Building trees based on aggregation efficiency in sensor networks

Sensor network protocols must minimize energy due to their resource-constrained nature. Large amounts of redundant data are produced by the sensors in such networks, however sending unnecessary data wastes energy. One common technique used to reduce the amount of data in sensor is data aggregation. Therefore, we consider the impact and cost of data aggregation in sensor networks to achieve energy efficient operation. We propose a new notion of energy efficiency that can be used to decide where aggregation points in the network should be placed. The main factor affecting energy efficiency is the location of the data aggregation points. The optimal choice of these points is determined by the aggregation efficiency, which determines the amount of data reduction. We present our aggregation tree algorithm “Oceanus” that produces energyefficient aggregation trees by taking into account the aggregation efficiency. Our evaluation shows that by using aggregation efficiency, Oceanus provides higher energy efficiency compared to existing solutions for data aggregation.