Reducing aggregation Bias and time in gossiping-based wireless sensor networks

Abstract

Wireless sensor networks are able to perform an aggregation of the data generated by sensors. In networks where no gateway or no central sensor is specified, gossiping algorithms are used such that sensors in the whole network can aggregate messages from all other sensors. In the gossiping algorithm, the bias problem limits the quality of the aggregation results and the lack of message identification results in large aggregation time. In this paper, we reveal the possibility of eliminating or reducing the bias at the sensors by using the concept of the divisible functions that are generally applied in a sensor network and by using the memory of the sensors. Furthermore, we show how the aggregation time can be reduced by using different communication strategies for sensors communicating with their neighbors. Simulation results show the reduction of the aggregation bias at sensors as well as a higher speed of the aggregation in the network.