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Energy-efficient cluster head selection algorithm for IoT using modified glow-worm swarm optimization

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Abstract

Internet of Things (IoT) is a new age technology that connects virtually every IP-enabled things in the world. Because of the constrained nature of the resources, energy-efficient clustering plays a very important role in successful implementation of these networks and also in increasing its lifetime. The proposed methodology provides an adaptive cluster head selection algorithm based on glow-worm swarm optimization algorithm. Most of the existing approaches groups nodes into clusters containing a fixed number of nodes. This is not applicable in certain cases, such as, when many of the nodes are dead. In the proposed approach, the number of nodes in each cluster is not fixed, and it automatically changes according to the number of alive nodes in the network, which increases the lifetime of the network. The proposed approach also ensures the minimum overlapping of the clusters by selecting geographically distributed cluster heads which increases the energy efficiency of the network by minimising the communication overhead. Repeated selection of cluster head also helps in load balancing in the network. We have implemented the proposed algorithm in OMNET++ simulator and demonstrated how it behaves with different densities of sensor deployment and communication ranges. The proposed algorithm is found to work notably well as compared to state-of-the-art IoT clustering protocol even when more than 20% nodes run out of energy. As time progresses, the improvement is even more apt.

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Authors and Affiliations

  1. Kendriya Vidyalaya AFS Bagdogra, Jadavpur University, Kolkata, India

    Mohana Bakshi

  2. Jadavpur University, Kolkata, India

    Chandreyee Chowdhury & Ujjwal Maulik

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  1. Mohana Bakshi
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  2. Chandreyee Chowdhury
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  3. Ujjwal Maulik
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Correspondence to Chandreyee Chowdhury.

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Bakshi, M., Chowdhury, C. & Maulik, U. Energy-efficient cluster head selection algorithm for IoT using modified glow-worm swarm optimization. J Supercomput 77, 6457–6475 (2021). https://doi.org/10.1007/s11227-020-03536-z

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