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Fairness-Aware Budgeted Edge Server Placement for Connected Autonomous Vehicles

IEEE Transactions on Mobile computing 2025年第06期24卷 4762-4776页

作者： Wu, Jintao Xu, Xiaolong Cui, Guangming Zhang, Yiwen Qi, Lianyong Dou, Wanchun Cai, Zhipeng Nanjing University of Information Science & Technology Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services School of Software Nanjing210044 China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Changchun130012 China Nanjing University of Information Science & Technology Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services School of Software China Nanjing University State Key Laboratory for Novel Software Technology China Anhui University School of Computer Science and Technology Anhui230031 China College of Computer Science and Technology Qingdao266580 China Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China Georgia State University Department of Computer Science United States

Mobile edge computing (MEC) considerably enhances the capabilities and performance of connected autonomous vehicles (CAVs) by deploying edge servers (ESs) on roadside units (RSUs) near CAVs, thereby ensuring low-latency services. Given the constrained and costly nature of ES resources (computing, storage, and bandwidth), equitable ES utilization is critical for CAV operations. However, fairness considerations are often overlooked in current budgeted edge server placement (ESP) strategies, potentially worsening resource imbalances and compromising user experience. This paper investigates the fairness-aware budgeted edge server placement (FESP) problem within RSUs, proving its NP-hardness. To address FESP, we first propose FESP-O, an integer programming-based optimal approach for small-scale problems, followed by FESP-APX, an approximation approach for large-scale scenarios that provides near-optimal solutions. We analyze the time complexity and approximation ratio of our proposed algorithms and validate their efficacy through experiments on real-world datasets. Extensive experimental results demonstrate significant performance improvements over baseline and state-of-the-art methods, indicating practical suitability and efficiency. © 2002-2012 IEEE.

关键词： Integer linear programming

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HEAT: Efficient Vision Transformer Accelerator with Hybrid-Precision Quantization

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IEEE Transactions on Circuits and Systems II: Express Briefs 2025年第5期72卷 758-762页

作者： Zhao, Pan Xue, Donghui Wu, Licheng Chang, Liang Tan, Haining Han, Yinhe Peng, Shouzhong Zhou, Jun University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Chinese Academy of Sciences Institute of Computing Technology Beijing100190 China Beihang University Anhui Province Key Laboratory of Spintronic Chip Research and Manufacturing Beijing100191 China

Quantization is an important technique for the acceleration of transformer-based neural networks. Prior related works mainly consider quantization from the algorithm level. Their hardware implementation is inefficient. In this work, we propose an efficient vision transformer accelerator with retraining-free and finetuning-free hybrid-precision quantization. At the algorithm level, the features and weights are divided into two parts: normal values and outlier values. These two parts are quantized with different bit widths and scaling factors. We use matrix transformation and group-wise quantization policy to improve hardware utilization. At the hardware level, we propose a two-stage FIFO group structure and a hierarchical interleaving data flow to further improve the utilization of the PE array. As a result, the input and weight matrices are quantized to 5.71 bits on average with 0.526% accuracy loss on Swin-T. The accelerator achieves a frame rate of 118.9 FPS and an energy efficiency of 43.58 GOPS/W on the ZCU102 FPGA board, better than state-of-the-art works. © 2025 IEEE.

关键词： Particle accelerators

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Symbol-Level Physical Layer Security Design in Space-Air-Ground Integrated Networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Xia, Guiyang Shi, Qianmei Hu, Xianxin Zhou, Xiaobo Shu, Feng School of Information and Artificial Intelligence Anhui Provincial Engineering Research Center for Agricultural Information Perception and Intelligent Computing China Anhui Agricultural University Anhui Province Key Laboratory of Smart Agricultural Technology and Equipment Hefei230036 China Hainan University School of Information and Communication Engineering Haikou570228 China

In the down-link secure space-air-ground integrated network (SAGIN) with practical discrete modulation symbols, we consider the joint design of the power of satellite and unmanned aerial vehicle (UAV) swarm beamforming to combat a formidable eavesdropper intercepting both the satellite-to-UAV and UAV-to-ground communication links. Given the challenge posed by the discrete symbols-based joint optimization problem, the security mechanism for SAGIN is developed through alternating optimization. Specifically, the optimal power of satellite is first determined with a fixed UAV beamformer. Based on this power, we propose a pair of solution approaches: convex optimization-based iteration (CObI) and heuristics-based semi-analytic iteration (HbSAI). The former focuses on achieving high performance while the latter emphasizes efficiency. The effectiveness of both CObI and HbSAI methods is validated through simulation results, demonstrating their ability to enhance the achievable secrecy rate. © 2025 IEEE.

关键词： Convex optimization

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Compromising Rechargeable Sensor Networks in Marine Environment

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IEEE Transactions on Mobile computing 2025年

作者： Wang, Qiwei Lin, Chi Dai, Haipeng Obaidat, Mohammad S. Hsiao, Kuei-Fang Fan, Xin Dalian University of Technology School of Software Technology & DUT-RU International School of Information Science and Engineering China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian116620 China Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China SRM University Department of Computational Intelligence School of Computing SRM Nagar TN Kattankulathur603203 India The Amity University School of Engineering UP Noida201301 India The King Abdullah II School of Information Technology The University of Jordan Amman11942 Jordan

Marine Wireless Rechargeable Sensor Networks (MWRSNs), enhanced by recent Wireless Power Transfer (WPT) technology, present a significant advancement in extending network life. Traditional methods improve network performance through algorithm optimization, but neglect charging security, exposing networks to potential attacks. This paper addresses this problem from an adversarial view and develops a novel attack for MWRSN through Denying of Charge (DoC) to maximize network destructiveness. We start by establishing a generalized on-demand charging model, essential for developing DoC tactics. Subsequently, we unveil the Collaborative DoC (CoDoC) algorithm, capable of manipulating and falsifying charging requests. Central to CoDoC is the Request Prediction Method (RPM), which forecasts the initiation of charging requests and facilitates rapid request surges to enhance the attack's efficacy. CoDoC is able to disguise the presence of the attack, which is able to escape from being detected by the base station. Theoretical analyses are provided to explore the features of the proposed scheme. To demonstrate the outperformed features of the proposed schemes, extensive simulations and test-bed experiments are conducted. Our analysis and extensive simulations demonstrate that CoDoC increases sensor node failures by 20% to 142% compared to traditional methods, highlighting its effectiveness in marine environments. © 2002-2012 IEEE.

关键词： Charging stations

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A Power Control Scheme for Target Localization in WSN

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电子学报(英文版) 2008年第3期17卷 443-448页

作者： GU Yu ZHAO Baohua Department of Computer Science University of Science and Technology of China Anhui Province Key Laboratory of Software in Computing and Communication Hefei 230027 China

In this paper, we focus on the target tracking problem in sensor networks and propose an Powersaving target localization scheme (PSTL) based on a conjecture model that reflects the moving pattern of a target, and also a corresponding two-step communication protocol between Base station (BS) and sensors. BS executes a query mechanism to determine which sensors should be used for detailed information according to a limited amount of data received from sensors. This scheme reduces both energy consumption and communication bandwidth requirement, prolongs the lifetime of the wireless sensor networks. Simulation results indicate that it can achieve a high accuracy while saving a large amount of energy.

关键词：无线传感器网络功率控制目标定位推测模型

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Minimum-Energy Broadcast Trees in All-Wireless Networks

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电子学报(英文版) 2008年第2期17卷 227-230页

作者： HU Tian ZHAO Baohua Department of Computer Science University of Science and Technology of China Anhui Province Key Laboratory of Software in Computing and Communication Hefei 230027 China

This paper focuses on the problem of constructing the minimum-energy broadcast trees in all-wireless networks. We proposed a greedy algorithm called Greedy maximum-branch replacement algorithm (GMBR) to decrease the total power of the broadcast tree further. This algorithm can be developed to a distributed one easily. Compared with the previous algorithms, GMBR was proved to have better performance, especially when the propagation loss exponent and the network size become larger.

关键词：最小能量广播天线网络技术性能

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Classifying and clustering in negative databases

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Frontiers of Computer Science 2013年第6期7卷 864-874页

作者： Ran LIU Wenjian LUO Lihua YUE School of Computer Science and Technology University of Science and Technology of China Hefei 230027 China Anhui Province Key Laboratory of Software Engineering in Computing and Communication University of Science and Technology of China Hefei 230027 China

Recently, negative databases （NDBs） are proposed for privacy protection. Similar to the traditional databases, some basic operations could be conducted over the NDBs, such as select, intersection, update, delete and so on. However, both classifying and clustering in negative databases have not yet been studied. Therefore, two algorithms, i.e., a k nearest neighbor （kNN） classification algorithm and a k-means clustering algorithm in NDBs, are proposed in this paper, respectively. The core of these two algorithms is a novel method for estimating the Hamming distance between a binary string and an NDB. Experimental results demonstrate that classifying and clustering in NDBs are promising.

关键词： negative databases classification clustering k nearest neighbor k-means hamming distance

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Secure Two-Party Point-Circle Inclusion Problem

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Journal of Computer Science & Technology 2007年第1期22卷 88-91页

作者：罗永龙黄刘生仲红 Department of Computer Science University of Science and Technology of China Hefei 230027 China Department of Computer Science Anhui Normal University Wuhu 241000 China Anhui Province Key laboratory of Software in Computing and Communication Hefei 230027 China

Privacy-preserving computational geometry is a special secure multi-party computation and has many applications. Previous protocols for determining whether a point is inside a circle are not secure enough. We present a two-round protocol for computing the distance between two private points and develop a more efficient protocol for the point-circle inclusion problem based on the distance protocol. In comparison with previous solutions, our protocol not only is more secure but also reduces the number of communication rounds and the number of modular multiplications significantly.

关键词： secure multi-party computation computational geometry homomorphic encryption scheme private comparison

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Clustering time-evolving data using an efficient differential evolution 6th

Clustering time-evolving data using an efficient differentia...

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6th International Conference on Swarm Intelligence, ICSI 2015 held in conjunction with the 2nd BRICS Congress on Computational Intelligence, CCI 2015

作者： Chen, Gang Luo, Wenjian Anhui Province key Laboratory of Software Engineering in Computing and Communication School of Computer Science and Technology University of Science and Technology of China HefeiAnhui China

ISBN: (纸本)9783319204659

The previous evolutionary clustering methods for time-evolving data usually adopt the temporal smoothness framework, which controls the balance between temporal noise and true concept drift of clusters. They, however, have two major drawbacks: (1) assuming a fixed number of clusters over time;(2) the penalty term may reduce the accuracy of the clustering. In this paper, a Multimodal Evolutionary Clustering (MEC) based on Differential Evolution (DE) is presented to cope with these problems. With an existing chromosome representation of the ACDE, the MEC automatically determines the cluster number at each time step. Moreover, instead of adopting the temporal smoothness framework, we try to deal with the problem from view of the multimodal optimization. That is, the species-based DE (SDE) for multimodal optimization is adopted in the MEC. Thus the MEC is a hybrid of the ACDE and the SDE, and designed for time-evolving data clustering. Experimental evaluation demonstrates the MEC achieves good results. © Springer International Publishing Switzerland 2015.

关键词： Cluster analysis

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A hybrid particle swarm optimization for high-dimensional dynamic optimization 11th

A hybrid particle swarm optimization for high-dimensional dy...

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11th International Conference on Simulated Evolution and Learning, SEAL 2017

作者： Luo, Wenjian Yang, Bin Bu, Chenyang Lin, Xin Anhui Province Key Laboratory of Software Engineering in Computing and Communication School of Computer Science and Technology University of Science and Technology of China HefeiAnhui230027 China

ISBN: (纸本)9783319687582

High-Dimensional Dynamic Optimization Problems (HDDOPs) commonly exist in real-world applications. In evolutionary computation field, most of existing benchmark problems, which could simulate HDDOPs, are non-separable. Thus, we give a novel benchmark problem, called high-dimensional moving peaks benchmark to simulate separable, partially separable, and non-separable problems. Moreover, a hybrid Particle Swarm Optimization algorithm based on Grouping, Clustering and Memory strategies, i.e. GCM-PSO, is proposed to solve HDDOPs. In GCM-PSO, a differential grouping method is used to decompose a HDDOP into a number of sub-problems based on variable interactions firstly. Then each sub-problem is solved by a species-based particle swarm optimization, where the nearest better clustering is adopted as the clustering method. In addition, a memory strategy is also adopted in GCM-PSO. Experimental results show that GCM-PSO performs better than the compared algorithms in most cases. © Springer International Publishing AG 2017.

关键词： Particle swarm optimization (PSO)

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