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A Cloud-Based Car Parking Middleware for IoT-Based Smart Cities: Design and Implementation

Open access
Ji, Zhanlin, Ganchev, Ivan, O'Droma, Máirtín, Zhao, Li, Zhang, Xueji·2014·Sensors

Abstract

This paper presents the generic concept of using cloud-based intelligent car parking services in smart cities as an important application of the Internet of Things (IoT) paradigm. This type of services will become an integral part of a generic IoT operational platform for smart cities due to its pure business-oriented features. A high-level view of the proposed middleware is outlined and the corresponding operational platform is illustrated. To demonstrate the provision of car parking services, based on the proposed middleware, a cloud-based intelligent car parking system for use within a university campus is described along with details of its design, implementation, and operation. A number of software solutions, including Kafka/Storm/Hbase clusters, OSGi web applications with distributed NoSQL, a rule engine, and mobile applications, are proposed to provide ‘best’ car parking service experience to mobile users, following the Always Best Connected and best Served (ABC&S) paradigm.

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Metadata

Authors
Ji, Z., Ganchev, I., O'Droma, M., Zhao, L., & Zhang, X.
Venue
Sensors
Publisher
MDPI AG
Published
2014-11-25
Volume
14
Issue
12
Pages
22372-22393
ISSN
1424-8220 (ISSN-L)
OpenAlex
W2156092677
Cited by
195
References
0
FWCI
18.63
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: A Cloud-Based Car Parking Middleware for IoT-Based Smart Cities: Design and Implementation. https://app.cochat.ai/papers/10.3390/s141222372
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Ji, Z., Ganchev, I., O'Droma, M., Zhao, L., & Zhang, X. (2014). A Cloud-Based Car Parking Middleware for IoT-Based Smart Cities: Design and Implementation. Sensors. https://doi.org/10.3390/s141222372

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.