Tree-based data aggregation approach in wireless sensor network using fitting functions

Sensor networks are a collection of sensor nodes that co-operatively transmit sensed data to a base station. One of the well-known characteristics of Wireless Sensor Networks (WSN) is its limited resources. Energy consumption of the network's nodes is considered one of the major challenges faced by researchers nowadays. On the other hand, data aggregation helps in reducing the redundant data transferred through the WSN. This fact implies that aggregation of data is considered a very crucial technique for reducing the energy consumption across the WSN. Local aggregation and Prefix filtering are two methods used in which they utilize a tree based bi-Ievel periodic data aggregation approach implemented on the source node and on the aggregator levels. In this paper our goal is to apply data aggregation on two nodes levels. We worked on sending fewer data from aggregator to the sink, along with the equation that expresses all data. We applied Bayesian belief network algorithm to measure the accuracy of this method.