An architecture for distributed wavelet analysis and processing in sensor networks

Abstract

Distributed wavelet processing within sensor networks holds promise for reducing communication energy and wireless bandwidth usage at sensor nodes. Local collaboration among nodes de-correlates measurements, yielding a sparser data set with significant values at far fewer nodes. Sparsity can then be leveraged for subsequent processing such as measurement compression, de-noising, and query routing. A number of factors complicate realizing such a transform in real-world deployments, including irregular spatial placement of nodes and a potentially prohibitive energy cost associated with calculating the transform in-network. In this paper, we address these concerns head-on; our contributions are fourfold. First, we propose a simple interpolatory wavelet transform for irregular sampling grids. Second, using ns-2 simulations of network traffic generated by the transform, we establish for a variety of network configurations break-even points in network size beyond which multiscale data processing provides energy savings. Distributed lossy compression of network measurements provides a representative application for this study. Third, we develop a new protocol for extracting approximations given only a vague notion of source statistics and analyze its energy savings over a more intuitive but naïve approach. Finally, we extend the 2-dimensional (2-D) spatial irregular grid transform to a 3-D spatio-temporal transform, demonstrating the substantial gain of distributed 3-D compression over repeated 2-D compression.

Adaptive distributed transforms for irregularly sampled Wireless Sensor Networks

“…The tree-based wavelet transform [8] is used to compare the "structure-dependent" filter designs of [4,8] against our distributed optimization method. We also compare these against the T-KLT and T-DPCM with adaptive filters.…”

“…In this context, in-network distributed transforms, e.g., [1,2,3,4] and references therein, have long been considered an attractive tool since they exploit the fact that data being gathered has to be routed over multiple hops from sensor to sensor and spatial data correlation exists across sensors. These transforms exploit existing spatial correlation in data in order to reduce the number of bits to be transmitted as data is routed towards the sink.…”

“…Thus, there is no learning cost. In the wavelet transform proposed in [4] using wavelet lifting [7], relative location information is used to design prediction filters at nodes that give more weight to data from closer neighbors and less to neighbors further away. However, unidirectional computation is not guaranteed for this transform since the transform is not developed along routing paths.…”

Optimized Local Superposition in Wireless Sensor Networks With T-Average-Mutual-Coherence

“…Distributed wavelet transform (DWT) [22] is successfully applied to sparsify the network data [4,22] acquired by the sensors deployed in an irregular grid. Once the fusion center knows the locations of all sensor nodes, DWT basis can be computed.…”

RHA: A robust hybrid architecture for information processing in wireless sensor networks

“…Distributed Wavelet Transform [12] addresses data compression rather than information processing, with the goal of minimizing communication costs. Junction Tree [13] ensures robust packet delivery for distributed inference, but does not use hybrid hardware.…”