7th International Conference on Body Area Networks

Research Article

Energy Harvesting Enabled Wireless Sensor Networks: Energy Model and Battery Dimensioning

Download562 downloads
  • @INPROCEEDINGS{10.4108/icst.bodynets.2012.249967,
        author={Raul Gomez Cid Fuentes and Albert Cabellos and Eduard Alarcon},
        title={Energy Harvesting Enabled Wireless Sensor Networks: Energy Model and Battery Dimensioning},
        proceedings={7th International Conference on Body Area Networks},
        publisher={ICST},
        proceedings_a={BODYNETS},
        year={2012},
        month={11},
        keywords={wireless sensor networks energy harvesting energy model battery dimensioning},
        doi={10.4108/icst.bodynets.2012.249967}
    }
    
  • Raul Gomez Cid Fuentes
    Albert Cabellos
    Eduard Alarcon
    Year: 2012
    Energy Harvesting Enabled Wireless Sensor Networks: Energy Model and Battery Dimensioning
    BODYNETS
    ICST
    DOI: 10.4108/icst.bodynets.2012.249967
Raul Gomez Cid Fuentes1,*, Albert Cabellos1, Eduard Alarcon1
  • 1: Universitat Politecnica de Catalunya
*Contact email: rgomez@ac.upc.edu

Abstract

Wireless Sensor Networks present a pending challenge for a complete deployability due to energy requirements. The low power density that these energy sources provide compared to the required energy for the communication process creates the necessity of temporal storage. Unfortunately, the random nature of the power sources implies that the energy storage unit might not be able to guarantee the communication at all time, thus giving a certain loss probability, which is a function of the energy storage capacity. Typical solutions reduce this loss probability by over-dimensioning the battery, producing a very large overhead in size. In this paper, a scalable energy model is presented for the estimation of the loss probability. Accordingly, this energy model is applied in order to provide battery dimensioning guidelines. The results show that, by means of an accurate energy model, a certain loss probability can be achieved, while reducing up to 4 times the needed energy storage capacity.