Advertisement

An Ontology-Driven Framework for Resource-Efficient Collaborative Sensing

  • Brayan Luna-Nuñez
  • Rolando Menchaca-Mendez
  • Jesus Favela
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8276)

Abstract

The massive adoption of smartphones that incorporate wireless connectivity and a growing set of embedded sensors is leveraging the emergence of personal and community-scale sensing applications. In these applications, the smartphones act as a cloud of sensors that move around with their human users and hence, are capable of gathering a rich variety of data from their users and from their environments. However, in order to realize their full potential, the designers of these applications face a set of technical challenges related with the limited resources available to mobile devices, their heterogeneity, and the dynamics of the scenarios where they are deployed. In this paper we introduce an ontology-driven framework aimed at efficiently supporting collaborative opportunistic sensing tasks. The proposed framework is composed of a set of local and distributed algorithms that support the establishment and coordination of sensing tasks by performing in-network processing to locate the devices that are most fit to perform the task and by establishing routes that can be used to exchange information among relevant devices. We present theorems that prove that the proposed algorithms are correct.

Keywords

Collaborative sensing distributed algorithms ontology 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Lane, N.D., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., Campbell, A.T.: A survey of mobile phone sensing. IEEE Communications Magazine 48(9), 140–150 (2010)CrossRefGoogle Scholar
  2. 2.
    Cuff, D., Hansen, M., Kang, J.: Urban sensing: out of the woods. Communications of the ACM 51(3), 24–33 (2008)CrossRefGoogle Scholar
  3. 3.
    Bannach, D., Lukowicz, P., Amft, O.: Rapid prototyping of activity recognition applications. IEEE Pervasive Computing 7(2), 22–31 (2008)CrossRefGoogle Scholar
  4. 4.
    Aharony, N., Pan, W., Ip, C., Khayal, I., Pentland, A.: Social fmri: Investigating and shaping social mechanisms in the real world. Pervasive and Mobile Computing 7(6), 643–659 (2011)CrossRefGoogle Scholar
  5. 5.
    Perez, M., Castro, L., Favela, J.: Incense: A research kit to facilitate behavioral data gathering from populations of mobile phone users. In: Proc. of UCAmI, Cancun, Mexico, pp. 25–34 (2011)Google Scholar
  6. 6.
    Sheng, X., Tang, J., Zhang, W.: Energy-efficient collaborative sensing with mobile phones. In: Proceedings IEEE INFOCOM 2012, pp. 1916–1924. IEEE (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Brayan Luna-Nuñez
    • 1
  • Rolando Menchaca-Mendez
    • 1
  • Jesus Favela
    • 2
  1. 1.Instituto Politécnico NacionalMexico
  2. 2.Centro de Investigación Científica y de Educación Superior de EnsenadaMexico

Personalised recommendations