Opportunistic Mobile Sensing in the Fog
Autor: | Rolando Quintero, Arturo Yee-Rendon, Brayan Luna-Nuñez, Rolando Menchaca-Mendez, Ricardo Menchaca-Mendez, Jesus Favela |
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Jazyk: | angličtina |
Rok vydání: | 2018 |
Předmět: |
Article Subject
lcsh:T Computer Networks and Communications business.industry Computer science Distributed computing 020206 networking & telecommunications 02 engineering and technology lcsh:Technology lcsh:Telecommunication 020202 computer hardware & architecture Variety (cybernetics) Set (abstract data type) Task (computing) Software Distributed algorithm lcsh:TK5101-6720 Face (geometry) 0202 electrical engineering electronic engineering information engineering Mobile sensing Electrical and Electronic Engineering Internet of Things business Information Systems |
Zdroj: | Wireless Communications and Mobile Computing, Vol 2018 (2018) |
ISSN: | 1530-8669 |
DOI: | 10.1155/2018/2796282 |
Popis: | The increasing adoption of mobile personal devices and Internet of Things devices is leveraging the emergence of a wide variety of opportunistic sensing applications. However, the designers of this type of applications face a set of technical challenges related to the limitations and heterogeneity of the hardware and software platforms and to the dynamics of the scenarios where they are deployed. In this paper, we introduce a Semantic-Centric Fog-based framework aimed at effectively and efficiently supporting this type of applications. The proposed framework is composed of local and distributed algorithms that support the establishment and coordination of sensing tasks in the Fog. First, it performs ontology-driven in-network processing to locate the most adequate devices to carry out a given sensing task and then, it establishes efficient multihop routes that are used to coordinate relevant devices and to transport the collected sensory data to Fog sinks. We present a set of theorems that prove that the proposed algorithms are correct and the results of a series of detailed simulation-based experiments in NS3 that characterize the performance of the proposed platform. The results show that the proposed framework outperforms traditional sensing platforms that are based on centralized services. |
Databáze: | OpenAIRE |
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