Distributed learning in sensor networks
Year: 2009
Type: dissertation
Abstract: The sensor networks of today tend to be built from “intelligent” sensor nodes. Such nodes have substantial processing capability and memory, are locally distributed and communicate wirelessly with one another. They are mostly battery-powered, possibly even with a lifetime battery. Power management is hence a prime concern. Such intelligent sensors, or “smart sensors”, are typically involved in some diagnosis task, which would benefit greatly from an ability to predict environment data. The best of those predictions are obtained by training a learning-model with environment data. This task is non-trivial, computationally intensive and thus expensive on energy, particularly if the model imposed by the environment data is dynamic and complex. As the training data usually come from diverse sources, not only from the nearest sensor, the learning node must communicate with other nodes to get at their measurement data. Data processors can be made very energy efficient, whereas radio is inherently wasteful. The ultimate aim is to find the right balance between prediction quality and energy consumption. Unlike conventional energy management solutions, which provide routing methods or communication protocols, this thesis introduces an efficient learning algorithm which improves prediction performance of sensors and reduces computational complexity. It introduces two techniques which both reduce the overall energy consumption of the sensor network: intra-node and inter-node solutions. Intra-node solution: A sensor’s duty cycle is the time fraction during which the sensor is active. Battery life can be extended by reducing the duty cycle of the sensor. Depending on the communication protocol, radio communication coincides with more or less of the sensor’s active time. The radio duty cycle can be reduced by communicating less frequently. This thesis introduces Spiral Recurrent Neural Networks (SpiralRNN ), a novel model for on-line prediction, where some received data get substituted by predicted data. It is shown that the SpiralRNN model works reliably, thus opening a way to significant energy savings. Inter-node solution: Communication between sensor nodes can also be diminished at network level. Transmitting data (and receiving them) consumes energy and blocks the airwaves, but the information transmitted is often irrelevant (depending on the application). This thesis introduces a heuristic evolutionary method, the evolution
DOI: 10.5282/edoc.9612
Author: Gao, Huaien
Institution: Ludwig-Maximilians-Universität München
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1750928526