Collaborative resource scheduling between edge terminals and cloud centers is regarded as a promising means of effectively completing computing tasks and enhancing quality of service. In this paper, to further improve the achievable performance, the edge cloud resource scheduling (ECRS) problem is transformed into a multi-objective Markov decision process based on task dependency and features extraction. A multi-objective ECRS model is proposed by considering the task completion time, cost, energy consumption and system reliability as the four objectives. Furthermore, a hybrid approach based on deep reinforcement learning (DRL) and multi-objective optimization are employed in our work. Specifically, DRL preprocesses the workflow, and a multi-objective optimization method strives to find the Pareto-optimal workflow scheduling decision. Various experiments are performed on three real data sets with different numbers of tasks. The results obtained demonstrate that the proposed hybrid DRL and multi-objective optimization design outperforms existing design approaches.

Mobile edge computing (MEC) is an effective computing tool to cope with the explosive growth of data traffic. It plays a vital role in improving the quality of service for user task computing. However, the existing solutions rarely address all the significant factors that impact the quality of service. To challenge this problem, a trusted many-objective model is built by comprehensively considering the task time delay, server energy consumption, trust metrics between task and server, and user experience utility factors in multi-server MEC networks. We decompose the original problem into task offloading (TO) and resource allocation (RA) to address the model. Then a novel hybrid many-objective optimization algorithm based on cascading clustering and incremental learning is designed to optimize the TO decision solutions. A low-complexity heuristic method is adopted based on the optimal TO decision solutions to optimize the RA problem continuously. To verify the model's validity and the optimisation algorithm's superiority, five other advanced many-objective algorithms are used for comparison. The results show that our algorithm has more than half the number of the superior values for the benchmark problem. And the obtained model solution shows good performance on different indicators metrics for the decomposition problem.

In-vehicle network security plays a vital role in ensuring the secure information transfer between vehicle and Internet. The existing research is still facing great difficulties in balancing the conflicting factors for the in-vehicle network security and hence to improve intrusion detection performance. To challenge this issue, we construct a many-objective intrusion detection model by including information entropy, accuracy, false positive rate and response time of anomaly detection as the four objectives, which represent the key factors influencing intrusion detection performance. We then design an improved intrusion detection algorithm based on many-objective optimization to optimize the detection model parameters. The designed algorithm has double evolutionary selections. Specifically, an improved differential evolutionary operator produces new offspring of the internal population, and a spherical pruning mechanism selects the excellent internal solutions to form the selected pool of the external archive. The second evolutionary selection then produces new offspring of the archive, and an archive selection mechanism of the external archive selects and stores the optimal solutions in the whole detection process. An experiment is performed using a real-world in-vehicle network data set to verify the performance of our proposed model and algorithm. Experimental results obtained demonstrate that our algorithm can respond quickly to attacks and achieve high entropy and detection accuracy as well as very low false positive rate with a good trade-off in the conflicting objective landscape.

The blockchain-based searchable symmetric encryption (SSE) scheme allows the retrieval and verification of outsourced data on cloud servers in sixth generation (6G) networks while ensuring the privacy of data. However, existing schemes are challenging to comprehensively meet the requirements of 6G-based intelligent application systems for low latency, high security, and high reliability. To address these limitations, we present VSSE, a novel blockchain-based SSE scheme designed for 6G-based intelligent application systems. Our scheme constructs a state chain structure to resist file injection attacks, thereby ensuring forward privacy. Moreover, we execute the search and verification operations separately on the cloud server and blockchain, while introducing a bitmap index structure and message authentication code (MAC) technology to achieve efficient searching and dynamic verification. Notably, VSSE also includes access control functionality, permitting only authorized users to access relevant files. The combination of remarkable efficiency and strong security establishes our VSSE as an ideal solution suitable for implementation in G-based intelligent application systems.

Edge intelligence is a critical enabler of intelligent application services in the Internet of Things (IoT). However, due to complex environmental factors, edge devices are subject to constant dynamic changes, which can result in security threats and sensitive information leakage. Therefore, it is essential to investigate data stream online analysis and detection strategies and implement an online releasing mechanism to ensure sensitive information is not leaked. Existing work rarely addresses these issues simultaneously or has poor performance, which poses a challenge. To address this challenge, we propose an intelligent edge dual-structure ensemble method (IEDSEM), consisting of three key components: data preprocessing, drift detection data analytics (IEDSEM-DDDA), and privacy-preserving data releasing (IEDSEM-PPDR). Data preprocessing is used primarily to enhance the quality of data streams to improve the performance of model learning. IEDSEM-DDDA involves three sequential operations: dynamic feature selection, model learning and selection, and online model ensemble deployment to achieve anomaly detection of online data streams. Meanwhile, IEDSEM-PPDR uses differential privacy and online optimization operations to achieve intelligent hierarchical protection of edge data. To validate the performance of our proposed IEDSEM method, we conducted two comprehensive simulation experiments on real data machines, verifying the accuracy of the concept drift component detection and the privacy optimization performance of the privacy-preserving component, respectively. Simulation results show that compared with several other advanced high-performance algorithms, our algorithm has over 99% accuracy in data stream analysis detection and more outstanding privacy-preserving ability.

As a new type of network model, edge computing has strong computing offload ability, which can effectively alleviate the burden of cloud center and network communication, match the urgent demand of accelerating wireless terminals operational capabilities, and obtain rapid development and application. However, the existing edge computing authentication methods are mainly based on traditional identity authentication, which is difficult to prevent the network access of untrusted wireless terminals. Based on the above, this paper proposes a trusted connection architecture TCA-EC for edge computing, which is based on the trusted connection architecture TCA. The architecture realizes two-way identity authentication, two-way platform integrity authentication and two-way behavior trusted authentication between MEC server and wireless terminal, and ensures the security of edge computing.