人工智能的快速发展将物联网推向了一个新阶段，面对数据的爆炸性增长和用户对更高服务质量的迫切需求，边缘计算和缓存被视为富有前景的技术解决手段。然而，边缘节点（Edge Nodes, ENs）中的资源并不是取之不尽的。本文提出了一种面向物联网的激励感知区块链辅助的智能边缘缓存与计算迁移方案，该方案致力于为协作ENs在资源优化和控制方面提供安全和智能的解决方案。具体地，该方案通过联合优化迁移和缓存决策以及计算和通信资源分配，以最大限度地降低EN中完成任务的总成本。此外，为解决上述优化问题，本文设计了区块链激励和贡献联合感知的联邦深度强化学习算法。在本地训练期间，该算法构建了一个激励感知区块链辅助的协作机制，即在安全保障前提下增强ENs参与协作的意愿。同时，提出了一种基于贡献的联邦聚合方法，即基于EN对全局模型性能提升所做贡献来计算其梯度的聚合权重，以提升训练效果。最后，与其它基准方案相比，数值结果证明本文方案具备高效的资源优化效用，同时在降低总成本和缓存性能方面具有显著优势。

The rapid development of artificial intelligence has pushed the Internet of Things (IoT) 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 IoT, 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.