Article

A supervised learning approach for routing optimizations in wireless sensor networks

Authors:

Margaret Martonosi,

Li-Shiuan PehAuthors Info & Claims

REALMAN '06: Proceedings of the 2nd international workshop on Multi-hop ad hoc networks: from theory to reality

Pages 79 - 86

https://doi.org/10.1145/1132983.1132997

Published: 26 May 2006 Publication History

Abstract

Routing in sensor networks maintains 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. In this paper, we explore using supervised learning techniques to address such challenges in wireless sensor networks. Machine learning has been very effective in discovering relations between attributes and extracting knowledge and patterns using a large corpus of samples.As a case study, we use link quality prediction to demonstrate the effectiveness of our approach. For this purpose, we present MetricMap,a link-quality aware collection protocol atop MintRoute that derives link quality information using knowledge acquired from a training phase. Our approach allows MetricMap to maintain efficient routing in situations where traditional approaches fail. Evaluation on a 30-node sensor network testbed shows that MetricMap can achieve up to 300% improvement on data delivery rate in a high data-rate application, with no negative impact on other performance metrics, such as data latency. Our approach is based on real-world measurement and provides a new perspective to routing optimizations in wireless sensor networks.

References

[1]

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.

[2]

D. Aguayo, J. Bicket, S. Biswas, G. Judd, and R. Morris. Link-level measurements from an 802.11b mesh network. In Proc. ACM SIGCOMM, 2004.

Digital Library

[3]

B. Calder, D. Grunwald, D. Lindsay, M. Jones, J. Martin, M. Mozer, and B. Zorn. Evidence-based static branch prediction using machine learning. ACM Transactions on Programming Languages and Systems, January 1997.

Digital Library

[4]

A. Cerpa, J. Wong, M. Potkonjak, and D. Estrin. Temporal properties of low-power wireless links: Modeling and implications on multi-hop routing. In Proc. ACM MobiHoc, 2005.

Digital Library

[5]

A. Cerpa, J. L. Wong, L. Kuang, M. Potkonjak, and D. Estrin. Statistical model of lossy links in wireless sensor networks. In Proc. IPSN, 2005.

Digital Library

[6]

M. Chen, A. X. Zheng, J. Lloyd, M. I. Jordan, and E. Brewer. Failure diagnosis using decision trees. In Proc. IEEE ICAC, 2004.

Digital Library

[7]

I. Cohen, M. Goldszmidt, T. Kelly, J. Symons, and J. Chase. Correlating instrumentation data to system states: A building block for automated diagnosis and control. In Proc. OSDI, 2004.

Digital Library

[8]

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

[9]

D. De Couto, D. Aguayo, J. Bicket, and R. Morris. A high-throughput path metric for multi-hop wireless routing. In Proc. ACM MobiCom, 2003.

Digital Library

[10]

C. Guestrin, P. Bodik, R. Thibaux, M. Paskin, and S. Madden. Distributed regression: an efficient framework for modeling sensor network data. In Proc. IPSN, 2004.

Digital Library

[11]

B. Hull, K. Jamieson, and H. Balakrishnan. Mitigating congestion in wireless sensor networks. In Proc. ACM SenSys, 2004.

Digital Library

[12]

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

[13]

C. E. Koksal and H. Balakrishnan. Quality-aware routing in time-varying wireless networks. to appear in IEEE Journal on Selected Areas of Communication Special Issue on Multi-hop Wireless Mesh Networks, 2005.

Digital Library

[14]

A. Krause, C. Guestrin, A. Gupta, and J. Kleinberg. Near-optimal sensor placements: maximizing information while minimizing communication cost. In Proc. IPSN, 2006.

Digital Library

[15]

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 the north sea and a semiconductor plant. In Proc. ACM SenSys, 2005.

Digital Library

[16]

B. Liblit, A. Aiken, A. X. Zheng, and M. I. Jordan. Bug isolation via remote program sampling. In Proc. ACM PLDI, 2003.

Digital Library

[17]

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

[18]

T. Mitchell. Machine Learning. McGraw Hill, 1997.

Digital Library

[19]

Motelab. http://motelab.eecs.harvard.edu/.

[20]

J. Paek, K. Chintalapudi, R. Govindan, J. Caffrey, and S. Masri. A wireless sensor network for structural health monitoring: Performance and experience. In Proc. IEEE EmNetS, 2005.

Digital Library

[21]

J. Polastre, R. Szewczyk, and D. Culler. Telos: Enabling ultra-low power wireless research. In Proc. IPSN/SPOTS, 2005.

Digital Library

[22]

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

Digital Library

[23]

C.-Y. Wan, S. B. Eisenman, and A. T. Campbell. CODA: congestion detection and avoidance in sensor networks. In Proc. ACM SenSys, 2003.

Digital Library

[24]

C.-Y. Wan, S. B. Eisenman, A. T. Campbell, and J. Crowcroft. Siphon: Overload traffic management using multi-radio virtual sinks. In Proc. ACM SenSys, 2005.

Digital Library

[25]

I. H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques, 2nd Edition. Morgan Kaufmann, 2005.

Digital Library

[26]

A. Woo, T. Tong, and D. Culler. Taming the underlying challenges of reliable multihop routing in sensor networks. In Proc. ACM SenSys, 2003.

Digital Library

Cited By

Ashtari SZhou IAbolhasan MShariati NLipman JNi W(2022)Knowledge-defined networking: Applications, challenges and future workArray10.1016/j.array.2022.10013614(100136)Online publication date: Jul-2022
https://doi.org/10.1016/j.array.2022.100136
Nandi SNandi APathak N(2022)Deep Learning Assisted Technology for MIMO OFDM 5G ApplicationProceedings of the International Conference on Computational Intelligence and Sustainable Technologies10.1007/978-981-16-6893-7_12(119-130)Online publication date: 12-Feb-2022
https://doi.org/10.1007/978-981-16-6893-7_12
Qu QMa ZClausen AJorgensen B(2021)A Comprehensive Review of Machine Learning in Multi-objective Optimization2021 IEEE 4th International Conference on Big Data and Artificial Intelligence (BDAI)10.1109/BDAI52447.2021.9515233(7-14)Online publication date: 2-Jul-2021
https://doi.org/10.1109/BDAI52447.2021.9515233
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Index Terms

A supervised learning approach for routing optimizations in wireless sensor networks
1. Computing methodologies
  1. Machine learning
2. Networks
  1. Network protocols
    1. Network layer protocols
      1. Routing protocols

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Published In

cover image ACM Conferences

REALMAN '06: Proceedings of the 2nd international workshop on Multi-hop ad hoc networks: from theory to reality

May 2006

142 pages

ISBN:1595933603

DOI:10.1145/1132983

General Chair:
Marco Conti
IIT-CNR, Italy
,
Program Chairs:
Jon Crowcroft
University of Cambridge, Computer Laboratory, UK
,
Andrea Passarella
IIT-CNR, Italy

Copyright © 2006 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: 26 May 2006

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REALMAN06: Multi-hop Ad Hoc Networks: From Theory to Reality Workshop 2006

May 26, 2006

Florence, Italy

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Cited By

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https://doi.org/10.1016/j.array.2022.100136
Nandi SNandi APathak N(2022)Deep Learning Assisted Technology for MIMO OFDM 5G ApplicationProceedings of the International Conference on Computational Intelligence and Sustainable Technologies10.1007/978-981-16-6893-7_12(119-130)Online publication date: 12-Feb-2022
https://doi.org/10.1007/978-981-16-6893-7_12
Qu QMa ZClausen AJorgensen B(2021)A Comprehensive Review of Machine Learning in Multi-objective Optimization2021 IEEE 4th International Conference on Big Data and Artificial Intelligence (BDAI)10.1109/BDAI52447.2021.9515233(7-14)Online publication date: 2-Jul-2021
https://doi.org/10.1109/BDAI52447.2021.9515233
Fontoura LDe Castro Lins HBertuleza AD'assuncao ANeto A(2021)Synthesis of Multiband Frequency Selective Surfaces Using Machine Learning With the Decision Tree AlgorithmIEEE Access10.1109/ACCESS.2021.30867779(85785-85794)Online publication date: 2021
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Barbahan IBaikalov VVyatkin VFilchenkov A(2021)Multi-Agent Deep Reinforcement Learning-Based Algorithm For Fast Generalization On Routing ProblemsProcedia Computer Science10.1016/j.procs.2021.10.023193(228-238)Online publication date: 2021
https://doi.org/10.1016/j.procs.2021.10.023
Nayak PSwetha GGupta SMadhavi K(2021)Routing in wireless sensor networks using machine learning techniques: Challenges and opportunitiesMeasurement10.1016/j.measurement.2021.108974178(108974)Online publication date: Jun-2021
https://doi.org/10.1016/j.measurement.2021.108974
Sharma NYadav N(2020)Machine Learning in Wireless CommunicationResearch Anthology on Artificial Intelligence Applications in Security10.4018/978-1-7998-7705-9.ch088(1979-1999)Online publication date: 27-Nov-2020
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Ghosh AHo CBestak R(2020)Secured Energy-Efficient Routing in Wireless Sensor Networks Using Machine Learning AlgorithmDeep Learning Strategies for Security Enhancement in Wireless Sensor Networks10.4018/978-1-7998-5068-7.ch002(23-41)Online publication date: 2020
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EL-Garoui LPierre SChamberland S(2020)A New SDN-Based Routing Protocol for Improving Delay in Smart City EnvironmentsSmart Cities10.3390/smartcities30300503:3(1004-1021)Online publication date: 9-Sep-2020
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Sharma NYadav N(2019)Machine Learning in Wireless CommunicationNext-Generation Wireless Networks Meet Advanced Machine Learning Applications10.4018/978-1-5225-7458-3.ch007(141-161)Online publication date: 2019
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