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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Zhou, Shasha Li, Ke College of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China University of Exeter Department of Computer Science Exeter United Kingdom

ISBN: (纸本)9798350337020

Optimization problems are ubiquitous in real-world engineering scenarios where the goals are to enhance interested aspects such as efficiency, productivity, and profitability. However, solving practical optimization problems could be non-trivial, partly due to the presence of a wide range of noises, including environmental noises, model biases, time-domain variations, measurement uncertainties and many other uncontrolled variables. In this paper, we empirically study the effect of noise range, sample size and resampling type on the solution quality of MOEAs when noise is added to decision variables. Our empirical results, conducted on three commonly used Multi-Objective Optimization Problems (MOEAs), i.e. NSGA-II, MOEA/D and IBEA, demonstrate that noise range has more significant impact on the robustness of optimization algorithms compared to sample size and resampling type. In addition, we introduce the concept of bad point, which is able to illustrate how noise affects the performance of different MOEAs. © 2023 IEEE.

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Preference-Based Multi-Objective Optimization with Gaussian Process

Preference-Based Multi-Objective Optimization with Gaussian ...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Huang, Tian Li, Ke University of Electronic Science and Technology of China Department of Computer Science and Engineering Chengdu China University of Exeter Department of Computer Science Exeter United Kingdom

ISBN: (纸本)9798350337020

Traditional evolutionary multi-objective optimization (EMO) algorithm is to generate a set of non-dominated solutions on the Pareto front (PF). However, this technique falls short of delivering the outcomes for multi-objective optimization problems (MOPs) containing user preference. In this paper, we present a novel EMO algorithm that incorporates user preferences via a decision maker (DM). Our approach comprises three modules: consultation, preference elicitation and optimization. The DM undertakes the consultation and preference elicitation using Gaussian process (GP) to provide preference information. We employ the decomposition-based EMO algorithm (i.e., MOEA/D) for optimization. The experiment comprises two sessions. Firstly, we simulate the decision maker module with GP. Secondly, we simulate our proposed method and compare its performance with existing interactive optimization algorithms. Our research proposes a new preference-based EMO algorithm that addresses the shortcomings of traditional techniques and unlocks new possibilities for multi-objective optimization. © 2023 IEEE.

关键词： evolutionary algorithms Gaussian process Multi-objective optimization preference learning

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DRL-Based Joint Optimization of Wireless Charging and Computation Offloading for Multi-Access Edge Computing

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IEEE Transactions on Services Computing 2025年第3期18卷 1352-1367页

作者： Zhu, Xinyuan Hao, Fei Ma, Lianbo Luo, Changqing Min, Geyong Yang, Laurence T. Shaanxi Normal University School of Computer Science Xi'an710119 China Northeastern University School of Software Shenyang100819 China Virginia Commonwealth University Department of Computer Science RichmondVA23084 United States University of Exeter Department of Computer Science United Kingdom Hainan University School of Computer Science and Technology China St. Francis Xavier University Department of Computer Science Canada

Wireless-powered multi-access edge computing (WP-MEC), as a promising computing paradigm with the great potential for breaking through the power limitations of wireless devices, is facing the challenges of reliable task offloading and charging power allocation. Towards this end, we formulate a joint optimization problem of wireless charging and computation offloading in socially-aware D2D-assisted WP-MEC to maximize the utility, characterized by wireless devices' residual energy and the strength of social relationship. To address this problem, we propose a deep reinforcement learning (DRL)-based approach with hybrid actor-critic networks including three actor networks and one critic network as well as with Proximal Policy Optimization (PPO) updating policy. Further, to prevent the policy collapse, we adopt the PPO-clip algorithm which limits the update steps to enhance the stability of algorithm. The experimental results show that the proposed algorithm can achieved superior convergence performance and, meanwhile, improves the average utility efficiently compared to other baseline approaches. © 2008-2012 IEEE.

关键词： Computation offloading

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Game-Based Service Requests and Channel Selection in Mobile Edge Computing 15

Game-Based Service Requests and Channel Selection in Mobile ...

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15th International Conference on Communication Software and Networks, ICCSN 2023

作者： Yin, Tong Chen, Xin Jiao, Libo Xing, Hua Min, Geyong School of Computer Science Beijing Information Science & Technology University Beijing China College of Engineering Mathematics and Physical Sciences University of Exeter Exeter United Kingdom

ISBN: (纸本)9798350329360

Mobile edge computing (MEC) can provide service requests and resource allocation for user equipments (UEs), effectively reducing service delay for massive Internet-of-Things (IoT). Application providers cache services in the edge servers to reduce service requests delay. However, limited channel resources will affect transmission delay and communication quality when UEs request services from edge servers, thus reducing the user's Quality-of-Experience. In this paper, we investigate the service offloading problem between UEs and base stations (BSs), as well as between BSs and remote data centers (RDCs) in MEC systems with heterogeneous networks, considering channel selection between UEs and BSs. To minimize the service delay of each UE and thus improve user satisfaction, we propose game theory to model the problem as a non-cooperative game of channel selection and then demonstrate the existence of Nash equilibrium (NE). In order to find the ideal channel selection scheme, this paper proposes a best response based on a multi-user service request channel selection (SRCS) algorithm. Simulation results reveal that the proposed channel selection mechanism can improve user satisfaction and verify the effectiveness and convergence of the proposed algorithm. © 2023 IEEE.

关键词： Game theory

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Priority-Aware Task Offloading and Resource Allocation in Satellite and HAP Assisted Edge-Cloud Collaborative Networks 15

Priority-Aware Task Offloading and Resource Allocation in Sa...

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15th International Conference on Communication Software and Networks, ICCSN 2023

作者： Dai, Xin Chen, Xin Jiao, Libo Wang, Yijie Du, Shougang Min, Geyong School of Computer Science Beijing Information Science & Technology University Beijing China College of Engineering Mathematics and Physical Sciences University of Exeter Exeter United Kingdom

ISBN: (纸本)9798350329360

Mobile edge computing (MEC) has been regarded as an effective technique to handle computing intensive tasks of IoT devices (IoTDs). In this paper, we present a priority-aware edge computing model for space-air-ground integrated networks (SAGIN), which contain a satellite, a high altitude platform (HAP) and numerous IoTDs. In such networks, the HAP provides edge computing services close to the IoTDs and the cloud servers connected to satellite provide cloud computing services. The network considers different services for IoTDs with various Quality-of-Service (QoS) requirements. First, we design a media access control MAC protocol named priority-aware time division multiple access (PTDMA) for controlling the large-scale access of IoTDs. Second, for tasks with different priorities, we propose a priority-aware deep deterministic policy gradient (DDPG) based task offloading and resource allocation scheme (PDOA). Simulation experimental results validate the performance of the PTDMA and PDOA scheme, which can effectively improve the average system utility in a stochastic time-varying environment. © 2023 IEEE.

关键词： Resource allocation

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Proactive video caching based on federated learning and implicit feedback in mobile edge computing

Proactive video caching based on federated learning and impl...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Qian, Zhen Li, Guanghui Qi, Tao Dai, Chenglong Hu, Jia Jiangnan University School of Artificial Intelligence and Computer Science Wuxi China University of Exeter Department of Computer Science United Kingdom

ISBN: (纸本)9798350351255

The rapid development of 5G and the widespread use of smart terminal devices bring explosive video traffic growth. Edge caching technology in mobile edge computing systems stores popular content of most interest to users in advance in edge servers closer to mobile users to alleviate the pressure of traffic congestion and excessive access latency caused by centralized storage. However, how to obtain popular videos while protect user data privacy with only implicit user feedback data, such as liking, viewing, favoriting, etc., is the key challenge. To tackle these challenges, we proposed a proactive video caching scheme based on federated learning and implicit feedback (FIPC). First, a mobile edge-cloud system model contained a three-tier network architecture is developed. Then, we introduced the federated process for training the denoised auto-encoder model and video caching in detail. Finally, experimental results in Movielens dataset show that, without user data leakage, the proposed FIPC scheme outperforms the baseline caching algorithm using user feedback data. © 2024 IEEE.

关键词： Mobile edge computing

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Solidification/stabilization of lead-contaminated soil using alkali-activated volcanic ash

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Environmental science and Pollution Research 2024年第26期31卷 38465-38484页

作者： Molaei, Mohammad Amin Miraki, Hania Morovati, Mohsen Ghadir, Pooria Javadi, Akbar A. School of Civil Engineering Iran University of Science and Technology Tehran Iran Department of Civil and Environmental Engineering University of Strathclyde GlasgowG1 1XJ United Kingdom Department of Engineering University of Exeter ExeterEX4 4QF United Kingdom

The bioaccumulation of lead in soil poses a significant human health risk. The solidification/stabilization (S/S) technique, employing binders like Portland cement or lime, is a common method for remediating lead-contaminated soil. However, cement production has adverse environmental impacts, prompting the exploration of eco-friendly alternatives like alkali-activated materials (AAMs). This study assesses AAM efficacy in the S/S of lead-contaminated soil. The effects of several factors, including varying amounts of volcanic ash (VA), lead concentration, curing temperatures, and curing times are investigated. Unconfined compressive strength (UCS), toxicity characteristic leaching procedure test (TCLP), X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), and field emission scanning electron microscope-energy-dispersive spectroscopy-mapping analyses (FESEM/EDS/mapping) analyses are used to study the specimens. The findings indicated a substantial increase in the UCS of lead-contaminated soil treated with 15% VA (under oven curing (OC) conditions), and 10% VA (under ambient curing (AC) conditions) exhibited remarkable increases of up to 600% and 458%, respectively. Moreover, the leaching of Pb2+ ions from samples contaminated with 10,000 mg/kg (under OC conditions) and 2500 mg/kg (under AC conditions) experienced significant reductions of 87% (from 135.14 to 13.36 ppm) and 91% (from 26.32 to 2.21 ppm), respectively. The S/S process in these samples operated through three primary mechanisms of chemical bonding, physical encapsulation, and the formation of insoluble silicate. The formation of N-A-S–H and hydroxy sodalite structures played a vital role in facilitating these mechanisms. Therefore, alkali-activated VA demonstrated excellent performance in the remediation of lead-contaminated soil. Graphical Abstract: (Figure presented.) © The Author(s) 2024.

关键词： Heavy metals

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Ethical Decision-Making for the Inside of Autonomous Buses Moral Dilemmas

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第10期5卷 5153-5166页

作者： Huang, Zijie Wu, Yulei Tempini, Niccolo Tang, Haina University of Exeter Faculty of Environment Science and Economy Department of Computer Science ExeterEX4 4QJ United Kingdom University of Bristol Faculty of Engineering Bristol Digital Futures Institute BristolBS8 1UB United Kingdom University of Exeter Faculty of Humanities Arts and Social Sciences Department of Sociology Philosophy and Anthropology ExeterEX4 4RJ United Kingdom University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China

The emergence of moral dilemmas inside autonomous buses can lead to unpredictable consequences for maneuver and amplify potential harms to passengers onboard. However, most existing approaches solely focus on ensuring ethical decision-making in scenarios outside of vehicles. To ensure ethical decision-making for autonomous buses when moral dilemmas occur inside, there are many urgent challenges that need to be addressed. First, the noncommensurability of ethical values presents difficulties in designing quantifiable environments and decision-making models. Moreover, ethical dilemmas involve multiple conflicting objectives, often necessitating the consideration of multiple moral theories to comprehensively evaluate different perspectives. Additionally, accurately representing these dilemmas and identifying optimal solutions that address conflicting objectives poses further challenges. This article proposes a general ethical decision-making system to handle ethical dilemmas inside autonomous buses. The system's design adheres to multiple ethical principles, and it comprises two stages: 1) develop a generative adversarial network (GAN) based human-value-aligned data collection scheme to gather representative moral values and generate comprehensive moral scenarios, which address the incommensurable ethical metrics issue;and 2) propose an ethical compliant multiobjective thresholded lexicographic Deep Q-learning method to ensure optimal policies that satisfy multiple ethical objectives. In a case study of autonomous bus route planning, our system outperforms benchmarks in showing a greater number and more evenly distributed policies in the Pareto Front and a 27% increase in coverage rate. Extensive experiments against nonethical systems show superior outcomes in convergence, average reward and cost. Finally, user studies demonstrate the system's accuracy and usability. © 2020 IEEE.

关键词： Decision making

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A Novel Approach to Recognising Physical Activity Intensity in Adults: Gravity-Based Acceleration Imaging with Vision Transformer 22

A Novel Approach to Recognising Physical Activity Intensity ...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Wang, Lin Zhang, Tianle Wang, Miaomin Wang, Gang Chen, Guohao Li, Zhuhui University of Exeter Faculty of Health and Life Sciences Exeter United Kingdom University of Liverpool Liverpool United Kingdom University of Exeter Faculty of Environment Science and Economy Exeter United Kingdom School of Computer Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9798331520526

Research on physical activity intensity (PAI) recognition often relies on tabular data, limiting the capture of global patterns compared to image-based data, while traditional models struggle with long-sequence dependencies. This study introduces a novel method that uses a gravity-based acceleration algorithm to generate images of different intensity levels, followed by classification with a Vision Transformer (ViT) model, aiming to enhance model performance and experimental efficiency. The method uses the Euclidean Norm Minus One to calculate gravity-based acceleration per second. Gramian Angular Field images are generated based on adult intensity thresholds and are subsequently classified using the ViT model. The results showed the ViT model's high performance in recognising different PAIs. Specifically, it demonstrated that using gravity-based images for recognising PAI improves accuracy compared to METs-based images (etabolic equivalents), achieving an overall accuracy of 98.77% compared to 97.52%. Additionally, this novel approach maintains high accuracy across different time windows, with accuracy ranging from 98.59% to 99.05%, further indicating the model's stability and robust performance across varying temporal windows. © 2025 IEEE.

关键词： Image enhancement

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Recovering Packet Collisions below the Noise Floor in Multi-gateway LoRa Networks 42

Recovering Packet Collisions below the Noise Floor in Multi-...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Mao, Wenliang Zhao, Zhiwei Zheng, Kaiwen Min, Geyong University of Electronic Science and Technology of China School of Computer Science and Engineering China University of Exeter Department of Computer Science United Kingdom

ISBN: (纸本)9798350334142

LoRa has been widely applied in various vertical areas such as smart grids, smart cities, etc. Packet collisions caused by concurrent transmissions have become one of the major limitations of LoRa networks due to the ALOHA MAC protocol and dense deployment. The existing studies on packet recovery usually assume that the collided packet signals are above the noise floor. However, considering the large-scale deployment and low-power nature of LoRa communications, many collided packets are below the noise floor. Consequently, the existing schemes will suffer from significant performance degradation in practical LoRa networks. To address this issue, we propose CPR, a Cooperative Packet Recovery mechanism aiming at recovering the collided packets below the noise floor. CPR firstly employs the incoherence of signals and noises at multiple gateways to detect and extract the frequency features of the collided packets hidden in the noise. Then, CPR adopts a novel gateway selection strategy to select the most appropriate gateways based on their packet power domain features extracted from collision detection, such that the interference can be eliminated and the original packets can be recovered. Extensive experimental results demonstrate that CPR can significantly increase the symbol recovery ratio in low-SNR scenarios. © 2023 IEEE.

关键词： Recovery

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