Publication | Closed Access
VANET-cloud: a generic cloud computing model for vehicular Ad Hoc networks
325
Citations
13
References
2015
Year
Vehicle CommunicationInternet Of VehicleEngineeringSmart CityEdge ComputingConnected CarCloud ComputingBusinessVehicle NetworkVehicular NetworksAutomotive SecurityMobile ComputingInternet Of ThingsGeneric CloudMobile Communication VehicleNew CloudTransportation EngineeringRoad Safety
Cloud computing offers ubiquitous access to shared computing resources, and its application to vehicular networks promises improved road safety and passenger comfort through services such as alternative routing and traffic‑light synchronization. The authors propose VANET‑Cloud, a cloud computing model for vehicular ad hoc networks, to enhance traffic safety and deliver computational services to road users. They review the transportation services enabled by VANET‑Cloud and outline future research directions, including security, privacy, data aggregation, energy efficiency, interoperability, and resource management.
Cloud computing is a network access model that aims to transparently and ubiquitously share a large number of computing resources. These are leased by a service provider to digital customers, usually through the Internet. Due to the increasing number of traffic accidents and dissatisfaction of road users in vehicular networks, the major focus of current solutions provided by intelligent transportation systems is on improving road safety and ensuring passenger comfort. Cloud computing technologies have the potential to improve road safety and traveling experience in ITSs by providing flexible solutions (i.e., alternative routes, synchronization of traffic lights, etc.) needed by various road safety actors such as police, and disaster and emergency services. In order to improve traffic safety and provide computational services to road users, a new cloud computing model called VANET-Cloud applied to vehicular ad hoc networks is proposed. Various transportation services provided by VANET-Cloud are reviewed, and some future research directions are highlighted, including security and privacy, data aggregation, energy efficiency, interoperability, and resource management.
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