Distributed transforms for efficient data gathering in arbitrary networks

Abstract

In this paper we present a simple distributed transform for data-gathering applications for arbitrary networks that achieves significant gains over raw data transmission, while requiring minimal coordination between nodes. In most spatial compression schemes some nodes (i.e., raw nodes) need to transmit raw data before spatial compression can be performed. Nodes that receive raw data (i.e., aggregating nodes) can then perform spatial compression. Thus, most spatial compression schemes require some raw-aggregating node assignment (RANA) to enable compression. Since transmitting raw data usually requires more bits than transmitting compressed data, we seek to find RANAs that select raw nodes in order to minimize overall energy consumption in the network. We formulate the problem of optimally selecting raw nodes as a set cover problem and propose distributed solutions for a variety of scenarios, including single-sink, multi-sink and gossip-based networks.