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computation-efficiency-oriented resource optimization in NOMA-assisted multi-UAV-enabled MEC networks

INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2024年第7期37卷 e5729-e5729页

作者： Liu, Qingde Tian, Jie Zhou, Xiaotian Yuan, Dongfeng Zhou, You Shandong Normal Univ Sch Informat Sci & Engn Jinan 250358 Peoples R China Shandong Univ Shandong Key Lab Wireless Commun Technol Jinan Peoples R China Qilu Inst Technol Sch Comp & Informat Engn Jinan Peoples R China

Unmanned aerial vehicles (UAVs) can serve as aerial mobile edge computing (MEC) servers to provide computing services to Internet of Things smart devices (ISDs) with insufficient computing capacity on the ground. However, how to provide energy-efficient computing services by designing appropriate resource optimization strategy is still a challenge issue in UAV-enabled MEC networks. To this end, this paper proposes a computation efficiency (CE)-oriented partial offloading framework for UAV-enabled MEC networks, where the ISDs' computation bits and energy consumption are taken into account simultaneously. Specifically, we firstly divide the ISDs into multiple clusters and determine the deployment of UAVs by k-means method. Meanwhile, ISDs occupying the same subchannel in the same cluster can offload data through non-orthogonal multiple access (NOMA) technology. Then, the problem of maximizing the CE of the system is formulated by optimizing subchannel allocation, transmit power and computation resources of ISDs. To solve it, we propose a staged optimization approach by using matching theory, Dinkelbach and sequential convex programming (SCP) methods. Numerical results demonstrate the proposed scheme can achieve higher CE compared with other baseline schemes.

关键词： computation efficiency mobile edge computing non-orthogonal multiple access unmanned aerial vehicles

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Joint Resource Allocation and 3D-Position Optimization for UAV-Assisted MEC Network With NOMA

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2025年第3期12卷 1440-1456页

作者： Yu, Xiangbin Zhang, Xinyi Rui, Yun Dang, Xiaoyu Jia, Guoqing Guizani, Mohsen Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 210016 Peoples R China East China Normal Univ Sch Commun & Elect Engn Shanghai 200050 Peoples R China Hangzhou Dianzi Univ Space Informat Res Inst Hangzhou 310005 Peoples R China Qinghai Minzu Univ Sch Intelligence Sci & Engn Xining 810007 Peoples R China Mohamed Bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates

In this article, the computation efficiency (CE) optimization of unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) network with non-orthogonal multiple access (NOMA) is addressed in the presence of imperfect successive interference cancelation. Specifically, joint design schemes of resource allocation (RA) and three-dimensional (3D) position are developed to improve the CE while ensuring the fairness of groundusers. In particular, we apply the max-min fairness criterion and optimize the beamforming (BF), power allocation (PA), local CPU frequency and UAV position jointly via two-step optimization method. Namely, we first optimize the 3D position by using an efficient iteration algorithm based on the alternating optimization and concave-convex procedure methods. Then, the joint design of BF, PA and CPU frequency is solved by an efficient iteration algorithm based on the block coordinate descent, sub-gradient methods and convex optimization tool. Additionally, a lower-complexity suboptimal PA scheme with closed-form expression for each iteration is developed. Simulation results indicate that the proposed two design schemes of joint RA and position are effective.

关键词： Autonomous aerial vehicles Optimization NOMA Three-dimensional displays Resource management Energy consumption Trajectory Symbols Interference Delays Mobile edge computing unmanned aerial vehicle resource allocation 3D position optimization computation efficiency

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Moss: Proxy Model-based Full-Weight Aggregation in Federated Learning with Heterogeneous Models

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PROCEEDINGS OF THE ACM ON INTERACTIVE MOBILE WEARABLE AND UBIQUITOUS TECHNOLOGIES-IMWUT 2025年第1期9卷 1-31页

作者： Cai, Yifeng Zhang, Ziqi Li, Ding Guo, Yao Chen, Xiangqun Peking Univ Sch Comp Sci MOE Key Lab HCST PKU Beijing Peoples R China Univ Illinois Dept Comp Sci Urbana IL USA

Modern Federated Learning (FL) has become increasingly essential for handling highly heterogeneous mobile devices. Current approaches adopt a partial model aggregation paradigm that leads to sub-optimal model accuracy and higher training overhead. In this paper, we challenge the prevailing notion of partial-model aggregation and propose a novel "full-weight aggregation" method named Moss, which aggregates all weights within heterogeneous models to preserve comprehensive knowledge. Evaluation across various applications demonstrates that Moss significantly accelerates training, reduces on- device training time and energy consumption, enhances accuracy, and minimizes network bandwidth utilization when compared to state-of-the-art baselines.

关键词： federated learning heterogeneous models computation efficiency

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Optimal Device Selection and Resource Allocation in Federated Learning

Optimal Device Selection and Resource Allocation in Federate...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Kushwaha, Deepali Hegde, Rajesh M. Department of Electrical Engineering Indian Institute of Technology Kanpur Kanpur India

ISBN: (纸本)9798350368741

With the advent of federated learning, the development of privacy-preserving learning models has assumed significance in several applications. However, challenges arise due to the participation of a massive number of edge devices and limited resources in a network. In this context, this paper addresses a joint device selection and resource allocation problem to improve the performance of federated learning in resource-constrained edge networks. The proposed method enhances network performance while optimally allocating limited network resources among selected devices. The optimal device selection and resource allocation problem is formulated as maximizing the number of data samples under latency, energy, and power constraints. A computationally efficient solution to this problem is proposed to ensure an optimal solution in terms of device selection and network resources. Comparison with existing methods demonstrates its ability to find optimal solutions while significantly reducing computation time by 82%. © 2025 IEEE.

关键词： computation efficiency device selection Federated learning resource allocation

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Hybrid Renewable Energy Systems-A Review of Optimization Approaches and Future Challenges

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APPLIED SCIENCES-BASEL 2025年第4期15卷 1744-1744页

作者： Giedraityte, Akvile Rimkevicius, Sigitas Marciukaitis, Mantas Radziukynas, Virginijus Bakas, Rimantas Lithuanian Energy Inst Breslaujos St 3 LT-44403 Kaunas Lithuania

The growing need for sustainable energy solutions has propelled the development of Hybrid Renewable Energy Systems (HRESs), which integrate diverse renewable sources like solar, wind, biomass, geothermal, hydropower and tidal. This review paper focuses on balancing economic, environmental, social and technical criteria to enhance system performance and resilience. Using comprehensive methodologies, the review examines state-of-the-art algorithms such as Multi-Objective Particle Swarm Optimization (MOPSO) and Non-Dominated Sorting Genetic Algorithm II (NSGA-II), alongside Crow Search Algorithm (CSA), Grey Wolf Optimizer (GWO), Levy Flight-Salp Swarm Algorithm (LF-SSA), Mixed-Integer Linear Programming (MILP) and tools like HOMER Pro 3.12-3.16 and MATLAB 9.1-9.13, which have been instrumental in optimizing HRESs. Key findings highlight the growing role of advanced, multi-energy storage technologies in stabilizing HRESs and addressing the intermittency of renewable sources. Moreover, the integration of metaheuristic algorithms with machine learning has enabled dynamic adaptability and predictive optimization, paving the way for real-time energy management. HRES configurations for cost-effectiveness, environmental sustainability, and operational reliability while also emphasizing the transformative potential of emerging technologies such as quantum computing are underscored. This review provides critical insights into the evolving landscape of HRES optimization, offering actionable recommendations for future research and practical applications in achieving global energy sustainability goals.

关键词： hybrid renewable energy systems optimization techniques optimization criteria computation efficiency sustainable energy innovations

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Automatic cassava disease recognition using object segmentation and progressive learning

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PEERJ COMPUTER SCIENCE 2025年 11卷 e2721-e2721页

作者： Che, Chang Xue, Nian Li, Zhen Zhao, Yilin Huang, Xin Harbin Univ Sch Civil Engn Elect & Informat Engn Harbin Heilongjiang Peoples R China Heilongjiang Urban Water Qual Monitoring Co Ltd Harbin Heilongjiang Peoples R China Shandong Univ Technol Sch Comp Sci & Technol Zibo Shandong Peoples R China NYU Tandon Sch Engn Dept Comp Sci & Engn Brooklyn NY USA Taiyuan Univ Technol Taiyuan Shanxi Peoples R China

Cassava is a vital crop for millions of farmers worldwide, but its cultivation is threatened by various destructive diseases. Current detection methods for cassava diseases are costly, time-consuming, and often limited to controlled environments, making them unsuitable for large-scale agricultural use. This study aims to develop a deep learning framework that enables early, accurate, and efficient detection of cassava diseases in real-world conditions. We propose a self-supervised object segmentation technique, combined with a progressive learning algorithm (PLA) that incorporates both triplet loss and classification loss to learn robust feature embeddings. Our approach achieves superior performance on the Cassava Leaf Disease Classification (CLDC) dataset from the Kaggle competition, with an accuracy of 91.43%, outperforming all other participants. The proposed method offers a practical and efficient solution for cassava disease detection, demonstrating the potential for large-scale, real-world application in agriculture.

关键词： Cassava disease recognition Progressive learning Object segmentation Deep learning computation efficiency

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Novel Planning Approach for Fast-charging Station in Integrated System

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CSEE Journal of Power and Energy Systems 2023年第5期9卷 1832-1844页

作者： Xiaoying Shi Yinliang Xu Qinglai Guo Yujie Sheng Hongbin Sun Feng Chen Yang Zhang Tsinghua-Berkeley Shenzhen Institute Tsinghua Shenzhen International Graduate SchoolTsinghua UniversityShenzhen 518055China the Department of Electrical Engineering State Key Laboratory of Power SystemsTsinghua UniversityBeijing 100084China the State Grid Zhejiang Electric Vehicle Service Co.Ltd. Hangzhou 310000China the State Grid Zhejiang Electric Power Co.Ltd. Hangzhou 310000China

A promising way to boost popularity of electric vehicles(EVs)is to properly layout fast charging stations(FCSs)by jointly considering interactions among EV drivers,power systems and traffic network *** paper proposes a novel sensitivity analysis-based FCS planning approach,which considers the voltage sensitivity of each sub-network in the distribution network and charging service availability for EV drivers in the transportation *** addition,energy storage systems are optimally installed to provide voltage regulation service and enhance charging *** tests conducted on two distribution network and transportation network coupled systems validate the efficacy of the proposed ***,comparison studies demonstrate the proposed approach outperforms a Voronoi graph and particle swarm optimization combined planning approach in terms of much higher computation efficiency.

关键词： computation efficiency fast charging station integrated system siting and sizing voltage sensitivity

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Fast solution to the free return orbit's reachable domain of the manned lunar mission by deep neural network

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Journal of Systems Engineering and Electronics 2024年第2期35卷 495-508页

作者： YANG Luyi LI Haiyang ZHANG Jin ZHU Yuehe College of Aerospace Science and Engineering National University of Defense TechnologyChangsha 410073China China Astronauts Research and Training Center Beijing 100094China Hunan Key Laboratory of Intelligent Planning and Simulation for Aerospace Missions Changsha 410073China

It is important to calculate the reachable domain(RD)of the manned lunar mission to evaluate whether a lunar landing site could be reached by the spacecraft. In this paper, the RD of free return orbits is quickly evaluated and calculated via the classification and regression neural networks. An efficient databasegeneration method is developed for obtaining eight types of free return orbits and then the RD is defined by the orbit’s inclination and right ascension of ascending node(RAAN) at the perilune. A classify neural network and a regression network are trained respectively. The former is built for classifying the type of the RD, and the latter is built for calculating the inclination and RAAN of the RD. The simulation results show that two neural networks are well trained. The classification model has an accuracy of more than 99% and the mean square error of the regression model is less than 0.01°on the test set. Moreover, a serial strategy is proposed to combine the two surrogate models and a recognition tool is built to evaluate whether a lunar site could be reached. The proposed deep learning method shows the superiority in computation efficiency compared with the traditional double two-body model.

关键词： manned lunar mission free return orbit reachable domain(RD) deep neural network computation efficiency

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Highly efficient contact detection strategy of 3D discontinuous deformation analysis in continuous-discontinuous simulation

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Journal of Rock Mechanics and Geotechnical Engineering 2025年

作者： Jingyu Kang Xiaodong Fu Qian Sheng Xing Wang Haifeng Ding Xuehan Zhao Tian Xi Shangwei Jiang State Key Laboratory of Geomechanics and Geotechnical Engineering Safety Institute of Rock and Soil Mechanics Chinese Academy of Sciences Wuhan 430071 China School of Engineering Science University of Chinese Academy of Sciences Beijing 100049 China

Contact detection is the most time-consuming stage in 3D discontinuous deformation analysis (3D-DDA) computation. Improving the efficiency of 3D-DDA is beneficial for its application in large-scale computing. In this study, aiming at the continuous-discontinuous simulation of 3D-DDA, a highly efficient contact detection strategy is proposed. Firstly, the global direct search (GDS) method is integrated into the 3D-DDA framework to address intricate contact scenarios. Subsequently, all geometric elements, including blocks, faces, edges, and vertices are divided into searchable and unsearchable parts. Contacts between unsearchable geometric elements would be directly inherited, while only searchable geometric elements are involved in contact detection. This strategy significantly reduces the number of geometric elements involved in contact detection, thereby markedly enhancing the computation efficiency. Several examples are adopted to demonstrate the accuracy and efficiency of the improved 3D-DDA method. The rock pillars with different mesh sizes are simulated under self-weight. The deformation and stress are consistent with the analytical results, and the smaller the mesh size, the higher the accuracy. The maximum speedup ratio is 38.46 for this case. Furthermore, the Brazilian splitting test on the discs with different flaws is conducted. The results show that the failure pattern of the samples is consistent with the results obtained by other methods and experiments, and the maximum speedup ratio is 266.73. Finally, a large-scale impact test is performed, and approximately 3.2 times enhanced efficiency is obtained. The proposed contact detection strategy significantly improves efficiency when the rock has not completely failed, which is more suitable for continuous-discontinuous simulation.

关键词： 3D discontinuous deformation analysis Contact detection computation efficiency Continuous-discontinuous simulation Fracture

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FLrce: Resource-Efficient Federated Learning With Early-Stopping Strategy

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第12期23卷 14514-14529页

作者： Niu, Ziru Dong, Hai Qin, A. K. Gu, Tao RMIT Univ Sch Comp Technol Melbourne Vic 3000 Australia Macquarie Univ Dept Comp Sydney NSW 2109 Australia

Federated Learning (FL) achieves great popularity in the Internet of Things (IoT) as a powerful interface to offer intelligent services to customers while maintaining data privacy. Under the orchestration of a server, edge devices (also called clients in FL) collaboratively train a global deep-learning model without sharing any local data. Nevertheless, the unequal training contributions among clients have made FL vulnerable, as clients with heavily biased datasets can easily compromise FL by sending malicious or heavily biased parameter updates. Furthermore, the resource shortage issue of the network also becomes a bottleneck. Due to overwhelming computation overheads generated by training deep-learning models on edge devices, and significant communication overheads for transmitting deep-learning models across the network, enormous amounts of resources are consumed in the FL process. This encompasses computation resources like energy and communication resources like bandwidth. To comprehensively address these challenges, in this paper, we present FLrce, an efficient FL framework with a relationship-based client selection and early-stopping strategy. FLrce accelerates the FL process by selecting clients with more significant effects, enabling the global model to converge to a high accuracy in fewer rounds. FLrce also leverages an early stopping mechanism that terminates FL in advance to save communication and computation resources. Experiment results show that, compared with existing efficient FL frameworks, FLrce improves the computation and communication efficiency by at least 30% and 43% respectively.

关键词： Federated learning computation efficiency communication efficiency early stopping client heterogeneity Internet of Things Federated learning computation efficiency communication efficiency early stopping client heterogeneity Internet of Things

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