D-DSC: Decoding Delay-based Distributed Source Coding for Internet of Sensing Things

Spatial correlation between densely deployed sensor nodes in a wireless sensor network (WSN) can be exploited to reduce the power consumption through a proper source coding mechanism such as distributed source coding (DSC). In this paper, we propose the Decoding Delay-based Distributed Source Coding...

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Veröffentlicht in:PloS one Jg. 13; H. 3; S. e0193154
Hauptverfasser: Aktas, Metin, Kuscu, Murat, Dinc, Ergin, Akan, Ozgur B.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Public Library of Science 14.03.2018
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Abstract Spatial correlation between densely deployed sensor nodes in a wireless sensor network (WSN) can be exploited to reduce the power consumption through a proper source coding mechanism such as distributed source coding (DSC). In this paper, we propose the Decoding Delay-based Distributed Source Coding (D-DSC) to improve the energy efficiency of the classical DSC by employing the decoding delay concept which enables the use of the maximum correlated portion of sensor samples during the event estimation. In D-DSC, network is partitioned into clusters, where the clusterheads communicate their uncompressed samples carrying the side information, and the cluster members send their compressed samples. Sink performs joint decoding of the compressed and uncompressed samples and then reconstructs the event signal using the decoded sensor readings. Based on the observed degree of the correlation among sensor samples, the sink dynamically updates and broadcasts the varying compression rates back to the sensor nodes. Simulation results for the performance evaluation reveal that D-DSC can achieve reliable and energy-efficient event communication and estimation for practical signal detection/estimation applications having massive number of sensors towards the realization of Internet of Sensing Things (IoST).
AbstractList Spatial correlation between densely deployed sensor nodes in a wireless sensor network (WSN) can be exploited to reduce the power consumption through a proper source coding mechanism such as distributed source coding (DSC). In this paper, we propose the Decoding Delay-based Distributed Source Coding (D-DSC) to improve the energy efficiency of the classical DSC by employing the decoding delay concept which enables the use of the maximum correlated portion of sensor samples during the event estimation. In D-DSC, network is partitioned into clusters, where the clusterheads communicate their uncompressed samples carrying the side information, and the cluster members send their compressed samples. Sink performs joint decoding of the compressed and uncompressed samples and then reconstructs the event signal using the decoded sensor readings. Based on the observed degree of the correlation among sensor samples, the sink dynamically updates and broadcasts the varying compression rates back to the sensor nodes. Simulation results for the performance evaluation reveal that D-DSC can achieve reliable and energy-efficient event communication and estimation for practical signal detection/estimation applications having massive number of sensors towards the realization of Internet of Sensing Things (IoST).
Spatial correlation between densely deployed sensor nodes in a wireless sensor network (WSN) can be exploited to reduce the power consumption through a proper source coding mechanism such as distributed source coding (DSC). In this paper, we propose the Decoding Delay-based Distributed Source Coding (D-DSC) to improve the energy efficiency of the classical DSC by employing the decoding delay concept which enables the use of the maximum correlated portion of sensor samples during the event estimation. In D-DSC, network is partitioned into clusters, where the clusterheads communicate their uncompressed samples carrying the side information, and the cluster members send their compressed samples. Sink performs joint decoding of the compressed and uncompressed samples and then reconstructs the event signal using the decoded sensor readings. Based on the observed degree of the correlation among sensor samples, the sink dynamically updates and broadcasts the varying compression rates back to the sensor nodes. Simulation results for the performance evaluation reveal that D-DSC can achieve reliable and energy-efficient event communication and estimation for practical signal detection/estimation applications having massive number of sensors towards the realization of Internet of Sensing Things (IoST).Spatial correlation between densely deployed sensor nodes in a wireless sensor network (WSN) can be exploited to reduce the power consumption through a proper source coding mechanism such as distributed source coding (DSC). In this paper, we propose the Decoding Delay-based Distributed Source Coding (D-DSC) to improve the energy efficiency of the classical DSC by employing the decoding delay concept which enables the use of the maximum correlated portion of sensor samples during the event estimation. In D-DSC, network is partitioned into clusters, where the clusterheads communicate their uncompressed samples carrying the side information, and the cluster members send their compressed samples. Sink performs joint decoding of the compressed and uncompressed samples and then reconstructs the event signal using the decoded sensor readings. Based on the observed degree of the correlation among sensor samples, the sink dynamically updates and broadcasts the varying compression rates back to the sensor nodes. Simulation results for the performance evaluation reveal that D-DSC can achieve reliable and energy-efficient event communication and estimation for practical signal detection/estimation applications having massive number of sensors towards the realization of Internet of Sensing Things (IoST).
Audience Academic
Author Dinc, Ergin
Aktas, Metin
Akan, Ozgur B.
Kuscu, Murat
AuthorAffiliation 2 Department of Electrical and Electronics Engineering, Koc University, 34450, Istanbul, Turkey
1 Aselsan, Inc., 06370, Ankara, Turkey
University of Texas at San Antonio, UNITED STATES
3 Electrical Engineering Division, Department of Engineering, University of Cambridge, CB3 0FA, Cambridge, United Kingdom
AuthorAffiliation_xml – name: 1 Aselsan, Inc., 06370, Ankara, Turkey
– name: 3 Electrical Engineering Division, Department of Engineering, University of Cambridge, CB3 0FA, Cambridge, United Kingdom
– name: 2 Department of Electrical and Electronics Engineering, Koc University, 34450, Istanbul, Turkey
– name: University of Texas at San Antonio, UNITED STATES
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/29538405$$D View this record in MEDLINE/PubMed
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CitedBy_id crossref_primary_10_1002_dac_4623
crossref_primary_10_1016_j_comcom_2019_11_046
crossref_primary_10_1515_freq_2020_0163
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2018 Aktas et al 2018 Aktas et al
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– notice: 2018 Aktas et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
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Snippet Spatial correlation between densely deployed sensor nodes in a wireless sensor network (WSN) can be exploited to reduce the power consumption through a proper...
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SubjectTerms Algorithms
Codes
Coding
Compression
Computer and Information Sciences
Correlation
Data compression
Decoding
Delay
Electrical engineering
Energy efficiency
Engineering and Technology
Engineering research
Evaluation
Information theory
Internet of Things
Management
Performance evaluation
Physical Sciences
Power consumption
Remote sensors
Research and Analysis Methods
Sensors
Signal detection
Signal processing
Wireless communications
Wireless networks
Wireless sensor networks
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Title D-DSC: Decoding Delay-based Distributed Source Coding for Internet of Sensing Things
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