Existing research on multi-QoS scheduling problems,due to its reliance solely on immediate reward feedback mechanisms,faces issues of poor scalability and resource wastage when handling delay-sensitive data and media data with con-tinuous transmission requirements in resource-constrained scenarios.To address this problem,this paper proposed a RB-DQN algorithm.This algorithm adjusted the current state's policy evaluation by backtracking future interactions,effectively identif-ying and resolving packet loss caused by suboptimal scheduling strategies.Additionally,it designed a LTT metric,which com-prehensively considered the service requirements of both delay-sensitive data and media-type data,allowing for weight adjust-ments to emphasize different priorities.Extensive simulation results demonstrate that the proposed algorithm significantly re-duces the delay and jitter of delay-sensitive data while ensuring the smoothness and stability of media-type data,outperforming other scheduling strategies.
The development of the internet and mobile technologies has driven the connection of devices such as smartphones to the network, resulting in a multitude of scattered data points. Restrictions on network bandwidth and the risk of privacy leaks have limited the widespread application of centralized machine learning. In contrast, federated learning technology, with its privacy- preserving feature, optimizes data processing by utilizing distributed network resources. Although federated learning reduces the demand for data transmission through parameter sharing, the exposure of parameters during network transmission still poses a risk to privacy security. In this study, a robust and privacy-preserving federated learning scheme is proposed, which performs user selection based on ciphertext. Firstly, the concept of user groups is constructed on the edge server, and a ciphertext-based user selection algorithm is designed to select users with large and evenly distributed datasets to participate for the training of the global model within the framework of Federated Learning. In addition, the algorithm effectively filters out anomalous gradients that significantly differ from the global model parameters while ensuring that the privacy of users' local model parameters is not compromised, thereby enhancing the overall robustness of the global model. Secondly, a lightweight encryption algorithm is designed based on cloud-edge collaboration, which not only effectively protects the privacy of users' local model parameters but also ensures the security of these parameters during the aggregation process, with high computational and communication efficiency. Based on the difficulty of the Decisional Diffie-Hellman (DDH) problem, the proposed scheme has been proven to be secure and effectively resistant to various attacks, ensuring the robustness of the global model and protecting users' data privacy. Compared with existing schemes, performance analysis and experimental results show that the proposed scheme performs better in terms of efficiency and accuracy.
The inspection of aircraft skin is critical to ensuring the safety of aircraft flights.Employing autonomous mobile ro-bots for inspection can significantly enhance inspection efficiency and reduce safety risks.However,achieving full coverage of aircraft skin with a single type of robot is challenging due to the complexity of aircraft structures.Therefore,this paper pro-posed an aerial-ground cooperative coverage path planning(AG-CCPP)method for heterogeneous robots.Firstly,this paper introduced a heterogeneous robot system consisting of unmanned aerial vehicles(UAV)and unmanned ground vehicles(UGV),analyzed the necessary constraints in the planning process,including workspace constraints,endurance constraints,and so on,and established an optimization model using integer linear programming.Subsequently,this paper proposed a dual-chromosome genetic algorithm with a multi-elite population based on a greedy allocation strategy for jointly solving task alloca-tion and path planning.By incorporating an allocation chromosome,it achieved joint solution for task allocation and path plan-ning,enabling global optimization.It conducted greedy allocation based on endurance constraints,fully leveraging the advan-tages of heterogeneous robots.It adopted a multi-level elite population design to reduce the number of inefficient crossover pop-ulations and enhance the algorithm's operational efficiency.Finally,simulation experiments and comparative analysis conduc-ted on a Boeing 737-300 demonstrate that the proposed method outperforms existing algorithms in terms of both the completion time for cooperative coverage by robots and the program execution time.
To address the problems of malicious node aggregation,high shard failure rate,and load imbalance in the current models of blockchain shard,this paper proposed a blockchain dynamic sharding model(CBDSM)based on node credibility.Firstly,this paper constructed a blockchain sharding architecture to solve the scalability bottlenecks faced by the blockchain.Secondly,it designed the reliability evaluation mechanism of nodes to quantify the ability and reliability of nodes,which rea-lized the efficient screening and classification of nodes.On this basis,it designed the dynamic network fragmentation algorithm and the network fragmentation flow.Through the dynamic allocation of nodes at different levels,it realized load balancing on the chain.Theoretical analysis and experimental results show that,compared with similar models,CBDSM boasts a 54％in-crease in throughput,a 46％reduction in consensus failure rate,and a 15.6％decrease in shard failure rate.This model can guarantee the dynamic equilibrium of nodes,and resist the single-point aggregation and collusion attacks of nodes.It ensures the dynamic security of the blockchain shard system,with the high performance and high scalability of the system.
Log parsing, the process of transforming raw logs into structured data, is a key step in the complex computer system's intelligent operation and maintenance and therefore has received extensive attention. Among all log parsing methods, heuristic log parsing methods are lightweight and can work in a streaming mode to well meet the real-time parsing requirements. However, the existing log representations used in the heuristic log parsing methods are not powerful in distinguishing log messages, which leads to low parsing accuracy and weak generality. Inspired by trigger word extraction of the event detection task in natural language processing (NLP), this paper proposes an online log parser, named PosParser, which employs the part-of-speech (PoS) tagging to extract a function token sequence (FTS) as the log message representation, and then identify event templates of log messages through the FTS. Experimental results on sixteen logs from real systems demonstrate that the FTS is powerful in distinguishing log messages from different event templates, and PosParser not only performs better in terms of parsing accuracy than state-of-the-art methods but is also comparable to them in efficiency.
