1-Bit Compressive Data Gathering for Wireless Sensor Networks

Jiping Xiong,Qinghua Tang

doi:10.1155/2014/805423

Abstract

Compressive sensing (CS) has been widely used in wireless sensor networks for the purpose of reducing the data gathering communication overhead in recent years. In this paper, we firstly apply 1-bit compressive sensing to wireless sensor networks to further reduce the communication overhead that each sensor needs to send. Furthermore, we propose a novel blind 1-bit CS reconstruction algorithm which outperforms other state-of-the-art blind 1-bit CS reconstruction algorithms under the settings of WSN. Experimental results on real sensor datasets demonstrate the efficiency of our method.

Highlights

Due to the vast practical and potential unlimited applications, wireless sensor networks keep attracting more and more attention from both research and industry community [1]
Guo et al [8] tried to find the potential law of history data with the help of particle swarm optimization and neural network and reduce the unnecessary transmissions based on the deviation between the actual and the predicted value at each sensor node
The first dataset is a real world trace from Intel Berkeley Research lab and we choose the temperature information collected by sensor node with number 1 on

Summary

Introduction

Due to the vast practical and potential unlimited applications, wireless sensor networks keep attracting more and more attention from both research and industry community [1]. Guo et al [8] tried to find the potential law of history data with the help of particle swarm optimization and neural network and reduce the unnecessary transmissions based on the deviation between the actual and the predicted value at each sensor node Beside those effective methods, another promising taxonomy is compressive sensing (CS) [10, 11] based approaches. Wang et al [12] first exploited the interspatial correlation of sensory data between sensor nodes in large scale wireless sensor networks using compressive sensing. Similar to CDG scheme, Luo et al [14] and Xiang et al [15] proposed a hybrid CS method In this approach, each leaf node only needs to send its own single sensory data instead of projection vector to its parent node. We conclude the paper and present future research work

Proposed Framework and Algorithm

Experimental Results

Conclusion and Future Work