Browsing by Author "Kanchan, P."

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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.
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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.
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    Quantum Optimizer Using MOEAD for WSN’s
    (Springer Science and Business Media Deutschland GmbH, 2024) Kanchan, P.; Shetty D, P.; Attea, B.A.
    Optimization of Wireless sensor networks is done with respect to several parameters like energy efficiency, coverage, etc. A WSN is an inter-related collection of sensors. A WSN can be Homogeneous or Heterogeneous. All nodes in homogeneous WSNs have comparable characteristics like energy of the nodes or radius of sensing, etc. In Heterogeneous WSNs, some of these properties differ. MultiObjective Opimization (MOO) simultaneously optimizes more than one objective. The Multi Objective Evolutionary Algorithm with Decomposition (MOEAD) splits/decomposes a problem into subproblems and all these subproblems are simultaneously optimized. In classical computing, a bit is usually represented by 0 or 1. In Quantum Computing, a bit is 0, 1 or a superposition of 0 and 1. In our research, we use MOEAD with quantum computing to optimize the multiple goals of network lifetime along with coverage for WSNs. These WSNs can be homogeneous or heterogeneous. We contrast our methodology with some of the standard methodologies. Simulations show the upsides of our methodology over different techniques referenced here. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
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    Quantum PSO algorithm for clustering in wireless sensor networks to improve network lifetime
    (2019) Kanchan, P.; Pushparaj, Shetty, D.
    Clustering is done in wireless sensor networks (WSN) to conserve the energy of sensor nodes in the network. The network lifetime of WSN can be defined as the duration for which the network remains operational. It is a critical design issue in WSN�s since once a node is deployed, it may not be feasible to replace or recharge the sensor nodes. In this paper, we proposed a quantum PSO algorithm for improving network lifetime called quantum PSO clustering algorithm to improve network lifetime(QPCINL). The QPCINL uses quantum bits. 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. We define a factor called network lifetime factor(NLF) which allows us to compare various algorithms. We test our algorithm by giving different values to the number of sensor nodes and cluster heads, varying the base station position, etc. Then, we compare our results to existing algorithms and demonstrate the superiority of our algorithm. � Springer Nature Singapore Pte Ltd. 2019.
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    Quantum PSO algorithm for clustering in wireless sensor networks to improve network lifetime
    (Springer Verlag service@springer.de, 2019) Kanchan, P.; Shetty D, D.
    Clustering is done in wireless sensor networks (WSN) to conserve the energy of sensor nodes in the network. The network lifetime of WSN can be defined as the duration for which the network remains operational. It is a critical design issue in WSN’s since once a node is deployed, it may not be feasible to replace or recharge the sensor nodes. In this paper, we proposed a quantum PSO algorithm for improving network lifetime called quantum PSO clustering algorithm to improve network lifetime(QPCINL). The QPCINL uses quantum bits. 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. We define a factor called network lifetime factor(NLF) which allows us to compare various algorithms. We test our algorithm by giving different values to the number of sensor nodes and cluster heads, varying the base station position, etc. Then, we compare our results to existing algorithms and demonstrate the superiority of our algorithm. © Springer Nature Singapore Pte Ltd. 2019.