A DISTRIBUTED WAVELET APPROACH FOR EFFICIENT INFORMATION REPRESENTATION AND DATA GATHERING IN SENSOR WEBS

In this work we investigate novel approaches for information processing and representation in a sensor web. Sensor nodes capture information that is both temporally and spatially correlated. Exploiting spatial correlation requires data exchange between sensors, which should be minimized in order to keep power consumption low and maximize the life of the system. We are investigating methods for sampling (which sensors should make measurements), routing (how does the information flow towards a fusion center), processing (how to perform a wavelet transform along a network route) and compression (how to compress the output of the wavelet transform). All of these aim at maximizing the quality of the data available at the fusion center for a given energy consumption target at the nodes. In this paper we will report algorithmic, analytical and implementation progress made in this work over the last year.

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