On Maximizing Residual Energy of Actors in Wireless Sensor and Actor Networks

  • Ka. Selvaradjou
  • C. Siva Ram Murthy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4308)


We consider the problem of residual energy maximization of actors by optimal assignment of mobile actors in Wireless Sensor and Actor Networks (WSANs) to the event spots in real-time. Finding the optimal tour of multiple actors towards the reported events can be shown to be NP-Complete. We formulate the optimization problem as Mixed Integer Non Linear Programming and propose heuristics that find near optimal schedule of actors in a large scale WSAN. Maximizing the residual energy of actors leads to increased service time. We also study the impact of optimal positioning of actors at the end of their tour so as to cover up new events that might occur with stringent deadline constraints. From the simulations, we observed that the inter-zone deadline based scheduling performs fairly better than others by minimizing the overall movement required by the actors and reducing the deadline miss ratio. It is also observed from the simulations that proactive positioning of actors at the end of their schedule such that every zone is guaranteed to have at least one actor, performs better both in terms of increased lifetime and controlled deadline miss ratio.


Sensor Network Sensor Node Wireless Sensor Network Cluster Head Actor Network 
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.
    Culler, D., Estrin, D., Srivastava, M.: Overview of Sensor Networks. IEEE Computer 37, 41–49 (2004)Google Scholar
  2. 2.
    Akyildiz, F.I., Kasimoglu, I.H.: Wireless Sensor and Actor Networks: Research Chal-lenges. Ad Hoc Networks 2(4), 351–367 (2004)CrossRefGoogle Scholar
  3. 3.
    He, T., Stankovic, J.A., Lu, C., Abdelzaher, T.F.: SPEED: A Stateless Protocol for Real-Time Communication in Sensor Networks. In: Proceedings of IEEE International Conference on Distributed Computing Systems, pp. 46–55 (2003)Google Scholar
  4. 4.
    Lu, C., Blum, B., Abdelzaher, T.F., Stankovic, J.A., He, T.: RAP: A Real-Time Communication Architecture for Large-Scale Wireless Sensor Networks. In: Proceedings of IEEE Real Time Technology and Applications Symposium, pp. 55–66 (2002)Google Scholar
  5. 5.
    Haghani, A., Hu, H., Tian, Q.: An Optimization Model for Real-Time Emergency Vehicle Dispatching and Routing. In: Proceedings of TRB Annual Meeting, pp. 1–23 (January 2003)Google Scholar
  6. 6.
    Melodia, T., Pompili, D., Gungor, V.C., Akyildiz, I.F.: A Distributed Coordination Framework for Wireless Sensor and Actor Networks. MobiHoc, 99–110 (2005)Google Scholar
  7. 7.
    Cyzyzk, J., Mesnier, M., More, J.: The NEOS server. IEEE Journal on Computational Sceince and Engineering 5(3), 68–75 (1998)CrossRefGoogle Scholar
  8. 8.
    Brooke, A., Kendrick, D., Meeraus, A., Raman, R., Rosenthal, R.E.: GAMS: A User’s Guide and Tutorial. GAMS Development Corporation (1998)Google Scholar
  9. 9.
    NEOS Server [Online], Available, http://neos.mcs.anl.gov
  10. 10.

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ka. Selvaradjou
    • 1
  • C. Siva Ram Murthy
    • 1
  1. 1.Department of Computer Science and EngineeringIndian Institute of TechnologyMadrasIndia

Personalised recommendations