Industrial wireless sensor networks (IWSNs) are usually fixedly deployed in industrial environments, and various sensor nodes cooperate with each other to complete industrial production tasks. The efficient work of each sensor node of IWSNs will improve the efficiency of the entire network. Automated robots need to perform timely inspection and maintenance of IWSNs in an industrial environment. Excessive inspection distance will increase inspection costs and increase energy consumption. Therefore, shortening the inspection distance can reduce production energy consumption, which is very important for the efficient operation of the entire system. However, the optimal detection path planning of IWSNs is an N-P problem, which can usually only be solved by heuristic mathematical methods. This paper proposes a new adaptive immune ant colony optimization (AIACO) for optimizing automated inspection path planning. Moreover, novel adaptive operator and immune operator are designed to prevent the algorithm from falling into the local optimum and increase the optimization ability. In order to verify the performance of the algorithm, the algorithm is compared with genetic algorithm (GA) and immune clone algorithm (ICA). The simulation results show that the inspection distance of IWSNs using AIACO is lower than that of GA and ICA. In addition, the convergence speed of AIACO is faster than that of GA and ICA. Therefore, the AIACO proposed in this paper can effectively reduce the inspection energy consumption of the entire IWSN system.

中文翻译：

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降低工业无线传感器网络自动检查路径规划能耗的自适应免疫蚁群优化算法

工业无线传感器网络（IWSN）通常固定部署在工业环境中，并且各种传感器节点相互协作以完成工业生产任务。IWSN的每个传感器节点的有效工作将提高整个网络的效率。自动化机器人需要在工业环境中对IWSN进行及时的检查和维护。检查距离过大会增加检查成本并增加能耗。因此，缩短检查距离可以减少生产能耗，这对于整个系统的高效运行非常重要。但是，IWSN的最佳检测路径规划是一个NP问题，通常只能通过启发式数学方法来解决。本文提出了一种新的自适应免疫蚁群优化算法（AIACO），用于优化自动检查路径计划。此外，设计了新颖的自适应算子和免疫算子，以防止算法陷入局部最优并提高优化能力。为了验证算法的性能，将该算法与遗传算法（GA）和免疫克隆算法（ICA）进行了比较。仿真结果表明，采用AIACO的IWSNs的检查距离要比GA和ICA的检查距离要短。另外，AIACO的收敛速度快于GA和ICA的收敛速度。因此，本文提出的AIACO可以有效降低整个IWSN系统的检查能耗。设计了新颖的自适应算子和免疫算子，以防止算法陷入局部最优，提高算法的优化能力。为了验证算法的性能，将该算法与遗传算法（GA）和免疫克隆算法（ICA）进行了比较。仿真结果表明，采用AIACO的IWSNs的检查距离要比GA和ICA的检查距离要短。另外，AIACO的收敛速度快于GA和ICA的收敛速度。因此，本文提出的AIACO可以有效降低整个IWSN系统的检查能耗。设计了新颖的自适应算子和免疫算子，以防止算法陷入局部最优，提高算法的优化能力。为了验证算法的性能，将该算法与遗传算法（GA）和免疫克隆算法（ICA）进行了比较。仿真结果表明，采用AIACO的IWSNs的检查距离要比GA和ICA的检查距离要短。另外，AIACO的收敛速度快于GA和ICA的收敛速度。因此，本文提出的AIACO可以有效降低整个IWSN系统的检查能耗。将该算法与遗传算法（GA）和免疫克隆算法（ICA）进行了比较。仿真结果表明，采用AIACO的IWSNs的检查距离要比GA和ICA的检查距离要短。另外，AIACO的收敛速度快于GA和ICA的收敛速度。因此，本文提出的AIACO可以有效降低整个IWSN系统的检查能耗。将该算法与遗传算法（GA）和免疫克隆算法（ICA）进行了比较。仿真结果表明，采用AIACO的IWSNs的检查距离要比GA和ICA的检查距离要短。另外，AIACO的收敛速度快于GA和ICA的收敛速度。因此，本文提出的AIACO可以有效降低整个IWSN系统的检查能耗。