Neural Networks based Real Time Classifier for Wireless Sensor Networks and Framework for VLSI Implementation

Wireless sensor network is highly data driven network. Communication between the nodes consumes higher quantum of battery power. Battery is the prime source for wireless sensor node to function. Hence every aspects of sensor node are designed with energy constraints. The paper discusses classificati...

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Bibliographic Details
Main Authors: Akojwar, S.G., Patrikar, R.M.
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Base stations
Batteries
Computer networks
Fuzzy neural networks
Head
Neural networks
Subspace constraints
Very large scale integration
Wireless sensor networks
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Summary:Wireless sensor network is highly data driven network. Communication between the nodes consumes higher quantum of battery power. Battery is the prime source for wireless sensor node to function. Hence every aspects of sensor node are designed with energy constraints. The paper discusses classification technique, which can reduce the energy need for communication. Neural Networks in particular the combination of ART1 and Fuzzy ART model are efficiently used for developing Real time Classifier. Wireless sensor networks demand for the real time classification of sensor data. In this paper clustering and classification techniques using ART1 and Fuzzy ART is discussed. ART1 and Fuzzy ART have very good architectural strategy, which makes it simple for VLSI implementation. The VLSI implementation of the proposed classifier can be a part of embedded microsensor.
ISSN:2163-5137
DOI:10.1109/ISIE.2007.4374802