Advertisement

Field-Based Coordination for Pervasive Computing Applications

  • Marco Mamei
  • Franco Zambonelli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5151)

Abstract

Emerging pervasive computing technologies such as sensor networks and RFID tags can be embedded in our everyday environment to digitally store and elaborate a variety of information. By having application agents access in a dynamic and wireless way such distributed information, it is possible to enforce a notable degree of context-awareness in applications, and increase the capabilities of interacting with the physical world. In particular, biologically inspired field-based data structures such as gradients and pheromones are suitable to represent information in a variety of pervasive computing applications. This paper discusses how both sensor networks and RFID tags can be used to that purpose, outlining the respective advantages and drawbacks of these technologies.

Keywords

Field-based coordination Ad-hoc networks RFID infrastructures Bio-inspired computing 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Parunak, V.: Go to the ant: Engineering principles from natural agent systems. Annals of Operations Research 75, 69–101 (1997)CrossRefzbMATHGoogle Scholar
  2. 2.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence. From Natural to Artificial Systems. Oxford University Press, Oxford (1999)zbMATHGoogle Scholar
  3. 3.
    Babaoglu, O., Meling, H., Montresor, A.: A framework for the development of agent-based peer-to-peer systems. In: 22nd International Conference on Distributed Computing Systems, pp. 15–22. IEEE CS Press, Vienna (2002)CrossRefGoogle Scholar
  4. 4.
    Mamei, M., Zambonelli, F.: Physical deployment of digital pheromones through rfid technology. In: IEEE Swarm Symposium, IEEE CS Press, Pasadena (2005)Google Scholar
  5. 5.
    Svennebring, J., Koenig, S.: Building terrain covering ant robots: a feasibility study. Autonomous Robots 16(3), 313–332 (2004)CrossRefGoogle Scholar
  6. 6.
    Paskin, M., Guestrin, C., McFadden, J.: A robust architecture for inference in sensor networks. In: International Symposium on Information Processing in Sensor Networks. ACM Press, Los Angeles (2005)Google Scholar
  7. 7.
    Werner-Allen, G., Lorincz, K., Ruiz, M., Marcillo, O., Johnson, J., Lees, J., Welsh, M.: Deploying a wireless sensor network on an active volcano. IEEE Internet Computing 10, 18–25 (2004)CrossRefGoogle Scholar
  8. 8.
    Patterson, D., Liao, L., Fox, D., Kautz, H.: Inferring high-level behavior from low-level sensors. In: International Conference on Ubiquitous Computing. ACM Press, Seattle (2003)Google Scholar
  9. 9.
    Choudhury, T., Pentland, A.: Sensing and modeling human networks using the sociometer. In: International Symposium on Wearable Computers. IEEE CS Press, White Plains (2003)Google Scholar
  10. 10.
    Mamei, M., Zambonelli, F.: Programming pervasive and mobile computing applications with the tota middleware. In: Proceedings of the International Conference On Pervasive Computing (Percom). IEEE CS Press, Orlando (2004)Google Scholar
  11. 11.
    Stoy, K., Nagpal, R.: Self-reconfiguration using directed growth. In: 7th International Symposium on Distributed Autonomous Robotic Systems. Springer, Heidelberg (2004)Google Scholar
  12. 12.
    Curino, C., Giani, M., Giorgetta, M., Giusti, A., Murphy, A., Picco, G.: Tinylime: Bridging mobile and sensor networks through middleware. IEEE CS Press, Los Alamitos (2005)Google Scholar
  13. 13.
    Weyns, D., Schelfthout, K., Holvoet, T.: Exploiting a virtual environment in a real-world application. In: Weyns, D., Van Dyke Parunak, H., Michel, F. (eds.) E4MAS 2005. LNCS, vol. 3830, pp. 218–234. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  14. 14.
    Riva, O., Nadeem, T., Borcea, C., Iftode, L.: Context-aware migratory services in ad hoc networks. IEEE Transaction on Mobile Computing (to appear, 2007)Google Scholar
  15. 15.
    Picco, G., Murphy, A., Roman, G.: Lime: a coordination model and middleware supporting mobility of hosts and agents. ACM Transactions on Software Engineering and Methodology 15, 279–328 (2006)CrossRefGoogle Scholar
  16. 16.
    Mascolo, C., Capra, L., Zachariadis, Z., Emmerich, W.: Xmiddle: A data-sharing middleware for mobile computing. Wireless Personal Communications 21, 77–103 (2002)CrossRefGoogle Scholar
  17. 17.
  18. 18.
  19. 19.
    Mamei, M., Zambonelli, F.: Pervasive pheromone-based interaction with rfid tags. ACM Transactions on Autonomous and Adaptive Systems 2, 1–28 (2007)CrossRefGoogle Scholar
  20. 20.
    Mamei, M., Quaglieri, R., Zambonelli, F.: Making tuple spaces physical with rfid tags. In: Proceedings of the Symposium on Applied Computing (SAC). ACM Press, Dijon (2006)Google Scholar
  21. 21.
    Payton, D., Daily, M., Estowski, R., Howard, M., Lee, C.: Pheromone robotics. Autonoumous Robots 11, 319–324 (2001)CrossRefzbMATHGoogle Scholar
  22. 22.
    Philipose, M., Fishkin, K., Perkowitz, M., Patterson, D., Fox, D., Kautz, H., Hahnel, D.: Inferring activities from interactions with objectsGoogle Scholar
  23. 23.
    Kulyukin, V., Gharpure, C., Nicholson, J., Pavithran, S.: Rfid in robot-assisted indoor navigation for visually impaired. In: Proceedings of the International Conference on Intelligent Robots and Systems. IEEE CS Press, Los Alamitos (2004)Google Scholar
  24. 24.
    Collins, G.: Next stretch for plastic electronics. Scientific American (August 2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Marco Mamei
    • 1
  • Franco Zambonelli
    • 1
  1. 1.Dipartimento di Scienze e Metodi dell’IngegneriaUniversity of Modena and Reggio EmiliaReggio EmiliaItaly

Personalised recommendations