research-article

Lance: optimizing high-resolution signal collection in wireless sensor networks

Authors:

Geoffrey Werner-Allen,

Stephen Dawson-Haggerty,

Matt WelshAuthors Info & Claims

SenSys '08: Proceedings of the 6th ACM conference on Embedded network sensor systems

Pages 169 - 182

https://doi.org/10.1145/1460412.1460430

Published: 05 November 2008 Publication History

Abstract

An emerging class of sensor networks focuses on reliable collection of high-resolution signals from across the network. In these applications, the system is capable of acquiring more data than can be delivered to the base station, due to severe limits on radio bandwidth and energy. Moreover, these systems are unable to take advantage of conventional approaches to in-network data aggregation, given the high data rates and need for raw signals. These systems face an important challenge: how to maximize the overall value of the collected data, subject to resource constraints.

In this paper, we describe Lance, a general approach to bandwidth and energy management for reliable data collection in wireless sensor networks. Lance couples the use of optimized, data-driven reliable data collection with a model of energy cost for extracting data from the network. Lance's design decouples resource allocation mechanisms from application-specific policies, enabling flexible customization of the system's optimization metrics.

We describe the Lance architecture in detail, demonstrating its use through a range of target applications and resource management policies. We present an extensive study driven by both real and synthetic data distributions, through simulations and runs on a large sensor testbed. We show that Lance maximizes the value of the collected data under a range of resource constraints, achieving near-optimal allocation of radio bandwidth and energy. Finally, we present results from a real sensor network deployment at Tungurahua volcano, Ecuador, in which Lance was used to drive data collection for an eight-node network collecting seismic and acoustic signals from the active volcano.

References

[1]

Cartel. http://cartel.csail.mit.edu/.

[2]

K. Aki and R. Richards. Quantitative Seismology: Theory and Methods. W. H. Freeman, San Francisco, 1980.

[3]

A. M. Ali, T. C. Collier, L. Girod, K. Yao, C. E. Taylor, and D. T. Blumstein. An empirical study of collaborative acoustic source localization. In Proc. IPSN 2007, Cambridge, MA, April 2007.

Digital Library

[4]

H. Balakrishnan, S. Madden, and K. Amaratunga. Wavescope: An adaptive wireless sensor network system for high data-rate applications. http://wavescope.csail.mit.edu/.

[5]

K. Chintalapudi, J. Paek, O. Gnawali, T. Fu, K. Dantu, J. Caffrey, R. Govindan, and E. Johnson. Structural Damage Detection and Localization Using NetSHM. In Proc. Fifth International Conference on Information Processing in Sensor Networks: Special track on Sensor Platform Tools and Design Methods for Networked Embedded Systems (IPSN/SPOTS'06), April 2006.

Digital Library

[6]

B. Greenstein, C. Mar, A. Pesterev, S. Farshchi, E. Kohler, J. Judy, and D. Estrin. Capturing high-frequency phenomena using a bandwidth-limited sensor network. In Proc. Sensys 2006, Boulder, CO, November 2006.

Digital Library

[7]

T. He, S. Krishnamurthy, J. A. Stankovic, T. Abdelzaher, L. Luo, R. Stoleru, T. Yan, L. Gu, G. Zhou, J. Hui, and B. Krogh. Vigilnet: An integrated sensor network system for energy-efficient surveillance. ACM Transactions on Sensor Networks, 2004.

Digital Library

[8]

J. Hill, R. Szewczyk, A. Woo, S. Hollar, D. E. Culler, and K. S. J. Pister. System architecture directions for networked sensors. In Proc. the 9th International Conference on Architectural Support for Programming Languages and Operating Systems, pages 93--104, Boston, MA, USA, Nov. 2000.

Digital Library

[9]

J. W. Hui and D. Culler. The dynamic behavior of a data dissemination protocol for network programming at scale. In Proc. 2nd ACM Conference on Embedded Networked Sensor Systems (SenSys'04), November 2004.

Digital Library

[10]

A. Husker, I. Stubailo, M. Lukac, V. Naik, R. Guy, P. Davis, and D. Estrin. Wilson: The wirelessly linked seismological network and its application in the middle american subduction experiment (mase). Seismological Research Letters, May/June 2008.

[11]

Intel Corporation. The SHIMMER Sensor Node Platform. 2006.

[12]

S. Kim, R. Fonseca, P. Dutta, A. Tavakoli, D. Culler, P. Levis, S. Shenker, and I. Stoica. Flush: A Reliable Bulk Transport Protocol for Multihop Wireless Networks. In Proc. SenSys'07, 2007.

Digital Library

[13]

S. Kim, S. Pakzad, D. Culler, J. Demmel, G. Fenves, S. Glaser, and M. Turon. Health monitoring of civil infrastructures using wireless sensor networks. In Proc. IPSN 2007, Cambridge, MA, April 2007.

Digital Library

[14]

L. Krishnamurthy, R. Adler, P. Buonadonna, J. Chhabra, M. Flanigan, N. Kushalnagar, L. Nachman, and M. Yarvis. Design and deployment of industrial sensor networks: experiences from a semiconductor plant and the north sea. In SenSys '05: Proceedings of the 3rd international conference on Embedded networked sensor systems, pages 64--75, New York, NY, USA, 2005. ACM Press.

Digital Library

[15]

J. Lees and G. Lindley. Three-dimensional attenuation tomography at Loma Prieta: Inverting t* for Q. J. Geophys. Res., 99(B4):6843--6863, 1994.

[16]

L. Luo, Q. Cao, C. Huang, T. Abdelzaher, J. A. Stankovic, and M. Ward. Enviromic: Towards cooperative storage and retrieval in audio sensor networks. In Proc. 27th International Conference on Distributed Computing Systems (ICDCS '07), June 2007.

Digital Library

[17]

J. P. Lynch, Y. Wang, K.-C. Lu, T.-C. Hou, and C.-H. Loh. Post-seismic damage assessment of steel structures instrumented with self-interrogating wireless sensors. In Proceedings of the 8th National Conference on Earthquake Engineering, 2006.

[18]

G. Mainland, M. Welsh, and G. Morrisett. Flask: A language for data-driven sensor network programs. Technical Report TR-13-06, Harvard University, May 2006.

[19]

M. Maroti, B. Kusy, G. Simon, and A. Ledeczi. The flooding time synchronization protocol. In Second ACM Conference on Embedded Networked Sensor Systems, November 2004.

Digital Library

[20]

T. Murray and E. Endo. A real-time seismic-amplitude measurement system (rsam). In Ewart and Swanson, editors, Monitoring Volcanoes: Techniques and Strategies Used by the Staff of the Cascades Volcano Observatory, 1980-1990, volume 1966, pages 5--10. USGS Bulletin, 1992.

[21]

J. Paek, K. Chintalapudi, J. Caffrey, R. Govindan, and S. Masri. A wireless sensor network for structural health monitoring: Performance and experience. In Proc. The Second IEEE Workshop on Embedded Networked Sensors (EmNetS-II), May 2005.

Digital Library

[22]

J. Paek and R. Govindan. RCRT: rate-controlled reliable transport for wireless sensor networks. In SenSys '07: Proceedings of the 5th international conference on Embedded networked sensor systems, pages 305--319, 2007.

Digital Library

[23]

M. Rahimi, R. Baer, O. I. Iroezi, J. C. Garcia, J. Warrior, D. Estrin, and M. Srivastava. Cyclops: in situ image sensing and interpretation in wireless sensor networks. In SenSys '05: Proceedings of the 3rd international conference on Embedded networked sensor systems, pages 192--204, New York, NY, USA, 2005. ACM Press.

Digital Library

[24]

G. Simon et al. Sensor network-based countersniper system. In Proc. ACM SenSys '04, November 2004.

Digital Library

[25]

G. Werner-Allen, J. Johnson, M. Ruiz, J. Lees, and M. Welsh. Monitoring volcanic eruptions with a wireless sensor network. In Proc. Second European Workshop on Wireless Sensor Networks (EWSN'05), January 2005.

[26]

G. Werner-Allen, K. Lorincz, J. Johnson, J. Lees, and M. Welsh. Fidelity and yield in a volcano monitoring sensor network. In Proc. 7th USENIX Symposium on Operating Systems Design and Implementation (OSDI 2006), Seattle, WA, November 2006.

Digital Library

[27]

G. Werner-Allen, P. Swieskowski, and M. Welsh. MoteLab: A Wireless Sensor Network Testbed. In Proc. the Fourth International Conference on Information Processing in Sensor Networks (IPSN'05), April 2005.

Digital Library

[28]

Y. Zhang, B. Hull, H. Balakrishnan, and S. Madden. ICEDB: Intermittently-Connected Continuous Query Processing. In International Conference on Data Engineering (ICDE), Istanbul, Turkey, April 2007.

Cited By

Kim-Hung LLe-Trung Q(2020)User-Driven Adaptive Sampling for Massive Internet of ThingsIEEE Access10.1109/ACCESS.2020.30114968(135798-135810)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3011496
Fahmy HFahmy H(2020)Energy Management Projects for WSNsWireless Sensor Networks10.1007/978-3-030-29700-8_8(611-637)Online publication date: 26-Jan-2020
https://doi.org/10.1007/978-3-030-29700-8_8
Bhuiyan MWu JWang GChen ZChen JWang T(2017)Quality-Guaranteed Event-Sensitive Data Collection and Monitoring in Vibration Sensor NetworksIEEE Transactions on Industrial Informatics10.1109/TII.2017.266546313:2(572-583)Online publication date: Apr-2017
https://doi.org/10.1109/TII.2017.2665463
Show More Cited By

Index Terms

Lance: optimizing high-resolution signal collection in wireless sensor networks
1. Computer systems organization
2. Software and its engineering
  1. Software organization and properties
    1. Software system structures
      1. Distributed systems organizing principles

Recommendations

Minimum k, ω-angle barrier coverage in wireless camera sensor networks

Barrier coverage is an important issue in wireless sensor networks, which guarantees to detect any intruder attempting to cross a barrier or penetrating a protected region monitored by sensors. However, the barrier coverage problem in wireless camera ...
A distributed clustering method for energy-efficient data gathering in sensor networks

Since sensor nodes operate on batteries, energy-efficient mechanisms for gathering sensor data are indispensable in prolonging the lifetime of a sensor network as long as possible. In this paper, we propose a novel clustering method where energy-...
In-network aggregation trade-offs for data collection in wireless sensor networks

This paper explores in-network aggregation as a power-efficient mechanism for collecting data in wireless sensor networks. In particular, we focus on sensor network scenarios where a large number of nodes produce data periodically. Such communication ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SenSys '08: Proceedings of the 6th ACM conference on Embedded network sensor systems

November 2008

468 pages

ISBN:9781595939906

DOI:10.1145/1460412

General Chair:
Tarek Abdelzaher
University of Illinois at Urbana-Champaign, USA
,
Program Chairs:
Margaret Martonosi
Princeton University, USA
,
Adam Wolisz
Technische Universität Berlin, Germany

Copyright © 2008 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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 November 2008

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

SenSys08

Sponsor:

SenSys08: The 6th ACM Conference on Embedded Networked Sensor Systems

November 5 - 7, 2008

NC, Raleigh, USA

Acceptance Rates

Overall Acceptance Rate 174 of 867 submissions, 20%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

65
Total Citations
View Citations
695
Total Downloads

Downloads (Last 12 months)5
Downloads (Last 6 weeks)1

Reflects downloads up to 01 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Kim-Hung LLe-Trung Q(2020)User-Driven Adaptive Sampling for Massive Internet of ThingsIEEE Access10.1109/ACCESS.2020.30114968(135798-135810)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3011496
Fahmy HFahmy H(2020)Energy Management Projects for WSNsWireless Sensor Networks10.1007/978-3-030-29700-8_8(611-637)Online publication date: 26-Jan-2020
https://doi.org/10.1007/978-3-030-29700-8_8
Bhuiyan MWu JWang GChen ZChen JWang T(2017)Quality-Guaranteed Event-Sensitive Data Collection and Monitoring in Vibration Sensor NetworksIEEE Transactions on Industrial Informatics10.1109/TII.2017.266546313:2(572-583)Online publication date: Apr-2017
https://doi.org/10.1109/TII.2017.2665463
Trihinas DPallis GDikaiakos M(2017)ADMin: Adaptive monitoring dissemination for the Internet of ThingsIEEE INFOCOM 2017 - IEEE Conference on Computer Communications10.1109/INFOCOM.2017.8057144(1-9)Online publication date: May-2017
https://doi.org/10.1109/INFOCOM.2017.8057144
Schmitt CKothmayr TErtl BHu WBraun LCarle G(2016)TinyIPFIXComputer Communications10.1016/j.comcom.2014.05.01274:C(63-76)Online publication date: 15-Jan-2016
https://dl.acm.org/doi/10.1016/j.comcom.2014.05.012
Wang SKim SLiu YTan GHe T(2015)CorLayerIEEE/ACM Transactions on Networking10.1109/TNET.2014.234780623:6(1970-1983)Online publication date: 1-Dec-2015
https://dl.acm.org/doi/10.1109/TNET.2014.2347806
Salmani VChou P(2014)Resilient Round RobinACM Transactions on Sensor Networks10.1145/266163811:2(1-38)Online publication date: 12-Dec-2014
https://dl.acm.org/doi/10.1145/2661638
Challen GHaseley SMaiti ANandugudi APrasad GPuri MWang J(2014)The mote is deadProceedings of the 15th Workshop on Mobile Computing Systems and Applications10.1145/2565585.2565598(1-6)Online publication date: 26-Feb-2014
https://dl.acm.org/doi/10.1145/2565585.2565598
Vigneron A(2014)Geometric optimization and sums of algebraic functionsACM Transactions on Algorithms10.1145/253264710:1(1-20)Online publication date: 1-Jan-2014
https://dl.acm.org/doi/10.1145/2532647
Har-Peled SRaichel B(2014)The fréchet distance revisited and extendedACM Transactions on Algorithms10.1145/253264610:1(1-22)Online publication date: 1-Jan-2014
https://dl.acm.org/doi/10.1145/2532646
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents