Scheduling algorithms for extending directional sensor network lifetime

Recently, directional sensor networks that are composed of a large number of directional sensors have attracted a great deal of attention. The main issues associated with the directional sensors are limited battery power and restricted sensing angle. Therefore, monitoring all the targets in a given area and, at the same time, maximizing the network lifetime has remained a challenge. As sensors are often densely deployed, a promising approach to conserve the energy of directional sensors is developing efficient scheduling algorithms. These algorithms partition the sensor directions into multiple cover sets each of which is able to monitor all the targets. The problem of constructing the maximum number of cover sets has been modeled as the multiple directional cover sets (MDCS), which has been proved to be an NP-complete problem. In this study, we design two new scheduling algorithms, a greedy-based algorithm and a learning automata (LA)-based algorithm, in order to solve the MDCS problem. In order to evaluate the performance of the proposed algorithms, several experiments were conducted. The obtained results demonstrated the efficiency of both algorithms in terms of extending the network lifetime. Simulation results also revealed that the LA-based algorithm was more successful compared to the greedy-based one in terms of prolonging network lifetime