Concerning the problem of limited computing capacity and storage capacity of lightweight smart devices in mobile edge computing, a computational offloading solution based on Stackelberg game was proposed. First, Combining with the blockchain technology, a computing power trading model based on cloud mining mechanism, named CPTP-BSG (Computing Power Trading and Pricing with Blockchain and Stackelberg Game), was built, which allows mobile smart devices (miners) to offload intensive and complex computing tasks to edge servers. Second, the computing power trading between miners and Edge computing Service Providers (ESPs) was modeled as a two-stage Stackelberg game process, and the expected profit functions for miners and ESP were formulated. Then, the existence and uniqueness of Nash equilibrium solution were respectively analyzed under uniform pricing and discriminatory pricing strategies by backward induction. Finally, a low gradient iterative algorithm was proposed to maximize the profits of miners and ESP. Experimental results show the effectiveness of the proposed algorithm, and it can be seen that the discriminatory pricing is more in line with the personalized computing power demand of miners than uniform pricing, and can achieve higher total demand of computing power and ESP profit.

For that the variety of smart contract applications on the blockchain platform exists more widely and manual filtering the suitable smart contract application services is more difficult, a hierarchical attention mechanism and Bidirectional Long Short-Term Memory(Bi-LSTM)neural network based model was proposed for automatic smart contract classification,namely HANN-SCA(Hierarchical Attention Neural Network with Source Code and Account). Firstly,the Bi-LSTM network was used to simultaneously model the smart contract source code and account information to extract the feature information of smart contract to the greatest extent. The source code perspective focused on the semantic features of code, and the account information perspective focused on the features of the account. Then,in the process of feature learning,the attention mechanism was introduced into the word level and the sentence level respectively to focus on the words and sentences that were important to the classification of smart contract. Finally,the code features and the account features were spliced to generate the document-level feature representation of the smart contract,and the classification task was completed through the Softmax layer. Experimental results on datasets of Dataset-E,Dataset-N and Dataset-EO show that the classification precisions of HANN-SCA model reach 93. 1%,91. 7% and 92. 1% respectively,which are better than those of the traditional Support Vector Machine(SVM)model and other neural network benchmark models,and the proposed model also has better stability and higher convergence speed.