Energy Consumption Averaging and Minimization for the Software Defined Wireless Sensor Networks With Edge Computing

The software-defined (SD) and edge computing (EC) are emerging technologies that have been used to improve the network operation efficiency of wireless sensor networks (WSNs). Due to the advantages of the SD and EC technologies, the area of WSNs has achieved a new dimension and breakthrough. However, the limited energy allocation mechanism in edge-SD wireless sensor networks (ESDWSNs) makes the energy consumption of different nodes unbalanced. In this paper, we propose an energy allocation optimization (EAO) algorithm that solves the energy averaging and minimization (ECAM) problem in ESDWSNs by selecting appropriate relay nodes and de-duplicated data flows. Specifically, we first establish a novel three-layer network architecture based on the edge computing and software-defined technologies. Then we proposed the ECAM problem, which minimizes the energy consumption in ESDWSNs. Furthermore, we propose an adaptive Levenberg-Marquardt algorithm and derive the optimization value of energy cost function. The extensive simulation results based on the NS-2 simulator demonstrate the energy balance efficiency of the EAO algorithm in ESDWSNs.

Abstract licence: CC BY-NC-ND

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