Building decentralized fog computing-based smart parking systems: from deterministic propagation modeling to practical deployment
Fecha
2020Autor
Versión
Acceso abierto / Sarbide irekia
Tipo
Artículo / Artikulua
Versión
Versión publicada / Argitaratu den bertsioa
Identificador del proyecto
AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TEC2016-75067-C4-1-R AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RED2018-102668-T AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-104958RB-C42/ES/
AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-095499-B-C31/ES/
Impacto
|
10.1109/ACCESS.2020.3004745
Resumen
The traditional process of finding a vacant parking slot is often inefficient: it increases driving time, traffic congestion, fuel consumption and exhaust emissions. To address such problems, smart parking systems have been proposed to help drivers to find available parking slots faster using latest sensing and communications technologies. However, the deployment of the communications infrastruct ...
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The traditional process of finding a vacant parking slot is often inefficient: it increases driving time, traffic congestion, fuel consumption and exhaust emissions. To address such problems, smart parking systems have been proposed to help drivers to find available parking slots faster using latest sensing and communications technologies. However, the deployment of the communications infrastructure of a smart parking is not straightforward due to multiple factors that may affect wireless propagation. Moreover, a smart parking system needs to provide not only accurate information on available spots, but also fast responses while guaranteeing the system availability even in the case of lacking connectivity. This article describes the development of a decentralized low-latency smart parking system: from its conception, design and theoretical simulation, to its empirical validation. Thus, this work first characterizes a real-world scenario and proposes a fog computing and Internet of Things (IoT) based communications architecture to provide smart parking services. Next, a thorough analysis on the wireless channel properties is carried out by means of an in-house developed deterministic 3D-Ray Launching (3D-RL) tool. The obtained results are validated through a real-world measurement campaign and then the communications architecture is implemented by using ZigBee sensor nodes. The implemented architecture also makes use of Bluetooth Low Energy beacons, an Android app, a decentralized database and fog computing gateways, whose performance is evaluated in terms of response latency and processing rate. Results show that the proposed system is able to deliver information to the drivers fast, with no need for relying on remote servers. As a consequence, the presented development methodology and communications evaluation tool can be useful for future smart parking developers, which can determine the optimal locations of the wireless transceivers during the simulation stage and then deploy a system that can provide fast responses and decentralized services. [--]
Materias
Smart parking,
Fog computing,
ZigBee,
BLE,
IoT,
Wireless channel,
3D ray launching,
IPFS
Editor
IEEE
Publicado en
IEEE Access, 2020, 8, 117666-117688
Departamento
Universidad Pública de Navarra. Departamento de Ingeniería Eléctrica, Electrónica y de Comunicación /
Nafarroako Unibertsitate Publikoa. Ingeniaritza Elektrikoa, Elektronikoa eta Telekomunikazio Ingeniaritza Saila /
Universidad Pública de Navarra/Nafarroako Unibertsitate Publikoa. Institute of Smart Cities - ISC
Versión del editor
Entidades Financiadoras
This work was supported in part by the School of Engineering and Sciences, Tecnológico de Monterrey, in part by the Xunta de Galicia under Grant ED431G2019/01, in part by the Agencia Estatal de Investigación of Spain under Grant TEC2016-75067-C4-1-R, Grant RED2018-102668-T, and Grant PID2019-104958RB-C42, in part by the European Regional Development Fund (ERDF) funds of the European Union (EU) (AEI/FEDER, UE), and in part by the Ministerio de Ciencia, Innovación y Universidades, Gobierno de España (MCI-U/AEI/FEDER,UE) under Grant RTI2018-095499-B-C31.