Localization from mere connectivity
Proceedings of the 4th ACM international symposium on Mobile ad hoc networking & computing
Semidefinite programming for ad hoc wireless sensor network localization
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Indoor people tracking based on dynamic weighted multidimensional scaling
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Did you see Bob?: human localization using mobile phones
Proceedings of the sixteenth annual international conference on Mobile computing and networking
Universal Rigidity and Edge Sparsification for Sensor Network Localization
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Social-Loc: improving indoor localization with social sensing
Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems
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This paper addresses the problem of tracking a group of mobile sensors in an environment where there is intermittent or no access to a localization service, such as the Global Positioning System. Example applications include tracking personnel underground or animals under dense tree canopies. We assume that each sensor uses inertial, visual or mechanical odometry to measure its relative movement as a series of displacement vectors. Each displacement vector suffers a small quantity of error which compounds, causing the overall accuracy of the positional estimate to decrease with time. The primary contribution of this paper is a novel offline method of counteracting this error by exploiting opportunistic radio encounters between sensors. We fuse encounter information with the displacement vectors to build a graph that models sensor mobility. We show that two dimensional sensor tracking is equivalent to finding an embedding of this graph in the plane. Finally, using radio, inertial and ground truth trace data, we conduct simulations to observe how the number of anchors, transmission range and radio noise affect the performance of the proposed model. We compare these results to those from a competing model in the literature.