| Title |
A continuous diversified vehicular cloud service availability framework for smart cities |
| ID_Doc |
42564 |
| Authors |
Al Ridhawi, I; Aloqaily, M; Kantarci, B; Jararweh, Y; Mouftah, HT |
| Title |
A continuous diversified vehicular cloud service availability framework for smart cities |
| Year |
2018 |
| Published |
|
| Abstract |
The intelligent and connected transportation system (ICTS) is a significant and mandatory component of the smart city architecture. Multimedia content sharing, vehicle power management, and road navigation are all examples of ICTS services. As smart cities continue to deploy different technologies to improve the performance and diversity of vehicular cloud services, one of the main issues that prevails is efficient and reliable service discovery and selection for smart vehicles. Furthermore, cloud service providers (SPs) are limited to the availability, variety and quality of services made available to vehicular cloud subscribers. Smart vehicles rely on a number of SPs to acquire the required services while moving. It therefore becomes challenging for vehicular cloud subscribers to acquire services that meet their quality of experience (QoE) preferences. This paper introduces a new service provision scheme to provide continuous availability of diversified cloud services targeting vehicular cloud users through a cluster-based trusted third party (TTP) framework. TTPs act as cloud service mediators between cloud service subscribers and providers. Vehicles that are considered to have similar patterns of movement and service acquisition characteristics are grouped into service-specific clusters. TTPs communicate with service providers and cluster heads to negotiate for services with high QoE characteristics. A location prediction method is adopted to determine a vehicle's future location and allow services to be negotiated for before the vehicle's arrival. We provide simulation results to show that our approach can adequately discover and deliver cloud services with increased QoE results, minimal overhead burden and reduced end-to-end latency. (C) 2018 Elsevier B.V. All rights reserved. |
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