Browsing by Author "Shetty D, D.S."

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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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    Quantum Inspired Multiobjective Optimization in Clustered Homogeneous Wireless Sensor Networks for Improving Network Lifetime and Coverage
    (Springer Science and Business Media Deutschland GmbH, 2021) Kanchan, P.; Shetty D, D.S.; Attea, B.A.
    The optimization technique in which many objectives are simultaneously optimized is called multiobjective optimization. A wireless sensor network (WSN) consists of many sensors forming a network. These sensor nodes mainly run on battery which deteriorates with time. Our aim is to optimize coverage and lifetime of the network. One of the most effective methods for minimizing energy and increasing lifetime of nodes is clustering. In this paper, we integrate the two objectives of improving network lifetime and increasing coverage. We use quantum bits or qubits in our representation instead of bits. A qubit can be in 0 state, 1 state or a super position of these two states at the same time. This is what makes quantum computing-based algorithms more powerful as we can have more diversity. The proposed algorithm, quantum inspired multiobjective evolutionary algorithm based on decomposition (QMOEAD) is compared with LEACH, SEP, NSGAII and MOEA/D on the basis of coverage and network lifetime. The results show that QMOEAD outperforms the other algorithms mentioned above. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.