A learning automata-based algorithm to solve imbalanced k -coverage in visual sensor networks
Ahmad Javan Bakht, Homayun Motameni, Hosein Mohamadi · Journal of Intelligent & Fuzzy Systems · 2020
One of the most important problems in directional sensor networks is k -coverage in which the orientation of a minimum number of directional sensors is determined in such a way that each target can be monitored at least k times. This problem has been already considered in two different environments: over provisioned where the number of sensors is enough to cover all targets, and under provisioned where there are not enough sensors to do the coverage task (known as imbalanced k -coverage problem). Due to the significance of solving the imbalanced k -coverage problem, this paper proposes a learning automata (LA)-based algorithm capable of selecting a minimum number of sensors in a way to provide k -coverage for all targets in a balanced way. To evaluate the efficiency of the proposed algorithm performance, several experiments were conducted and the obtained results were compared to those of two greedy-based algorithms. The results confirmed the efficiency of the proposed algorithm in terms of solving the problem.