Abstract
For large-scale wireless sensor networks, the nonlinear localization problem where only neighboring distances are available to each individual sensor nodes have been attracting great research attention. In general, distributed algorithms for this problem are likely to suffer from the failures that localizations are trapped in local minima. Focusing on this issue, this article considers a fully distributed algorithm by introducing a novel mechanism, where each individual node is allowed to computationally interact with a random subset of its neighbors, for helping localizations escape from local minima. Theoretical analyses reveal that with the proposed algorithm, any local minimum of the localization will be unstable, and the global optimum would finally be achieved with probability 1 after enough time of iterations. Numerical simulations are given as well to demonstrate the effectiveness of the algorithm.
Highlights
Wireless sensor networks have been attracting great attention since a large number of simple sensor nodes working cooperatively can bring plenty of advantages
Focusing on the local-minima issue in distance-based localization problems, this article considers a concise topology-controlled mass–spring relaxation (TCMSR) algorithm that allows node to computationally interact with only a random subset of its neighbors, for help localizations escape from local minima
Given a sensor network with an undirected graph G containing a collection of N ordinary nodes and M anchor nodes, and the distance measurements of each node to its neighbors, the goal is to produce a set of position estimates ri that are consistent with the actual positions of all ordinary nodes, that is, an assignment of points ri such that ri = rià for i 2 N
Summary
Wireless sensor networks have been attracting great attention since a large number of simple sensor nodes working cooperatively can bring plenty of advantages. Keywords Node localization, distributed algorithm, global optimum, sensor networks, topology control The information of hop-counts among nodes can be used in advance to generate an excellent starting point that is close to the global minimum, for the followed localization process using MSR algorithm.[9] But the starting point cannot be always good enough, especially when the network connectivity is large.
Sign-in/Register to view highlights & summary 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 callMore From: International Journal of Distributed Sensor Networks
Disclaimer: 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.
