Abstract
Random deployment of wireless sensors in the network environment and the need for determining sensors location has made the localization problem as a critical challenge in wireless sensor networks. One of the efficient approaches to solve this problem is the heuristic algorithm based on simulated annealing. Despite the advantages of such an algorithm, its major weakness is accuracy reduction in low density networks and the increase of the localization processing time associated with the growth of the network density. This paper aims at providing a solution to increase the efficiency of this algorithm. To this end, first we applied Iterative Multilateration method in a base station. With this idea the completely random initial estimation is replaced by an appropriate estimation location of sensor nodes, so while the prevention of localization error propagation in the whole network, the computation is decreased considerably at the beginning of the algorithm. In addition, by changing the probability distribution function P(Δf), the localization error has been reduced even in highly noisy environments. The evaluation results indicate a significant increase in the localization accuracy and speed in all networks especially in high noisy ones.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
