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Author: search.filters.author.Kanchan, P.Subject: search.filters.subject.PSO

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    Optimizing Network Lifetime and Energy Consumption in Homogeneous Clustered WSNs Using Quantum PSO Algorithm
    (Springer Science and Business Media Deutschland GmbH, 2021) Kanchan, P.; Shetty D, D.S.
    A Wireless Sensor Network (WSN) is a group of sensors which communicate with each other and perform some specific task. Clustering is used to conserve energy in a WSN. In this work, the aim is to minimize the energy consumption and maximize the network lifetime of a homogeneous WSN using PSO (Particle Swarm Optimization) based Clustering algorithm in conjunction with quantum computing. In quantum computing, a bit is known as a qubit and it can exist in the following states: a ‘0’, a ‘1’ or a superposition of ‘0’ and ‘1’. In this chapter, the Quantum Computing based PSO clustering algorithm for Optimizing Energy consumption and Network lifetime (QCPOEN) algorithm for homogeneous wireless sensor networks is proposed. The proposed algorithm is compared with the PSO-ECHS algorithm and the LEACH algorithm. The superiority of the algorithm can be verified from the results. © 2021, Springer Nature Singapore Pte Ltd.
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    A quantum inspired PSO algorithm for energy efficient clustering in wireless sensor networks
    (Cogent OA info@CogentOA.com, 2018) Kanchan, P.; Shetty D, S.D.
    Clustering is done in wireless sensor networks (WSN) to conserve the energy of sensor nodes in the network. Decreasing the energy consumption of nodes prolongs the lifetime of the WSN. A quantum bit can exist in “0” state, “1” state or a linear superposition of “0” and “1” states, unlike the binary bit which can exist in only “0” state or “1” state. In this paper, we propose a Quantum inspired PSO (particle swarm optimization) called Quantum-inspired PSO for Energy Efficient Clustering (QPSOEEC). The algorithm is tested by giving different values to the number of sensor nodes and cluster heads, varying the base station position, etc. Then, our results are compared to existing algorithms that demonstrate the superiority of our algorithm. © 2018, © 2018 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.