Deployment optimization in wireless sensor networks using advanced artificial bee colony algorithm

With the widespread implementation of artificial intelligence techniques and self-driving systems in smart cars, providing excellent coverage of wireless sensor networks is critical for stable and effective tasks. Coverage control is an essential task for the design of wireless sensor networks. However, considering the influence of network resources and coverage features, several normal optimization methods are hard to carry out, yet heuristic iterative algorithms could generate an estimated ideal feasible solution for this issue. We present an artificial bee colony algorithm based on random dual strategies, called RDABC. Specifically, RDABC modifies the optimization direction through alternating between dual search techniques with the goal to find further excellent feasible solution. At the same time, through incorporating cross-mutation strategy to improve variety, and increase the algorithm’s optimization efficiency. According to simulation experiments, RDABC outperforms four well-known algorithms in terms of coverage optimization. As a whole, RDABC optimizes the location and deployment of wireless sensors, enhances the overall performance and stability of intelligent transportation systems, and simplifies vehicle monitoring and traffic sign tasks.