Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
The Cricket location-support system
MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
Dynamic fine-grained localization in Ad-Hoc networks of sensors
Proceedings of the 7th annual international conference on Mobile computing and networking
Range-free localization schemes for large scale sensor networks
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Resilient Localization for Sensor Networks in Outdoor Environments
ICDCS '05 Proceedings of the 25th IEEE International Conference on Distributed Computing Systems
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Proceedings of the 7th ACM international symposium on Mobile ad hoc networking and computing
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Proceedings of the 12th annual international conference on Mobile computing and networking
StarDust: a flexible architecture for passive localization in wireless sensor networks
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MSP: multi-sequence positioning of wireless sensor nodes
Proceedings of the 5th international conference on Embedded networked sensor systems
Quality of Trilateration: Confidence Based Iterative Localization
ICDCS '08 Proceedings of the 2008 The 28th International Conference on Distributed Computing Systems
Spinning beacons for precise indoor localization
Proceedings of the 6th ACM conference on Embedded network sensor systems
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Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems
Organizing a global coordinate system from local information on an ad hoc sensor network
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
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Locating sensors in the wild: pursuit of ranging quality
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
Estimation From Relative Measurements: Electrical Analogy and Large Graphs
IEEE Transactions on Signal Processing
Providing reliable and real-time delivery in the presence of body shadowing in breadcrumb systems
ACM Transactions on Embedded Computing Systems (TECS)
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Previous localization solutions in wireless sensor networks mainly focus on using various techniques to estimate node positions. In this paper, we argue that quantifying the uncertainty of these estimates is equally important in practice. By using the quantitative uncertainty of measurements and estimates, we can derive more accurate estimates by better fusing the measurements, provide confidence information for confidence-based applications, and know how to select the best anchor nodes so as to minimize the total mean square errors of the whole network. This paper quantifies the estimation uncertainty as an error covariance matrix, and presents an efficient incremental centralized algorithm---INOVA and a decentralized algorithm---OSE-COV for calculating the error covariance matrix. Furthermore, we present how to use the error covariance matrix to infer the confidence region of each node's estimate, and provide an optimal strategy for the anchor selection problem. Extensive simulation results show that INOVA significantly improves the computation efficiency when the network changes dynamically; the confidence region inference is accurate when the measurement number to node number ratio is more than 2; and the optimal anchor selection strategy reduces the total mean square error by four times as much as the variation-based algorithm in best case.