Optimum Temporal Coverage with Rotating Directional Sensors
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Advances in directional sensors technology and impressive development of wireless sensor networks, created a new class of wireless sensor networks called directional sensor networks. According to the nature of directional nodes, the coverage problem in directional sensor networks is substantial. The coverage measurement in the directional sensor network can be positional or temporal. In temporal coverage, directional sensors periodically repeat rotating around themselves. Therefore in each period of time, targets that exist in the radius of sensor nodes are covered in the interval of time. In this model, when a target has not been covered by sensors in any interval of time, it is said that the target has remained in dark. Temporal coverage model is defined by minimizing dark time for all targets. This paper presents two solutions for solving the temporal coverage problem. The first solution formulates the problem of temporal coverage as an integer linear programming (ILP) optimization problem. By using this method, the optimal solution can be achieved for temporal coverage problem. Due to NP-Hardness of temporal coverage problem and since ILP is a centralized method, we develop a heuristics solution, namely distributed initial orientation algorithm (DIOA). This algorithm uses local information and tries to be near-optimal. Simulation results show that in ILP, we have up to 14.19% reduction on average sum of dark time and in DIOA we have up to 6.74%. Additionally, the number of perfect temporal coverage (0-dark time) in ILP method improves up to 69.29% and in DIOA we have up to 25.23% improvements compared to related algorithms.
KeywordsTemporal coverage Directional sensor networks Rotating directional sensors Dark time Integer linear programing
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