ACM Home Page
Please provide us with feedback. Feedback
Lance: optimizing high-resolution signal collection in wireless sensor networks
Full text PdfPdf (1.39 MB)
Source
Conference On Embedded Networked Sensor Systems archive
Proceedings of the 6th ACM conference on Embedded network sensor systems table of contents
Raleigh, NC, USA
SESSION: Data analysis table of contents
Pages 169-182  
Year of Publication: 2008
ISBN:978-1-59593-990-6
Authors
Geoffrey Werner-Allen  Harvard University, Cambridge, MA, USA
Stephen Dawson-Haggerty  University of California, Berkeley, Berkeley, CA, USA
Matt Welsh  Harvard University, Cambridge, MA, USA
Sponsors
SIGCOMM: ACM Special Interest Group on Data Communication
SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
SIGOPS: ACM Special Interest Group on Operating Systems
SIGMETRICS: ACM Special Interest Group on Measurement and Evaluation
ACM: Association for Computing Machinery
SIGARCH: ACM Special Interest Group on Computer Architecture
SIGBED: ACM Special Interest Group on Embedded Systems
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 13,   Downloads (12 Months): 299,   Citation Count: 0
Additional Information:

abstract   references   index terms   collaborative colleagues  

Tools and Actions: Request Permissions Request Permissions    Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/1460412.1460430
What is a DOI?

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

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked 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
 
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
6
7
8
9
 
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
13
14
 
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
 
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
 
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
22
23
24
 
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
 
27
 
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.

Collaborative Colleagues:
Geoffrey Werner-Allen: colleagues
Stephen Dawson-Haggerty: colleagues
Matt Welsh: colleagues