Aggregating Correlated Data in Sensor Networks
Consider a network where each node gathers information from its vicinity and sends this information to a centralized processing agent. If the information is geographically correlated, then a large saving in data transmission costs may be obtained by aggregating information from nearby nodes before sending it to the central agent. This is particularly relevant to sensor networks where battery limitations dictate that data transmission be kept to a minimum, and where sensed data is often geographically correlated. In-network aggregation for sensor networks has been extensively studied over the last few years. In this paper we show that a very simple opportunistic aggregation scheme can result in near-optimum performance under widely varying (and unknown) scales of correlation.