Wireless Communications: Principles and Practice
Wireless Communications: Principles and Practice
Localization from mere connectivity
Proceedings of the 4th ACM international symposium on Mobile ad hoc networking & computing
Using proximity and quantized RSS for sensor localization in wireless networks
WSNA '03 Proceedings of the 2nd ACM international conference on Wireless sensor networks and applications
A Partial-Range-Aware Localization Algorithm for Ad-hoc Wireless Sensor Networks
LCN '04 Proceedings of the 29th Annual IEEE International Conference on Local Computer Networks
Wireless localization using self-organizing maps
Proceedings of the 6th international conference on Information processing in sensor networks
Cramér-Rao-type bounds for localization
EURASIP Journal on Applied Signal Processing
Localization using signal strength: to range or not to range?
Proceedings of the first ACM international workshop on Mobile entity localization and tracking in GPS-less environments
On the error characteristics of multihop node localization in ad-hoc sensor networks
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Relative location estimation in wireless sensor networks
IEEE Transactions on Signal Processing
Understanding the limits of RF-based collaborative localization
IEEE/ACM Transactions on Networking (TON)
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Connectivity-based localization schemes compute the node positions using proximity information collected within the network. In many cases of practical interest, Received Signal Strength (RSS) measurements are available, and connectivity data can be obtained by comparing the RSS against a threshold. We use the Cramér-Rao bound (CRB) analysis to determine the threshold value that minimizes the localization error. The CRB is based on knowledge of the propagation model's parameters and the true node positions. Since this information is not available to a localization scheme, we approximate the optimal threshold value using a function that depends only on the number of nodes in the network. We use extensive simulations and RSS data from in-field experiments to validate the results of the proposed approach.