Wireless Communications: Principles and Practice
Wireless Communications: Principles and Practice
IEEE Transactions on Signal Processing
Distributed sensor localization in random environments using minimal number of anchor nodes
IEEE Transactions on Signal Processing
Relative location estimation in wireless sensor networks
IEEE Transactions on Signal Processing
Higher dimensional consensus: learning in large-scale networks
IEEE Transactions on Signal Processing
Energy-efficient and coverage-specific node scheduling for wireless sensor networks
Proceedings of the 13th ACM international conference on Modeling, analysis, and simulation of wireless and mobile systems
Robust power allocation for energy-efficient location-aware networks
IEEE/ACM Transactions on Networking (TON)
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We present an algorithm for distributed sensor localization with noisy distance measurements (DILAND) that extends and makes the DLRE more robust. DLRE is a distributed sensor localization algorithm in Rm (m ≥ 1) introduced in our previouswork (IEEE Trans. Signal Process., vol. 57, no. 5, pp. 2000-2016, May 2009). DILAND operates when: 1) the communication among the sensors is noisy; 2) the communication links in the network may fail with a nonzero probability; and 3) the measurements performed to compute distances among the sensors are corrupted with noise. The sensors (which do not know their locations) lie in the convex hull of at least m+1 anchors (nodes that know their own locations). Under minimal assumptions on the connectivity and triangulation of each sensor in the network, we show that, under the broad random phenomena described above, DILAND converges almost surely (a.s.) to the exact sensor locations.