Collaborative Neural Network Algorithm for Event-Driven Deployment in Wireless Sensor and Robot Networks

• Published in: Sensors (Basel, Switzerland) Vol. 20; no. 10; p. 2779
• Main Authors: Zhuang, Yaoming, Wu, Chengdong, Wu, Hao, Zhang, Zuyuan, Gao, Yuan, Li, Li
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 13.05.2020; MDPI
• Subjects: Algorithms; Collaboration; collaborative neural network; event-driven deployment; maximum entropy function; Methods; multiple constraints; Neural networks; Robots; Sensors; Wireless networks; wireless sensor and robot networks; wireless sensor and robot networks; maximum entropy function; collaborative neural network; event-driven deployment; multiple constraints
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20102779

Wireless sensor and robot networks (WSRNs) often work in complex and dangerous environments that are subject to many constraints. For obtaining a better monitoring performance, it is necessary to deploy different types of sensors for various complex environments and constraints. The traditional event-driven deployment algorithm is only applicable to a single type of monitoring scenario, so cannot effectively adapt to different types of monitoring scenarios at the same time. In this paper, a multi-constrained event-driven deployment model is proposed based on the maximum entropy function, which transforms the complex event-driven deployment problem into two continuously differentiable single-objective sub-problems. Then, a collaborative neural network (CONN) event-driven deployment algorithm is proposed based on neural network methods. The CONN event-driven deployment algorithm effectively solves the problem that it is difficult to obtain a large amount of sensor data and environmental information in a complex and dangerous monitoring environment. Unlike traditional deployment methods, the CONN algorithm can adaptively provide an optimal deployment solution for a variety of complex monitoring environments. This greatly reduces the time and cost involved in adapting to different monitoring environments. Finally, a large number of experiments verify the performance of the CONN algorithm, which can be adapted to a variety of complex application scenarios.