TY - CONF T1 - On Computing Compression Trees for Data Collection in Wireless Sensor Networks T2 - 2010 Proceedings IEEE INFOCOM Y1 - 2010 A1 - Li,Jian A1 - Deshpande, Amol A1 - Khuller, Samir KW - Approximation algorithms KW - Base stations KW - Communications Society KW - Computer networks KW - Computer science KW - computing compression trees KW - Costs KW - data collection KW - Data communication KW - data compression KW - designing algorithms KW - Educational institutions KW - Entropy KW - graph concept KW - Monitoring KW - Protocols KW - trees (mathematics) KW - weakly connected dominating sets KW - Wireless sensor networks AB - We address the problem of efficiently gathering correlated data from a wireless sensor network, with the aim of designing algorithms with provable optimality guarantees, and understanding how close we can get to the known theoretical lower bounds. Our proposed approach is based on finding an optimal or a near-optimal compression tree for a given sensor network: a compression tree is a directed tree over the sensor network nodes such that the value of a node is compressed using the value of its parent. We focus on broadcast communication model in this paper, but our results are more generally applicable to a unicast communication model as well. We draw connections between the data collection problem and a previously studied graph concept called weakly connected dominating sets, and we use this to develop novel approximation algorithms for the problem. We present comparative results on several synthetic and real-world datasets showing that our algorithms construct near-optimal compression trees that yield a significant reduction in the data collection cost. JA - 2010 Proceedings IEEE INFOCOM PB - IEEE SN - 978-1-4244-5836-3 M3 - 10.1109/INFCOM.2010.5462035 ER -