WSN01-5: Efficient Data Collection through Compression-Centric Routing

Efficient sensor data fusion is one of the more critical and challenging tasks in building practical sensor networks. It is widely understood that transmitting raw sensor data to a central location for processing is severely hampered by scaling in large scale wireless networks, both in terms of energy consumption and latency costs. However, many detection, classification, estimation, and phenomena modeling algorithms rely heavily on the individual data from each sensor and thus, require raw data collection if not from the entire network, then at least among localized node clusters of varying sizes, hi order to make the data collection as efficient as possible, various proposed sampling and compression techniques have been, and are being investigated. As we demonstrate, in addition to the compression algorithm, the topology of the aggregation (e.g. the routes used) can play a significant role in the achievable compression rates, hi this paper, we propose a compression-centric data collection algorithm for use in wireless sensor networks. The algorithm relies on the construction of a Minimum Cost Tree (MCT), using neighbor data correlation as the optimization heuristic along the tree. By selecting routes that traverse nodes with higher degrees of data correlation, it is possible to achieve superior compression results when compared to more naive minimum spanning tree algorithm variations, hi addition to presenting the details of our distributed MCT algorithm, we use simulated data as well as actual data from a real sensor network to validate and demonstrate the performance of the new compression-centric routing scheme.