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

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Abstract

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.

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Funding

The authors have no relevant financial or non-financial interests to disclose.

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Authors and Affiliations

  1. Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Mozhdeh Norouzi Shad & Meisam Nesari Moghadam

  2. Department of Electrical Engineering, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran

    Mohsen Maadani

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  1. Mozhdeh Norouzi Shad
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  2. Mohsen Maadani
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Contributions

The paper is based on the Mozhdeh Norouzi Shad’s MSc. thesis. Mohsen Maadani (the corresponding author) and Meisam Nesari Moghadam are the thesis supervisor and advisor respectively. All authors contributed to the idea development, algorithm design, analytical method verification, implementation of the research and simulation, analysis of the results, and writing of the manuscript.

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Correspondence to Mohsen Maadani.

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Norouzi Shad, M., Maadani, M. & Nesari Moghadam, M. GAPSO-SVM: An IDSS-based Energy-Aware Clustering Routing Algorithm for IoT Perception Layer. Wireless Pers Commun 126, 2249–2268 (2022). https://doi.org/10.1007/s11277-021-09051-5

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