Dealing with nonuniformity in data centric storage for wireless sensor networks (Articolo in rivista)

Type
Label
  • Dealing with nonuniformity in data centric storage for wireless sensor networks (Articolo in rivista) (literal)
Anno
  • 2011-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1109/TPDS.2011.18 (literal)
Alternative label
  • Albano M., Chessa S., Nidito F., Pelagatti S. (2011)
    Dealing with nonuniformity in data centric storage for wireless sensor networks
    in IEEE transactions on parallel and distributed systems (Print); IEEE, Institute of electrical and electronics engineers, New York (Stati Uniti d'America)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Albano M., Chessa S., Nidito F., Pelagatti S. (literal)
Pagina inizio
  • 1398 (literal)
Pagina fine
  • 1406 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
  • Albano, Michele (1); Chessa, Stefano (1); Nidito, Francesco (2); Pelagatti, Susanna (2) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5680897&tag=1 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 22 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
  • 9 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • 8 (literal)
Note
  • Scopu (literal)
  • PuMa (literal)
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • CNR-ISTI, Pisa, Italy; Dipartimento di Informatica, Università di Pisa, Italy (literal)
Titolo
  • Dealing with nonuniformity in data centric storage for wireless sensor networks (literal)
Abstract
  • In-network storage of data in Wireless Sensor Networks (WSNs) is considered a promising alternative to external storage since it contributes to reduce the communication overhead inside the network. Recent approaches to data storage rely on Geographic Hash Tables (GHT) for efficient data storage and retrieval. These approaches, however, assume that sensors are uniformly distributed in the sensor field, which is seldom true in real applications. Also they do not allow tuning the redundancy level in the storage according to the importance of the data to be stored. To deal with these issues, we propose an approach based on two mechanisms. The first is aimed at estimating the real network distribution. The second exploits data dispersal method based on the estimated network distribution. Experiments through simulation show that our approach approximates quite closely the real distribution of sensors and that our dispersal protocol sensibly reduces data losses due to unbalanced data load. (literal)
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