Skip to main content
SpringerLink
Log in

Decentralized Multi-Agent Clustering in Scale-free Sensor Networks

  • Chapter
Book cover Computational Intelligence: A Compendium

Part of the book series: Studies in Computational Intelligence ((SCI,volume 115))

Many interaction processes in complex adaptive systems occur in groups, and in order to organize knowledge, collaboration and a proper distribution of functions and tasks, there is a need to analyze, model and develop computational systems in which several autonomous units interact, adapt and work together in a common open environment, combining individual strategies into overall behavior. The approach to engineering a desired system-level behavior, adopted in this work, is based on a multi-agent system [11], in which the preferred responses emerge as a result of inter-agent interactions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 389.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 499.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Albert R, Jeong H, Barabási A-L (1999) Diameter of the world-wide web. Nature, 401: 130-131.

    Article  Google Scholar 

  2. Albert R, Jeong H, Barabási A-L (2000) Error and attack tolerance of complex networks. Nature, 406: 378-382.

    Article  Google Scholar 

  3. Albert R, Barabási A-L (2002) Statistical mechanics of complex networks. Reviews of Modern Physics, 74(1): 47-97.

    Article  MathSciNet  Google Scholar 

  4. Butler M, Prokopenko M, Howard T (2001) Flexible synchronisation within RoboCup environment: a comparative analysis. In: Stone P, Balch TR, Kraet-zschmar GK (eds) RoboCup 2000: Robot Soccer World Cup IV (Proc. 4th RoboCup-2000 Workshop, 31 August - 1 September, Melbourne, Australia), Lecture Notes in Computer Science 2019: 119-128, Springer-Verlag, Berlin.

    Chapter  Google Scholar 

  5. Bonabeau E, Theraulaz G, Deneubourg J-L, Camazine S (1997) Self-organisation in social insects. Trends in Ecology and Evolution, 12 (5): 188-193.

    Article  Google Scholar 

  6. Boschetti F, Prokopenko M, Macreadie I, Grisogono A-M (2005) Defining and detecting emergence in complex networks. In: Khosla R, Howlett RJ, Jain LC (eds) Proc. 9th Intl. Conf. Knowledge-Based Intelligent Information and Engi-neering Systems - KES 2005, 14-16 September, Melbourne, Australia, Lecture Notes in Computer Science 3684: 573-580, Springer-Verlag, Berlin.

    Google Scholar 

  7. Dhamala M, Lai YC, Kostelich EJ (2001) Analyses of transient chaotic time series. Physical Review E, 64: 1-9.

    Article  Google Scholar 

  8. Dorigo M, Gambardella LM (1997) Ant colonies for the Traveling Salesman Problem. BioSystems, 43: 73-81.

    Article  Google Scholar 

  9. Durrant-Whyte, HF, Stevens M (2001) Data fusion in decentralised sensing networks. Proc. 4th Intl. Conf. Information Fusion, 7-10 August, Montreal, Canada, International Society of Information Fusion, Sunnyvale, CA.

    Google Scholar 

  10. Faloutsos M, Faloutsos P, Faloutsos C (1999) On power-law relationships of the internet topology. Computer Communication Review, 29: 251-262.

    Article  Google Scholar 

  11. Ferber J (1999) Multi-Agent Systems. Addison Wesley Professional, Reading, MA.

    Google Scholar 

  12. Foreman M, Prokopenko M, Wang P (2003) Phase transitions in self-organising sensor networks. In: Banzhaf W, Christaller T, Dittrich P, Kim JT, Ziegler J (eds) Advances in Artificial Life (Proc. 7th European Conf. Artificial Life -ECAL2003), 14-17 September, Dortmund, Germany, Lecture Notes in Artificial Intelligence 2801: 781-791, Springer-Verlag, Berlin.

    Google Scholar 

  13. Gerasimov V, Healy G, Prokopenko M, Wang P, Zeman A (2006) Symbiotic sensor networks in complex underwater terrains: a simulation framework. In: Gabrys B, Howlett RJ, Jain LC (eds) Proc. 10th Intl. Knowledge-Based Intel-ligent Information and Engineering Systems Conf. - KES 2006, 9-11 October, Bournemouth, UK, Lecture Notes in Artificial Intelligence 4253(III): 315-323, Springer-Verlag, Berlin.

    Google Scholar 

  14. Ghanem M, Guo Y, Hassard J, Osmond M, Richards M (2004) Sensor grid for air pollution monitoring. In: Cox SJ (ed) Proc. 3rd UK e-Science All-hands Conf. - AHM 2004, 31 August - 3 September, Nottingham, UK, Engineering and Physical Sciences Research Council (EPSRC), UK: 106-113.

    Google Scholar 

  15. Grassberger P, Procaccia I (1983) Estimation of the Kolmogorov entropy from a chaotic signal. Physical Review A, 28(4): 2591-2593.

    Article  Google Scholar 

  16. Jánosi IM, Tél T (1994) Time series analysis of transient chaos. Physical Review E, 49(4): 2756-2763.

    Article  Google Scholar 

  17. Jeong H, Tombor B, Albert R, Oltvai ZN, Barabási, A-L (2000) The large-scale organization of metabolic networks. Nature, 407: 651-654.

    Article  Google Scholar 

  18. Jones T, James G (2005) The management and control of distributed energy resources(extended version). In: Proc. 18th Intl. Conf. and Exhibition on Electricity Distribution - CIRED, June 2005, Turin, Italy, IEE, London, UK: 987-998.

    Google Scholar 

  19. Kolmogorov AN (1959) Entropy per unit time as a metric invariant of automorphisms (in Russian). Doklady Akademii Nauk SSSR, 124: 754-755.

    MATH  MathSciNet  Google Scholar 

  20. Lin R, Gerla M (1997) Adaptive clustering for mobile wireless networks. IEEE J. Selected Areas in Communications, September: 1265-1275.

    Google Scholar 

  21. Makarenko A, Kaupp T, Grocholsky B, Durrant-Whyte HF (2003) Human-robot interactions in active server networks. In: Computational Intelligence in Robotics and Automation for the New Millennium (Proc. 2003 IEEE Intl. Symposium Computational Intelligence in Robotics and Automation), 16-20 July, Kobe, Japan, IEEE Computer Society Press, Piscataway, NJ, 1: 247-252.

    Google Scholar 

  22. Mathews GM, Durrant-Whyte HF, Prokopenko M (2006) Scalable decen-tralised decision making and optimisation in heterogeneous teams. In: Proc. IEEE Intl. Conf. Multisensor Fusion and Integration for Intelligent Systems -MFI2006, 3-6 September, Heidelberg, Germany, IEEE Computer Society Press, Piscataway, NJ: 383-388.

    Google Scholar 

  23. Newman MEJ (2002) The structure and function of networks. In: Hossfeld F, Binder E (eds) Proc. Europhysics Conf. Computational Physics - CCP2001, 5-8 September, 2001, Aachen, Germany, Elsevier Science, Amsterdam, 147(1-2): 40-45.

    Google Scholar 

  24. Ogston E, Overeinder B, Van Steen M, Brazier F (2003) A Method for decentral-ized clustering in large multi-agent systems. In: Rosenschein JS, Sandholm T, Wooldridge M, Yokoo M (eds) Proc. 2nd Intl. Joint Conf. Autonomous Agents and Multi-Agent Systems, 14-18 July, Melbourne, Australia, ACM Press, New York, NY: 798-796.

    Google Scholar 

  25. Olsson L, Nehaniv CL, Polani D (2004) Sensory channel grouping and struc-ture from uninterpreted sensor data. In: Zebulum RS, Gwaltney D, Hornby G, Keymeulen D, Lohn J, Stoica A (eds) Proc. NASA/DoD Conf. Evolvable Hard-ware - EH’04, 24-26 June, Seattle, WA, IEEE Computer Society Press, Los Alamitos, CA: 153-160.

    Chapter  Google Scholar 

  26. Piraveenan M, Prokopenko M, Wang P, Price DC (2005) Towards adaptive clustering in self-monitoring multi-agent networks. In: Khosla R, Howlett RJ, Jain LC (eds) Proc. 9th Intl. Conf. Knowledge-Based Intelligent Information and Engineering Systems - KES’2005, 14-16 September, Melbourne, Australia. Lecture Notes in Computer Science 3682(II), Springer-Verlag, Berlin: 796-805.

    Google Scholar 

  27. Prokopenko M (1999) On situated reasoning in multi-agent systems. In: Hybrid Systems and AI: Modeling, Analysis and Control of Discrete and Continuous Systems, AAAI Technical Report SS-99-05, March, AAAI Press, Menlo Park, CA: 158-163.

    Google Scholar 

  28. Prokopenko M, Butler M, Howard T (2001) On emergence of scalable tactical and strategic behavior. In: Stone P, Balch TR, Kraetzschmar GK (eds) RoboCup 2000: Robot Soccer World Cup IV (Proc. 4th RoboCup-2000 Workshop), 31 August - 1 September, Melbourne, Australia, Lecture Notes in Computer Science 2019, Springer-Verlag, Berlin: 357-366.

    Chapter  Google Scholar 

  29. Prokopenko M, Wang P (2004) On self-referential shape replication in robust aerospace vehicles. In: Pollack J, Bedau MA, Husbands P, Ikegami T, Watson RA (eds) Artificial Life IX (Proc. 9th Intl. Conf. Simulation and Synthesis of Living Systems), 12-15 September, Boston, MA, MIT Press, Cambridge, MA: 27-32.

    Google Scholar 

  30. Prokopenko M, Wang P (2004) Evaluating team performance at the edge of chaos. In: Polani D, Browning B, Bonarini A, Yoshida K (eds) RoboCup 2003: Robot Soccer World Cup VII (Proc. 7th RoboCup-2003 Springer-Verlag, Berlin:Symposium), Padua, July, Lecture Notes in Computer Science 3020: 89-101.

    Google Scholar 

  31. Prokopenko M, Piraveenan M, Wang P (2005) On convergence of dynamic clus-ter formation in multi-agent networks. In: Capcarrére MS, Freitas AA, Bentley PJ, Johnson CG, Timmis J (eds) Advances in Artificial Life (Proc. 8th European Conference - ECAL 2005), 5-9 September, Canterbury, UK. Lecture Notes in Computer Science 3630, Springer-Verlag, Berlin: 884-894.

    Google Scholar 

  32. Prokopenko M, Wang P, Price DC, Valencia P, Foreman M, Farmer AJ (2005) Self-organising hierarchies in sensor and communication networks. Artificial Life (Special issue on Dynamic Hierarchies), 11(4): 407-426.

    Google Scholar 

  33. Prokopenko M, Wang P, Foreman M, Valencia P, Price DC, Poulton G (2005) On connectivity of reconfigurable impact networks in ageless aerospace vehicles. J. Robotics and Autonomous Systems, 53: 36-58.

    Article  Google Scholar 

  34. Prokopenko M, Wang P, Price DC (2005) Complexity metrics for self-monitoring impact sensing networks. In: Lohn J, Gwaltney D, Hornby G, Zebulum R, Keymeulen D, Stoica A (eds) Proc. NASA/DoD Conf. Evolvable Hardware -EH-05, 29 June - 1 July, Washington, DC, IEEE Computer Society Press, Los Alamitos, CA: 239-246.

    Google Scholar 

  35. Prokopenko M, Poulton GT, Price DC, Wang P, Valencia P, Hoschke N, Farmer AJ, Hedley M, Lewis C, Scott DA (2006) Self-organising impact sensing net-works in robust aerospace vehicles. In: Fulcher, J (ed) Advances in Applied Artificial Intelligence. Idea Group, Hershey, PA: 186-223.

    Google Scholar 

  36. Prokopenko M, Gerasimov V, Tanev I (2006) Evolving spatiotemporal coor-dination in a modular robotic system. In: Nolfi S, Baldassarre G, Calabretta R, Hallam JCT, Marocco D, Meyer J-A, Miglino O, Parisi D (eds) From Ani-mals to Animats 9 (Proc. 9th Intl. Conf. Simulation of Adaptive Behavior -SAB2006), 25-29 September, Rome, Italy, Lecture Notes in Computer Science 4095, Springer-Verlag, Berlin: 558-569.

    Google Scholar 

  37. Pynadath DV, Tambe M (2002) Multiagent teamwork: analyzing the optimality and complexity of key theories and models. In: Castelfranchi C, Johnson WL (eds) Proc. 1st Intl. Joint Conf. Autonomous Agents and Multiagent Systems -AAMAS2002, 15-19 July, Bologna, Italy, ACM Press, New York, NY: 873-880.

    Chapter  Google Scholar 

  38. Rasmussen S, Baas NA, Mayer B, Nilsson M, Olesen MW (2001) Ansatz for dynamical hierarchies. Artificial Life, 7(4): 329-353.

    Article  Google Scholar 

  39. Rényi, A(1970) Probability theory. North-Holland, Amsterdam, The Netherlands.

    Google Scholar 

  40. Sandholm T, Lesser V (1995) Coalition formation among bounded rational agents. In: Mellish C (ed) Proc. 14th Intl. Joint Conf. Artificial Intelligence -IJCAI95, 20-25 August, Montréal, Québec, Canada, Morgan Kaufmann, San Francisco, CA: 662-671.

    Google Scholar 

  41. Sinai YG (1959) On the concept of entropy of a dynamical system (in Russian). Doklady Akademii Nauk SSSR, 124: 768-771.

    MATH  MathSciNet  Google Scholar 

  42. Solé RV, Ferrer-Cancho R, Montoya JM, Valverde S (2002) Selection, tinkering and emergence in complex networks - crossing the land of tinkering. Complexity, 8(1): 20-33.

    Article  MathSciNet  Google Scholar 

  43. Takens F (1981) Detecting strange attractors in turbulence. Dynamical systems and Turbulence, Lecture Notes in Mathematics 898, Springer-Verlag, Berlin: 366-381.

    Google Scholar 

  44. US Department of Energy (2006) Benefits of Demand Response in Electricity Markets and Recommendations for Achieving Them. A Report to the United Stated Congress Pursuant to Section 1252 of the Energy Policy Act of 2005, February.

    Google Scholar 

  45. Wieselthier JE, Nguyen GD, Ephremides A (2000) On the construction of energy-efficient broadcast and multicast trees in wireless networks. Proc. 19th Annual Joint Conf. IEEE Computer and Communications Societies -INFOCOM2000, 26-30 March, Tel Aviv, Israel: 585-594.

    Google Scholar 

  46. White JG, Southgate E, Thompson JN, Brenner S (1986) The structure of the nervous system of the nematode C. elegans. Philosophical Transactions of the Royal Society of London - Series B: Biological Sciences, 314(1165): 1-340.

    Article  Google Scholar 

  47. Williams, RJ, Martinez ND (2000) Simple rules yield complex food webs. Nature, 404: 180-183.

    Article  Google Scholar 

  48. Ygge F, Akkermans JM (1996) Power load management as a computational mar-ket. In: Tokoro M (ed) Proc. 2nd Intl. Conf. Multi-Agent Systems - ICMAS’96, 9-13 December, Kyoto, Japan, AAAI Press, Menlo Park, CA: 393-400.

    Google Scholar 

  49. Zhang T, Ramakrishnan R, Livny M (1997) BIRCH: a new data clustering algorithm and its applications. Data Mining and Knowledge Discovery, 1(2): 141-182.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. CSIRO Information and Communication Technologies Centre, Sydney, Australia

    Mahendra Piraveenan, Mikhail Prokopenko, Peter Wang & Astrid Zeman

Authors
  1. Mahendra Piraveenan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mikhail Prokopenko
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Peter Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Astrid Zeman
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Computer Science and Software Engineering Faculty of Informatics, University of Wollongong, Northfields Ave, Wollongong, NSW, 2522, Australia

    John Fulcher

  2. Knowledge-Based Engineering Founding Director of the KES Centre, University of South Australia, SCT-Building Mawson Lakes Campus, Adelaide, South Australia SA, 5095, Australia

    L. C. Jain

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Piraveenan, M., Prokopenko, M., Wang, P., Zeman, A. (2008). Decentralized Multi-Agent Clustering in Scale-free Sensor Networks. In: Fulcher, J., Jain, L.C. (eds) Computational Intelligence: A Compendium. Studies in Computational Intelligence, vol 115. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78293-3_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-78293-3_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78292-6

  • Online ISBN: 978-3-540-78293-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation