Quality of Service in Heterogeneous Networks. 6th International ICST Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2009 and 3rd International Workshop on Advanced Architectures and Algorithms for Internet Delivery and Applications, AAA-IDEA 2009, Las Palmas, Gran Canaria, November 23-25, 2009 Proceedings

Research Article

Cooperative Training in Wireless Sensor and Actor Networks

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  • @INPROCEEDINGS{10.1007/978-3-642-10625-5_36,
        author={Francesco Sorbelli and Roberto Ciotti and Alfredo Navarra and Cristina Pinotti and Vlady Ravelomanana},
        title={Cooperative Training in Wireless Sensor and Actor Networks},
        proceedings={Quality of Service in Heterogeneous Networks. 6th International ICST Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2009 and 3rd International Workshop on Advanced Architectures and Algorithms for Internet Delivery and Applications, AAA-IDEA 2009, Las Palmas, Gran Canaria, November 23-25, 2009 Proceedings},
        proceedings_a={QSHINE},
        year={2012},
        month={10},
        keywords={wireless sensor network training localization distributed algorithms},
        doi={10.1007/978-3-642-10625-5_36}
    }
    
  • Francesco Sorbelli
    Roberto Ciotti
    Alfredo Navarra
    Cristina Pinotti
    Vlady Ravelomanana
    Year: 2012
    Cooperative Training in Wireless Sensor and Actor Networks
    QSHINE
    Springer
    DOI: 10.1007/978-3-642-10625-5_36
Francesco Sorbelli1, Roberto Ciotti1, Alfredo Navarra1,*, Cristina Pinotti1,*, Vlady Ravelomanana2,*
  • 1: University of Perugia
  • 2: University of Paris
*Contact email: navarra@dmi.unipg.it, pinotti@dmi.unipg.it, vlad@lipn.univ-paris13.fr

Abstract

Exploiting features of high density wireless sensor networks represents a challenging issue. In this work, the training of a sensor network which consists of anonymous and asynchronous sensors, randomly and massively distributed in a circular area around a more powerful device, called actor, is considered. The aim is to partition the network area in concentric coronas and sectors, centered at the actor, and to bring each sensor autonomously to learn to which corona and sector belongs. The new protocol, called , is the fastest training algorithm for asynchronous sensors, and it matches the running time of the fastest known training algorithm for synchronous sensors. Moreover, to be trained, each sensor stays awake only a constant number of time slots, independent of the network size, consuming very limited energy. The performances of the new protocol, measured as the number of trained sensors, the accuracy of the achieved localization, and the consumed energy, are also experimentally tested under different network density scenarios.