Trusted Access Authentication Technology for Large-Scale Heterogeneous Terminals in a Vehicle Charging Network System

A vehicle charging network system has to access large-scale heterogeneous terminals to collect charging pile status information, which may also give malicious terminals an opportunity to access. Though some general access authentication solutions aimed at only allowing trusted terminals have been proposed, they are difficult to work with in a vehicle charging network system. First, among various heterogeneous terminals with significant differences in computing capabilities, there are inevitably terminals that cannot support computations required for cryptography-based access authentication schemes. Second, though access authentication schemes based on device fingerprints are independent of terminal computing capabilities, their authentication performance is weak in robustness and high in overhead. Third, the access authentication delay is huge since the system cannot withstand heavy concurrent access requests from large-scale terminals. To address the above problems, we propose a reliable and lightweight trusted access authentication solution for terminals in the vehicle charging network system. By cloud, edge, and local servers cooperating to execute authentication tasks, our Cloud-Edge-End Collaborative architecture effectively alleviates the authentication delay caused by high concurrent requests. Each server in the architecture deploys our well-designed unified trusted access authentication (UATT) model based on device fingerprints. With ingenious data construction and the powerful swin-transformer network, the UATT model can provide robust and low-overhead authentication services for heterogeneous terminals. To minimize authentication latency, we further design an A2C-based authentication task scheduling scheme to decide which server executes the current task. Comprehensive experiments demonstrate our solution can authenticate terminals with an accuracy higher than 98% while reducing the required data packets by two orders of magnitude, and it can effectively reduce authentication latency

Privacy-Friendly Task Offloading for Smart Grid in 6G Satellite–Terrestrial Edge Computing Networks

Through offloading computing tasks to visible satellites for execution, the satellite edge computing architecture effectively issues the high-delay problem in remote grids (e.g., mountain and desert) when tasks are offloaded to the urban terrestrial cloud (TC). However, existing works are usually limited to offloading tasks in pure satellite networks and make offloading decisions based on the predefined models. Additionally, runtime consumption for offloading decisions is rather high. Furthermore, privacy information may be maliciously sniffed since computing tasks are transmitted via vulnerable satellite networks. In this paper, we study the task-offloading problem in satellite–terrestrial edge computing networks, where tasks can be executed by satellite or urban TC. A privacy leakage scenario is described, and we consider preserving privacy by sending extra random dummy tasks to confuse adversaries. Then, the offloading cost with privacy protection consideration is modeled, and the offloading decision that minimizes the offloading cost is formulated as a mixed-integer programming (MIP) problem. To speed up solving the MIP problem, we propose a deep reinforcement learning-based task-offloading (DRTO) algorithm. In this case, offloading location and bandwidth allocation only depend on the current channel states. Simulation results show that the offloading overhead is reduced by 17.5% and 23.6% compared with pure TC computing and pure SatEC computing, while the runtime consumption of DRTO is reduced by at least 42.6%. The dummy tasks are exhibited to effectively mitigate privacy leakage during offloading

Perfluorooctanoic Acid Degradation Using UV–Persulfate Process: Modeling of the Degradation and Chlorate Formation

In this study, we investigated the destruction and by-product formation of perfluorooctanoic acid (PFOA) using ultraviolet light and persulfate (UV–PS). Additionally, we developed a first-principles kinetic model to simulate both PFOA destruction and by-product and chlorate (ClO₃^–) formation in ultrapure water (UW), surface water (SW), and wastewater (WW). PFOA degradation was significantly suppressed in the presence of chloride and carbonate species and did not occur until all the chloride was converted to ClO₃^– in UW and for low DOC concentrations in SW. The model was able to simulate the PS decay, pH changes, radical concentrations, and ClO₃^– formation for UW and SW. However, our model was unable to simulate PFOA degradation well in WW, possibly from PS activation by NOM, which in turn produced sulfate radicals