Sensor Systems and Software. Second International ICST Conference, S-Cube 2010, Miami, FL, USA, December 13-15, 2010, Revised Selected Papers

Research Article

Pro-active Strategies for the Frugal Feeding Problem in Wireless Sensor Networks

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  • @INPROCEEDINGS{10.1007/978-3-642-23583-2_14,
        author={Elio Velazquez and Nicola Santoro and Mark Lanthier},
        title={Pro-active Strategies for the Frugal Feeding Problem in Wireless Sensor Networks},
        proceedings={Sensor Systems and Software. Second International ICST Conference, S-Cube 2010, Miami, FL, USA, December 13-15, 2010, Revised Selected Papers},
        proceedings_a={S-CUBE},
        year={2012},
        month={5},
        keywords={},
        doi={10.1007/978-3-642-23583-2_14}
    }
    
  • Elio Velazquez
    Nicola Santoro
    Mark Lanthier
    Year: 2012
    Pro-active Strategies for the Frugal Feeding Problem in Wireless Sensor Networks
    S-CUBE
    Springer
    DOI: 10.1007/978-3-642-23583-2_14
Elio Velazquez1,*, Nicola Santoro1,*, Mark Lanthier1,*
  • 1: Carleton University
*Contact email: evelazqu@scs.carleton.ca, santoro@scs.carleton.ca, lanthier@scs.carleton.ca

Abstract

This paper proposes a pro-active solution 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 entities (sensors). Our pro-active solution reduces the FFP to finding energy-efficient routes in a dynamic Compass Directed unit Graph (CDG). The proposed CDG incorporates ideas from forward progress routing and the directionality of compass routing in an energy-aware unit sub-graph. Navigating the CDG 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 the shared resource (recharge station). We also examine the impact of critical parameters such as transmission range, cost of mobility and sensor knowledge in the overall performance.