Efficient localization for mobile sensor networks based on constraint rules optimized Monte Carlo method

Wireless sensor networks (WSNs) have been widely used in many fields. The issue of node localization is a fundamental problem in WSNs. And it is the basis and prerequisite for many applications. Due to the mobility of the sensor nodes, it is more challenging to locate nodes in the mobile WSNs than in the static ones. The existing localization schemes for mobile WSNs are almost based on the Sequential Monte Carlo (SMC) localization method. The SMC-based schemes may suffer from low sampling efficiency resulted from a large sampling area, which makes them difficult to achieve high localization accuracy and efficiency. Some schemes try to reduce the sampling area by further employing position relationship with neighbor common nodes, while we have found that the movements of the neighbor beacon nodes have not been fully exploited. Addressing this issue, in this paper, some new constraint rules are developed and some existing constraint rules are optimized with the consideration of the moving distance and direction of neighbor beacons. A series of distance constraint conditions are further created, by which, the scope/size of the sampling area can be further reduced, and the samples can be filtered more accurately. The performance of our algorithm is evaluated by extensive simulation experiments. The simulation results show that the localization error and computation cost of our proposed algorithm are lower than those of the existing ones, even when the speed of the sensor nodes is relative high.