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Node localization using mobile robots in delay-tolerant sensor networks

Pathirana, Pubudu, Bulusu, Nirupama, Savkin, Andrey and Jha, Sanjay 2005, Node localization using mobile robots in delay-tolerant sensor networks, IEEE transactions on mobile computing, vol. 4, no. 3, May/June, pp. 285-296.

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Title Node localization using mobile robots in delay-tolerant sensor networks
Author(s) Pathirana, Pubudu
Bulusu, Nirupama
Savkin, Andrey
Jha, Sanjay
Journal name IEEE transactions on mobile computing
Volume number 4
Issue number 3
Season May/June
Start page 285
End page 296
Publisher Institute of Electrical and Electronics Engineers
Place of publication New York, N.Y.
Publication date 2005-05
ISSN 1536-1233
Keyword(s) localization
delay-tolerant sensor networks
robust extended Kalman filter
mobile robot
mobility
Summary We present a novel scheme for node localization in a Delay-Tolerant Sensor Network (DTN). In a DTN, sensor devices are often organized in network clusters that may be mutually disconnected. Some mobile robots may be used to collect data from the network clusters. The key idea in our scheme is to use this robot to perform location estimation for the sensor nodes it passes based on the signal strength of the radio messages received from them. Thus, we eliminate the processing constraints of static sensor nodes and the need for static reference beacons. Our mathematical contribution is the use of a Robust Extended Kalman Filter (REKF)-based state estimator to solve the localization. Compared to the standard extended Kalman filter, REKF is computationally efficient and also more robust. Finally, we have implemented our localization scheme on a hybrid sensor network test bed and show that it can achieve node localization accuracy within 1m in a large indoor setting.
Language eng
Field of Research 100599 Communications Technologies not elsewhere classified
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2005, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30002984

Document type: Journal Article
Collection: School of Engineering and Information Technology
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