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.

中文翻译：

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工业量子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

工业无线传感器网络（IWSN）的应用在现代工业中经常出现，通常是在监视区域中部署大量传感器节点。这种部署方式提高了IWSN的健壮性，但引入了许多冗余节点，从而增加了不必要的开销。本文的目的是延长IWSN的寿命，而无需更改物理设施并尽可能确保传感器的覆盖范围。因此，我们提出了一种量子克隆灰狼优化（QCGWO）算法，根据实际工厂条件设计了传感器占空比模型（SDCM），并使用QCGWO优化了SDCM。具体而言，QCGWO结合了量子计算的概念和克隆操作，以避免算法陷入局部最优状态。随后，我们将提出的算法与遗传算法（GA）和模拟退火（SA）算法进行了比较。实验结果表明，基于QCGWO的IWSN的寿命比GA和SA更长，并且QCGWO的收敛速度也比GA和SA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的使用寿命，从而在不替换实际工业生产中传感器节点的情况下，大大降低了维护成本。