TY - GEN
T1 - An ontology-driven framework for resource-efficient collaborative sensing
AU - Luna-Nuñez, Brayan
AU - Menchaca-Mendez, Rolando
AU - Favela, Jesus
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - Collaborative sensing
KW - Distributed algorithms
KW - Ontology
UR - http://www.scopus.com/inward/record.url?scp=84893092943&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-03176-7_47
DO - 10.1007/978-3-319-03176-7_47
M3 - Contribución a la conferencia
SN - 9783319031750
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 366
EP - 369
BT - Ubiquitous Computing and Ambient Intelligence
T2 - 7th International Conference on Ubiquitous Computing and Ambient Intelligence, UCAmI 2013
Y2 - 2 December 2013 through 6 December 2013
ER -