Data Distribution Optimization Strategy in Wireless Sensor Networks With Edge Computing

Guozhi Li,Xuekun Song

doi:10.1109/access.2020.3041356

Abstract

Resource-constrained wireless sensor networks (WSNs) impose a substantial challenge to the transmission and processing of non-redundant perceptual data. Moreover, breakthroughs in this area have opened up a new dimension that integrates edge computing technology in wireless sensor networks. However, achieving reliable data distribution in wireless sensor networks with edge computing (EWSN) and balancing the link bandwidth and node energy resources have become challenging problems. In this article, we propose a joint bandwidth allocation and energy consumption (JBAEC) algorithm for distributing different perceptual data to different sensor nodes of the EWSN by considering the available transmission links between the source sensor and destination nodes under the condition of early perceived energy. Therefore, we first establish the model of different data packet types and energy detection costs. Second, we describe the problem of effective bandwidth allocation and prove its NP-hard feature. In addition, we formulate the bandwidth allocation problem as an optimization problem that is divided into the two states of sufficient energy and lack of energy. Afterwards, we present an available node selection (ANS) algorithm for sorting the available relay nodes under the condition of different energy-sufficient nodes. The effective transmission network construction (ETNC) algorithm selects the available transmission links based on the set of available nodes. Finally, we implement the JBAEC algorithm by using the NS-2 simulator and present extensive simulation results to verify the data distribution efficiency of the JBAEC algorithm in the EWSN.

Highlights

Perceptual data are the data sensed, measured and transmitted by wireless sensors in wireless sensor networks (WSNs)
We study the problem of bandwidth allocation efficiency based on minimal energy consumption in edge wireless sensor network (EWSN)
The measurement samples are generated by using a Poisson process, and the arrival rate of each source sensor node is λ. λ represents the size of data distribution requests sent by the source node to the edge computing server per unit time

Summary

Introduction

Perceptual data are the data sensed, measured and transmitted by wireless sensors in WSNs. The proposed flow splitting optimization (FSO) algorithm [14] can transmit the perceptual data with the least redundancy from the source node to the destination node, and proves that the FSO algorithm can minimize the transmission load of the wireless sensor network These mechanisms of perceived data distribution in [13], [14] are based on the traditional structure of a wireless sensor network. In the process of data analysis and scheduling, each source node first needs to detect the resource state of other sensor nodes, and the source node obtains the idle bandwidth of the transmission link and the residual energy of relay nodes before the transmission of perceptual data [15] These mechanisms cannot sense the data and node states of the entire network, thereby transmitting a large quantity of redundant perceived data to the cloud data centre, which increases the transmission load of perceived data and energy consumption. Not all of these data are needed by cloud data centres

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Results

Conclusion