Conference Papers

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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.