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Vehicular micro cloud prototype implementation / vorgelegt von Kiran Narayanaswamy ; angefertigt in der Fachgruppe Distributed Embedded Systems (CCS Labs), Heinz Nixdorf Institut, Universität Paderborn ; Gutachter: Prof. Dr.-Ing. habil. Falko Dressler, Prof. Dr. Christian Scheideler
AuthorNarayanaswamy, Kiran
ParticipantsDressler, Falko ; Scheideler, Christian
PublishedPaderborn, 2019
Elektronische Ressource
Description1 Online-Ressource (iv, 55 Seiten) : Diagramme
Institutional NoteUniversität Paderborn, Masterarbeit, 2019
Tag der Abgabe: 11.03.2019
Date of Submission11/03/2019
Document TypesMaster Thesis
Vehicular micro cloud prototype implementation [1.47 mb]
Abstract (English)

One of the important paradigms of vehicular networking applications is information based services where the vehicles with communication capabilities interact with the internet cloud servers. This is facilitated by cellular networks for uploading and downloading the relevant data. In future, the number of such vehicles is expected to increase. This is going to result in an increase in the usage of cellular bandwidth (which is limitedly available). This may cause vehicles to suffer a higher end to end communication latency. The solution to this problem is addressed by a newly proposed architecture called Vehicular Micro Cloud architecture. In this architecture, the connected vehicles are bundled together forming small clusters called Vehicular Micro Clouds. The pool of computing, storage, sensing and communication resources contributed by vehicles themselves can be used to aggregate and process the upstream data and cache the downstream data. This minimizes the bandwidth consumption in vehicular networks. In this thesis, the first prototype of the Vehicular Micro Cloud architecture is implemented along with two Micro Cloud services - Data Collection and Aggregation, and Task Distribution.The prototype implemented offers the scope for real-world experimentation and serves as proof of concept. This has been evaluated using the Manhattan grid scenario at different traffic densities and data size transfers.

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