandates the provision of semantically structured, appropriate information
sources in this information age. The advent of numerous smart devices, operators, and
IoT service providers subject to time-consuming and complex operations, inadequate
research and innovations give rise to design complexity. For efficient functioning and
effective implementation of the domain requires the inclusion of semantics and the
desired interoperability among these factors. This motivates the authors to review
and emphasizes a few of the emerging trends of the semantic technology impacting
the IoT. Particularly, the work focuses on different aspects as information modeling,
ontology design, machine learning, network tools, security policy and processing of
semantic data—and discuss the issues and challenges in the current scenario.
Keywords Internet of things · Semantic IoT · Interoperability · Ontology ·
Semantic web
H. K. Palo (B)
Department of ECE, Institute of Technical Education and Research, Siksha ‘O’ Anusandhan
(Deemed To Be University), Bhubaneswar, Odisha, India
e-mail: hemantapalo@soa.ac.in
© Springer Nature Switzerland AG 2021
R. Pandey et al. (eds.), Semantic IoT: Theory and Applications, Studies in Computational
Intelligence 941, https://doi.org/10.1007/978-3-030-64619-6_4
81