Abstract
Trilateration is an effective way to localize a sensor network based on relative distance measures, but the conditions that guarantee the existence of a solution are quite restrictive. If the network topology is a unit disk graph, however, the localization of the network can be achieved also when the standard trilateration fails, using a priori information about “not being connected”. Such an information can be modeled as additional links, namely shadow edges, that can be used to localize also networks that are not localizable via trilateration. In this paper we inspect the applicability of shadow edge localization in the noisy setting, showing some conditions that guarantee the existence of solution and comparing the results of trilateration and shadow edge localization algorithms in a noisy setting, with respect to the error after a post processing done by means of a recursive least square algorithm. The results show that, besides localizing more nodes, the shadow edge approach has better results in terms of localization error.
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.
