A simulated annealing algorithm to support the sensor placement for target location

In this paper, we develop an algorithm to cope with the sensor placement problem for target location under constraints of cost limitation and complete coverage. We adopt the grid-based placement scenario that deploys exact one sensor in one grid point at most. A target in a grid point can be positioned by a set of sensors whose transmission radius covers the grid point. The optimal sensor placement for target location is to find a sensor deployment such that targets can be positioned in any grid point of the sensor fields. However, due to the cost limitation, the optimal sensor deployment cannot be achieved frequently. Consequently, the positioning accuracy is the major issue of the problem. The distance error is one of the most natural criteria to measure the positioning accuracy. In this paper, the distance error of two indistinguishable grid points is defined as the Euclidean distance between them. We formulate the sensor placement problem as a combinatorial optimization problem for minimizing the maximum distance error in the sensor field under constraints. The sensor placement problem is NP-complete for arbitrary sensor fields. We present an efficient algorithm that is based on the simulated annealing approach to address the problem. We first compare our algorithm with the brute force approach in the case of smaller sensor fields. The evidence indicates that our algorithm can find the optimal sensor placement under the minimum cost limitation. Moreover, the simulation results also show that the proposed algorithm is very superior in terms of the positioning accuracy even in the case of larger sensor fields.