Title of Presentation: Novel Distributed Wavelet Transforms and Routing Algorithms for Efficient Data Gathering in Sensor Webs

Primary (Corresponding) Author: Goodwin Shen

Organization of Primary Author: University of Southern California

Co-Authors: S. Pattem, S. Lee, S. Lee, A. Tu, B. Krishnamachari, A. Ortega, M. Cheng, S. Dolinar,A. Kiely and H. Xie

Abstract: In this work we present our ongoing investigation of novel approaches for information processing and representation in a sensor web. Since sensor nodes capture spatially and temporally correlated information, (a) sensors can exploit this spatial correlation by first exchanging data and then compressing it in a distributed manner, (b) sensors can exploit temporal correlation locally, or (c) sensors can even exploit correlation across time and space. This reduces the total amount of data to be transferred in the sensor web at the expense of some additional (potentially minor) power consumption. We are investigating methods for sampling, routing, processing and compression. 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. Two types of methods for exploiting spatio-temporal correlation between sensors are presented here. First we consider a compression scheme based on a distributed 2D wavelet transform along arbitrary routing trees and discuss its extension to include a temporal component of the transform. Second, we explore techniques that involve "sub-sampling", i.e., where data is not captured by all nodes, (including methods based on traditional sampling, as well as new approaches based on compressed sensing). Investigation of joint routing and compression optimization is also underway for both class of methods, with preliminary results presented here. The techniques presented here provide a variety of ways to exploit data correlation through the routing choices made, the compression choices made across time and space, and the joint decisions on compression and routing, ultimately leading to lower cost and higher quality data. Given the variety of tools we have developed, we also attempt to identify a set of use scenarios that can employ our technology along with an appropriate combination of tools for each scenario. We also discuss the current status of an implementation of the distributed wavelet transform on a practical sensor platform, as well as extensions of our algorithms to take advantage of radio range characteristics of these sensors.