2019
DOI: 10.3390/electronics8121483
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WSN Hardware for Automotive Applications: Preliminary Results for the Case of Public Transportation

Abstract: The ubiquitous nature and great potential of Wireless Sensors Network has not yet been fully exploited in automotive applications. This work deals with the choice of the cost-effective hardware required to face the challenges and issues proposed by the new trend in the development of intelligent transportation systems. With this aim, a preliminary WSN architecture is proposed. Several commercially available open-source platforms are compared and the Raspberry Pi stood out as a suitable and viable solution. The… Show more

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Cited by 11 publications
(8 citation statements)
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“…However, from the critical aspects and open issues discussed in this paper, the proposed WSN can be improved by relying on a standardization, which, unfortunately, is still lacking, in a general sense [6,55]. Moreover, the security aspects need to be carefully investigated [7,10].…”
Section: Discussionmentioning
confidence: 99%
“…However, from the critical aspects and open issues discussed in this paper, the proposed WSN can be improved by relying on a standardization, which, unfortunately, is still lacking, in a general sense [6,55]. Moreover, the security aspects need to be carefully investigated [7,10].…”
Section: Discussionmentioning
confidence: 99%
“…In the work of Baires [14], a Wireless Sensor Network for an Intelligent transportation system was developed using a Raspberry Pi. The sensing layer of the developed device has the goal of detecting if mechanical or environmental stresses during bus rides could be harmful to the device or to its performances.…”
Section: Related Workmentioning
confidence: 99%
“…Their design is based on the application-specific requirements, since each industrial process has multiple objectives and different infrastructure needs [ 3 ]. In the past few years, IWSNs have emerged in numerous application fields, including personal health monitoring [ 19 ], building and civil infrastructure monitoring [ 20 , 21 ], automotive applications [ 22 ], power converters [ 23 ], power and smart grids [ 24 ], energy harvesting [ 25 ], smart cities [ 26 ], agriculture [ 27 ], food processing [ 28 ], underwater wireless sensor networks [ 29 , 30 ], and environment monitoring [ 31 ].…”
Section: Background Of Process Automationmentioning
confidence: 99%