Ring-Based Optimal-Level Distributed Wavelet Transform with Arbitrary Filter Length for Wireless Sensor Networks

Siwang Zhou,Yonghe Liu,Yaping Lin

doi:10.1155/2008/396126

Abstract

We propose an optimal-level distributed transform for wavelet-based spatiotemporal data compression in wireless sensor networks. Although distributed wavelet processing can efficiently decrease the amount of sensory data, it introduces additional communication overhead as the sensory data needs to be exchanged in order to calculate the wavelet coefficients. This tradeoff is explored in this paper with the optimal transforming level of wavelet transform. By employing a ring topology, our scheme is capable of supporting a broad scope of wavelets rather than specific ones, and the border effect generally encountered by wavelet-based schemes is also eliminated naturally. Furthermore, the scheme can simultaneously explore the spatial and temporal correlations among the sensory data. For data compression in wireless sensor networks, in addition to minimizing energy and consumption, it is also important to consider the delay and the quality of reconstructed sensory data, which is measured by the ratio of signal to noise (PSNR). We capture this with energy × delay/PSN R metric and using it to evaluate the performance of the proposed scheme. Theoretically and experimentally, we conclude that the proposed algorithm can effectively explore the spatial and temporal correlation in the sensory data and provide significant reduction in energy and delay cost while still preserving high PSNR compared to other schemes.

Highlights

Edging toward real world deployments, wireless sensor networks have revealed vast potentials in a plethora of applications including battle field monitoring, environmental exploration, and precision agriculture [1, 2]
Wavelet compression is first performed in a single sensor node and the wavelet coefficients are sent for further processing at a central location
Aiming at time-series sampled by a single sensor node, RACE [4] proposes a rate adaptive Haar wavelet compression algorithm

Summary

Introduction

Edging toward real world deployments, wireless sensor networks have revealed vast potentials in a plethora of applications including battle field monitoring, environmental exploration, and precision agriculture [1, 2]. Extensive research efforts have been focusing on wavelet data compression in wireless sensor networks, with a goal of data amount reduction and energy conservation. The WISDEN system [3] is designed for structural monitoring In this system, wavelet compression is first performed in a single sensor node and the wavelet coefficients are sent for further processing at a central location. Aiming at time-series sampled by a single sensor node, RACE [4] proposes a rate adaptive Haar wavelet compression algorithm. Dimensions [5, 6] propose a hierarchical routing scheme with its wavRoute protocol This scheme exploits the temporal data redundancy at the bottom level of the routing hierarchy firstly, and performs spatial data reduction in the middle. There exists the transmission of spatially redundant data from the bottom to the middle of the hierarchy

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Conclusion