Comparison of MLP Neural Networks and Kalman Filter for Localization in Wireless Sensor Networks, in Proceedings of 19th IASTED International Conference on Parallel and Distributed Computing Systems, November 19-21, 2007, Cambridge, Massachusetts, USA, Pages 323-330 (acceptance rate: 49%).
Abstract
Localization with noisy distance measurements is a critical problem in many applications of wireless sensor networks. Different localization algorithms offer different tradeoffs between accuracy and hardware resource requirements. In order to provide insight into selecting the best algorithm that optimizes this tradeoff, this paper evaluates the accuracy, memory, and computational requirements of two approaches that may be taken in localization: neural net works and Kalman ?lters. In this paper, we quantitatively compare the localization performance of a Multi-Layer Perceptron (MLP) neural network, PV, and PVA models of the Extended Kalman ?lter. Our experimental results show that the MLP neural network has weaker self-adaptivity than the Extended Kalman ?lters; however, the MLP can potentially achieve the highest localization accuracy and requires the least amount of computational and memory resources.
BibTeX Entry
	@inproceedings{zhu_pdcs07, author = {Ali Shareef and Yifeng Zhu and Mohamad Musavi and Bingxin Shen}, title = {Comparison of {MLP} Neural Networks and {K}alman Filter for Localization in Wireless Sensor Networks}, booktitle = {Proceedings of 19th {IASTED} International Conference on Parallel and Distributed Computing Systems ({PDCS'07})}, year = {2007}, pages = {323--330}, address = { Cambridge, Massachusetts, USA}, }
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Last modified on October 16, 2007