The Effect of Re-sampling on Incremental Nelder-Mead Simplex Algorithm: Distributed Regression in Wireless Sensor Networks

Wireless sensor networks (WSNs) have been of great interest among academia and industry, due to their diverse applications in recent years. The main goal of a WSN is data collection. As the amount of the collected data increases, it would be essential to develop some techniques to analyze them. In this paper, we propose an in-network optimization algorithm based on Nelder-Mead simplex (NM simplex) to incrementally do regression analysis over distributed data. Then improve the regression accuracy by the use of re-sampling in each node. Simulation results show that the proposed algorithm not only increases the accuracy to more than that of the centralized approach, but is also more efficient in terms of communication compared to its counterparts.

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