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Accelerated Evolution: A Biologically-Inspired Approach for Augmenting Self-star Properties in Wireless Sensor Networks

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Book cover Transactions on Computational Science XV

Part of the book series: Lecture Notes in Computer Science ((TCOMPUTATSCIE,volume 7050))

Abstract

Wireless sensor networks (WSNs) possess inherent tradeoffs among conflicting performance objectives such as data yield, data fidelity and power consumption. In order to address this challenge, this paper proposes a biologically-inspired application framework for WSNs. The proposed framework, called El Niño, models an application as a decentralized group of software agents. This is analogous to a bee colony (application) consisting of bees (agents). Agents collect sensor data on individual nodes and carry the data to base stations. They perform this data collection functionality by autonomously sensing their local network conditions and adaptively invoking biological behaviors such as pheromone emission, swarming, reproduction and migration. Each agent carries its own operational parameters, as genes, which govern its behavior invocation and configure its underlying sensor nodes. El Niño allows agents to evolve and adapt their operational parameters to network dynamics and disruptions by seeking the optimal tradeoffs among conflicting performance objectives. This evolution process is augmented by a notion of accelerated evolution. It allows agents to evolve their operational parameters by learning dynamic network conditions in the network and approximating their performance under the conditions. This is intended to expedite agent evolution to adapt to network dynamics and disruptions.

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References

  1. Karl, H., Willig, A.: Protocols and Architectures for Wireless Sensor Networks. Wiley Interscience (2007)

    Google Scholar 

  2. Han, Q., Hakkarinen, D., Boonma, P., Suzuki, J.: Quality-Aware Sensor Data Collection. International Journal of Sensor Networks 7, 127–140 (2010)

    Article  Google Scholar 

  3. Gershenson, C., Heylighen, F.: When Can We Call a System Self-Organizing? In: Banzhaf, W., Ziegler, J., Christaller, T., Dittrich, P., Kim, J.T. (eds.) ECAL 2003. LNCS (LNAI), vol. 2801, pp. 606–614. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  4. Seeley, T.: The Wisdom of the Hive. Harvard University Press (2005)

    Google Scholar 

  5. Levis, P., Madden, S., Polastre, J., Szewczyk, R., Whitehouse, K., Woo, A., Gay, D., Hill, J., Welsh, M., Brewer, E., et al.: TinyOS: An operating system for sensor networks. In: Ambient Intelligence, pp. 115–148. Springer, Heidelberg (2005)

    Google Scholar 

  6. Shnayder, V., Hempstead, M., Chen, B.-R., Werner-Allen, G., Welsh, M.: Simulating the power consumption of large-scale sensor network applications. In: Proc. of IEEE Conference on Embedded Networked Sensor Systems (2004)

    Google Scholar 

  7. Levis, P., Lee, N., Welsh, M., Culler, D.: TOSSIM: Accurate and Scalable Simulation of Entire TinyOS Applications. In: Proc. of IEEE Int’l Conference on Embedded Networked Sensor System, pp. 126–137 (2003)

    Google Scholar 

  8. Xu, N., Rangwala, S., Chintalapudi, K.K., Ganesan, D., Broad, A., Govindan, R., Estrin, D.: Wireless Sensor Network for Structural Monitoring. In: Proc. of ACM Int’l Conference on Embedded Networked Sensor Systems, pp. 13–24 (2005)

    Google Scholar 

  9. Boonma, P., Suzuki, J.: Exploring Self-star Properties in Cognitive Sensor Networking. In: Proc. of IEEE/SCS Int’l Symposium on Performance Evaluation of Computer and Telecommunication Systems, pp. 36–43 (2008)

    Google Scholar 

  10. Baldi, P., Nardis, L.D., Benedetto, M.G.D.: Modeling and Optimization of UWB Communication Networks Through a Flexible Cost Function. IEEE J. on Sel. Areas in Commun. 20, 1733–1744 (2002)

    Article  Google Scholar 

  11. Khanna, R., Liu, H., Chen, H.: Self-organisation of Sensor Networks using Genetic Algorithms. Inderscience Int’l J. of Sensor Networks 1(3), 241–252 (2006)

    Article  Google Scholar 

  12. Hussain, S., Matin, A.W., Islam, O.: Genetic Algorithm for Hierarchical Wireless Sensor Networks. Journal of Networks 2(5), 87–97 (2007)

    Article  Google Scholar 

  13. Jin, S., Zhou, M., Wu, A.S.: Sensor Network Optimization using a Genetic Algorithm. In: Proc. of IIIS World Multi-Conference on Systemics, Cybernetics and Informatics (2003)

    Google Scholar 

  14. Ferentinos, K.P., Tsiligiridis, T.A.: Adaptive Design Optimization of Wireless Sensor Networks using Genetic Algorithms. Computer Networks: The International Journal of Computer and Telecommunications Networking 51(4), 1031–1051 (2007)

    Article  MATH  Google Scholar 

  15. Buczaka, A.L., Wangb, H.: Optimization of Fitness Functions with Non-ordered Parameters by Genetic Algorithms. In: Proc. of IEEE Congress on Evolutionary Computation, pp. 199–206 (2001)

    Google Scholar 

  16. Hauser, J., Purdy, C.: Sensor Data Processing using Genetic Algorithms. In: Proc. of IEEE Midwest Symposium on Circuits and Systems, pp. 1112–1115 (2000)

    Google Scholar 

  17. Tam, V., Cheng, K.Y., Lui, K.S.: Using Micro-Genetic Algorithms to Improve Localization in Wireless Sensor Networks. Journal of Communications 1(4), 1–10

    Google Scholar 

  18. Zhang, Q., Wang, J., Jin, C., Ye, J., Ma, C., Zhang, W.: Genetic Algorithm Based Wireless Sensor Network Localization. In: Proc. of IEEE Int’l Conference on Natural Computation, pp. 608–613 (2008)

    Google Scholar 

  19. Guo, H.Y., Zhang, L., Zhang, L.L., Zhou, J.X.: Optimal Placement of Sensors for Structural Health Monitoring using Improved Genetic Algorithms. Smart Materials and Structures 13(3), 528–534 (2004)

    Article  Google Scholar 

  20. Zhao, J., Wen, Y., Shang, R., Wang, G.: Optimizing Sensor Node Distribution with Genetic Algorithm in Wireless Sensor Network. In: Yin, F.-L., Wang, J., Guo, C. (eds.) ISNN 2004. LNCS, vol. 3174, pp. 242–247. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  21. Rajagopalan, R., Mohan, C., Varshney, P., Mehrotra, K.: Multi-objective Mobile Agent Routing in Wireless Sensor Networks. In: Proc. of IEEE Congress on Evolutionary Computation, pp. 1730–1737 (2005)

    Google Scholar 

  22. Rajagopalan, R., Varshney, P.K., Mehrotra, K.G., Mohan, C.K.: Fault Tolerant Mobile Agent Routing in Sensor Networks: A Multi-Objective Optimization Approach. In: Proc. of IEEE Upstate New York Workshop on Communication and Networking (2005)

    Google Scholar 

  23. Xue, F., Sanderson, A., Graves, R.: Multi-Objective Routing in Wireless Sensor Networks with a Differential Evolution Algorithm. In: Proc. of IEEE Int’l Conference on Networking, Sensing and Control, pp. 880–885 (2006)

    Google Scholar 

  24. Sin, H., Lee, J., Lee, S., Yoo, S., Lee, S., Lee, J., Lee, Y., Kim, S.: Agent-based Framework for Energy Efficiency in Wireless Sensor Networks. World Academy of Science, Engineering and Technology 46, 305–309 (2008)

    Google Scholar 

  25. Jourdan, D.B., de Weck, O.L.: Multi-Objective Genetic Algorithm for the Automated Planning of a Wireless Sensor Network to Monitor A Critical Facility. In: Proc. of SPIE Defense and Security Symposium, pp. 565–575 (2004)

    Google Scholar 

  26. Rajagopalan, R., Varshney, P.K., Mohan, C.K., Mehrotra, K.G.: Sensor Placement for Energy Efficient Target Detection in Wireless Sensor Networks: A multi-objective Optimization Approach. In: Proc. of Annual Conference on Information Sciences and Systems (2005)

    Google Scholar 

  27. Raich, A.M., Liszkai, T.R.: Multi-Objective Genetic Algorithm Methodology for Optimizing Sensor Layouts to Enhance Structural Damage Identification. In: Proc. of Int’l Workshop on Structural Health Monitoring, pp. 650–657 (2003)

    Google Scholar 

  28. Jia, J., Chen, J., Chang, G., Tan, Z.: Energy Efficient Coverage Control in Wireless Sensor Networks based on Multi-Objective Genetic Algorithm. Computers & Mathematics with Applications 57(11-12), 1756–1766 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  29. Molina, G., Alba, E., Talbi, E.G.: Optimal Sensor Network Layout Using Multi-Objective Metaheuristics. J. of Universal Computer Science 14(15), 2549–2565 (2008)

    Google Scholar 

  30. Yang, E., Erdogan, A.T., Arslan, T., Barton, N.: Multi-Objective Evolutionary Optimizations of a Space-Based Reconfigurable Sensor Network under Hard Constraints. Soft Computing - A Fusion of Foundations, Methodologies and Applications 15(1), 25–36 (2011)

    Google Scholar 

  31. Liu, C., Wu, K., Tsao, M.: Energy Efficient Information Collection with the ARIMA model in Wireless Sensor Networks. In: Proc. of IEEE Global Telecommunication Conference, pp. 2470–2474 (2005)

    Google Scholar 

  32. Li, M., Ganesan, D., Shenoy, P.: PRESTO: Feedback-Driven Data Management in Sensor Networks. In: Proc. of ACM/USENIX Symposium on Networked Systems Design and Implementation, pp. 311–324 (2006)

    Google Scholar 

  33. Vassev, E., Hinchey, M., Nixon, P.: Prototyping Home Automation Wireless Sensor Networks with ASSL. In: Proc. of ACM Int’l Conference on Autonomic Computing, pp. 71–72 (2010)

    Google Scholar 

  34. Vassev, E., Hinchey, M., Nixon, P.: Developing Intelligent Sensor Networks: A Technological Convergence Approach. In: Proc. of ACM/IEEE Int’l Workshop on Software Engineering for Sensor Network Applications, pp. 66–71 (2010)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Chiang Mai University, Thailand

    Pruet Boonma

  2. Department of Computer Science, University of Massachusetts, Boston, USA

    Junichi Suzuki

Authors
  1. Pruet Boonma
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  2. Junichi Suzuki
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Editors and Affiliations

  1. Department of Computer Science, University of Calgary, 2500 University Drive N.W., T2N 1N4, Calgary, AB, Canada

    Marina L. Gavrilova

  2. Exascala Ltd., Unit 9, 97 Rickman Drive, B15 2AL, Birmingham, UK

    C. J. Kenneth Tan

  3. NTT University, 298A-300A Nguyen Tat Thanh Street, Ho Chi Minh City, Vietnam

    Cong-Vinh Phan

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© 2012 Springer-Verlag Berlin Heidelberg

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Boonma, P., Suzuki, J. (2012). Accelerated Evolution: A Biologically-Inspired Approach for Augmenting Self-star Properties in Wireless Sensor Networks. In: Gavrilova, M.L., Tan, C.J.K., Phan, CV. (eds) Transactions on Computational Science XV. Lecture Notes in Computer Science, vol 7050. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28525-7_4

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  • DOI: https://doi.org/10.1007/978-3-642-28525-7_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28524-0

  • Online ISBN: 978-3-642-28525-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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