Application of wavelet neural-networks in wireless sensor networks

• Published in: Sixth International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Network pp. 262 - 267
• Main Authors: Kulakov, A., Davcev, D., Trajkovski, G.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2005
• Subjects: Clustering algorithms; Computer architecture; Computer networks; Concurrent computing; Costs; Data analysis; Distributed computing; Robustness; Unsupervised learning; Wireless sensor networks
• ISBN: 0769522947; 9780769522944
• DOI: 10.1109/SNPD-SAWN.2005.23

Some of the algorithms developed within the artificial neural-networks tradition can be easily adopted to wireless sensor network platforms and in the same time they can meet the requirements for sensor networks like: simple parallel distributed computation, distributed storage, data robustness and auto-classification of sensor readings. Dimensionality reduction, obtained simply from the outputs of the neural-networks clustering algorithms, leads to lower communication costs and energy savings. Two different data aggregation architectures are presented, with algorithms which use wavelets for initial data-processing of the sensory inputs and artificial neural-networks which use unsupervised learning methods for categorization of the sensory inputs. They are analyzed on a data obtained from a set of several motes, equipped with several sensors each. Results from deliberately simulated malfunctioning sensors show the data robustness of these architectures.