The invention discloses a task unloading system and method in vehicle fog calculation. The system comprises a base station, a user vehicle, a vehicle fog server, a threat model and a time delay model.Each base station and vehicle have a wallet with digital currency and generate a key pair for encryption and decryption, wherein the key pair of the base station is shared by three components. All vehicles need to register with a registration authorization component to obtain a certificate. Secondly, the user sends the request to the base station, and the base station executes the intelligent contract of task unloading; and then the user unloads the encrypted data to a corresponding fog server, and the fog server executes a calculation task and feeds back a calculation result to the user andthe base station. The base station checks the whole process and carries out corresponding rewards and punishment; and finally, the base station creates a block and uploads the block to the block chain, and other base stations verify the legality of the workload proof and transaction of the base station. Once the block is accepted by most of the base stations, it will be added to the end of the block chain.

Task unloading system and method in vehicle fog calculation

The invention relates to a server recruitment and task unloading optimization scheme applied to vehicle fog computing. And the vehicle fog computing recruits the vehicle with idle resources as a fog server, and unloads the computing task of the user vehicle from the edge server to the fog server. The architecture is realized by adopting a two-stage task unloading mechanism. In the first stage, wepropose a contract optimization algorithm based on a concave-convex process to recruit a fog server, and the expected effectiveness of an operator is maximized under the condition of asymmetric information. In the second stage, firstly, a pricing matching-based task unloading mechanism is provided under the condition that the information is complete, so that the matching conflict between user vehicles is solved, and the network time delay is minimized. Besides, when the user vehicle is extended to a scene with uncertain information, a volatile upper confidence limit task unloading algorithm based on matching is provided, matching conflicts and volatility of the candidate fog servers are comprehensively considered, and through online learning, long-term delay performance of the user vehicleis minimized.

server recruitment and task unloading optimization scheme based on vehicle fog computing

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server recruitment and task unloading optimization scheme based on vehicle fog computing