Neighbor-Aided Spatial-Temporal Compressive Data Gathering in Wireless Sensor Networks

Lei, Qunfang; Xiao, Song; Xue, Xiao; Lu, Cunbo

doi:10.1109/lcomm.2016.2519031

Cited by 41 publications

(19 citation statements)

References 7 publications

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“…Lei Quan et.al [7] developed a neighbor-aided compressive sensing (NACS) scheme for efficient data gathering in varies mode of WSN. In every sensing period, sensor nodes send random readings within sensing period to randomly selected neighbor.…”

Section: Related Workmentioning

confidence: 99%

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Investigation on Energy Based Data Gathering Approach for WSN

Bharathidasan¹,

Ramamoorthy²

2017

JAC

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Section: Related Workmentioning

confidence: 99%

“…We compare our proposed approach REEDGA with NSTCDG [7], ADGS [10] and MDF [12] in presence of data gathering environment. Figure. 3 shows the results of packet delivery ratio while mobility.…”

Section: Control Overheadmentioning

confidence: 99%

Investigation on Energy Based Data Gathering Approach for WSN

Bharathidasan¹,

Ramamoorthy²

2017

JAC

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“…To deal with big data generated in WSNs, recent studies [23][24][25] pay a great attention to inter-nodes data correlation techniques and scheduling nodes strategies. The aim of such approaches is to schedule sensors that generate high spatial-temporal correlation into sleep/active mode thus, enhancing the network lifetime.…”

Section: Introductionmentioning

confidence: 99%

Energy-efficient scheduling strategies for minimizing big data collection in cluster-based sensor networks

Harb

Makhoul

2018

Peer-to-Peer Netw. Appl.

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Today, wireless sensor networks (WSNs) have been widely used in monitoring various applications, such as environment, military and healthcare, etc. The explosive growth of the data volume generated in these applications has led to one of the most challenging research issues of the big data era.To deal with such amounts of data, exploring data correlation and scheduling strategies have received great attention in WSNs. In this paper, we propose an efficient mechanism based on the Euclidean distance for searching the spatialtemporal correlation between sensor nodes in periodic applications. Based on this correlation, we propose two sleep/active strategies for scheduling sensors in the network. The first one searches the minimum number of active sensors based on the set covering problem while the second one takes advantages from the correlation degree and the residual energy of the sensors for scheduling them in the network. Our mechanism with the proposed strategies were successfully tested on real sensor data. Compared to other existing techniques, the simulation results show that our mechanism significantly extends the lifetime of the network while conserving the quality of the collected data and the coverage of the monitored area.

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“…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.…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

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confidence: 99%

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

Jiang

2017

TELKOMNIKA

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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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Neighbor-Aided Spatial-Temporal Compressive Data Gathering in Wireless Sensor Networks

Cited by 41 publications

References 7 publications

Investigation on Energy Based Data Gathering Approach for WSN

Investigation on Energy Based Data Gathering Approach for WSN

Energy-efficient scheduling strategies for minimizing big data collection in cluster-based sensor networks

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

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