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

Guozhi Li,Yulong Xu

doi:10.1109/access.2019.2955691

Guozhi Li, Yulong Xu

Open Access

https://doi.org/10.1109/access.2019.2955691

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Journal: IEEE Access	Publication Date: Jan 1, 2019
Citations: 6	License type: CC BY 4.0

Affiliation: First Affiliated Hospital of Henan University

Abstract

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.

Highlights

Wireless sensor networks (WSNs) with limited energy and computing capabilities make the scheduling strategy of data transmission less complicated
We employ the energy balance level (EBL) to analyze the level of average energy consumption, and the total network throughput demonstrates the ability of the edge-SD wireless sensor networks (ESDWSNs) for transmitting data flow
WORK The main challenge of energy consumption averaging and minimization (ECAM) problem in wireless sensor networks (WSNs) depends on the selection of available data packets and the optimal allocation of remaining energy in effective relay nodes

Summary

Introduction

Wireless sensor networks (WSNs) with limited energy and computing capabilities make the scheduling strategy of data transmission less complicated. Unbalanced energy allocation method shortens the life cycle of WSNs [1], [2]. Traditional network architecture and data scheduling strategies are difficult to save limited physical resources. The network architecture, data processing and forwarding mechanisms need to be redesigned [3]. There are some breakthroughs in the scheduling algorithms of wireless sensor networks. The proposed flow split optimization (FSO) algorithm in [4] selects the relay nodes with the smallest redundant data flow as the optimal transmission path, which can minimize the traffic load of software defined wireless sensor networks (SDWSNs). The profit maximization multi-round auction (PMMRA) algorithm in [5] is

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