Approximate Continuous Aggregation via Time Window Based Compression and Sampling in WSNs

In many applications continuous aggregation of sensed data is usually required. The existing aggregation schemes usually compute every aggregation result in a continuous aggregation either by a complete aggregation procedure or by partial data update at each epoch. To further reduce the energy cost, we propose a sampling-based approach with time window based linear regression for approximate continuous aggregation. We analyze the approximation error of the aggregation results and discuss the determinations of parameters in our approach. Simulation results verify the effectiveness of our approach.