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QoS-Aware Computation Offloading in LEO Satellite Edge Computing for IoT:A Game-Theoretical Approach

Chinese Journal of Electronics 2024年第4期33卷 875-885页

作者： Ying CHEN Jintao HU Jie ZHAO Geyong MIN School of Computer Science Beijing Information Science and Technology University University of Exeter

Low earth orbit(LEO) satellite edge computing can overcome communication difficulties in harsh environments, which lack the support of terrestrial communication infrastructure. It is an indispensable option for achieving worldwide wireless communication coverage in the future. To improve the quality-of-service(QoS) for Internet-of-things(IoT) devices, we combine LEO satellite edge computing and ground communication systems to provide network services for IoT devices in harsh environments. We study the QoS-aware computation offloading(QCO) problem for IoT devices in LEO satellite edge computing. Then we investigate the computation offloading strategy for IoT devices that can minimize the total QoS cost of all devices while satisfying multiple constraints, such as the computing resource constraint, delay constraint, and energy consumption constraint. We formulate the QoSaware computation offloading problem as a game model named QCO game based on the non-cooperative competition game among IoT devices. We analyze the finite improvement property of the QCO game and prove that there is a Nash equilibrium for the QCO game. We propose a distributed QoS-aware computation offloading(DQCO) algorithm for the QCO game. Experimental results show that the DQCO algorithm can effectively reduce the total QoS cost of IoT devices.

关键词： Satellites Costs Computational modeling Low earth orbit satellites Quality of service Games Nash equilibrium

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Effect of CO_(2)exposure on the mechanical strength of geopolymerstabilized sandy soils

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Journal of Rock Mechanics and Geotechnical engineering 2024年第2期16卷 670-681页

作者： Hamid Reza Razeghi Armin Geranghadr Fatemeh Safaee Pooria Ghadir Akbar A.Javadi School of Civil Engineering Iran University of Science and TechnologyTehran***Iran Department of Engineering University of ExeterExeterEX44QFUK

In recent years,there has been growing interest in developing methods for mitigating greenhouse effect,as greenhouse gas emissions continue to contribute to global temperature *** the other hand,investigating geopolymers as environmentally friendly binders to mitigate the greenhouse effect using soil stabilization has been widely ***,the effect of CO_(2)exposure on the mechanical properties of geopolymer-stabilized soils is rarely *** this context,the effect of CO_(2)exposure on the mechanical and microstructural features of sandy soil stabilized with volcanic ash-based geopolymer was *** factors were concerned,for example the binder content,relative density,CO_(2)pressure,curing condition,curing time,and carbonate *** results showed that the compressive strength of the stabilized sandy soil specimens with 20%volcanic ash increased from 3 MPa to 11 *** was also observed that 100 kPa CO_(2)pressure was the optimal pressure for strength development among the other *** mechanical strength showed a direct relationship with binder content and carbonate ***,in the ambient curing(AC)condition,the mechanical strength and carbonate content increased with the curing ***,the required water for carbonation evaporated after 7 d of oven curing(OC)condition and as a result,the 14-d cured samples showed lower mechanical strength and carbonate content in comparison with 7-d cured ***,the rate of strength development was higher in OC cured samples than AC cured samples until 7 d due to higher geopolymerization and carbonation rate.

关键词： Soil stabilization CO_(2)effect Geopolymer

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Novel cable-like tin@carbon whiskers derived from the Ti_(2)SnC MAX phase for ultra-wideband electromagnetic wave absorption

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Carbon Energy 2024年第12期6卷 1-13页

作者： Feiyue Hu Pei Ding Fushuo Wu Peigen Zhang Wei Zheng Wenwen Sun Rui Zhang Longzhu Cai Bingbing Fan ZhengMing Sun School of Materials Science and Engineering Southeast UniversityNanjingChina School of Materials Science and Engineering Zhengzhou UniversityZhengzhouChina The State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast UniversityNanjingChina Department of Engineering Faculty of EnvironmentScience and EconomyUniversity of ExeterExeterUK

One-dimensional(1D)metals are well known for their exceptional conductivity and their ease of formation of interconnected networks that facilitate electron migration,making them promising candidates for electromagnetic(EM)***,the impedance mismatch from high conductivity and their singular mode of energy loss hinder effective EM wave *** of cable structures not only optimizes impedance matching but also introduces a multitude of heterojunctions,increasing attenuation modes and potentially enhancing EM wave absorption(EMA)***,we showcase the scalable synthesis of tin(Sn)whiskers from a Ti_(2)SnC MAX phase precursor,followed by creation of a 1D tin@carbon(Sn@C)cable structure through polymerization of PDA on their surface and annealing in *** EMA capabilities of Sn@C significantly surpass those of uncoated Sn whiskers,with an effective absorption bandwidth reaching 7.4 ***,its maximum radar cross section reduction value of 27.85 dBm2 indicates its exceptional stealth *** enhanced EMA performance is first attributed to optimized impedance matching,and furthermore,the Sn@C cable structures have rich SnO2/C and Sn/SnO2 heterointerfaces and the associated defects,which increase interfacial and defect-induced polarization losses,as visually demonstrated by off-axis electron *** development of the Sn@C cable structure represents a notable advancement in broadening the scope of materials with potential applications in stealth technology,and this study also contributes to the understanding of how heterojunctions can improve EMA performance.

关键词： cable structure electromagnetic wave absorption interfacial polarization Sn whiskers Sn@C

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Formal Modeling of Hybrid System Based on Semi-continuous Colored Petri Net: A Case Study of Adaptive Cruise Control System

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ACM Transactions on Embedded Computing Systems 2025年第3期24卷 1-21页

作者： Yu, Wangyang Guo, Qi Cheng, Yumeng Liu, Lu Hao, Fei Zhai, Xiaojun Chen, Minsi The School of Computer Science Shaanxi Normal University Shaanxi Xi'an China The School of Computer Science Shaanxi Normal University Xi'an China University of Exeter Department of Computer Science Exeter United Kingdom University of Essex School of Computer Science and Electronic Engineering Colchester United Kingdom University of Huddersfield School of Computing and Engineering Huddersfield United Kingdom

Many Next-Generation consumer electronic devices would be distributed hybrid electronic systems, such as UAVs (Unmanned Aerial Vehicles) and smart electronic cars. The safety and risk control are the key issues for the sustainability of such consumer electronic systems. The modeling of hybrid electronic systems is difficult to be abstracted by traditional Petri Nets. This also makes the reachable marking graph unable to be applied to Petri Nets of the hybrid electronic systems. This paper proposes a novel Petri Net to model and analyze the hybrid electronic systems. We name it a Semi-continuous Colored Petri Net (SCPN) that inherits the excellent modeling capabilities and analysis methods of Petri Nets, and can formally depict hybrid quantities. In addition, we propose the construction algorithm for an SCPN reachable marking graph and prove its finiteness. Finally, we model and analyze an Adaptive Cruise Control (ACC) system of smart electronic cars as an example to prove the validity of SCPN. We use the proposed SCPN to model and analyze the running process of an ACC system under the continuous deceleration scenario of the front vehicle. The application study shows that the ACC system has logic flaws under the constant headway strategy when the front vehicle continues to decelerate. Based on this analysis, improvements to the SCPN of the ACC system are made, effectively enhancing its safety and logical correctness. Copyright © 2025 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Adaptive cruise control

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Joint Mobile Energy Replenishment and Data Gathering in Wireless Sensor Networks via Federated Deep Reinforcement Learning

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6460-6473页

作者： Huang, Haojun Zhang, Junhao Wang, Bang Miao, Wang Min, Geyong Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan430074 China University of Exeter Department of Computer Science ExeterEX44QF United Kingdom

Recent years have witnessed the proliferation of wireless energy transfer for Wireless Sensor Networks (WSNs), which are mainly used for data gathering in real-world applications. A number of studies have investigated mobile vehicle scheduling to charge sensor nodes via wireless Mobile Chargers (MCs). Unfortunately, most of them cannot parallelly charge all nodes in an intelligent manner with the global network attributes. Furthermore, the time-variable charging ignores the optimal data gathering, resulting in poor Joint Energy Replenishment and Data Gathering (JERDG). To fill this gap, this paper proposes a Federated Deep Reinforcement Learning (FDRL)-based JERDG (FERG) solution for WSNs. To this end, FERG firstly partitions the networks into a set of clusters to distribute the workload evenly among multiple MCs, and then designs an FDRL-based framework that incorporates various time-variant network attributes to determine the optimal schedule for charging and data gathering via multiple MCs and a base station (BS). The BS as the cloud server is responsible for global training of JERDG models, while multiple MCs will parallelly train local models to jointly charge energy-exhausted nodes and gather the data from all nodes in clusters. To reserve more personalized characteristics of each cluster, a density-based partial aggregation strategy is designed to train the global model. Furthermore, a reward-weighted update and selection solution is proposed to generate and exploit reference samples with high rewards. Simulation results obtained from various scenarios demonstrate that FERG significantly outperforms the state-of-the-art approaches in terms of network lifetime, energy efficiency and data collection latency. © 2002-2012 IEEE.

关键词： Deep reinforcement learning

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Dynamic Task Offloading for Digital Twin-Empowered Mobile Edge Computing via Deep Reinforcement Learning

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China Communications 2023年第11期20卷 164-175页

作者： Ying Chen Wei Gu Jiajie Xu Yongchao Zhang Geyong Min School of Computer Beijing Information Science and Technology UniversityBeijing 100101China University of Exeter ExeterEX44QFUnited Kingdom

Limited by battery and computing re-sources,the computing-intensive tasks generated by Internet of Things(IoT)devices cannot be processed all by *** edge computing(MEC)is a suitable solution for this problem,and the gener-ated tasks can be offloaded from IoT devices to *** this paper,we study the problem of dynamic task offloading for digital twin-empowered *** twin techniques are applied to provide information of environment and share the training data of agent de-ployed on IoT *** formulate the task offload-ing problem with the goal of maximizing the energy efficiency and the workload balance among the ***,we reformulate the problem as an MDP problem and design DRL-based energy efficient task offloading(DEETO)algorithm to solve *** experi-ments are carried out which show the superiority of our DEETO algorithm in improving energy efficiency and balancing the workload.

关键词： deep reinforcement learning digital twin Internet of Things mobile edge computing

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Synthetic Privacy-Preserving Trajectories with Semantic-aware Dummies for Location-Based Services

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IEEE Transactions on Services Computing 2025年第03期18卷 1811-1824页

作者： Huang, Haojun Sun, Hao Wu, Weimin Wang, Chen Liu, Wuwu Miao, Wang Min, Geyong Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan430074 China University of Exeter Department of Mathematics and Computer Science ExeterEX4 4QF United Kingdom

Trajectory synthesis with a series of fake locations has been deemed as a promising obfuscation technology to preserve the individual privacy of users in Location-Based Services (LBSs). However, a number of previous approaches fail to take into consideration the geographic distance and motion direction of the real locations to synthesize trajectories. As a result, most of them always cannot represent the statistical characteristics of real trajectories in a privacy-preserving manner, and thus suffer from various attacks through data analysis. To tackle this issue, this paper presents SPSD, a novel privacy-preserving trajectory synthesis approach with a k-anonymous guarantee, through extracting the semantic, geographic and directional similarity of locations from the real trajectories to create plausible trajectories. SPSD first classifies all historical trajectory data into a series of sets for location identity, by introducing the visiting time and visiting duration, which can clearly represent the semantic information of locations. Then, 4k locations and 2k of 4k ones have been selected from each set to act as the initial disguises of each corresponding real location, with quantitative semantic and geographic similarities, respectively. In order to find enough fake locations for each real location in less time, the candidate locations have been narrowed down to k in direction recovery through step-by-step screening, with the k-anonymous property. Experiment results built on the real-world trajectory datasets indicate that SPSD has outperformed the previous approaches in terms of semantic similarity, directional accuracy and security resistance to synthesize privacy-preserving trajectories at the tolerable time cost. © 2008-2012 IEEE.

关键词： Differential privacy

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Interpretability and spatial efficacy of a deep-learning-based on-site early warning framework using explainable artificial intelligence and geographically weighted random forests

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Geoscience Frontiers 2024年第5期15卷 182-196页

作者： Jawad Fayaz Carmine Galasso Department of Computer Science University of ExeterUK Department of Civil Environmentaland Geomatic EngineeringUniversity College London(UCL)LondonUK

Earthquakes pose significant risks globally,necessitating effective seismic risk mitigation strategies like earthquake early warning(EEW)***,developing and optimizing such systems requires thoroughly understanding their internal procedures and coverage *** study examines a deep-learning-based on-site EEW framework known as ROSERS(Real-time On-Site Estimation of Response Spectra)proposed by the authors,which constructs response spectra from early recorded ground motion waveforms at a target *** study has three primary goals:(1)evaluating the effectiveness and applicability of ROSERS to subduction seismic sources;(2)providing a detailed interpretation of the trained deep neural network(DNN)and surrogate latent variables(LVs)implemented in ROSERS;and(3)analyzing the spatial efficacy of the framework to assess the coverage area of on-site EEW *** is retrained and tested on a dataset of around 11,000 unprocessed Japanese subduction ground ***-of-fit testing shows that the ROSERS framework achieves good performance on this database,especially given the peculiarities of the subduction seismic *** trained DNN and LVs are then interpreted using game theory-based Shapley additive explanations to establish cause-effect ***,the study explores the coverage area of ROSERS by training a novel spatial regression model that estimates the LVs using geographically weighted random forest and determining the radius of *** results indicate that on-site predictions can be considered reliable within a 2–9 km radius,varying based on the magnitude and distance from the earthquake *** information can assist end-users in strategically placing sensors,minimizing blind spots,and reducing errors from regional extrapolation.

关键词： Earthquake early warning systems Spatial regression Neural networks Japanese subduction Explainable artificial intelligence

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DF-EGM: Personalised Knowledge Tracing with Dynamic Forgetting and Enhanced Gain Mechanisms 21

DF-EGM: Personalised Knowledge Tracing with Dynamic Forgetti...

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21st International Conference on Networking, Sensing and Control, ICNSC 2024

作者： Wang, Hongyun Shi, Leilei Han, ZiXuan Liu, Lu Sun, Xiang Aziz, Furqan School of Computer Science and communication Engineering Jiangsu University China University of Exeter Department of Computer Science Exeter United Kingdom School of Computing and Mathematical Sciences University of Leicester United Kingdom

ISBN: (纸本)9798350365221

In current online education, Knowledge Tracing (KT) technology plays a pivotal role, in monitoring and updating students' evolving knowledge states throughout their educational journey. Previous knowledge tracing models like DKT and DKVMN, have made some progress in personalised learning. However, they fall short in fully capturing the dynamic changes in the learning process, including forgetting behaviour and individual learning gains. To address these issues, this paper introduces a novel knowledge tracing model named dynamic forgetting and enhanced gain mechanisms (DF-EGM), which builds on Deep-Irt. The model initially utilises a CART decision tree classifier to extract pre-classification labels by analyzing response times and learning abilities. Subsequently, the model incorporates forgetting factors and personalised learning gains. It introduces attempt counts and time intervals to simulate knowledge forgetting. and the realization of personalised learning gains is achieved through a deeply refined knowledge gain module. This module models the perceived difficulty of exercises by students and dynamically adjusts learning gains based on their answer performance and current knowledge state. Experiments on public datasets indicate that the DF-EGM model can more accurately track students' knowledge states. © 2024 IEEE.

关键词： Federated learning

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An Automated Few-Shot Learning for Time-Series Forecasting in Smart Grid under Data Scarcity

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2482-2492页

作者： Xu, Jiangjiao Li, Ke Li, Dongdong Shanghai University of Electric Power Department of Electrical Engineering Shanghai20090 China University of Exeter Department of Computer Science ExeterEX4 4QF United Kingdom

Microgrid can improve greenhouse gas emissions and reduce operational costs. To forecast both energy generation and load demand, time-series prediction has been a key tool in real-time control and optimization. Developing an adequate predictive model is difficult when there is a lack of historical data. Moreover, hyperparameters have a tangible impact on the performance of machine learning models. Bearing these considerations in mind, this article develops a BiLO-Auto-TSF/ML framework that automates the optimal design of a few-shot learning pipeline from a bilevel programming perspective. Specifically, a lower level metalearner helps mitigate the small data challenge, whereas an upper level optimization optimizes both hyperparameters for lower and upper level learners. Note that the proposed framework is designed to accommodate a wide range of machine learning methods, allowing for easy integration through a plug-in mechanism. Comprehensive experiments demonstrate the effectiveness of our proposed BiLO-Auto-TSF/ML to search for a high-performance few-shot learning pipeline for various energy sources. © 2020 IEEE.

关键词： Forecasting

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