With wide adoption of Internet of Things across the world, the IoT devices are facing more and more intensive computation task nowadays. However, the IoT devices are usually limited by their computing capability and battery lifetime. Mobile Edge Computing provides new opportunities for developments of IoT, since edge computing servers which are close to devices can provide more powerful computing resources. The IoT devices can offload the intensive computing tasks to edge computing servers, while saving their own computing resources and reducing energy consumption. However, the benefits come at the cost of higher latency, mainly due to additional transmission time, and it may be unacceptable for many IoT applications. In this paper, we try to find a trade-off between energy consumption and latency, in order to satisfy user demands of various IoT applications. We formalize the problem into a constrained multi-objective optimization problem and find the optimal solutions by a modified fast and elitist nondominated sorting genetic algorithm (NSGA-II). To improve performance of the algorithm, we propose a novel problem-specific encoding scheme and genetic operators in the proposed modified NSGA-II. We also conduct extensive simulation experiments to evaluate the proposed algorithm and its sensitivity under certain major parameters. The experimental results show that the proposed algorithm can find a large number of optimal solutions to adjust the corresponding offloading decision according to the real-world situation.

Edge computing architectures can help us quickly process the data collected by Internet of Things (IoT) and caching files to edge nodes can speed up the response speed of IoT devices requesting files. Blockchain architectures can help us ensure the security of data transmitted by IoT. Therefore, we have proposed a system that combines IoT devices, edge nodes, remote cloud, and blockchain. In the system, we designed a new algorithm in which blockchain-assisted compressed algorithm of federated learning is applied for content caching, called CREAT to predict cached files. In the CREAT algorithm, each edge node uses local data to train a model and then uses the model to learn the features of users and files, so as to predict popular files to improve the cache hit rate. In order to ensure the security of edge nodes’ data, we use federated learning (FL) to enable multiple edge nodes to cooperate in training without sharing data. In addition, for the purpose of reducing communication load in FL, we will compress gradients uploaded by edge nodes to reduce the time required for communication. What is more, in order to ensure the security of the data transmitted in the CREAT algorithm, we have incorporated blockchain technology in the algorithm. We design four smart contracts for decentralized entities to record and verify the transactions to ensure the security of data. We used MovieLens data sets for experiments and we can see that CREAT greatly improves the cache hit rate and reduces the time required to upload data.