Abstract
We consider the sensor deployment problem in the context of distance uncertainty. It is characterized by differentiated arrangement of specific detection probability thresholds at different locations. The problem is formulated as an integer linear programming (ILP) model firstly, aiming at optimizing the number of sensors and their locations. Based on the robust discrete optimization methodology, the uncertain model is transformed into an equivalent ILP problem considering distance uncertainty. The proposed approach can control the tradeoff between optimality and robustness by varying the parameters named protection levels. Uniform and non-uniform event detection probabiliy distributions are considered in the experiment. The results show that, as the distance uncertainty increases, the constraint violation can be avoided in the robust model and the robust solution can provide a significant improvement at the expense of a small loss in optimality when compared to the optimal solution of a deterministic scenario.
Highlights
With the rapid progress of sensor design and communication technique, sensor networks have been quickly evolving in both research and practical domains in the last few decades [1, 2]
Distance usually suffers from many uncertain factors, and few deployment problems have taken into account the distance uncertainty
Experimental results show that the tradeoff between the optimality and robustness can be adjusted only by varying the protection levels in the robust model
Summary
With the rapid progress of sensor design and communication technique, sensor networks have been quickly evolving in both research and practical domains in the last few decades [1, 2]. As one of the fundamental issues in such a network, coverage is important to determine how well an area of interest is monitored and a service is provided [3]. In pre-determined deployment, the locations of nodes are specified and the quality of service can be provided in terms of coverage. It is mainly applied when sensors are expensive or when their operation is significantly affected by their positions. We use the robust discrete optimization methodology to deal with distance uncertainty for the deployment problem in sensor networks. With the distance uncertainty increasing, the robust solution for the problem provides a significant performance improvement at the expense of a small loss in optimality when compared to its primitive algorithm.
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