The Directional Sensor Networks (DSNs) have recently drawn considerable attention with respect to their extensive applications in various situations. In this regard, covering a set of targets in a specific region while maximizing network lifetime is considered as a major problem related to the DSN, which is resulted from limitation in sensing angle and battery power of directional sensors. The problem gets more challenging when the targets have different coverage quality requirements. In the present study, this problem is referred to as Priority-based Target Coverage (PTC) that has been proved to be an NP-complete problem. In this regard, a genetic-based algorithm along with a repair operator is developed, which is able to select a proper subset of directional sensors for providing the coverage quality requirements for all targets. In order to evaluate the performance of the proposed algorithm, several experiments were performed and the results were compared to those of another algorithms already introduced to literature. تفاصيل المقالة

A mobile ad-hoc network (MANET), a set of wirelessly connected sensor nodes, is a dynamic system that executes hop-by-hop routing independently with no external help of any infrastructure. Proper selection of cluster heads can increase the life time of the Ad-hoc network by decreasing the energy consumption. Although different methods have been successfully proposed by researchers to tackle this problem, nearly all of them have the deficiency of providing a single combination of head clusters as the solution. On the contrary, in our proposed method, using a Multi-Objective Genetic Algorithm, a set of near optimum solutions is provided. In the proposed method, energy consumption, number of cluster heads, coverage and degree difference are considered as objectives. Numerical results reveal that the proposed algorithm can find better solutions when compared to conventional methods in this area namely, weighted clustering algorithm (WCA), comprehensive learning particle swarm optimization (CLPSO) and multi objective particle swarm optimization(MOPSO). تفاصيل المقالة