Big Data Reduction and Optimization in Sensor Monitoring Network

Bin He,Yonggang Li

doi:10.1155/2014/294591

Abstract

Wireless sensor networks (WSNs) are increasingly being utilized to monitor the structural health of the underground subway tunnels, showing many promising advantages over traditional monitoring schemes. Meanwhile, with the increase of the network size, the system is incapable of dealing with big data to ensure efficient data communication, transmission, and storage. Being considered as a feasible solution to these issues, data compression can reduce the volume of data travelling between sensor nodes. In this paper, an optimization algorithm based on the spatial and temporal data compression is proposed to cope with these issues appearing in WSNs in the underground tunnel environment. The spatial and temporal correlation functions are introduced for the data compression and data recovery. It is verified that the proposed algorithm is applicable to WSNs in the underground tunnel.

Highlights

It is well known that traditional structural health monitoring mainly relies on manual work, which is a labor-intensive and time-consuming process
Distributed compression approaches are broadly classified into four main techniques: distributed source modeling (DSM), distributed transform coding (DTC), distributed source coding (DSC), and compressed sensing (CS) techniques [5]
Transmitting the variation of the sensing signals, rather than the original signals, to the base station can reduce the volume of data stream in the routing path and save the energy, thereby prolonging the lifetime of the network

Summary

Introduction

It is well known that traditional structural health monitoring mainly relies on manual work, which is a labor-intensive and time-consuming process. Our algorithm is considered as an extension to spatial and temporal data compression algorithms [10], lying in the fact that we further explore the correlation property among sensor nodes to carry out the corresponding data compression and recovery based on the correlation degree [11, 12]. We proposed an optimization algorithm based on the temporal and spatial data compression. The proper choice for the values helps ensure effective compression performance and high recovery degree

Wireless Sensor Network Deployment Model

Optimization Algorithm Based on Temporal and Spatial Correlation

Simulation and Experimental Results

Conclusion and Future Work