Energy-Efficient Data Recovery via Greedy Algorithm for Wireless Sensor Networks. (10th February 2016)

• Record Type: Journal Article
• Authors: Zou, Zhi-qiang; Li, Ze-ting; Shen, Shu; Wang, Ru-chuan
• Other Names: Balakrishnan Narayanaswamy Academic Editor.
• Is Part Of: International journal of distributed sensor networks. (2016)
• Publication Date: 2016-02-10
• Subjects: Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1155/2016/7256396
• Languages: English
• ISSNs: 1550-1329

Abstract

Abstract : Accelerating energy consumption and increasing data traffic have become prominent in large-scale wireless sensor networks (WSNs). Compressive sensing (CS) can recover data through the collection of a small number of samples with energy efficiency. General CS theory has several limitations when applied to WSNs because of the high complexity of itsl 1 -based conventional convex optimization algorithm and the large storage space required by its Gaussian random observation matrix. Thus, we propose a novel solution that allows the use of CS for compressive sampling and online recovery of large data sets in actual WSN scenarios. Thel 0 -based greedy algorithm for data recovery in WSNs is adopted and combined with a newly designed measurement matrix that is based on LEACH clustering algorithm integrated into a new framework called data acquisition framework of compressive sampling and online recovery (DAF_CSOR). Furthermore, we study three different greedy algorithms under DAF_CSOR. Results of evaluation experiments show that the proposed sparsity-adaptive DAF_CSOR is relatively optimal in terms of recovery accuracy. In terms of overall energy consumption and network lifetime, DAF_CSOR exhibits a certain advantage over conventional methods.