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首页> 外文期刊>Journal of Sensors >Sensor Duty Cycle for Prolonging Network Lifetime Using Quantum Clone Grey Wolf Optimization Algorithm in Industrial Wireless Sensor Networks
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Sensor Duty Cycle for Prolonging Network Lifetime Using Quantum Clone Grey Wolf Optimization Algorithm in Industrial Wireless Sensor Networks

机译：在工业无线传感器网络中使用量子克隆灰狼优化算法延长网络寿命的传感器占空比

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The application of industrial wireless sensor networks (IWSNs) frequently appears in modern industry, and it is usually to deploy a large quantity of sensor nodes in the monitoring area. This way of deployment improves the robustness of the IWSNs but introduces many redundant nodes, thereby increasing unnecessary overhead. The purpose of this paper is to increase the lifetime of IWSNs without changing the physical facilities and ensuring the coverage of sensors as much as possible. Therefore, we propose a quantum clone grey wolf optimization (QCGWO) algorithm, design a sensor duty cycle model (SDCM) based on real factory conditions, and use the QCGWO to optimize the SDCM. Specifically, QCGWO combines the concept of quantum computing and the clone operation for avoiding the algorithm from falling into a local optimum. Subsequently, we compare the proposed algorithm with the genetic algorithm (GA) and simulated annealing (SA) algorithm. The experimental results suggest that the lifetime of the IWSNs based on QCGWO is longer than that of GA and SA, and the convergence speed of QCGWO is also faster than that of GA and SA. In comparison with the traditional IWSN working mode, our model and algorithm can effectively prolong the lifetime of IWSNs, thus greatly reducing the maintenance cost without replacing sensor nodes in actual industrial production.
机译：工业无线传感器网络（IWSNS）的应用经常出现在现代行业中，通常可以在监控区域部署大量的传感器节点。这种部署方式改善了IWSN的鲁棒性，但引入了许多冗余节点，从而增加了不必要的开销。本文的目的是增加IWSN的寿命而不改变物理设施并尽可能确保传感器的覆盖范围。因此，我们提出了一个量子克隆灰太狼优化（QCGWO）算法，设计基于真实工厂条件传感器工作周期模型（SDCM），并使用QCGWO优化SDCM。具体地，QCGWO结合了量子计算的概念和克隆操作，以避免算法落入局部最佳状态。随后，我们将所提出的算法与遗传算法（GA）和模拟退火（SA）算法进行比较。实验结果表明，基于QCGWO的IWSNs的寿命也比GA和SA的长，QCGWO的收敛速度也比GA和SA的速度更快。与传统的IWSN工作模式相比，我们的模型和算法可以有效地延长IWSN的寿命，从而大大降低了在实际工业生产中的传感器节点的情况下降低了维护成本。

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