Dynamic energy-efficient surveillance routing in uncertain group-based industrial wireless sensor networks

Smart factories in harsh large-scale environments are achieved by installation of group-based industrial wireless sensor networks (GIWSNs), in which a group of sensors are deployed on each machine target, for security and saving deployment time. In GIWSNs, enormous data for production (e.g., 3D digital twin and automated optical inspection images) and surveillance is transmitted frequently among multiple machines, and consumes huge energy. Furthermore, a real-world factory whose radio environment is interfered by mobile devices is dynamic and uncertain. Therefore, this paper investigates reliable energy-efficient routing for surveillance in dynamic uncertain GIWSNs, and further proposes a fuzzy improved global-best harmony search approach, where improved operators are integrated to efficiently explore the search space locally and globally; and a fuzzy evaluation scheme is employed to address uncertain factors. Through simulation under various parameter settings, this approach can find reliable routing in dynamic environments, and shows high performance as compared with other approaches. In addition, this approach can always find the reliable surveillance routing under various data amounts, while activating fewer crucial sensors to effectively reduce energy consumption.