Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Robust Positioning Algorithms for Distributed Ad-Hoc Wireless Sensor Networks
ATEC '02 Proceedings of the General Track of the annual conference on USENIX Annual Technical Conference
Autonomous Localization Method in Wireless Sensor Networks
PERCOMW '05 Proceedings of the Third IEEE International Conference on Pervasive Computing and Communications Workshops
A survey of practical issues in underwater networks
WUWNet '06 Proceedings of the 1st ACM international workshop on Underwater networks
Sensor networks of freely drifting autonomous underwater explorers
WUWNet '06 Proceedings of the 1st ACM international workshop on Underwater networks
An Energy-Efficient Localization Scheme with Specified Lower Bound for Wireless Sensor Networks
CIT '06 Proceedings of the Sixth IEEE International Conference on Computer and Information Technology
Relative location estimation in wireless sensor networks
IEEE Transactions on Signal Processing
Recursive algorithms for computing the Cramer-Rao bound
IEEE Transactions on Signal Processing
Performance analysis of relative location estimation for multihop wireless sensor networks
IEEE Journal on Selected Areas in Communications
IEEE Transactions on Wireless Communications
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In their quest to study the intricate underwater ecosystems, oceanographic scientists need spatially-rich data that is collected within the moving reference frame created by the oceans' currents. To this end, we are developing a system of networked underwater ocean explorers that drift freely with the currents. However, effective interpretation of collected sensor data hinges on knowing the positions of these drifters while submerged. Due to their uncontrollable motion, positions have to be tracked in time. Unlike in static networks, position estimation therefore represents a recurring cost, which should be minimized for reasons of limited on-board energy supplies. In this paper, we present a low-overhead scheme that is able to trade accuracy for energy savings by cleverly selecting the specific links that are required for the self-localization algorithm. Our technique is also agile enough to quickly adapt to on-demand changes in accuracy requirements, and cuts the position estimation costs by 40% or more under various ocean current dynamics.