Simultaneous aggregate sum retrieval from multiple regions in sensor networks by distributed data cubes

Abstract

Several data aggregation algorithms aiming at the retrieval of data aggregates from a single set of sensors in a sensor network have been proposed. However, when data aggregates of several sets of sensors are needed, the only solution these techniques provide is to build multiple distributed data structures or gossip groups in the sensor network. Hence in a sensor network containing N sensors, we may need 2 N distributed data structures or gossip groups in order to retrieve the aggregates from all possible sets of sensors. In this paper, we propose to build distributed data cubes for the fast retrieval of aggregate sums from multiple regions in a sensor network, such that only one distributed data structure is needed. The distributed data cube construction algorithms we propose are based on the inclusion-exclusion principle, and they are capable of building distributed Prefix Sum (PS) and Local Prefix Sum (LPS) data cubes in sensor networks.