article

Predicting link quality using supervised learning in wireless sensor networks

Authors:

Margaret Martonosi,

Li-Shiuan PehAuthors Info & Claims

ACM SIGMOBILE Mobile Computing and Communications Review, Volume 11, Issue 3

Pages 71 - 83

https://doi.org/10.1145/1317425.1317434

Published: 01 July 2007 Publication History

Abstract

Routing protocols in sensor networks maintain information on neighbor states and potentially many other factors in order to make informed decisions. Challenges arise both in (a) performing accurate and adaptive information discovery and (b) processing/analyzing the gathered data to extract useful features and correlations. To address such challenges, this paper explores using supervised learning techniques to make informed decisions in the context of wireless sensor networks.

We investigate the design space of both offline learning and online learning and use link quality estimation as a case study to evaluate their effectiveness. For this purpose, we present MetricMap, a metric-based collection routing protocol atop MintRoute that derives link quality using classifiers learned in the training phase, when the traditional ETX approach fails. The offline learning approach is evaluated on a 30-node sensor network testbed, and our results show that MetricMap can achieve up to 300% improvement over MintRoute in data delivery rate for high data rate situations, with no negative impact on other performance metrics. We also explore the possibility of using online learning in this paper.

References

[1]

Douglas De Couto, Daniel Aguayo, John Bicket, and Robert Morris. A high-throughput path metric for multi-hop wireless routing. In Proc. ACM MobiCom, 2003.

Digital Library

[2]

Alec Woo, Terence Tong, and David Culler. Taming the underlying challenges of reliable multihop routing in sensor networks. In Proc. ACM SenSys, 2003.

Digital Library

[3]

Hongwei Zhang, Anish Arora, and Prasun Sinha. Learn on the fly: Data-driven link estimation and routing in sensor network backbones. In Proc. IEEE INFOCOM, 2006.

[4]

Jeongyeup Paek, Krishna Chintalapudi, Ramesh Govindan, John Caffrey, and Sami Masri. A wireless sensor network for structural health monitoring: Performance and experience. In Proc. IEEE Workshop on Embedded Networked Sensors (EmNetS), 2005.

Digital Library

[5]

Lakshman Krishnamurthy, Robert Adler, Phil Buonadonna, Jasmeet Chhabra, Mick Flanigan, Nandakishore Kushalnagar, Lama Nachman, and Mark Yarvis. Design and deployment of industrial sensor networks: Experiences from the north sea and a semiconductor plant. In Proc. ACM SenSys, 2005.

Digital Library

[6]

Samuel Madden, Michael J. Franklin, Joseph M. Hellerstein, and Wei Hong. TAG: A tiny aggregation service for ad-hoc sensor networks. In Proc. Symposium on Operating Systems Design and Implementation (OSDI), 2002.

Digital Library

[7]

Tian He, Brian M. Blum, John A. Stankovic, and Tarek F. Abdelzaher. AIDA: Adaptive application independent data aggregation in wireless sensor networks. ACM Transactions on Embedded Computing System Special issue on Dynamically Adaptable Embedded Systems, May 2004.

Digital Library

[8]

Christopher M. Sadler and Margaret Martonosi. Data compression algorithms for energy-constrained devices in delay tolerant networks. In Proc. ACM SenSys, 2006.

Digital Library

[9]

S. Sandeep Pradhan, Julius Kusuma, and Kannan Ramchandran. Distributed compression in a dense microsensor network. IEEE Signal Processing, March 2002.

[10]

Mistlab. http://mistlab.csail.mit.edu/.

[11]

IEEE Standard 802, part 15.4: Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low Rate Wireless Personal Area Networks (LR-WPANs). 2003.

[12]

Joseph Polastre, Robert Szewczyk, and David Culler. Telos: Enabling ultra-low power wireless research. In Proc. IPSN/SPOTS, 2005.

Digital Library

[13]

Tom Mitchell. Machine Learning. McGraw Hill, 1997.

Digital Library

[14]

Ian H. Witten and Eibe Frank. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, second edition, 2005.

Digital Library

[15]

Daniel Aguayo, John Bicket, Sanjit Biswas, Glenn Judd, and Robert Morris. Link-level measurements from an 802.11b mesh network. In Proc. ACM SIGCOMM, 2004.

Digital Library

[16]

Chieh-Yih Wan, Shane B. Eisenman, and Andrew T. Campbell. CODA: congestion detection and avoidance in sensor networks. In Proc. ACM SenSys, 2003.

Digital Library

[17]

Chieh-Yih Wan, Shane B. Eisenman, Andrew T. Campbell, and Jon Crowcroft. Siphon: Overload traffic management using multi-radio virtual sinks. In Proc. ACM SenSys, 2005.

Digital Library

[18]

William W. Cohen. Fast effective rule induction. In Proc. the International Conference on Machine Learning (ICML), 1995.

[19]

J. Ross Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann, 1993.

Digital Library

[20]

Raj Jain. The Art of Computer Systems Performance Analysis: Techniques for Experimental Design, Measurement, Simulation and Modeling. John Wiley & Sons, Inc., 1991.

[21]

Geoff Werner-Allen, Pat Swieskowski, and Matt Welsh. MoteLab: A wireless sensor network testbed. In Proc. IPSN/SPOTS, 2005.

Digital Library

[22]

Bret Hull, Kyle Jamieson, and Hari Balakrishnan. Mitigating congestion in wireless sensor networks. In Proc. ACM SenSys, 2004.

Digital Library

[23]

Omprakash Gnawali, Ben Greenstein, Ki-Young Jang, August Joki, Jeongyeup Paek, Marcos Vieira, Deborah Estrin, Ramesh Govindan, and Eddie Kohler. The Tenet architecture for tiered sensor networks. In Proc. ACM SenSys, 2006.

Digital Library

[24]

G. Hulten, L. Spencer, and P. Domingos. Mining time-changing data streams. In Proc. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2001.

Digital Library

[25]

VFML Toolkit, http://www.cs.washington.edu/dm/vfml/main.html.

[26]

Nithya Ramanathan, Kevin Chang, Rahul Kapur, Lewis Girod, Eddie Kohler, and Deborah Estrin. Sympathy for the sensor network debugger. In Proc. ACM SenSys, 2005.

Digital Library

[27]

Gilman Tolle and David Culler. Design of an application-cooperative management system for wireless sensor networks. In Proc. EWSN, 2005.

[28]

Brad Calder, Dirk Grunwald, Donald Lindsay, Michael Jones, James Martin, Michael Mozer, and Benjamin Zorn. Evidence-based static branch prediction using machine learning. ACM Transactions on Programming Languages and Systems, January 1997.

Digital Library

[29]

Ira Cohen, Moises Goldszmidt, Terence Kelly, Julie Symons, and Jeff Chase. Correlating instrumentation data to system states: A building block for automated diagnosis and control. In Proc. Symposium on Operating Systems Design and Implementation (OSDI), 2004.

Digital Library

[30]

Mike Chen, Alice X. Zheng, Jim Lloyd, Michael I. Jordan, and Eric Brewer. Failure diagnosis using decision trees. In Proc. IEEE International Conference on Autonomic Computing (ICAC), 2004.

Digital Library

[31]

Ben Liblit, Alex Aiken, Alice X. Zheng, and Michael I. Jordan. Bug isolation via remote program sampling. In Proc. ACM PLDI, 2003.

Digital Library

[32]

Justin A. Boyan and Michael L. Littman. Packet routing in dynamically changing networks: A reinforcement learning approach. In Advances in Neural Information Processing Systems, volume 6. Morgan Kaufmann Publishers, 1994.

[33]

Venkata N. Padmanabhan, Lili Qiu, and Helen J. Wang. Passive network tomography using bayesian inference. In Proc. ACM SIGCOMM Workshop on Internet Measurment, 2002.

Digital Library

[34]

Carlos Guestrin, Peter Bodik, Romain Thibaux, Mark Paskin, and Samuel Madden. Distributed regression: an efficient framework for modeling sensor network data. In Proc. IPSN, 2004.

Digital Library

[35]

Andreas Krause, Carlos Guestrin, Anupam Gupta, and Jon Kleinberg. Near-optimal sensor placements: maximizing information while minimizing communication cost. In Proc. IPSN, 2006.

Digital Library

[36]

Joseph Polastre, Jonathan Hui, Philip Levis, Jerry Zhao, David Culler, Scott Shenker, and Ion Stoica. A unifying link abstraction for wireless sensor networks. In Proc. ACM SenSys, 2005.

Digital Library

[37]

Can Emre Koksal and Hari Balakrishnan. Quality-aware routing metrics for time-varying wireless mesh networks. IEEE Journal on Selected Areas of Communication Special Issue on Multi-hop Wireless Mesh Networks, 24(11), November 2006.

[38]

Alberto Cerpa, Jennifer L. Wong, Louane Kuang, Miodrag Potkonjak, and Deborah Estrin. Statistical model of lossy links in wireless sensor networks. In Proc. IPSN, 2005.

Digital Library

[39]

Alberto Cerpa, Jennifer Wong, Miodrag Potkonjak, and Deborah Estrin. Temporal properties of low-power wireless links: Modeling and implications on multi-hop routing. In Proc. ACM MobiHoc, 2005.

Digital Library

[40]

Hung X. Nguyen and Patrick Thiran. Using end-to-end data to infer lossy links in sensor networks. In Proc. IEEE INFOCOM, 2006.

[41]

Qing Cao, Tian He, Lei Fang, Tarek Abdelzaher, John Stankovic, and Sang Son. Efficiency centric communication model for wireless sensor networks. In Proc. IEEE INFOCOM, 2006.

[42]

Kannan Srinivasan, Prabal Dutta, Arsalan Tavakoli, and Philip Levis. Understanding the causes of packet delivery success and failure in dense wireless sensor networks. Technical Report SING-06-00, Stanford University, 2006.

Digital Library

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Predicting link quality using supervised learning in wireless sensor networks

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Published In

cover image ACM SIGMOBILE Mobile Computing and Communications Review

ACM SIGMOBILE Mobile Computing and Communications Review Volume 11, Issue 3

July 2007

97 pages

ISSN:1559-1662

EISSN:1931-1222

DOI:10.1145/1317425

Issue’s Table of Contents

Copyright © 2007 Authors.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 July 2007

Published in SIGMOBILE Volume 11, Issue 3

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https://doi.org/10.1007/s42979-025-03730-x
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