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Particle swarm optimization based sleep scheduling and clustering protocol in wireless sensor network

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Abstract

Energy management is considered as a challenging task for applications related to the wireless sensor network. The cluster-based networks are among the most effective solutions for the energy-related issue of the wireless sensor network. In this paper, a clustering protocol based on the sleep scheduling approach named Cluster based Sleep Scheduling Protocol (CSSP) is proposed for the lifetime enhancement of the network. The proposed scheme employs a particle swarm optimization based sleep scheduling technique that uses the remaining energy of nodes, distance to neighbors, and coverage neighbor parameter to choose the active and sleep nodes in the network to minimize the energy expenditure. The proposed scheme uses a probability based cluster head selection process which considers the initial energy and remaining energy of sensor nodes to choose the most energy efficient node for the cluster head job and form clusters with the selected cluster heads. The performance of the proposed scheme is compared with the various existing protocol for the different values of heterogeneity to show the effectiveness of the proposed scheme. The proposed protocol has improved the lifetime of the network by 257%, 172%, 119%, 128%, and 64% as compared to the existing CACP, EDHRP, ECDC, E2DR-MCS, and EBCS protocols. The stability period in the proposed scheme has enhanced by 413%, 240%, 145%, 125%, and 95% as compared to the existing CACP, EDHRP, ECDC, E2DR-MCS, and EBCS protocols.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology, Hamirpur, H.P., India

    Piyush Rawat & Siddhartha Chauhan

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  1. Piyush Rawat
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  2. Siddhartha Chauhan
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Correspondence to Piyush Rawat.

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Rawat, P., Chauhan, S. Particle swarm optimization based sleep scheduling and clustering protocol in wireless sensor network. Peer-to-Peer Netw. Appl. 15, 1417–1436 (2022). https://doi.org/10.1007/s12083-022-01307-6

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