Optimisation of target coverage in wireless sensor network using novel learning automata approach

Wireless sensor networks (WSNs) technology is employed in multiple areas like battleground surveillance, home security etc. In WSN, most algorithms are based on the maximum cover set for energy-efficient target coverage (TC). But it generates the NP-complete problem of constructing maximum cover sets (CS). These formations consume more energy because each node participates in the building of sets. To reduce the average energy consumption of networks, we propose learning automata based on a scheduling algorithm called self-adaptive minimum energy consumption algorithm (SAMECA). The SAMECA assists each sensor to choose the proper state (active or sleep) at any given time. The purpose of SAMECA is to increase the network lifetime by maximising the sleep state presence of nodes. Besides, it ensures that fewer sensors are required to cover all the targets. The results indicate that the SAMECA is a good option to analyse all the targets by consuming less energy power.