research-article

Compressive data gathering for large-scale wireless sensor networks

Authors:

Chang Wen ChenAuthors Info & Claims

MobiCom '09: Proceedings of the 15th annual international conference on Mobile computing and networking

Pages 145 - 156

https://doi.org/10.1145/1614320.1614337

Published: 20 September 2009 Publication History

Abstract

This paper presents the first complete design to apply compressive sampling theory to sensor data gathering for large-scale wireless sensor networks. The successful scheme developed in this research is expected to offer fresh frame of mind for research in both compressive sampling applications and large-scale wireless sensor networks. We consider the scenario in which a large number of sensor nodes are densely deployed and sensor readings are spatially correlated. The proposed compressive data gathering is able to reduce global scale communication cost without introducing intensive computation or complicated transmission control. The load balancing characteristic is capable of extending the lifetime of the entire sensor network as well as individual sensors. Furthermore, the proposed scheme can cope with abnormal sensor readings gracefully. We also carry out the analysis of the network capacity of the proposed compressive data gathering and validate the analysis through ns-2 simulations. More importantly, this novel compressive data gathering has been tested on real sensor data and the results show the efficiency and robustness of the proposed scheme.

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Cited By

Shi TCai ZLi JGao H(2024)Optimize the Age of Useful Information in Edge-assisted Energy-harvesting Sensor NetworksACM Transactions on Sensor Networks10.1145/364034220:2(1-26)Online publication date: 16-Feb-2024
https://dl.acm.org/doi/10.1145/3640342
Tanygin MTanygina E(2024)The Model for the Formation of Time Intervals for Receiving Messages in Distributed Information Systems2024 International Russian Automation Conference (RusAutoCon)10.1109/RusAutoCon61949.2024.10694647(8-12)Online publication date: 8-Sep-2024
https://doi.org/10.1109/RusAutoCon61949.2024.10694647
Ghaderi MTabataba Vakili VSheikhan M(2023)Energy Consumption Analysis based on Compressive Sensing Model in Wireless Sensor NetworksSignal and Data Processing10.61186/jsdp.20.2.19520:2(195-210)Online publication date: 1-Sep-2023
https://doi.org/10.61186/jsdp.20.2.195
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Index Terms

Compressive data gathering for large-scale wireless sensor networks
1. Networks
  1. Network protocols
  2. Network types
    1. Mobile networks
    2. Wireless access networks

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Published In

cover image ACM Conferences

MobiCom '09: Proceedings of the 15th annual international conference on Mobile computing and networking

September 2009

368 pages

ISBN:9781605587028

DOI:10.1145/1614320

General Chairs:
Kang G. Shin
University of Michigan, USA
,
Yongguang Zhang
Microsoft Research Asia, China
,
Program Chairs:
Rajive Bagrodia
University of California, Los Angeles, USA
,
Ramesh Govindan
University of Southern California, USA

Copyright © 2009 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 20 September 2009

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MobiCom'09

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MobiCom'09: Annual International Conference on Mobile Computing and Networking

September 20 - 25, 2009

Beijing, China

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Overall Acceptance Rate 440 of 2,972 submissions, 15%

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Cited By

Shi TCai ZLi JGao H(2024)Optimize the Age of Useful Information in Edge-assisted Energy-harvesting Sensor NetworksACM Transactions on Sensor Networks10.1145/364034220:2(1-26)Online publication date: 16-Feb-2024
https://dl.acm.org/doi/10.1145/3640342
Tanygin MTanygina E(2024)The Model for the Formation of Time Intervals for Receiving Messages in Distributed Information Systems2024 International Russian Automation Conference (RusAutoCon)10.1109/RusAutoCon61949.2024.10694647(8-12)Online publication date: 8-Sep-2024
https://doi.org/10.1109/RusAutoCon61949.2024.10694647
Ghaderi MTabataba Vakili VSheikhan M(2023)Energy Consumption Analysis based on Compressive Sensing Model in Wireless Sensor NetworksSignal and Data Processing10.61186/jsdp.20.2.19520:2(195-210)Online publication date: 1-Sep-2023
https://doi.org/10.61186/jsdp.20.2.195
Zhang HNa JZhang B(2023)Autonomous Internet of Things (IoT) Data Reduction Based on Adaptive ThresholdSensors10.3390/s2323942723:23(9427)Online publication date: 26-Nov-2023
https://doi.org/10.3390/s23239427
Wang XChen HLi S(2023)A reinforcement learning-based sleep scheduling algorithm for compressive data gathering in wireless sensor networksEURASIP Journal on Wireless Communications and Networking10.1186/s13638-023-02237-42023:1Online publication date: 13-Mar-2023
https://doi.org/10.1186/s13638-023-02237-4
Zhang KShi YKarnouskos SSauter TFang HColombo A(2023)Advancements in Industrial Cyber-Physical Systems: An Overview and PerspectivesIEEE Transactions on Industrial Informatics10.1109/TII.2022.319948119:1(716-729)Online publication date: Jan-2023
https://doi.org/10.1109/TII.2022.3199481
Zhang YGuo PLiu XZhang K(2023)In-Network Processing or Feature Compressive Sensing? Case Study of Structural Health Monitoring With Wireless Sensor NetworksIEEE Internet of Things Journal10.1109/JIOT.2022.322858710:8(7051-7061)Online publication date: 15-Apr-2023
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Kumar GSharma PPradhan P(2023)A Hybrid CDG-CSF Based Approach for Enhancing Lifetime of Wireless Sensor Network2023 7th International Conference on Computer Applications in Electrical Engineering-Recent Advances (CERA)10.1109/CERA59325.2023.10455557(1-6)Online publication date: 27-Oct-2023
https://doi.org/10.1109/CERA59325.2023.10455557
Ghaderi MSheikhan M(2023)An Energy-Aware Model for Wireless Sensor Networks: Hierarchical Compressive Data Gathering for Hierarchical Grid-Based Routing (HCDG-HGR)Wireless Personal Communications10.1007/s11277-023-10200-1129:3(1645-1668)Online publication date: 21-Feb-2023
https://doi.org/10.1007/s11277-023-10200-1
Sureshkumar CSabena S(2023)Design of an adaptive framework with compressive sensing for spatial data in wireless sensor networksWireless Networks10.1007/s11276-023-03291-y29:5(2203-2216)Online publication date: 14-Mar-2023
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