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.
Highlights
There are many types of data transmission in WSN (Wireless Sensor Networks), and multi-point to point transmission is the mainstream
The main energy-saving methods include optimizing topology and routing to make energy consumption more uniform [1], [2], compressing and converging data packets to reduce the number of transmissions [3], [4], and so on
The MinST model is based on data convergence as an energy-saving idea, it does not focus on planning the overall topological layout and traffic distribution of fragmentation, and only pursues uniform energy consumption and prolongs the death time of the first node
Summary
This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFB1800302, in part by the Beijing Natural Science Foundation under Grant 4172019, in part by the National Nature Science Foundation of China (NSFC) Project under Grant 61300171, in part by the Joint of Beijing Natural Science Foundation and Education Commission under Grant KZ201810009011, and in part by the Science and Technology Innovation Project of North China University of Technology under Grant 18XN053.
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