Balancing energy consumption and memory usage in sensor data processing

Abstract

Algorithms for data processing in sensor nodes of wireless sensor networks should be able to handle the resource limitations the nodes face (i.e. energy and memory). An important issue to be considered is that the materialization of large amounts of data in sensor nodes may cause memory overflows and consequently data losses. On the other hand, the excessive sending of data packets to other nodes may result in unacceptable energy consumption levels. Some alternatives to save energy and also avoid memory overflows involve in-network aggregation and reductions in sensor activities (when sensor nodes experience resource constraints). In this paper we study energy-memory tradeoffs in sensor nodes and how they affect the accuracy of query results in wireless sensor networks. We propose an adaptive data processing strategy to balance energy consumption and memory usage at sensor node level. Our goal is to maximize sensor lifetime while also maintaining the accuracy of query results. We implemented our approach by means of an algorithm called ADAGA (ADaptive AGgregation Algorithm for sensor networks), which process in-network aggregation in sensor nodes. ADAGA is able to adapt its behavior according to energy and memory availabilities by dynamically adjusting data collection and data sending intervals. The results on the efficiency of our approach are also presented at the end of the paper.