Data Aggregation for Spatially Correlated data using Polynomial Regression in 3D Wireless Sensor Network

Abstract: Sensor nodes observing events, occurring in close proximity, gather data that are highly co-related. This work uses a polynomial regression technique to exploit the spatial correlation of the data in a three dimensional sensor network. The sensing nodes, sense the physical attribute and report their position coordinates (x, y, z) and the sensed value to the nearest tree node. Another set of nodes, categorized as the tree nodes, are responsible for generating a polynomial function of the received data and transmit the coefficients of regression to the parent tree node. The approach proceeds from the bottom to the top. The query from the sink, receives a polynomial function which is generated by the root node of the tree in each cluster to compute the attribute value at any location within the boundary. The proposed approach aims to save a lot of energy in the sensor network. Simulations, performed for different tree heights, indicate that a tree with a depth of four gives the best results.

Keywords: Data aggregation, Energy efficiency, Spatial correlation.