Distributed Data Aggregation Using Slepian–Wolf Coding in Cluster-Based Wireless Sensor Networks

Zheng, Jun; Wang, Pu; Li, Cheng

doi:10.1109/tvt.2010.2042186

Cited by 69 publications

(24 citation statements)

References 23 publications

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“…Basically, the increase of network lifetime will be proportional to the reduction in the number of transmitted data packets. Following this principle, data reduction has become one of the most enhanced solutions that is aimed to reduce the amount of data transmissions [15,16,17,18].…”

Section: Introductionmentioning

confidence: 99%

Data prediction, compression, and recovery in clustered wireless sensor networks for environmental monitoring applications

Tan

Xiong

2016

Information Sciences

213

134

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Section: Introductionmentioning

confidence: 99%

Data prediction, compression, and recovery in clustered wireless sensor networks for environmental monitoring applications

Tan

Xiong

2016

Information Sciences

213

134

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“…In our simulations, the source sequence from Source 2 has a temporal correlation of ρ te2 = 0.51 for R s = 1, 2 and 3. 6 In this case, since u 2 is uncompressed and it is perfectly known at the receiver, we have R 2 = R s . Hence, the SW-gap with respect to the SW bound is shown in Fig.…”

Section: B Ser Performance and Rate Region Analysismentioning

confidence: 99%

Distributed Joint Source Coding and Trellis Coded Modulation for Symbol-Based Markov Sources

Izhar

Aljohani

et al. 2018

IEEE Trans. Veh. Technol.

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A distributed joint source-channel coding scheme based on a unity-rate code (URC)-assisted trellis coded modulation (TCM) is proposed, which exploits the spatio-temporal correlation of symbol-based sources. More specifically, asymmetric distributed source coding of two spatially correlated Markov sources is considered, where one of the sources is assumed to be perfectly decoded and to be available at the receiver of the other source as side information. In order to exploit the temporal correlation statistics, an iterative decoding process exchanging extrinsic information between the amalgamated URC-assisted TCM and a soft-symbol source decoder employing a modified symbol-based maximum a posteriori algorithm is invoked. Furthermore, the Slepian-Wolf (SW) bound of symbol-based sources having spatiotemporal correlation is derived and the benefits of exploiting the spatio-temporal correlation using the proposed coding scheme are demonstrated by our extrinsic information transfer chart analysis. It is shown from our simulation results that upon exploiting the spatio-temporal correlation of the sources, the proposed coding scheme is capable of operating within 0.02 bit of the SW bound.

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“…To reduce the amount of communication data in WSN, a lot of correlation-based data aggregation methods have been proposed in [7,[16][17][18][19][20][21][22][23][24]. The traditional data aggregation methods which are used in WSN mainly include two categories, the first type is based on least square method, Bayesian estimation method, D-S evidence theory, and so on; the other is based on artificial intelligence theory of artificial neural network method, fuzzy reasoning method, and rough set method [25].…”

Section: Related Studiesmentioning

confidence: 99%

Research on Monitoring and Prewarning System of Accident in the Coal Mine Based on Big Data

Chen

Wei

2018

Scientific Programming

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More and more big data come from sensor nodes. There are many sensor nodes placed in the monitoring and prewarning system of the coal mine in China for the purpose of monitoring the state of the environment. It works every day and forms the coal mine big data. Traditional coal mine monitoring and prewarning systems are mainly based on mine communication cable, but they are difficult to place at coal working face tunnels. We use WSN to replace mine communication cable and build the monitoring and prewarning system. The sensor nodes in WSN are energy limited and the sensor data are complicated so it is very difficult to use these data directly to prewarn the accident. To solve these problems, in this paper, a new data aggregation strategy and fuzzy comprehensive assessment model are proposed. Simulations compared the energy consumption, delay time, cooperation cost, and prewarning time with our previous work. The result shows our method is reasonable.

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Distributed Data Aggregation Using Slepian–Wolf Coding in Cluster-Based Wireless Sensor Networks

Cited by 69 publications

References 23 publications

Data prediction, compression, and recovery in clustered wireless sensor networks for environmental monitoring applications

Data prediction, compression, and recovery in clustered wireless sensor networks for environmental monitoring applications

Distributed Joint Source Coding and Trellis Coded Modulation for Symbol-Based Markov Sources

Research on Monitoring and Prewarning System of Accident in the Coal Mine Based on Big Data

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