A new hybrid genetic algorithm with tabu search forsolving the temporal coverage problem using rotatingdirectional sensors.

In recent years, technological developments of directional sensors and wireless sensor networks have resulted in the emergence of an innovative class of wireless sensor networks termed ”directional sensor networks”. The nature of this type of sensors has led to substantial problems associated with these networks. One of the most important problems in this context is coverage problem that arises due to the nature of the directional sensor nodes. The coverage can be measured in two ways: positional or temporal. In temporal coverage, the directional sensors rotate periodically round themselves in a repetitive process. Thus, in each time slot, those targets that are positioned within the sensor nodes radius receive their desired coverage. In this model, if a target is left uncovered, it is said that the target has remained in darkness. The main task defined for the temporal coverage model is the minimization of the total dark time for all the targets in the network. To solve the temporal coverage problem, this paper presents a hybridized model comprising Genetic Algorithms (GAs) and Tabu Search (TS). Such hybridization was performed to get benefits of solution space explorations done by GA together with the solution exploitation that is realized by TS. In the proposed model, GA acts as the main algorithm and TS is added to enhance the individuals of the population. The proposed hybrid model is mainly aimed to identify the best initial directions of the existing sensors in such a way that the total dark time could be minimized. The results obtained from the experiments conducted for evaluation of the algorithm performance confirmed the superiority of the proposed hybrid algorithm over the other algorithms (whose results were compared to those of the proposed one) in terms of minimizing the total dark time of the targets.