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Engineering 2023年第12期31卷 127-138页

作者： Qian Wang Siguang Chen Meng Wu Jiangsu Key Laborotary of Broadband Wireless Communication and Internet of Things Nanjing University of Posts and TelecommunicationsNanjing 210003China School of Internet of Things Nanjing University of Posts and TelecommunicationsNanjing 210003China

The rapid development of artificial intelligence has pushed the Internet of Things(Io T)into a new stage.Facing with the explosive growth of data and the higher quality of service required by users,edge computing and caching are regarded as promising solutions.However,the resources in edge nodes(ENs)are not inexhaustible.In this paper,we propose an incentive-aware blockchain-assisted intelligent edge caching and computation offloading scheme for Io T,which is dedicated to providing a secure and intelligent solution for collaborative ENs in resource optimization and controls.Specifically,we jointly optimize offloading and caching decisions as well as computing and communication resources allocation to minimize the total cost for tasks completion in the EN.Furthermore,a blockchain incentive and contribution co-aware federated deep reinforcement learning algorithm is designed to solve this optimization problem.In this algorithm,we construct an incentive-aware blockchain-assisted collaboration mechanism which operates during local training,with the aim to strengthen the willingness of ENs to participate in collaboration with security guarantee.Meanwhile,a contribution-based federated aggregation method is developed,in which the aggregation weights of EN gradients are based on their contributions,thereby improving the training effect.Finally,compared with other baseline schemes,the numerical results prove that our scheme has an efficient optimization utility of resources with significant advantages in total cost reduction and caching performance.

关键词： Computation offloading Caching Incentive Blockchain federated deep reinforcement learning

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