A genetic algorithm for target coverage problem in directional sensor networks
Abstract:
Unlike traditional sensors with full-angle sensing range, directional sensors can only monitor for limited sensing ranges and angles due to technical limitations or cost considerations. The directional sensor network is composed of a number of directional sensor nodes. Therefore, it is possible that when directional sensors are randomly deployed in the field, some interested targets cannot be sensed even if these targets are located within the sensing range of the directional sensors. we study the target coverage problem in directional sensor networks with rotatable sensors. A rotatable sensor in a directional sensor network is a sensor whose sensing orientaion can be rotated to any particular direction. The target coverage problem is to achieve the higher coverage rate by rotating the sensor orentation while minimizing the active sensors after deployment. In this paper, we first present a greedy algorithm to solve the target coverage problem by scheduling each sensor to appropriate direction. This greedy scheme is used as a baseline for the performance comparison. We then propose a genetic algorithm-based target coverage scheme that can find the better coverage rate while minimizing the active sensors to prolong the network lifetime by evolutionary global search technique. Simulation results showed that the genetic algorithm-based scheme outperforms than the greedy algorithm in terms of maxmizing the coverage rate and minimizing the active sensors.
Keywords:
Directional sensor networks, target coverage problem, genetic algorithms
pages:
333-336
Year:
2019
Published in:
2nd Eurasian Conference on Educational Innovation 2019
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