Energy-efficient Compressive Data Gathering Utilizing Virtual Multi-input Multi-output

Abstract

Data gathering is an attractive operation for obtaining information in wireless sensor networks (WSNs). But one of important challenges is to minimize energy consumption of networks. In this paper, an integration of distributed compressive sensing (CS) and virtual multi-input multi-output (vMIMO) Keywords: data gathering, compressive sensing, virtual MIMO, energy optimizationCopyright © 2017 Universitas Ahmad Dahlan. All rights reserved. IntroductionWSNs are typically self-organizing netwoks consisiting of nunmerous low-cost, feature riched and energy-limited sensor nodes, which can be widely applied in environmental monitoring, intelligent home furnishing, military monitoring, security monitoring and other fields [1,2]. Such applications usually require that sensor nodes in surveillance region periodically sense data and report to sink noedes or base stations. So data gathering is an important operation to collect and transmit the sensed data to sink nodes. At present, WSNs have severe energy constraints, the problem for data gathering is that the transmssion of huge amounts of monitoring data causes large consumptions of nodes and reduces network life cycle.Many CS-based data gathering methods have been studied to improve the energy efficiency of WSNs [3][4][5][6][7][8][9][10][11]. Chong Luo, et al., [3] applied CS theory to tree-based and chainbased data gathering in WSNs to obtain efficient data compression. In [4], You proposed a CSbased dynamic source and transmission control algorithm to prolong the lifetime of networks. In [5], a CS model for data gathering was proposed that used spatial and temporal relativity of signals. This model reduced the quantity of transmitted data and achieved better reconstruction performance in sink nodes. In [6] and [7], combined with random walk routing and CS measurement matrices, compressive data gathering schemes were proposed. In [8] and [9], CS was applied in clustered networks, cluster heads received raw data from their member-nodes and sent them to sink by the way of single-hop network. Besides that, some researches about sparse projections [10] and joint optimization of transport cost and recovery [11] in WSNs have had some useful explorations. These CS based methods can decrease cost by data compression and reducing the number of in-network data packets. But these researches are all based on transmission model with single input and single output.As multi-antenna transmission in wireless networks can achieve spatial diversity. And spatial diversity is considered to be an effective solution to resist channel fading and reduce power consumption. Virtual MIMO mechanism with single-antenna nodes had been introduced in data gathering [12]. In [13] and [14], virtual MIMO was applied to improve data gathering cost in clustered wireless sensor networks. We can notice that above data gathering methods based on virtual MIMO need an effective data fusion, this mechanism will make systems more

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