Neighbour Selection and Sensor Knowledge: Proactive Approach for the Frugal Feeding Problem in Wireless Sensor Networks

  • Elio Velazquez
  • Nicola Santoro
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 89)

Abstract

This paper examines new proactive solutions to the Frugal Feeding Problem (FFP) in Wireless Sensor Networks. The FFP attempts to find energy-efficient routes for a mobile service entity to rendezvous with each member of a team of mobile robots. Although the complexity of the FFP is similar to the Traveling Salesman Problem (TSP), we propose an efficient solution, completely distributed and localized for the case of a fixed rendezvous location (i.e., service facility with limited number of docking ports) and mobile capable sensors. Our proactive solution reduces the FFP to finding energy-efficient routes in a dynamic Compass Directed Gabriel Graph (CDGG) or Compass Directed Relative Neighbour Graph (CDRNG). The proposed graphs incorporate ideas from forward progress routing and the directionality of compass routing in an energy-aware graph. Navigating the CDGG or CDRNG guarantees that each sensor will reach the rendezvous location in a finite number of steps. The ultimate goal of our solution is to achieve energy equilibrium (i.e., no further sensor losses due to energy starvation) by optimizing the use of a shared recharge station. We also examine the impact of critical parameters such as transmission range, number of recharge ports and sensor knowledge for the two proposed graphs.

Keywords

Wireless Sensor Network Transmission Range Travel Salesman Problem Sensor Loss Network Survivability 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Arwin, F., Samsudin, K., Ramli, A.R.: Swarm robots long term autonomy using moveable charger. In: Proceedings of the 2009 International Conference on Future Computer and Communication, pp. 127–130 (2009)Google Scholar
  2. 2.
    Drenner, A., Papanikolopoulos, N.: Docking station relocation for maximizing longevity of distributed robotic teams. In: Proceedings of the 2006 IEEE International Conference on Robotics and Automation, pp. 2436–2441 (2006)Google Scholar
  3. 3.
    Drytkiewicz, W., Sroka, S., Handziski, V.: A mobility framework for omnet++. In: Proceesings of the 3rd International OMNeT++ Workshop (2003)Google Scholar
  4. 4.
    Feeney, L.: An energy consumption model for performance analysis of routing protocols for mobile ad hoc networks. Mobile Network Applications 6, 239–249 (2001)CrossRefMATHGoogle Scholar
  5. 5.
    Feeney, L., Nilsson, M.: Investigating the energy consumption of a wireless network interface in an ad hoc networking environment. In: Proceedings of the 20th Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Infocom 2001), vol. 3, pp. 1548–1557 (2001)Google Scholar
  6. 6.
    Frey, H., Ruhrup, S., Stojmenovic, I.: Routing in wireless sensor networks. In: Guide to Wireless Sensor Networks, ch. 4, pp. 81–111 (2009)Google Scholar
  7. 7.
    Kranakis, E., Singh, H., Urrutia, J.: Compass routing on geometric networks. In: Proceedings of the 11th Canadian Conference on Computational Geometry, pp. 51–54 (1999)Google Scholar
  8. 8.
    Litus, Y., Vaughan, R., Zebrowski, P.: The frugal feeding problem: energy-efficient, multi-robot, multi-place rendezvous. In: Proceedings of the 2007 IEEE International Conference on Robotics and Automation, pp. 27–32 (2007)Google Scholar
  9. 9.
    Litus, Y., Zebrowski, P., Vaughan, R.T.: A distributed heuristic for energy-efficient multirobot multiplace rendezvous. IEEE Transactions on Robotics 25, 130–135 (2009)CrossRefGoogle Scholar
  10. 10.
    Sharifi, M., Sedighian, S., Kamali, M.: Recharging sensor nodes using implicit actor coordination in wireless sensor actor networks. Wireless Sensor Network 2, 123–128 (2010)CrossRefGoogle Scholar
  11. 11.
    Stojmenovic, I., Lin, X.: Power-aware localized routing in wireless networks. IEEE Transactions on Parallel and Distributed Systems 12, 1122–1133 (2001)CrossRefGoogle Scholar
  12. 12.
    Vargas, A.: The omnet++ discrete event simulation system. In: Proceedings of the European Simulation Multi-conference (ESM 2001), pp. 319–324 (2001)Google Scholar
  13. 13.
    Velazquez, E., Santoro, N.: Mobility-based strategies for energy restoration in wireless sensor networks. In: Proceedings of the 6th International Conference on Mobile Ah-hoc and Sensor Networks (MSN 2010), pp. 161–168 (2010)Google Scholar
  14. 14.
    Velazquez, E., Santoro, N., Lanthier, M.: Pro-active Strategies for the Frugal Feeding Problem in Wireless Sensor Networks. In: Par, G., Morrow, P. (eds.) S-CUBE 2010. LNICST, vol. 57, pp. 189–204. Springer, Heidelberg (2011)CrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2012

Authors and Affiliations

  • Elio Velazquez
    • 1
  • Nicola Santoro
    • 1
  1. 1.School of Computer ScienceCarleton UniversityCanada

Personalised recommendations