Nothing Special   »   [go: up one dir, main page]

skip to main content
research-article

Energy-efficient data gathering in wireless sensor networks with asynchronous sampling

Published: 24 June 2010 Publication History

Abstract

A low sampling rate leads to reduced congestion and hence energy consumption in the resource-constrained wireless sensor networks. In this article, we propose asynchronous sampling that shifts the sampling time instances of sensor nodes from each other. For lossy data gathering scenarios, the proposed approach provides more information about the physical phenomena in terms of increased entropy at a low sampling rate. For lossless data gathering scenarios, on the other hand, the sampling rate is lowered without sacrificing critical knowledge required for signal reconstruction. As lower sampling rates lead to smaller energy consumption for processing and transmitting the collected sensory data, the proposed asynchronous sampling strategies are capable of achieving a better trade-off between the lifetime of the network and the quality of collected information. In addition to mathematical analysis, simulation results based on real data also verify the benefits of our asynchronous sampling.

References

[1]
Arora, S. and Frieze, A. 1996. A new rounding procedure for the assignment problem with applications to dense graph arrangement problems. In Proceedings of the 37th IEEE Symposium on Foundations of Computer Science. 21--30.
[2]
Bandyopadhyay, S., Tian, Q., and Coyle, E. J. 2005. Spatio-Temporal sampling rates and energy efficiency in wireless sensor networks. IEEE/ACM Trans. Netw. 13, 6, 1339--1352.
[3]
Bian, F., Kempe, D., and Govindan, R. 2006. Utility based sensor selection. In Proceedings of the 5th International Conference on Information Processing in Sensor Networks. 11--18.
[4]
Burer, S. and Lee, J. 2007. Solving maximum-entropy sampling problems using factored masks. Math. Program. 109, 2, 263--281.
[5]
Chou, J., Petrovic, D., and Ramchandran, K. 2004. A distributed and adaptive signal processing approach to exploiting correlation in sensor networks. Ad Hoc Netw. 2, 4, 387--403.
[6]
Diggle, P. J., Tawn, J. A., and Moyeed, R. A. 1998. Model-based geostatistics. Appl. Statist. 47, 3, 299--350.
[7]
Donoho, D. 2006. Compressed sensing. IEEE Trans. Inf. Theory 52, 4, 1289--1306.
[8]
Duarte, M. F., Sarvotham, S., Baron, D., Wakin, M. B., and Baraniuk, R. G. 2005. Distributed compressed sensing of jointly sparse signals. In Proceedings of the 39th Asilomar Conference on Signals, Systems, and Computers.
[9]
Feichtinger, H. G. and Gröchenig, K. 1994. Theory and practice of irregular sampling. In Wavelets: Mathematics and Applications. 305--363.
[10]
Guestrin, C., Bodi, P., Thibau, R., Paski, M., and Madde, S. 2004. Distributed regression: An efficient framework for modeling sensor network data. In Proceedings of the 3rd International Symposium on Information Processing in Sensor Networks (IPSN'04). 1--10.
[11]
Guestrin, C., Krause, A., and Singh, A. P. 2005. Near-Optimal sensor placements in gaussian processes. In Proceedings of the 22nd International Conference on Machine Learning (ICML'05). 265--272.
[12]
Hoang, A. T. and Motani, M. 2005. Exploiting wireless broadcast in spatially correlated sensor networks. In Proceedings of the IEEE International Conference on Communications (ICC'05). 2807--2811.
[13]
Hou, Y. T., Shi, Y., and Sherali, H. D. 2004. Rate allocation in wireless sensor networks with network lifetime requirement. In Proceedings of the 5th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc'04). 67--77.
[14]
J. Kho, A. R. and Jennings, N. R. 2007. Decentralised adaptive sampling of wireless sensor networks. In Proceedings of the 1st International Workshop on Agent Technology for Sensor Networks. 55--62.
[15]
Jain, A. and Chang, E. Y. 2004. Adaptive sampling for sensor networks. In Proceeedings of the 1st International Workshop on Data Management for Sensor Networks (DMSN'04). 10--16.
[16]
Jindal, A. and Psounis, K. 2006. Modeling spatially-correlated sensor network data. ACM Trans. Sensor Netw. 5, 1--35.
[17]
Julius Degesys, Ian Rose, A. P. and Nagpal, R. 2007. Desync: Self-Organizing desynchronization and tdma on wsns. In Proceeedings of the 6th International Symposium on Information Processing in Sensor Networks (IPSN'07).
[18]
Krishnamachari, L., Estrin, D., and Wicker, S. 2002. The impact of data aggregation in wireless sensor networks. In Proceedings of the 22nd International Conference on Distributed Computing Systems Workshops (ICDCSW'02). 575--578.
[19]
Lab, I. B. 2004. http://db.lcs.mit.edu/labdata/labdata.html.
[20]
Liu, X., Wang, Q., He, W., Caccamo, M., and Sha, L. 2006. Optimal real-time sampling rate assignment for wireless sensor networks. ACM Trans. Sensor Netw. 2, 2, 263--295.
[21]
Luo, H., Liu, Y., and Das, S. K. 2009. Distributed algorithm for en route aggregation decision in wireless sensor networks. IEEE Trans. Mobile Comput. 8, 1, 1--13.
[22]
Luo, H., Luo, J., Liu, Y., and Das, S. K. 2006. Adaptive data fusion for energy efficient routing in wireless sensor networks. IEEE Trans. Comput. 55, 10, 1286--1299.
[23]
Mackay, D. 1997. Gaussian processes—A replacement for supervised neural networks? In Lecture Notes for a Tutorial at Neural Information Processing Systems Conference.
[24]
Osborne, M. A., Roberts, S. J., Rogers, A., Ramchurn, S. D., and Jennings, N. R. 2008. Towards real-time information processing of sensor network data using computationally efficient multi-output gaussian processes. In Proceedings of the 7th International Conference on Information Processing in Sensor Networks (IPSN'08). 109--120.
[25]
Paek, J. and Govindan, R. 2007. RCRT: Rate-Controlled reliable transport for wireless sensor networks. In Proceedings of the 5th International Conference on Embedded Networked Sensor Systems (SenSys'07). 305--319.
[26]
Pattem, S., Krishnamachari, B., and Govindan, R. 2004. The impact of spatial correlation on routing with compression in wireless sensor networks. In Proceedings of the 3rd International Symposium on Information Processing in Sensor Networks (IPSN'04). 28--35.
[27]
Rabbat, M. and Nowak, R. 2004. Distributed optimization in sensor networks. In Proceedings of the 3rd International Symposium on Information Processing in Sensor Networks (IPSN'04). 20--27.
[28]
Shenoi, B. A. 2005. Introduction to Digital Signal Processing and Filter Design. Wiley-Interscience.
[29]
Sivrikaya, F. and Yener, B. 2004. Time synchronization in sensor networks: A survey. IEEE Netw. 18, 4, 45--50.
[30]
Sun, K., Ning, P., and Wang, C. 2005. Fault-Tolerant cluster-wise clock synchronization for wireless sensor networks. IEEE Trans. Depend. Secure Comput. 2, 3, 177--189.
[31]
Tang, C. and Raghavendra, C. S. 2004. Correlation analysis and application in wireless microsensor networks. In Proceedings of the 1st Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services (MobiQuitous'04). 184--193.
[32]
Vuran, M. C. and Akyildiz, I. F. 2006. Spatial correlation-based collaborative medium access control in wireless sensor networks. IEEE/ACM Trans. Netw. 14, 2, 316--329.
[33]
Wan, C.-Y., Eisenman, S. B., and Campbell, A. T. 2003. Coda: Congestion detection and avoidance in sensor networks. In Proceedings of the 1st International Conference on Embedded Networked Sensor Systems (SenSys'03). 266--279.
[34]
Wang, J., Liu, Y., and Das, S. 2007. Asynchronous sampling of correlated data in wireless sensor networks. In Proceedings of the IEEE Wireless Communications and Networking Conference (WCNC'07). 2768--2772.
[35]
Wang, J., Liu, Y., and Das, S. 2008. Asynchronous sampling benefits wireless sensor networks. In Proceedings of the 27th IEEE Conference on Computer Communications (INFoCoM'08). 2207--2215.
[36]
Willett, R., Martin, A., and Nowak, R. 2004. Backcasting: Adaptive sampling for sensor networks. In Proceedings of the 3rd International Symposium on Information Processing in Sensor Networks (IPSN'04). 124--133.
[37]
Xiao, J.-J., Ribeiro, A., Luo, Z.-Q., and Giannakis, G. 2006. Distributed compression-estimation using wireless sensor networks. IEEE Signal Process. Mag. 23, 4, 27--41.
[38]
Yu, Y., Krishnamachari, B., and Prasanna, V. 2008. Data gathering with tunable compression in sensor networks. IEEE Trans. Parallel Distrib. Syst. 19, 2, 276--287.
[39]
Yuen, K., Liang, B., and Li, B. 2008. A distributed framework for correlated data gathering in sensor networks. IEEE Trans. Vehicular Technol. 57, 1, 578--593.
[40]
Zhao, F., Liu, J., Liu, J., Guibas, L., and Reich, J. 2003. Collaborative signal and information processing: An information-directed approach. Proc. IEEE 91, 8, 1199--1209.
[41]
Zhu, J., Hung, K.-L., and Bensaou, B. 2006. Tradeoff between network lifetime and fair rate allocation in wireless sensor networks with multi-path routing. In Proceedings of the 9th ACM International Symposium on Modeling Analysis and Simulation of Wireless and Mobile Systems (MSWiM'06). 301--308.
[42]
Zhu, Y., Vedantham, R., Park, S.-J., and Sivakumar, R. 2008. A scalable correlation aware aggregation strategy for wireless sensor networks. Inf. Fusion 9, 3, 354--369.

Cited By

View all
  • (2023)Walk‐to‐Charge Technology: Exploring Efficient Energy Harvesting Solutions for Smart ElectronicsJournal of Sensors10.1155/2023/66146582023:1Online publication date: 10-Oct-2023
  • (2023)UAV and UGV Assisted Path Planning for Sensor Data Collection in Precision Agriculture2023 11th International Symposium on Electronic Systems Devices and Computing (ESDC)10.1109/ESDC56251.2023.10149861(1-6)Online publication date: 4-May-2023
  • (2022)Age-of-Information Oriented Scheduling for Multichannel IoT Systems With Correlated SourcesIEEE Transactions on Wireless Communications10.1109/TWC.2022.317930521:11(9775-9790)Online publication date: Nov-2022
  • Show More Cited By

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Transactions on Sensor Networks
ACM Transactions on Sensor Networks  Volume 6, Issue 3
June 2010
320 pages
ISSN:1550-4859
EISSN:1550-4867
DOI:10.1145/1754414
Issue’s Table of Contents
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]

Publisher

Association for Computing Machinery

New York, NY, United States

Journal Family

Publication History

Published: 24 June 2010
Accepted: 01 July 2009
Revised: 01 July 2009
Received: 01 November 2008
Published in TOSN Volume 6, Issue 3

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Models
  2. asynchronous sampling
  3. data gathering
  4. energy efficiency
  5. temporal-spatial correlation
  6. wireless sensor networks

Qualifiers

  • Research-article
  • Research
  • Refereed

Funding Sources

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)5
  • Downloads (Last 6 weeks)0
Reflects downloads up to 25 Nov 2024

Other Metrics

Citations

Cited By

View all
  • (2023)Walk‐to‐Charge Technology: Exploring Efficient Energy Harvesting Solutions for Smart ElectronicsJournal of Sensors10.1155/2023/66146582023:1Online publication date: 10-Oct-2023
  • (2023)UAV and UGV Assisted Path Planning for Sensor Data Collection in Precision Agriculture2023 11th International Symposium on Electronic Systems Devices and Computing (ESDC)10.1109/ESDC56251.2023.10149861(1-6)Online publication date: 4-May-2023
  • (2022)Age-of-Information Oriented Scheduling for Multichannel IoT Systems With Correlated SourcesIEEE Transactions on Wireless Communications10.1109/TWC.2022.317930521:11(9775-9790)Online publication date: Nov-2022
  • (2021)Data Collection Utility Maximization in Wireless Sensor Networks via Efficient Determination of UAV Hovering Locations2021 IEEE International Conference on Pervasive Computing and Communications (PerCom)10.1109/PERCOM50583.2021.9439126(1-10)Online publication date: 22-Mar-2021
  • (2020)Energy Management Techniques for WSNs (2): Data-Driven ApproachWireless Sensor Networks10.1007/978-3-030-29700-8_5(259-398)Online publication date: 26-Jan-2020
  • (2019)Performance Evaluation of Unitary Measurement Matrix in Compressed Data Gathering for Real-Time Wireless Sensor Network ApplicationsPervasive Computing: A Networking Perspective and Future Directions10.1007/978-981-13-3462-7_9(93-102)Online publication date: 30-Jan-2019
  • (2018)Data Volume Based Data Gathering in WSNs using Mobile Data CollectorProceedings of the 22nd International Database Engineering & Applications Symposium10.1145/3216122.3216166(199-207)Online publication date: 18-Jun-2018
  • (2018)Energy efficient cluster head formation in wireless sensor networkMicrosystem Technologies10.1007/s00542-018-3873-724:12(4775-4784)Online publication date: 1-Dec-2018
  • (2017)Building the Data Association Network of Sensors in the Internet of ThingsAutomatika10.7305/automatika.54-4.41954:4(459-470)Online publication date: 20-Jan-2017
  • (2017)e-SamplingACM Transactions on Autonomous and Adaptive Systems10.1145/299415012:1(1-29)Online publication date: 27-Mar-2017
  • Show More Cited By

View Options

Login options

Full Access

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media