An Optimization Algorithm Based on the Monte Carlo Node Localization of Mobile Sensor Network
Special Issues Editor (Nottingham Tent University, United Kingdom (Great Britain))
In classic Monte Carlo localization algorithm, Communication radius cannot be determined. In order to avoid the flaw, the paper presents an improved Monte Carlo localization algorithm based on transformation model between Hops and Hop Distance. The algorithm can estimate node's co-ordinates by obtaining hop information among nodes. Then a circular sampling area is produced. So the sampling efficiency is improved. The simulation results show that optimized algorithm can not only reduce times of positioning sample obviously, but also improve the accuracy of positioning effectively. In addition, whole network performance can also be improved when anchor nodes are a low-density. In classic Monte Carlo localization algorithm, variety of communication radius will impact effect of sampling directly. In the novel method, communication radius is replaced by hop. The flaw is solved thoroughly. Furthermore Positioning accuracy and sampling efficiency are upgraded as well.
Journal: International Journal of Simulation: Systems, Science & Technology, IJSSST V17
Published: Jul 14, 2016