Issue No. 03 - March (2011 vol. 10)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TMC.2010.158
Zhong Zhou , University of Connecticut, Storrs
Zheng Peng , University of Connecticut, Storrs
Jun-Hong Cui , University of Connecticut, Storrs
Zhijie Shi , University of Connecticut , Storrs
Amvrossios C. Bagtzoglou , University of Connecticut, Storrrs
Due to harsh aqueous environments, non-negligible node mobility and large network scale, localization for large-scale mobile underwater sensor networks is very challenging. In this paper, by utilizing the predictable mobility patterns of underwater objects, we propose a scheme, called Scalable Localization scheme with Mobility Prediction (SLMP), for underwater sensor networks. In SLMP, localization is performed in a hierarchical way, and the whole localization process is divided into two parts: anchor node localization and ordinary node localization. During the localization process, every node predicts its future mobility pattern according to its past known location information, and it can estimate its future location based on the predicted mobility pattern. Anchor nodes with known locations in the network will control the localization process in order to balance the trade-off between localization accuracy, localization coverage, and communication cost. We conduct extensive simulations, and our results show that SLMP can greatly reduce localization communication cost while maintaining relatively high localization coverage and localization accuracy.
Network architecture and design, network communications, network protocols, applications, miscellaneous, localization, underwater sensor networks.
Z. Zhou, J. Cui, Z. Shi, A. C. Bagtzoglou and Z. Peng, "Scalable Localization with Mobility Prediction for Underwater Sensor Networks," in IEEE Transactions on Mobile Computing, vol. 10, no. , pp. 335-348, 2010.