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Digital Twin-Assisted Space-Air-Ground Integrated Multi-Access Edge Computing for Low-Altitude Economy: An Online Decentralized Optimization Approach

arXiv

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arXiv 2024年

作者： He, Long Sun, Geng Sun, Zemin Wang, Jiacheng Du, Hongyang Niyato, Dusit Liu, Jiangchuan Leung, Victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong School of Computing Science Simon Fraser University BurnabyBCV5A 1S6 Canada R&D Department Jiangxing Intelligence Inc. Nanjing210000 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518115 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

The emergence of space-air-ground integrated multi-access edge computing (SAGIMEC) networks opens a significant opportunity for the rapidly growing low altitude economy (LAE), facilitating the development of various applications by offering efficient communication and computing services. However, the heterogeneous nature of SAGIMEC networks, coupled with the stringent computational and communication requirements of diverse applications in the LAE, introduces considerable challenges in integrating SAGIMEC into the LAE. In this work, we first present a digital twin-assisted SAGIMEC paradigm for LAE, where digital twin enables reliable network monitoring and management, while SAGIMEC provides efficient computing offloading services for Internet of Things sensor devices (ISDs). Then, a joint satellite selection, computation offloading, communication resource allocation, computation resource allocation and UAV trajectory control optimization problem (JSC4OP) is formulated to maximize the quality of service (QoS) of ISDs. Given the complexity of JSC4OP, we propose an online decentralized optimization approach (ODOA) to address the problem. Specifically, JSC4OP is first transformed into a real-time decision-making optimization problem (RDOP) by leveraging Lyapunov optimization. Then, to solve the RDOP, we introduce an online learning-based latency prediction method to predict the uncertain system environment and a game theoretic decision-making method to make real-time decisions. Finally, theoretical analysis confirms the effectiveness of the ODOA, while the simulation results demonstrate that the proposed ODOA outperforms other alternative approaches in terms of overall system performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Quality of service

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STAR-RIS and UAV Combination in MEC Networks: Simultaneous Task Offloading and Communications

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IEEE Transactions on Communications 2025年

作者： Xiao, Han Hu, Xiaoyan Wang, Wenjie Su, Zhou Wong, Kai-Kit Yang, Kun Xi'an Jiaotong University School of Information and Communications Engineering Xi'an710049 China Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Xi'an Jiaotong University School of Cyber Science and Engineering Xi'an710049 China University College London Department of Electronic and Electrical Engineering Torrington Place LondonWC1E 7JE United Kingdom Yonsei University Yonsei Frontier Lab Seoul Korea Republic of Nanjing University State Key Laboratory of Novel Software Technology Nanjing210008 China School of Intelligent Software and Engineering Suzhou215163 China University of Essex School of Computer Science and Electronic Engineering Essex United Kingdom

This paper explores a simultaneous tasks offloading and communications (STOC) scheme in mobile edge computing (MEC) networks, supported by the combination of simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and the unmanned aerial vehicle (UAV). Different from the traditional MEC schemes, the proposed scheme concurrently considers the computation and communication capabilities of the MEC networks, which is actually more practical in reality. Specifically, an optimization problem is devised to maximize the weighted sum of the minimum computed task data and communication data, while ensuring the quality of service (QoS) constraints for STOC through joint design of time scheduling, resource allocation, active and passive beamforming, alongside with the UAV trajectory planning. This non-convex problem with strong couplings among variables is challenging to solve directly. Then, a novel alternating optimization method is proposed, leveraging the successive convex approximation (SCA) and semi-definite relaxation (SDR) techniques. We provide sufficient numerical results to validate the effectiveness of the proposed STOC scheme, which demonstrate that the proposed scheme supported by STAR-RIS and UAV outperforms five benchmark schemes in terms of performance gain. It is important to note that the proposed scheme offers a feasible and realistic way for the implementations of STOC in practical MEC networks. © 1.72-201. IEEE.

关键词： Convex optimization

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An Explainable AutoML-Driven Meta-Learning Scheme for Intrusion Prevention in Zero-Touch Networks Within Carbon Intelligent IIoT

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IEEE Internet of Things Journal 2025年

作者： Islam, Anik Karimipour, Hadis Gadekallu, Thippa Reddy University of Calgary Schulich School of Engineering Deptartment of Electrical and Software Engneering CalgaryT2N1N4 Canada Zhejiang A and F University The College of Mathematics and Computer Science Hangzhou311300 China Lovely Professional University Division of Research and Development Phagwara India Chitkara University Center of Research Impact and Outcome Punjab Rajpura140401 India

Carbon Intelligent Industrial Internet of Things (IIoT) systems are critical for achieving sustainable industrial automation but face challenges such as scalability, operational complexity, and security vulnerabilities. Zero-Touch Networks (ZTN), with their autonomous management capabilities, offer solutions to operational challenges but remain vulnerable to sophisticated cyber intrusions due to their high level of autonomy and interconnectedness. While Artificial Intelligence (AI), especially Deep Learning (DL), shows potential in intrusion detection, current approaches often encounter obstacles such as insufficient datasets, challenges in automated data preprocessing, and a lack of transparency. This paper introduces an AutoML-enabled Meta Learning-based Intrusion Prevention Scheme designed specifically for ZTN within Carbon Intelligent IIoT. The proposed framework integrates AutoML and meta-learning to streamline data preprocessing and improve model adaptability in dynamic and evolving threat environments. To ensure transparency, an Integrated Gradient-based Explainable AI (XAI) mechanism is employed, offering insights into the impact of individual features on model predictions, thereby addressing concerns related to trust and accountability in industrial applications. Experimental evaluations demonstrate the framework’s effectiveness in enhancing intrusion prevention, bolstering security, and improving transparency for ZTN in carbon intelligent IIoT, providing a comprehensive solution to prevailing challenges. © 201. IEEE.

关键词： Network intrusion

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SCMM: Calibrating Cross-modal Representations for Text-Based Person Search

arXiv

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arXiv 2023年

作者： Liu, Jing Wei, Donglai Liu, Yang Zhang, Sipeng Yang, Tong Leung, Victor C.M. School of Information Science and Technology Fudan University Shanghai200433 China Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada Academy for Engineering & Technology Fudan University Shanghai200433 China Department of Computer Science University of Toronto TorontoONM5S 1A1 Canada MEGVII Technology Beijing100096 China Academy of Artificial Intelligence SMBU Guangdong Shenzhen518115 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

Text-Based Person Search (TBPS) is a crucial task in the Internet of Things (IoT) domain that enables accurate retrieval of target individuals from large-scale galleries with only given textual caption. For cross-modal TBPS tasks, it is critical to obtain well-distributed representation in the common embedding space to reduce the inter-modal gap. Furthermore, learning detailed image-text correspondences is essential to discriminate similar targets and enable fine-grained search. To address these challenges, we present a simple yet effective method named Sew Calibration and Masked Modeling (SCMM) that calibrates cross-modal representations by learning compact and well-aligned embeddings. SCMM introduces two novel losses for fine-grained cross-modal representations: Sew calibration loss that aligns image and text features based on textual caption quality, and Masked Caption Modeling (MCM) loss that establishes detailed relationships between textual and visual parts. This dual-pronged strategy enhances feature alignment and cross-modal correspondences, enabling accurate distinction of similar individuals while maintaining a streamlined dual-encoder architecture for real-time inference, which is essential for resource-limited sensors and IoT systems. Extensive experiments on three popular TBPS benchmarks demonstrate the superiority of SCMM, achieving 73.81., 64.25%, and 57.35% Rank-1.accuracy on CUHK-PEDES, ICFG-PEDES, and RSTPReID, respectively. Copyright © 2023, The Authors. All rights reserved.

关键词： Calibration

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End-to-End Delay Performance for Mobile Cyber-Physical Systems with Edge Computing 11

End-to-End Delay Performance for Mobile Cyber-Physical Syste...

引用

1.th International Conference on Networks, Communication and Computing, ICNCC 2022

作者： Wu, Yan Yang, Qinghai Qiao, Feiyu Kwak, K.S. Leung, C.M. Pan, Guangchuan State Key Laboratory of ISN School of Telecommunications Engineering Xidian University No.2 South Taibai Road Shaanxi Xi'an710071 China Communication Engineering Inha University #100 Inha-Ro Nam-gu Incheon22212 Korea Republic of College of Computer Science and Software Engineering Shenzhen University 518060 China The Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada Xidian University No.2 South Taibai Road Shaanxi Xi'an710071 China

ISBN: (纸本)9781450398039

In this letter, we investigate the end-to-end (E2E) delay performance for mobile cyber-physical systems (M-CPS) with edge computing. The traditional delay analysis is based on flow conservation law, whereas the flow is not conserved in the M-CPS with edge computing due to the existence of edge computing node and remote controller. To tackle this problem, we introduce two scaling factors to ensure the flow conservation, then the E2E delay bound of M-CPS with edge computing is derived via stochastic network calculus. In addition, to cater for the delay demand for M-CPS stability, the stable-aware delay bound is obtained. Numerical results show the gap between the analytic bound and simulation can be narrower as the average signal-to-noise (SNR) growing. © 2022 ACM.

关键词： Cyber Physical System

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Collaborative Learning-Based Scheduling for Kubernetes-Oriented Edge-Cloud Network

arXiv

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arXiv 2023年

作者： Shen, Shihao Han, Yiwen Wang, Xiaofei Wang, Shiqiang Leung, Victor C.M. The College of Intelligence and Computing Tianjin University Tianjin300350 China IBM T. J. Watson Research Center Yorktown HeightsNY10598 United States The College of Computer Science and Software Engineering Shenzhen University Shenzhen518052 China The Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

Kubernetes (k8s) has the potential to coordinate distributed edge resources and centralized cloud resources, but currently lacks a specialized scheduling framework for edge-cloud networks. Besides, the hierarchical distribution of heterogeneous resources makes the modeling and scheduling of k8soriented edge-cloud network particularly challenging. In this paper, we introduce KaiS, a learning-based scheduling framework for such edge-cloud network to improve the long-term throughput rate of request processing. First, we design a coordinated multiagent actor-critic algorithm to cater to decentralized request dispatch and dynamic dispatch spaces within the edge cluster. Second, for diverse system scales and structures, we use graph neural networks to embed system state information, and combine the embedding results with multiple policy networks to reduce the orchestration dimensionality by stepwise scheduling. Finally, we adopt a two-time-scale scheduling mechanism to harmonize request dispatch and service orchestration, and present the implementation design of deploying the above algorithms compatible with native k8s components. Experiments using real workload traces show that KaiS can successfully learn appropriate scheduling policies, irrespective of request arrival patterns and system scales. Moreover, KaiS can enhance the average system throughput rate by 1..9% while reducing scheduling cost by 38.4% compared to baselines. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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UAV Swarm-enabled Collaborative Secure Relay Communications with Time-domain Colluding Eavesdropper

arXiv

引用

arXiv 2023年

作者： Zhang, Chuang Sun, Geng Wu, Qingqing Li, Jiahui Liang, Shuang Niyato, Dusit Leung, Victor C.M. The College of Computer Science and Technology Jilin University Changchun130012 China The Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China Pillar of Engineering Systems and Design Singapore University of Technology and Design 487372 Singapore The School of Information Science and Technology Northeast Normal University Changchun130024 China The School of Computer Science and Engineering Nanyang Technological University 639798 Singapore The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

Unmanned aerial vehicles (UAVs) as aerial relays are practically appealing for assisting Internet of Things (IoT) network. In this work, we aim to utilize the UAV swarm to assist the secure communication between the micro base station (MBS) equipped with the planar array antenna (PAA) and the IoT terminal devices by collaborative beamforming (CB), so as to counteract the effects of collusive eavesdropping attacks in time-domain. Specifically, we formulate a UAV swarm-enabled secure relay multi-objective optimization problem (US2RMOP) for simultaneously maximizing the achievable sum rate of associated IoT terminal devices, minimizing the achievable sum rate of the eavesdropper and minimizing the energy consumption of UAV swarm, by jointly optimizing the excitation current weights of both MBS and UAV swarm, the selection of the UAV receiver, the position of UAVs and user association order of IoT terminal devices. Furthermore, the formulated US2RMOP is proved to be a non-convex, NP-hard and large-scale optimization problem. Therefore, we propose an improved multi-objective grasshopper algorithm (IMOGOA) with some specific designs to address the problem. Simulation results exhibit the effectiveness of the proposed UAV swarm-enabled collaborative secure relay strategy and demonstrate the superiority of IMOGOA. Copyright © 2023, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Joint Task Offloading and Resource Allocation in Aerial-Terrestrial UAV Networks with Edge and Fog Computing for Post-Disaster Rescue

arXiv

引用

arXiv 2023年

作者： Sun, Geng He, Long Sun, Zemin Wu, Qingqing Liang, Shuang Li, Jiahui Niyato, Dusit Leung, Victor C.M. The College of Computer Science and Technology Jilin University Changchun130012 China The Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China The School of Information Science and Technology Northeast Normal University Changchun130024 China Pillar of Engineering Systems and Design Singapore University of Technology and Design 487372 Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

Unmanned aerial vehicles (UAVs) play an increasingly important role in assisting fast-response post-disaster rescue due to their fast deployment, flexible mobility, and low cost. However, UAVs face the challenges of limited battery capacity and computing resources, which could shorten the expected flight endurance of UAVs and increase the rescue response delay during performing mission-critical tasks. To address this challenge, we first present a three-layer post-disaster rescue computing architecture by leveraging the aerial-terrestrial edge capabilities of mobile edge computing (MEC) and vehicle fog computing (VFC), which consists of a vehicle fog layer, a UAV client layer, and a UAV edge layer. Moreover, we formulate a joint task offloading and resource allocation optimization problem (JTRAOP) with the aim of maximizing the time-average system utility. Since the formulated JTRAOP is proved to be NP-hard, we propose an MEC-VFC-aided task offloading and resource allocation (MVTORA) approach, which consists of a game theoretic algorithm for task offloading decision, a convex optimization-based algorithm for MEC resource allocation, and an evolutionary computation-based hybrid algorithm for VFC resource allocation. Simulation results validate that the proposed approach can achieve superior system performance compared to the other benchmark schemes, especially under heavy system workloads. Copyright © 2023, The Authors. All rights reserved.

关键词： Fog computing

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Number plate recognition smart parking management system using IoT

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Measurement: Sensors 2025年 37卷

作者： Ditta, Allah Ahmed, Muhammad Maroof Mazhar, Tehseen Shahzad, Tariq Alahmed, Yazan Hamam, Habib Department of Information Sciences Division of Science and Technology University of Education Lahore54000 Pakistan School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Science and Information Technology School Education Department Government of Punjab Layyah31200 Pakistan Department of Computer Science COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Software Engineering Al Ain University Al Ain University Abu Dhabi United Arab Emirates Faculty of Engineering University of de Moncton MonctonNBE1A3E9 Canada School of Electrical Engineering University of Johannesburg Johannesburg2006 South Africa Av. Grandes Ecoles LibrevilleBP 1989 Gabon Bridges for Academic Excellence - Spectrum Tunis Tunisia

This study aims to address the urban vehicle parking issues by proposing a solution using Automatic Number Plate Recognition (ANPR) through image processing and a sensor-based hardware system. Integrating these technologies forms a Smart Parking Management System (SPMS) to automate parking processes and enhance the parking experience. The study aims to create an efficient system that eliminates manual vehicle registration and optimizes space utilization. ANPR and IoT-based sensors help users identify the available slots and pay only for the actual parking duration, which will help to minimize the fixed billing rates. The proposed ANPR system processes vehicle number plates at entry, ensuring seamless identification and eliminating manual registration. IoT sensors monitor real-time slot occupancy, transmitting data to a web admin panel. This panel provides insights such as entry and exit times, total parking duration, and billing costs, facilitating efficient management and remote monitoring. The ANPR-based SPMS reduces reliance on manual processes, streamlining entry procedures. By dynamically assessing slot availability through IoT sensors, users can locate unoccupied spaces quickly, which enhances user convenience. The web admin panel allows administrators to monitor the system remotely, ensuring smooth operations and maintaining accurate records. This study introduces a comprehensive solution to urban parking challenges by integrating ANPR and IoT technologies. The SPMS improves efficiency, reduces human resource needs, and enhances user experience with flexible billing based on actual duration. The combination of hardware and software provides a foundation for effective urban parking management. © 2024 The Authors

关键词： Digital storage

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Reconfigurable Intelligence Surface Assisted Opportunistic Multiple Access in UAV-IoT Networks

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IEEE Internet of Things Journal 2025年

作者： Zhang, Xi-Ran Sun, Wen-Bin Zhang, Zhaolin Wang, Ling Gao, Ang Cheng, Nan Meng, Wei-Xiao Leung, Victor C. M. Northwestern Polytechnical University School of Electronics And Information Xi'an China Xidian University State Key Laboratory of ISN Xi'an710071 China Xidian University School of Cyber Engineering Xi'an710071 China Harbin Institute of Technology Communication Research Center Harbin150001 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T1Z4 Canada

Due to the advantages of flexible deployment and strong environmental adaptability of an unmanned aerial vehicle (UAV), UAVs serve as aerial base stations (BSs) to meet Quality of Services (QoSs) of ground users in internet of things (IoT) networks. NOMA (Non-Orthogonal Multiple Access) is a potential technique in wireless communications area, which can significantly improve sum spectrum efficiency (SE) of systems. To avoid the limitation of perfect CSI, opportunistic beamforming (OBF) is proposed, where a set of randomly generated weights is used to preprocess transmitted signals. Due to multiuser diversity gain introduced by OBF, OBF-NOMA systems can achieve approximate sum SE to conventional NOMA systems. Additionally, reconfigurable intelligent surfaces (RISs) are involved to overcome obstruction and obtain further improvements of SE. Therefore, this paper proposes a RIS-aid OBF-NOMA system in UAV-IoT networks, where random weights and opportunistic phase matrix are respectively applied in a UAV and RIS. Statistical characteristics of equivalent channels are derived in Nakagami-m (m≥1. fading channels. Theoretical asymptotic analyses of SE and bit error rate (BER) are then presented. Furthermore, a non-convex optimization problem is formulated to maximize SE. To obtain the optimal solution, we divide the problem into two sub-optimization problems and apply a joint iterative algorithm. Numerical results show that the proposed method achieves a satisfactory SE without complex channel estimation and perfect CSI. © 201. IEEE.

关键词： Micro air vehicle (MAV)

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