Transform-based Distributed Data Gathering

TL;DR

This paper introduces a class of unidirectional transforms for distributed data gathering in networks, enabling efficient, invertible data compression that exploits data correlation and improves existing methods, especially in wireless sensor networks.

Contribution

The paper proposes a new general framework for unidirectional transforms that are computable in a distributed manner and invertible under certain conditions, with novel wavelet transforms for better performance.

Findings

Transforms can be computed along routing trees in a distributed manner.

Proposed transforms exploit data correlation and wireless broadcast capabilities.

New wavelet transforms outperform existing unidirectional transforms.

Abstract

A general class of unidirectional transforms is presented that can be computed in a distributed manner along an arbitrary routing tree. Additionally, we provide a set of conditions under which these transforms are invertible. These transforms can be computed as data is routed towards the collection (or sink) node in the tree and exploit data correlation between nodes in the tree. Moreover, when used in wireless sensor networks, these transforms can also leverage data received at nodes via broadcast wireless communications. Various constructions of unidirectional transforms are also provided for use in data gathering in wireless sensor networks. New wavelet transforms are also proposed which provide significant improvements over existing unidirectional transforms.