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