research-article

SILENCE: distributed adaptive sampling for sensor-based autonomic systems

Authors:

Hariharasudhan Viswanathan,

Dario PompiliAuthors Info & Claims

ICAC '11: Proceedings of the 8th ACM international conference on Autonomic computing

Pages 61 - 70

https://doi.org/10.1145/1998582.1998594

Published: 14 June 2011 Publication History

Abstract

Adaptive sampling and sleep scheduling can help realize the much needed resource efficiency in densely deployed autonomic sensor-based systems that monitor and reconstruct physical or environmental phenomena. This paper presents a data-centric approach to distributed adaptive sampling aimed at minimizing the communication and processing overhead in autonomic networked sensor-based systems. The proposed solution exploits the spatio-temporal correlation in sensed data and eliminates redundancy in transmitted data through selective representation without compromising on accuracy of reconstruction of the monitored phenomenon at a remote monitor node. In addition, the solution also exploits the same correlations for adaptive sleep scheduling aimed at saving energy in Wireless Sensor Networks (WSNs) while also providing a mechanism for ensuring connectivity to the monitor node. The data-centric joint adaptive-sampling and sleep-scheduling solution, SILENCE, has been evaluated through real experiments on a testbed monitoring temperature and humidity distribution in a rack of servers as well as through extensive simulations on TOSSIM, the TinyOS simulator.

References

[1]

Building a network topology for tossim. http://www.tinyos.net/tinyos-2.x/doc/html/tutorial/usc-topologies.html.

[2]

Z. Abbasi, G. Varsamopoulos, and S. K. S. Gupta. Thermal aware server provisioning and workload distribution for internet data centers. In Proc. of Intl. Symp. on High Performance Distributed Computing (HPDC), Chicago, IL, June 2010.

Digital Library

[3]

Ayan Banerjee and Tridib Mukherjee and Georgios Varsamopoulos and Sandeep K. S. Gupta. Cooling-Aware and Thermal-Aware Workload Placement for Green HPC Data Centers. In Proc. of Intl. Green Computing Conf. (IGCC), Chicago, IL, Aug. 2010.

Digital Library

[4]

S. Bandyopadhyay and E. Coyle. An Energy-Efficient Hierarchical Clustering Algorithm for Wireless Sensor Networks. In Proc. of IEEE Conf. on Computer Communications (INFOCOM), San Francisco, CA, Apr. 2003.

[5]

S. Bandyopadhyay and E. J. Coyle. Minimizing Communication Costs in Hierarchically Clustered Networks of Wireless Sensors. Computer Networks, 44(1):1--16, Jan. 2004.

Digital Library

[6]

M. Bhardwaj and A. P. Chandrakasan. Bounding the Lifetime of Sensor Networks Via Optimal Role Assignments. In Proc. of IEEE Conf. on Computer Communications (INFOCOM), New York, NY, June 2002.

[7]

S. Chachra and M. Marefat. Distributed Algorithms for Sleep Scheduling in Wireless sensor Networks. In Proc. of Intl. Conf. on Robotics and Automation (ICRA), Orlando, FL, May 2006.

[8]

T. Cui, L. Chen, T. Ho, S. Low, and L. Andrew. Opportunistic Source Coding for Data Gathering in Wireless Sensor Networks. In Proc. of Mobile Adhoc and Sensor Systems (MASS), Pisa, Italy, Oct. 2007.

[9]

W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan. Energy-Efficient Communication Protocol for Wireless Microsensor Networks. In Proc. of Hawaii Intl. Conf. On System Science (HICSS), Maui, HI, Jan. 2000.

Digital Library

[10]

B. Krishnamachari, D. Estrin, and S. Wicker. The Impact of Data Aggregation in Wireless Sensor Networks. In Proc. of IEEE Intl. Conf. on Distributed Event-Based Systems (DEBS), Vienna, Austria, July 2002.

Digital Library

[11]

J. Kusuma, L. Doherty, and K. Ramchandran. Distributed Compression for Sensor Networks. In Proc. of IEEE Intl. Conf. on Image Processing (ICIP), Thessaloniki, Greece, Oct. 2001.

[12]

E. K. Lee, I. Kulkarni, D. Pompili, and M. Parashar. Proactive Thermal Management in Green Datacenter. The Journal of Supercomputing (Springer), June 2010.

Digital Library

[13]

X.-Y. Li, X. Xu, S. Wang, S. Tang, G. Dai, J. Zhao, and Y. Qi. Efficient Data Aggregation in Multi-hop Wireless Sensor Networks under Physical Interference Model. In Proc. of Intl. Conf. Mobile Adhoc and Sensor Systems (MASS), Macau (S.A.R.), China, Oct. 2009.

[14]

T. Melodia, D. Pompili, and I. F. Akyildiz. A Communication Architecture for Mobile Wireless Sensor and Actor Networks. In Proc. of IEEE Conf. on Sensor, Mesh and Ad Hoc Communications and Networks (SECON), Reston, VA, Sept. 2006.

[15]

V. Mhatre and C. Rosenberg. Design Guidelines for Wireless Sensor Networks: Communication, Clustering and Aggregation. Ad Hoc Networks Journal, 2(1):45--63, Jan. 2004.

[16]

J. Moore, J. S. Chase, and P. Ranganathan. Weatherman: Automated, Online and Predictive Thermal Mapping and Management for Data Centers. In Proc. IEEE Conf. on Autonomic Computing (ICAC), pages 155--164, Dublin, Ireland, June 2006.

Digital Library

[17]

P. Ogren, E. Fiorelli, and N. E. Leonard. Cooperative Control of Mobile Sensor Networks: Adaptive Gradient Climbing in a Distributed Environment. IEEE Transactions on Automatic Control, 49(8):1292--1302, Aug. 2004.

[18]

S. S. Pradhan, J. Kusuma, and K. Ramchandran. Distributed Compression in a Dense Microsensor Network. IEEE Signal Processing Magazine, 19(2):51--60, Mar. 2002.

[19]

S. S. Pradhan and K. Ramchandran. Distributed Source Coding: Symmetric Rates and Applications to Sensor Network. In Proc. of Data Compression Conf. (DCC), Snowbird, UT, Mar. 2000.

Digital Library

[20]

A. Scaglione. Routing and Data Compression in Sensor Networks: Stochastic Models for Sensor Data that Guarantee Scalability. In Proc. IEEE Intl. Symp. on Information Theory (ISIT), Yokohama, Japan, July 2003.

[21]

A. Scaglione and S. D. Servetto. On the Interdependence of Routing and Data Compression in Multi-Hop Sensor Networks. In Proc. of Intl. Conf. on Mobile Computing and Networking (MobiCom), Atlanta, GA, Sept. 2002.

Digital Library

[22]

K. Sohrabi, J. Gao, V. Ailawadhi, and G. J. Pottie. Protocols for Self-Organization of a Wireless Sensor Network. IEEE Personal Communications, 7(1):16--27, Oct. 2000.

[23]

J. A. Stankovic, T. F. Abdelzaher, C. Lu, L. Sha, and J. Hou. Real-time Communication and Coordination in Embedded Sensor Networks. Proc. of the IEEE, 91(7):1002--1022, July 2003.

[24]

M. C. Vuran, O. B. Akan, and I. F. Akyildiz. Spatio-temporal Correlation: Theory and Applications for Wireless Sensor Networks. Computer Networks (Elsevier Science), 45(3):245--259, June 2004.

Digital Library

[25]

R. Willett, A. Martin, and R. Nowak. Backcasting: Adaptive Sampling for Sensor Networks. In Proc. of the Intl. Conf. on Information Processing in Sensor Networks (IPSN), Berkeley, CA, Apr. 2004.

Digital Library

[26]

X. Xu, Y.-H. Hu, W. Liu, and J. Bi. Data-Coverage Sleep Scheduling in Wireless Sensor Networks. In Proc. of Intl. Conf. on Grid and Cooperative Computing (GCC), Shenzhen, China, Oct. 2008.

Digital Library

[27]

O. Younis and S. Fahmy. Distributed Clustering in Ad-hoc Sensor Networks: A Hybrid, Energy-Efficient Approach. In Proc. of IEEE Conf. on Computer Communications (INFOCOM), Hong Kong (S.A.R.), China, Mar. 2004.\endthebibliography

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Casadei RMariani SPianini DViroli MZambonelli F(2022)Space-Fluid Adaptive Sampling: A Field-Based, Self-organising ApproachCoordination Models and Languages10.1007/978-3-031-08143-9_7(99-117)Online publication date: 14-Jun-2022
https://doi.org/10.1007/978-3-031-08143-9_7
Mukherjee PMukherjee A(2019)Advanced processing techniques and secure architecture for sensor networks in ubiquitous healthcare systemsSensors for Health Monitoring10.1016/B978-0-12-819361-7.00001-4(3-29)Online publication date: 2019
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Index Terms

SILENCE: distributed adaptive sampling for sensor-based autonomic systems
1. Networks
  1. Network protocols

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Published In

ICAC '11: Proceedings of the 8th ACM international conference on Autonomic computing

June 2011

278 pages

ISBN:9781450306072

DOI:10.1145/1998582

General Chairs:
Hartmut Schmeck
Karlsruhe Institute of Technology, Germany
,
Wolfgang Rosenstiel
University of Tübingen, Germany
,
Program Chairs:
Tarek Abdelzaher
University of Illinois at Urbana-Champaign, USA
,
Joseph Hellerstein
Google, USA

Copyright © 2011 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: 14 June 2011

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ICAC '11

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University of Arizona
SIGARCH

ICAC '11: 8th International Conference on Autonomic Computing

June 14 - 18, 2011

Karlsruhe, Germany

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Cited By

Pianini DCasadei RMariani SAguzzi GViroli MZambonelli F(2024)Space-Fluid and Time-Fluid ProgrammingFluidware10.1007/978-3-031-62146-8_6(107-134)Online publication date: 13-May-2024
https://doi.org/10.1007/978-3-031-62146-8_6
Casadei RMariani SPianini DViroli MZambonelli F(2022)Space-Fluid Adaptive Sampling: A Field-Based, Self-organising ApproachCoordination Models and Languages10.1007/978-3-031-08143-9_7(99-117)Online publication date: 14-Jun-2022
https://doi.org/10.1007/978-3-031-08143-9_7
Mukherjee PMukherjee A(2019)Advanced processing techniques and secure architecture for sensor networks in ubiquitous healthcare systemsSensors for Health Monitoring10.1016/B978-0-12-819361-7.00001-4(3-29)Online publication date: 2019
https://doi.org/10.1016/B978-0-12-819361-7.00001-4
Silvestri SUrgaonkar RZafer MKo B(2018)A Framework for the Inference of Sensing Measurements Based on CorrelationACM Transactions on Sensor Networks10.1145/327203515:1(1-28)Online publication date: 15-Dec-2018
https://dl.acm.org/doi/10.1145/3272035
Pandey PViswanathan HPompili D(2018)Robust orchestration of concurrent application workflows in mobile device cloudsJournal of Parallel and Distributed Computing10.1016/j.jpdc.2018.05.004120(101-114)Online publication date: Oct-2018
https://doi.org/10.1016/j.jpdc.2018.05.004
Lee EViswanathan HPompili D(2015)Distributed Data-Centric Adaptive Sampling for Cyber-Physical SystemsACM Transactions on Autonomous and Adaptive Systems (TAAS)10.1145/26448209:4(1-27)Online publication date: 14-Jan-2015
https://dl.acm.org/doi/10.1145/2644820
Silvestri SUrgaonkar RZafer MKo B(2015)An Online Method for Minimizing Network Monitoring Overhead2015 IEEE 35th International Conference on Distributed Computing Systems10.1109/ICDCS.2015.35(268-277)Online publication date: Jun-2015
https://doi.org/10.1109/ICDCS.2015.35
Sabri-e-Zaman Gupta MMondragon RBodanese E(2014)An eigendecomposition based adaptive spatial sampling technique for wireless sensor networks39th Annual IEEE Conference on Local Computer Networks10.1109/LCN.2014.6925809(430-433)Online publication date: Sep-2014
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Viswanathan HLee ERodero IPompili DMilojicic DXu DTalwar V(2012)An autonomic resource provisioning framework for mobile computing gridsProceedings of the 9th international conference on Autonomic computing10.1145/2371536.2371550(79-84)Online publication date: 18-Sep-2012
https://dl.acm.org/doi/10.1145/2371536.2371550
Viswanathan HLee EPompili D(2012)Mobile grid computing for data- and patient-centric ubiquitous healthcare2012 The First IEEE Workshop on Enabling Technologies for Smartphone and Internet of Things (ETSIoT)10.1109/ETSIoT.2012.6311263(36-41)Online publication date: Jun-2012
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