GAPSO-SVM: An IDSS-based Energy-Aware Clustering Routing Algorithm for IoT Perception Layer

With the emergence of Internet of Things (IoT) having large scale and generating huge amount of data, Intelligent Decision Support Systems (IDSSs) have attracted a lot of attention for provisioning the required Quality of Service. IoT perception layer is responsible for data dissemination of the “Th...

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Vydané v:	Wireless personal communications Ročník 126; číslo 3; s. 2249 - 2268
Hlavní autori:	Norouzi Shad, Mozhdeh, Maadani, Mohsen, Nesari Moghadam, Meisam
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.10.2022
Predmet:	Communications Engineering Computer Communication Networks Engineering Networks Signal,Image and Speech Processing Intelligent Decision Support System (IDSS) Convergence rate Internet of Things (IoT) Support Vector Machine (SVM) Genetic Algorithm-Particle Swarm Optimization (GAPSO) Clustering routing
ISSN:	0929-6212, 1572-834X
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Shrnutí:	With the emergence of Internet of Things (IoT) having large scale and generating huge amount of data, Intelligent Decision Support Systems (IDSSs) have attracted a lot of attention for provisioning the required Quality of Service. IoT perception layer is responsible for data dissemination of the “Things”, and energy efficient clustering protocols play an important role in providing them with long-time battery operation. Clustering routing protocols are among the most efficient methods in large scale IoT networks and using location-based decision support can highly simplify the routing problem. Existing literature either assume that the nodes’ location is known, or rely on the expensive and energy consuming GPS modules which are not practical in most IoT use cases. Developing a low-cost and low-energy localization solution is an ongoing challenge. In this paper, an IDSS based clustering routing protocol, named GAPSO-SVM, is proposed for the IoT perception layer utilizing a Support Vector Machine (SVM) based algorithm to estimate the nodes’ locations, and a hybrid Genetic Algorithm-Particle Swarm Optimization (GAPSO) based mechanism for clustering optimization. Simulation results show that, although the exact location of the nodes is not available, compared with recent similar works the convergence rate and network lifetime is enhanced by up to 80% and 11%, respectively.
ISSN:	0929-6212 1572-834X
DOI:	10.1007/s11277-021-09051-5