The Georgia Tech Network Simulator
MoMeTools '03 Proceedings of the ACM SIGCOMM workshop on Models, methods and tools for reproducible network research
Design Considerations for Energy-Efficient Radios in Wireless Microsensor Networks
Journal of VLSI Signal Processing Systems
Robust distributed estimation in sensor networks using the embedded polygons algorithm
Proceedings of the 3rd international symposium on Information processing in sensor networks
Robust distributed network localization with noisy range measurements
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Proceedings of the conference on Design, Automation and Test in Europe - Volume 1
Distributed Estimation from Relative Measurements in Sensor Networks
ICISIP '05 Proceedings of the 2005 3rd International Conference on Intelligent Sensing and Information Processing
Exploiting low complexity motion for ad-hoc localisation
International Journal of Sensor Networks
Order-optimal consensus through randomized path averaging
IEEE Transactions on Information Theory
Approximate distributed kalman filtering for cooperative multi-agent localization
DCOSS'10 Proceedings of the 6th IEEE international conference on Distributed Computing in Sensor Systems
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We consider the problem of estimating vector-valued variables from noisy “relative” measurements. The measurement model can be expressed in terms of a graph, whose nodes correspond to the variables being estimated and the edges to noisy measurements of the difference between the two variables. This type of measurement model appears in several sensor network problems, such as sensor localization and time synchronization. We consider the optimal estimate for the unknown variables obtained by applying the classical Best Linear Unbiased Estimator, which achieves the minimum variance among all linear unbiased estimators. We propose a new algorithm to compute the optimal estimate in an iterative manner, the Overlapping Subgraph Estimator algorithm. The algorithm is distributed, asynchronous, robust to temporary communication failures, and is guaranteed to converges to the optimal estimate even with temporary communication failures. Simulations for a realistic example show that the algorithm can reduce energy consumption by a factor of two compared to previous algorithms, while achieving the same accuracy.