A Lossless Convergence Method for Reducing Data Fragments on WSN

Abstract

This article focuses on the most common application scenarios for data collection and uploading in WSN (Wireless Sensor Networks). First, we measure the energy consumption of widely used hardware. According to the characteristics of transmission energy consumption, a MIP (mixed integer programming) model called FAT-WSN (fragmentation aggregation transmission WSN) is proposed to minimize the number of data fragments. Moreover, we propose an iterative solution for this MIP problem with elasticity and low complexity. The main optimization method for this model is to adjust topology and traffic distribution. It focuses on optimizing the number of data transfers without modifying any data and without introducing a compression calculation burden. Finally, simulation and small-scale real node verifications are performed for the FAT-WSN scheme. The experimental results show that FAT-WSN can effectively reduce the number of data transmission and reception, thereby reducing energy consumption and improving network life. Compared with the MinST model, JGDC (Jointly Gaussian Distributed Compress) model and AMREST (Approximately Maximum min-Residual Energy Steiner Tree) model, the network life can be increased by 10%-30% without extending the calculation time.