research-article

Distributed genetic evolution in WSN

Authors:

Philip Valencia,

Raja JurdakAuthors Info & Claims

IPSN '10: Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks

Pages 13 - 23

https://doi.org/10.1145/1791212.1791215

Published: 12 April 2010 Publication History

Abstract

Wireless Sensor Actuator Networks (WSANs) extend wireless sensor networks through actuation capability. Designing robust logic for WSANs however is challenging since nodes can affect their environment which is already inherently complex and dynamic. Fixed (offline) logic does not have the ability to adapt to significant environmental changes and can fail under changed conditions. To address this challenge, we present In situ Distributed Genetic Programming (IDGP) as a framework for evolving logic post-deployment (online) and implement this framework on a physically deployed WSAN. To demonstrate the features of the framework including individual, cooperative and heterogeneous evolution, we apply it to two simple optimisation problems requiring sensing, communications and actuation. The experiments confirm that IDGP can evolve code to achieve a system wide objective function and is resilient to unexpected environmental changes.

References

[1]

I. Akyildiz and I. Kasimoglu. Wireless sensor and actor networks: Research challenges. In Ad Hoc Networks Journal, volume 2, no. 4, pages 351--367. 2004.

[2]

P. A. N. Bosman and H. L. Poutré. Learning and anticipation in online dynamic optimization with evolutionary algorithms: The stochastic case. In Proc 9th Ann Conf on Genetic and Evolutionary Computation, pages 1165--1172, 2007.

Digital Library

[3]

D. Floreano and F. Mondada. Automatic creation of an autonomous agent: Genetic evolution of a neural-network driven robot. In Proc 3rd Int Conf on Simulation of Adaptive Behavior, 1994.

Digital Library

[4]

S. Hettiarachchi. Distributed online evolution for swarm robotics. Presented at Autonomous Agents and Multi Agent Systems, Doctoral Mentoring Program, 2006.

[5]

S. Hettiarachchi, W. Spears, W. Kerr, D. Zarzhitsky, and D. Green. Distributed agent evolution with dynamic adaptation to local unexpected scenarios. In Proc. Second GSFC/IEEE Workshop on Radical Agent Concepts. Springer-Verlag, 2006.

Digital Library

[6]

J. H. Holland. Escaping brittleness: The possibilities of general-purpose learning algorithms applied to parallel rule-based systems. In T. M. M. Ryszard S. Michalski, Jaime G. Carbonell, editor, Machine learning: An artificial intelligence approach, volume II, pages 593--623. Morgan Kaufmann, 1986.

[7]

J. H. Holland. Adaptation in Natural and Artificial Systems. MIT Press, Cambridge, MA, USA, 1992.

Digital Library

[8]

D. M. Johnson, A. Teredesai, and R. T. Saltarelli. Genetic programming in wireless sensor networks. In 8th European Conf onGenetic Programming, LNCS 3447, pages 96--107, 2005.

Digital Library

[9]

J. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, 1992.

Digital Library

[10]

W. Martin, J. Lienig, and J. Cohoon. Population structures: island (migration) models: evolutionary algorithms based on punctuated equilibria. In Back, Fogel, and Michalewicz, editors, Evolutionary Computation, chapter 6.3:1--6.3:16. Institute of Physics Publishing, Oxford University Press, New York, 1997.

[11]

G. Nan and M. Li. Evolutionary based approaches in wireless sensor networks: A survey. In Fourth International Conference on Natural Computation, volume 5, pages 217--222, 2008.

Digital Library

[12]

H. N. Pham, D. Pediaditakis, and A. Boulis. From simulation to real deployments in wsn and back. In Proc. IEEE Int. Symposium on a World of Wireless, Mobile and Multimedia Networks, pages 1--6, June 2007.

[13]

J. Pintér. Global optimization: Software, test problems, and applications. In P. Pardalos and H. Romeijn, editors, Handbook of Global Optimization, volume 2, chapter 15, pages 515--569. Kluwer Academic Publishers, Dordrecht Boston London, 2002.

[14]

R. Poli, W. B. Langdon, and N. F. McPhee. A field guide to genetic programming. Published via http://lulu.com and freely available at http://www.gp-field-guide.org.uk, 2008.

Digital Library

[15]

Q. Qiu, Q. Wu, D. Burns, and D. Holzhauer. Lifetime aware resource management for sensor network using distributed genetic algorithm. In Proc. of the Int. symposium on Low power electronics and design, pages 191--196. New York, NY, USA, 2006.

Digital Library

[16]

P. Sikka, P. Corke, L. Overs, P. Valencia, and T. Wark. Fleck - a platform for real-world outdoor sensor networks. In 3rd International Conference on Intelligent Sensors, Sensor Networks and Information, pages 709--714. 2007.

[17]

A. Silva, A. Neves, and E. Costa. Evolving controllers for autonomous agents using genetically programmed networks. In Proc. 2nd European Workshop on Genetic Programming, LNCS 1598, pages 255--269. Springer-Verlag, 1999.

Digital Library

[18]

T. Wark, P. Corke, P. Sikka, L. Klingbeil, Y. Guo, C. Crossman, P. Valencia, D. Swain, and G. Bishop-Hurley. Transforming agriculture through pervasive wireless sensor networks. Pervasive Computing, IEEE, 6(2):50--57, April-June 2007.

Digital Library

[19]

T. Weise and K. Geihs. Genetic programming techniques for sensor networks. In P. J. Marron, editor, Proceedings of 5. GI/ITG KuVS Fachgesprach Drahtlose Sensornetze, pages 21--25. University of Stuttgart, Stuttgart, Germany, 2006.

[20]

D. H. Wolpert and W. G. Macready. No free lunch theorems for search. In Technical report TR-95-02-010. Santa Fe Institute, Sante Fe, NM, USA, 1995.

Cited By

Carbognin ACustode LIacca G(2022)Genetic Improvement of TCP Congestion AvoidanceBioinspired Optimization Methods and Their Applications10.1007/978-3-031-21094-5_9(114-126)Online publication date: 10-Nov-2022
https://doi.org/10.1007/978-3-031-21094-5_9
Shrit OFilliat DSebag M(2021)Iterative Learning for Model Reactive Control: Application to Autonomous Multi-agent Control2021 7th International Conference on Automation, Robotics and Applications (ICARA)10.1109/ICARA51699.2021.9376454(140-146)Online publication date: 4-Feb-2021
https://doi.org/10.1109/ICARA51699.2021.9376454
Dorri AKanhere SJurdak R(2018)Multi-Agent Systems: A SurveyIEEE Access10.1109/ACCESS.2018.28312286(28573-28593)Online publication date: 2018
https://doi.org/10.1109/ACCESS.2018.2831228
Show More Cited By

Recommendations

Distributed Coordinate-free Boundary Nodes Identification in Wireless Sensor Networks
Q2SWinet '17: Proceedings of the 13th ACM Symposium on QoS and Security for Wireless and Mobile Networks

In a wireless sensor network (WSN), coverage holes may appear due to sensor random deployment or node failure. They may restrain the network from providing comprehensive and quality data for end users. In order to heal the coverage holes, the network ...
Fitness importance for online evolution
GECCO '10: Proceedings of the 12th annual conference companion on Genetic and evolutionary computation

To complement standard fitness functions, we propose "Fitness Importance" (FI) as a novel meta-heuristic for online learning systems. We define FI and show how it can be used to dynamically bias the population composition in order to vary the ...
Energy Efficient Distributed Face Recognition in Wireless Sensor Network

Face recognition enhances the security through wireless sensor network and it is a challenging task due to constrains involved in wireless sensor network. Image processing and image communication in wireless sensor network reduces the life time of ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

IPSN '10: Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks

April 2010

460 pages

ISBN:9781605589886

DOI:10.1145/1791212

Conference Chairs:
Tarek Abdelzaher
UIUC
,
Thiemo Voigt
SICS
,
Adam Wolisz
TU Berlin

Copyright © 2010 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

IEEE-SPS: Signal Processing Society
SIGBED: ACM Special Interest Group on Embedded Systems

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 April 2010

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

IPSN '10

Sponsor:

IEEE-SPS
SIGBED

IPSN '10: The 9th International Conference on Information Processing in Sensor Networks

April 12 - 16, 2010

Stockholm, Sweden

Acceptance Rates

Overall Acceptance Rate 143 of 593 submissions, 24%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

7
Total Citations
View Citations
317
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)0

Reflects downloads up to 24 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Carbognin ACustode LIacca G(2022)Genetic Improvement of TCP Congestion AvoidanceBioinspired Optimization Methods and Their Applications10.1007/978-3-031-21094-5_9(114-126)Online publication date: 10-Nov-2022
https://doi.org/10.1007/978-3-031-21094-5_9
Shrit OFilliat DSebag M(2021)Iterative Learning for Model Reactive Control: Application to Autonomous Multi-agent Control2021 7th International Conference on Automation, Robotics and Applications (ICARA)10.1109/ICARA51699.2021.9376454(140-146)Online publication date: 4-Feb-2021
https://doi.org/10.1109/ICARA51699.2021.9376454
Dorri AKanhere SJurdak R(2018)Multi-Agent Systems: A SurveyIEEE Access10.1109/ACCESS.2018.28312286(28573-28593)Online publication date: 2018
https://doi.org/10.1109/ACCESS.2018.2831228
Iacca G(2018)Distributed optimization in wireless sensor networksSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-013-1091-x17:12(2257-2277)Online publication date: 29-Dec-2018
https://dl.acm.org/doi/10.1007/s00500-013-1091-x
Valencia PHaak ACotillon AJurdak R(2014)Genetic programming for smart phone personalisationApplied Soft Computing10.1016/j.asoc.2014.08.05825:C(86-96)Online publication date: 1-Dec-2014
https://dl.acm.org/doi/10.1016/j.asoc.2014.08.058
Cotillon AValencia PJurdak R(2012)Android genetic programming frameworkProceedings of the 15th European conference on Genetic Programming10.1007/978-3-642-29139-5_2(13-24)Online publication date: 11-Apr-2012
https://dl.acm.org/doi/10.1007/978-3-642-29139-5_2
Valencia PJurdak RLindsay PPelikan MBranke J(2010)Fitness importance for online evolutionProceedings of the 12th annual conference companion on Genetic and evolutionary computation10.1145/1830761.1830890(2117-2118)Online publication date: 7-Jul-2010
https://dl.acm.org/doi/10.1145/1830761.1830890

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