Abstract
The mobile wireless networks (MWNs) are flexible and scalable. They have been widely used in largescale monitoring, search and rescue tasks, pursuit and escape operations and other scenarios. However, due to the high cost of wireless sensors in MWNs, the cost of increasing wireless sensor nodes is relatively high, so it is generally monitored by the limited nodes moving in the monitoring range. Having this in mind, we design an original algorithm to improve the monitoring efficiency of mobile wireless nodes, named Distributed Guidance Anti-flocking Algorithm (DGAA). Because most of the dynamic coverage algorithms are difficult to adapt to the complex operating environment. Furthermore the high overlapping rate of the wireless nodes is easy to cause resource waste. In light of these factors, the DGAA has a low overlapping rate of mobile wireless nodes in the complex environment with obstacles. DGAA mimics the predatory behavior of solitary animals. These animals can go out on their own to catch prey, and they do not leave their homes. At the same time, they can prevent the invasion of foreign individuals and ensure their maximum profits. The experiment is simulated on a map of 50m*50m, and the results are compared with those produced by competitive algorithms. Experimental results show that DGAA exhibits better performance than other algorithms. (C) 2021 Elsevier B.V. All rights reserved.