Grid generation is an important step of computational fluid dynamics.In the process of large-scale numerical simulation,the time consumption of grid generation increases with the number of grids which often increases with the simulation accuracy.Based on the grid generation algorithm in an open-source software called OpenFoam,this paper proposes a hybrid parallel me-thod of OpenMP and MPI for polyhedral grid generation.By theoretical analysis,we show that when the hybrid parallel method is used to generate the same quality grids,increasing the number of threads and grid cells will reduce the time consumption of grid generation.Three numerical simulations using different solvers show that the grids generated by the hybrid parallel method and the original method have close qualifications,and the simulation results are almost indistinguishable from those of the original method.Furthermore,the time consumption of this method to generate the same quality and quantity grids can be reduced to less than a quarter of the time consumption without using OpenMP parallel method.

Users of big data analytics systems want the execution time of tasks to be as short as possible.However,during task execution,both network and computational moments may become resource bottlenecks that hinder task execution.Through the observation and analysis of the big data analysis system,the following conclusions are drawn:1)the data-parallel framework should switch between multiple working modes depending on the current resource bottlenecks;2)the scheduling of subtasks should fully consider the new tasks that may arrive in the future,not only the currently submitted tasks.Based on the above observations,a new task scheduling system Duopoly is designed and implemented,which consists of two parts:cans,a network scheduler that senses computational resources,and nats,a sub-task scheduler that senses network resources.The effectiveness of Duopoly is evaluated by small-scale physical clusters and large-scale simulation experiments,and the experimental results show that Duopoly can reduce the average task completion time by 37.30%~76.16% compared with existing work.

In hierarchical federated learning,energy constrained mobile devices will consume their own resources for participating in model training.In order to reduce the energy consumption of mobile devices,this paper proposes the problem of minimizing the sum of energy consumption of mobile devices without exceeding the maximum tolerance time of hierarchical federated learning.Different training rounds of edge server can select different mobile devices,and mobile devices can also train models under diffe-rent edge servers concurrently.Therefore,this paper proposes ODAM-DS algorithm based on an online double auction mechanism.Based on the optimal stopping theory,the edge server is supported to select the mobile device at the best time,so as to minimize the average energy consumption of the mobile device.Then,the theoretical analysis of the proposed online double auction mechanism proves that it meets the characteristics of incentive compatibility,individual rationality and weak budget equilibrium constraints.Simulation results show that the energy consumption of ODAM-DS algorithm is 19.04% lower than that of the existing HFEL algorithm.

Federated learning has solved the problem of data interoperability under the premise of satisfying user privacy protection and data security.However,traditional federated learning lacks an incentive mechanism to encourage and attract data owners to participate in federated learning.Meanwhile,the lack of a federated learning audit mechanism provides the possibility for malicious nodes to conduct sabotage attacks.In response to this problem,this paper proposes a reliable federated learning incentive mechanism for electric metering data based on blockchain technology.This method starts from two aspects:rewarding data parti-cipants for training participation and evaluating data reliability for all of them.We design an algorithm to evaluate the training effect of data participants.The contribution of data participants is determined from the perspective of training effect and training cost,and the participants are rewarded according to the contribution.At the same time,a reputation model is established for the reliability of the data participants,and the reputation of the data participants is updated according to the training effect,so as to achieve the reliability assessment for data participants.Based on the open-source framework of federated learning and real electric metering data,a case study is carried out,and the obtained results verify the effectiveness of our method.

Existing model synchronization mechanisms of federated learning (FL) are mostly based on single-layer parameter server architecture,which are difficult to adapt to current heterogeneous wireless network scenarios.There are some problems such as excessive communication load on single-point and poor scalability of FL.In response to these problems,this paper proposes an efficient model synchronization scheme for FL in hybrid wireless edge networks.In a hybrid edge wireless network,edge devices transmit local models to nearby small base stations.After receiving local models from edge devices,small base stations exe-cute the aggregation algorithm and send the aggregated models to the macro base station to update the global model.Considering the heterogeneity of channel performance and the competitive relationship of data transmission on the wireless channel,this paper proposes a new type of grouping asynchronous model synchronization scheme and designs a transmission rate aware channel allocation algorithm.Experiments are carried out on real data sets.Experimental results show that the proposed transmission rate aware channel allocation algorithm in grouping asynchronous model synchronization scheme can reduce communication time by 25%～60% and greatly improve the training efficiency of FL.

Using deep learning technology to control vehicles at intersections is a research hotspot in the field of intelligent transportation.Previous studies suffer from the inability to adapt to dynamic changes in the number of self-driving vehicles,slow convergence of training,and locally optimal training results.This work focuses on how autonomous vehicles can use distributed deep reinforcement methods to improve the efficiency of intersections at unsignalized intersections.First,an efficient reward function is proposed to apply the distributed reinforcement learning algorithm to the unsignalized intersection scenario,which can effectively improve the efficiency of intersection passage by relying on only local information even if the vehicle cannot obtain the whole intersection state information.Then,to address the problem of inefficient training of reinforcement learning methods in open intersection scenarios,a transfer learning approach is used to improve the training efficiency by using the trained strategy in the closed figure-of-eight scenario as a warm start and continuing the training in the unsignalized intersection scenario.Finally,this paper proposes a strategy that can be adapted to all proportions of autonomous vehicles,and this strategy can improve intersection access efficiency in scenarios with any proportion of autonomous vehicles.The algorithm is validated on the simulation platform Flow,and the experimental results show that the proposed smart body model converges quickly in training,can adapt to dynamic changes in the proportion of self-driving vehicles,and can effectively improve the efficiency of intersections.

Based on blockchain technology,Ethereum Solidity smart contract as a computer protocol is designed to spread,verify,or execute contracts in an informative way,and it provides a foundation for various distributed application services.Although implemented for less than six years,its security problems have frequently broken out and caused substantial financial losses,which attracts more attention in the security inspection research.This paper firstly introduces some specific mechanisms and operating principles of smart contracts based on Ethereum related techniques,and analyzes some smart contract vulnerabilities occurring frequently and deriving from the characteristics of smart contracts.Then,this paper explains the traditional mainstream smart contract vulnerability detecting tools in terms of symbolic execution,fuzzing,formal verification,and taint analysis.In addition,in order to cope with the endless new vulnerabilities and the need to improve the efficiency of detection,vulnerabilities detection based on machine learning in recent years is classified and summarized according to the various ways of problem transformation in three perspectives including text processing,non-Euclidean graph and standard image.Finally,this paper proposes to formulate more extensive and accurate standardized information database and measurement indicators towards the insufficiency of the detection methods in two directions.

In order to overcome the difficulties that current attack graph model cannot reflect real-time network attack events,a method is proposed including a forward risk probability update algorithm and a forward-backward combined risk probability update algorithm,which meets the needs of real-time analyzing network security.It first performs specific quantitative analysis on the uncertainty of each node in the graph,and uses Bayesian networks to calculate their static probabilities.After that,it updates the dynamic probability of each node along the forward and backward paths according to the real-time network security events,instantly reflecting the changes of external conditions and assessing real-time risk levels across the network.Experimental results show that the method can calibrate and adjust the risk probability of each node according to the actual situation,which helps the network operator correctly understand the dangerous levels of the network and make better decision for defense and prevention of the next attack.

Most of the existing watermarking and encryption schemes are difficult to ensure the commutativity of watermarking and encryption as well as the visual quality of the protected image.These schemes complete watermark embedding and image encryption processes in a fixed order,and they modify the protected image content more or less.Few of them complete the commutativity of watermarking and encryption process without affecting the quality of the protected image content.Therefore,a homomorphic and commutative fragile zero-watermarking based on SVD (singular value decomposition) is proposed.At the sender side,the content owner adopts homomorphic modular encryption to encrypt the original image content,and the two stages of image encryption and watermarking generation do not affect each other.The zero-watermarking information can be generated from the encrypted image and the original host image,respectively.At the receiver end,the legitimate receiver first decrypts the image and then performs watermarking detection on the decrypted image content,and the extracted watermarking information can detect and locate the deliberately tampered area of the watermarked image.Experimental results confirm that the use of zero-watermarking will not lead to gray level value alteration of the image content,and the deliberately tampered area of the watermarked image can be located perfectly while ensuring the commutativity.

In the traditional ciphertext-policy attribute-based encryption (CP-ABE) scheme,the access policy exists together with the ciphertext.This may leak the privacy of the data owner and bring potential security risks to the data owner in medicalscena-rios Therefore,solutions supporting access policy hiding have been proposed.However,most solutions need to generate redundant ciphertexts or key components in the process of implementing the decryption test,which increases the computing overhead of data owners and the storage overhead of data users.At the same time,malicious users may be motivated by its own interest to reveal their decryption keys.In order to solve the problems above,a lightweight medical data sharing scheme with access policy hiding and key tracking is proposed.Firstly,part of the master key is stored in the Enclave in advance by using software guard extensions(SGX) technology,so that the test results can be calculated accurately and quickly,and the generation of redundant ciphertexts and key components are avoided.Then,verifiable outsourcing technology is employed to reduce user’s computing overhead,ensuring the correctness and completeness of decryption result.Finally,key tracking is realized by embedding the identity identifier in the decryption key of the data user.Performance analysis shows that the proposed scheme has certain advantages in terms of function and computing.The security analysis proves that the proposed scheme is secure under the selected plaintext attack.