A new learning automata-based approach for maximizing network lifetime in wireless sensor networks with adjustable

Abstract

Recently, several algorithms have been proposed to solve the problem of target coverage in wireless sensor networks (WSNs). A conventional assumption is that sensors have a single power level (i.e., fixed sensing range); however, in real applications, sensors might have multiple power levels, which determines different sensing ranges and, consequently, different power consumptions. Accordingly, one of the most important problems in WSNs is to monitor all the targets in a specific area and, at the same time, maximize the network lifetime in a network in which sensors have multiple power levels. To solve the problem, this paper proposes a learning-automata based algorithm equipped with a pruning rule. The proposed algorithm attempts to select a number of sensor nodes with minimum energy consumption to monitor all the targets in the network. To investigate the efficiency of the proposed algorithm, several simulations were conducted, and the obtained results were compared with those of two greedy-based algorithms. The results showed that, compared to the greedy-based algorithms, the proposed learning automata-based algorithm was more successful in prolonging the network lifetime and constructing higher number of cover sets.