Environmental wireless sensor networks (EWSNs) are essential in environmental monitoring and are widely used in gas monitoring, soil monitoring, natural disaster early warning and other fields. EWSNs are limited by the sensor battery capacity and data collection range, and the usual deployment method is to deploy many sensor nodes in the monitoring zone. This deployment method improves the robustness of EWSNs, but introduces many redundant nodes, resulting in a problem of duty cycle design, which can be effectively solved by duty cycle optimization. However, the duty cycle optimization in EWSNs is an NP-Hard problem, and the complexity of the problem increases exponentially with the number of sensor nodes. In this way, non-heuristic algorithms often fail to obtain a deployment solution that meets the requirements in reasonable time. Therefore, this paper proposes a novel heuristic algorithm, the Quantum Evolutionary Golden Jackal Optimization Algorithm (QEGJOA), to solve the duty cycle optimization problem. Specifically, QEGJOA can effectively prolong the lifetime of EWSNs by duty cycle optimization and can quickly get a deployment solution in the face of multi-sensor nodes. New quantum exploration and exploitation operators are designed, which greatly improves the global search ability of the algorithm and enables the algorithm to effectively solve the problem of excessive complexity in duty cycle optimization. In addition, this paper designs a new sensor duty cycle model, which has the advantages of high accuracy and low complexity. The simulation shows that the QEGJOA proposed in this paper improves by 18.69$ % $, 20.15$ % $ and 26.55$ % $ compared to the Golden Jackal Optimization (GJO), Whale Optimization Algorithm (WOA) and the Simulated Annealing Algorithm (SA).
Citation: Zhonghua Lu, Min Tian, Jie Zhou, Xiang Liu. Enhancing sensor duty cycle in environmental wireless sensor networks using Quantum Evolutionary Golden Jackal Optimization Algorithm[J]. Mathematical Biosciences and Engineering, 2023, 20(7): 12298-12319. doi: 10.3934/mbe.2023547
Environmental wireless sensor networks (EWSNs) are essential in environmental monitoring and are widely used in gas monitoring, soil monitoring, natural disaster early warning and other fields. EWSNs are limited by the sensor battery capacity and data collection range, and the usual deployment method is to deploy many sensor nodes in the monitoring zone. This deployment method improves the robustness of EWSNs, but introduces many redundant nodes, resulting in a problem of duty cycle design, which can be effectively solved by duty cycle optimization. However, the duty cycle optimization in EWSNs is an NP-Hard problem, and the complexity of the problem increases exponentially with the number of sensor nodes. In this way, non-heuristic algorithms often fail to obtain a deployment solution that meets the requirements in reasonable time. Therefore, this paper proposes a novel heuristic algorithm, the Quantum Evolutionary Golden Jackal Optimization Algorithm (QEGJOA), to solve the duty cycle optimization problem. Specifically, QEGJOA can effectively prolong the lifetime of EWSNs by duty cycle optimization and can quickly get a deployment solution in the face of multi-sensor nodes. New quantum exploration and exploitation operators are designed, which greatly improves the global search ability of the algorithm and enables the algorithm to effectively solve the problem of excessive complexity in duty cycle optimization. In addition, this paper designs a new sensor duty cycle model, which has the advantages of high accuracy and low complexity. The simulation shows that the QEGJOA proposed in this paper improves by 18.69$ % $, 20.15$ % $ and 26.55$ % $ compared to the Golden Jackal Optimization (GJO), Whale Optimization Algorithm (WOA) and the Simulated Annealing Algorithm (SA).
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Zhonghua Lu, Min Tian, Jie Zhou, Xiang Liu. Enhancing sensor duty cycle in environmental wireless sensor networks using Quantum Evolutionary Golden Jackal Optimization Algorithm[J]. Mathematical Biosciences and Engineering, 2023, 20(7): 12298-12319. doi: 10.3934/mbe.2023547
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