Coverage Enhancement Strategy in WMSNs Based on a Novel Swarm Intelligence Algorithm: Army Ant Search Optimizer

As one of the most crucial scenarios Internet Things (IoT), wireless multimedia sensor networks (WMSNs) pay more attention to information-intensive data (e.g., audio, video, image) for remote environments. The area coverage reflects perception WMSNs surrounding environment, where a good effect can ensure effective collection. Given harsh and complex physical environment WMSNs, which easily form sensing overlapping regions holes by random deployment. intention our research is deal with optimization problem maximizing rate in WMSNs. By proving NP-hard enhancement inspired predation behavior army ants, this article proposes novel swarm intelligence (SI) technology ant search optimizer (AASO) solve above problem, implemented five operators: prey initialization, recruited prey, attack update build bridge. simulation results demonstrate that shows performance terms exploration exploitation on benchmark suites when compared other representative SI algorithms. More importantly, AASO-based has better merits existing approaches.