A Scalable Graph Model and Coordination Algorithms for Mobile Sensor Networks

  • Jindong Tan
Part of the Signals and Communication Technology book series (SCT)

A mobile sensor network consists of a collection of wireless connected mobile robots equipped with a variety of sensors, as shown in Figure 1. In such a system, each mobile robot has sensing, computation, communication, and locomotion capabilities. The mobile robots spread out across certain areas and share sensory information through an ad hoc wireless network. A mobile sensor network is therefore a wireless sensor network with reconfigurable sensing capabilities. Mobile sensor networks have a myriad of civilian and military applications ranging from foraging, surveillance, search and rescue to mobile target tracking. A mobile sensor network can be rapidly deployed in hostile environments, inaccessible terrains or disaster relief operations for sensing and reconnaissance tasks, where a task is generally achieved by coordination of the robots’ activities. The variety of task specifications and the ever-changing environment require the control algorithms of the reconfigurable mobile sensor network to be flexible, scalable and adaptive. This chapter presents a distributed model and algorithms for locally optimized control of mobile sensor networks. Multi-robot coordination and formation control is addressed to the continuous reconfiguration of the mobile robots for varying task requirements and changing environments.


Sensor Network Mobile Robot Voronoi Diagram Delaunay Triangulation Topological Event 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    F. Araujo and L. Rodrigues. Fast localized delaunay triangulation. In Proceedings of the 8th International Conference on Principles of Distributed Systems (OPODIS), Grenoble, France, December 2004.Google Scholar
  2. 2.
    T. Balch and R. Arkin. Behavior-based formation control for multirobot teams. IEEE Transactions on Robotics and Automation, 14(6):926-939, 1998.CrossRefGoogle Scholar
  3. 3.
    M. Batalin and G. S. Sukhatme. Coverage, exploration and deployment by a mobile robot and communication network. Telecommunication Systems Journal, Special Issue on Wireless Sensor Networks, 26(2), 2004.Google Scholar
  4. 4.
    M. Batalin and G. S. Sukhatme. Using a sensor network for distributed multi-robot task allocation. In IEEE International Conference on Robotics and Automation, pages 158-164, New Orleans, Louisiana, Apr 2004.Google Scholar
  5. 5.
    M. Batalin, G. S. Sukhatme, and M. Hattig. Mobile robot navigation using a sensor network. In IEEE International Conference on Robotics and Automation, pages 636-642, New Orleans, Louisiana, Apr 2004.Google Scholar
  6. 6.
    M. S. Branicky. Multiple lyapunov functions and other analysis tools for switched and hybrid systems. IEEE Transactions on Automatic Control, 43(4):475-482, 1998.MATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    R. Brooks. A robust layered control system for a mobile robot. International Journal of Robotics and Automation, 2(1):14-23, 1986.Google Scholar
  8. 8.
    N. Bulusu, J. Heidemann, and D. Estrin. Adaptive beacon placement. In Twenty-first International Conference on Distributed Computing Systems (ICDCS). IEEE Computer Society, April 2001.Google Scholar
  9. 9.
    R. Burridge, A. Rizzi, and D. Koditschek. Sequential compostion of dynamically dex-terous robot behaviors. The International Journal of Robotics Research, 18(6):534-555, 1999.CrossRefGoogle Scholar
  10. 10.
    Z. Butler and D. Rus. Event-based motion control for mobile sensor networks. IEEE Pervasive Computing, 2(4):34-43, 2003.CrossRefGoogle Scholar
  11. 11.
    P. Corke, R. Peterson, and D. Rus. Networked robots: Flying robot navigation using a sensor net. In Proceedings of the 11th International Symposium of Robotics Research, 2003.Google Scholar
  12. 12.
    P. I. Corke, S. E. Hrabar, R. Peterson, D. Rus, S. Saripalli, and G. S. Sukhatme. Au-tonomous deployment and repair of a sensor network using an unmanned aerial vehicle. In IEEE International Conference on Robotics and Automation, pages 3602-3609, Apr 2004.Google Scholar
  13. 13.
    P. I. Corke, S. E. Hrabar, R. Peterson, D. Rus, S. Saripalli, and G. S. Sukhatme. De-ployment and connectivity repair of a sensor net with a flying robot. In International Symposium on Experimental Robotics, 2004.Google Scholar
  14. 14.
    J. Cortes, S. Martinez, T. Karatas, and F. Bullo. Coverage control for mobile sensing networks. IEEE Transactions on Robotics and Automation, 20(2):243-255, 2004.CrossRefGoogle Scholar
  15. 15.
    A. Das, J. Spletzer, V. Kumar, and C. Taylor. Ad hoc networks for localization and control. In Proceedings of the 41st IEEE Conference on Decision and Control, pages 2978-2983, December 2002.Google Scholar
  16. 16.
    M. de Berg, M. van Kreveld, M. Overmars, and O. Schwarzkopf. Computational Geom-etry Algorithms and Applications. Springer, 2000.Google Scholar
  17. 17.
    J. P. Desai, J. P. Ostrowski, and V. Kumar. Modeling and control of formations of nonholonomic mobile robots. IEEE Transactions on Robotics and Automation, 17(6):905-908, 2001.CrossRefGoogle Scholar
  18. 18.
    Q. Du, V. Faber, and M. Gunzburger. Centroidal voronoi tessellations: Applications and algorithms. SIAM Review, 41(4):637-676, 1999.MATHCrossRefMathSciNetGoogle Scholar
  19. 19.
    J. A. Fax. Optimal and Cooperative Control of Vehicle Formations. Dissertation of California Institute of Technology, Pasadena, California, 2002.Google Scholar
  20. 20.
    J. F. Feddema, C. Lewis, and D. A. Schoenwald. Decentralized control of cooperative robotic vehicles: Theory and application. IEEE Transactions on Robotics and Automa-tion, 18(5):852-864, 2002.CrossRefGoogle Scholar
  21. 21.
    R. Fierro, A. K. Das, R. V. Kumar, and J. P. Ostrowsk. Hybrid control of formations of robots. In Proceedings of IEEE International Conference on Robotics and Automation, pages 157-162, 2001.Google Scholar
  22. 22.
    A. Howard, M. J. Mataric, and G. S. Sukhatme. An incremental self-deployment algo-rithm for mobile sensor networks. Autonomous Robots, 13:113-126, 2002.MATHCrossRefGoogle Scholar
  23. 23.
    A. Howard, M. J. Mataric, and G. S. Sukhatme. Mobile sensor network deployment using potential fields: A distributed, scalable solution to the area coverage problem. In Proceedings of the 6th International Conference on Distributed Autonomous Robotic Systems, pages 299-308, Fukuoka, Japan, 2002.Google Scholar
  24. 24.
    A. Jadbabaie, J. Lin, and A. Morse. Coordination of groups of mobile autonomous agents using nearest neighbor rules. IEEE Transactions on Automatic Control, 48(6):988-1001, 2003.CrossRefMathSciNetGoogle Scholar
  25. 25.
    O. Khatib. Real-time obstacle avoidance for manipulators and mobile robots. The Inter-national Journal of Robotics Research, 5(1), 1986.Google Scholar
  26. 26.
    Q. Li, M. De Rosa, and D. Rus. Distributed algorithms for guiding navigation across a sensor network. In Proceedings of the 9th annual international conference on Mobile computing and networking, pages 313-325. ACM Press, 2003.Google Scholar
  27. 27.
    X.-Y. Li, G. Calinescu, P.-J. Wan, and Y. Wang. Localized delaunay triangulation with application in ad hoc wireless networks. IEEE Transactions on Parallel and Distributed Systems, 14(10):1035-1047, 2003.CrossRefGoogle Scholar
  28. 28.
    M. J. Mataric. Behavior-based robotics as a tool for synthesis of artificial behavior and analysis of natural behavior. Trends in Cognitive Science, 2(3):82-87, 1998.CrossRefGoogle Scholar
  29. 29.
    S. Meguerdichian, S. Slijepcevic, V. Karayan, and M. Potkonjak. Localized algorithms in wireless ad-hoc networks: location discovery and sensor exposure. pages 106-116, Long Beach, CA, July 2001.Google Scholar
  30. 30.
    P. Ogren, E. Fiorelli, and N. E. Leonard. Cooperative control of mobile sensor networks: Adaptive gradient climbing in a distributed environment. IEEE Transactions on Auto-matic Control, 49(8):1292-1302, 2004.CrossRefMathSciNetGoogle Scholar
  31. 31.
    .A. Okabe, B. Boots, K. Sugihara, and S. N. Chiu. Spatial Tessellations. John Wiley and Sons, 1999.Google Scholar
  32. 32.
    L. E. Parker. Distributed algorithms for multi-robot observation of multiple moving targets. Autonomous Robots, 12:231-255, 2002.MATHCrossRefGoogle Scholar
  33. 33.
    D. Payton, M. Daily, R. Estkowski, M. Howard, and C. Lee. Pheromone robotics. Au-tonomous Robots, 11(3):319-324, 2001.MATHCrossRefGoogle Scholar
  34. 34.
    S. Poduri and G. S. Sukhatme. Constrained coverage for mobile sensor networks. In IEEE International Conference on Robotics and Automation, pages 165-172, New Orleans, LA, May 2004.Google Scholar
  35. 35.
    P. Sander, D. Peleshcuk, and B. Grosz. A scalable, distributed algorithm for efficient task allocation. In Proceedings of the First Joint Conference on Autonomous Agents and Multi-agent Systems, pages 1191-1198, Bologna, Italy, July 2002.Google Scholar
  36. 36.
    J. Tan, N. Xi, A. Goradia, and W. Sheng. Coordination of human and mobile manipu-lator formation in a perceptive reference frame. In Proceedings of IEEE International Conference on Robotics and Automation, 2003.Google Scholar
  37. 37.
    A. Winfield. Distributed sensing and data collection via broken ad hoc wireless connected networks of mobile robots. Distributed Autonomous Robotic Systems 4, ed. L. E. Parker, G. Bekey, J. Barhen, Springer-Verlag, pages 273-282, 2000.Google Scholar
  38. 38.
    Y. Zou and K. Chakrabarty. Sensor deployment and target localization based on virtual forces. In Proceedings of the Twenty-Second Annual Joint Conference of the IEEE Com-puter and Communications Societies, pages 1293-1303, April 2003.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Jindong Tan
    • 1
  1. 1.Department of Electrical and Computer EngineeringMichigan Technological UniversityHoughtonUSA

Personalised recommendations