Browsing M.Sc. Computer Science by Subject "Vehicular Cloud Computing"
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Game Theory-based Allocation Management in VCC NetworksVehicular Ad-hoc Networks (VANETs) have contributed significantly towards improving road traffic management and safety. VANETs, integrated with Vehicular Clouds, enable underutilized vehicular resources for efficient resource management, fulfilling service requests. However, due to the frequently changing network topology of vehicular cloud networks, the vehicles frequently move out of the coverage area of roadside units (RSUs), disconnecting from the RSUs and interrupting the fulfillment of ongoing service requests. In addition, working with heterogeneous vehicles makes it difficult to match the service requests with the varying resources of individual vehicles. Therefore, to address these challenges, this work introduces the concept of clustering resources from nearby vehicles to form Combined Resource Units (CRUs). These units contribute to maximizing the rate of fulfillment of service requests. CRU composition is helpful, especially for the heterogeneity of vehicles, since it allows clustering the varying resources of vehicles into a single unit. The vehicle resources are clustered into CRUs based on three different sized pools, making the service matching process more time-efficient. Previous works have adopted stochastic models for resource clustering configurations. However, this work adopts distinct search algorithms for CRU composition, which are computationally less complex. Results showed that light-weight search algorithms, such as selective search algorithm (SSA), achieved close to 80% of resource availability without over-assembling CRUs in higher density scenarios. Following CRU composition, a game-theoretical approach is opted for allocating CRUs to service requests. Under this approach, the CRUs play a non-cooperative game to maximize their utility, contributing to factors such as fairness, efficiency, improved system performance and reduced system overhead. The utility value takes into account the RSS (Received Signal Strength) value of each CRU and the resources required in fulfilling a request. Results of the game model showed that the proposed approach of CRU composition obtained 90% success rate towards matching and fulfilling service requests.