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Task Offloading and Resource Allocation Based on Reinforcement Learning and Load Balancing in Vehicular networking

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IEEE Transactions on Consumer Electronics 2025年

作者： Tian, Shujuan Xiang, Shuhuan Zhou, Ziqi Dai, Haipeng Yu, Enze Deng, Qingyong Xiangtan University Key Laboratory of Hunan Province for Internet of Things and Information Security Hunan International Scientific and Technological Cooperation Base of Intelligent network School of Computer Science and School of Cyberspace Security Xiangtan411105 China Nanjing University School of Computer Science and Technology Nanjing211189 China Guangxi Normal University Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guilin541004 China Guangxi Normal University Guangxi Key Lab of Multi-source Information Mining and Security Guilin541004 China

Due to limited on-board resources and the mobility characteristics of vehicles in a multi-access edge computing (MEC)-based vehicular network, efficient task offloading and resource allocation schemes are essential for achieving low-latency and low-energy consumption applications in the Internet of Vehicles (IoV). The spatial distribution of vehicles, influenced by various factors, leads to significant workload variations across MEC servers. In this paper, we address task offloading and resource allocation as a joint optimization problem and propose a Load-Balancing Deep Deterministic Policy Gradient (LBDDPG) algorithm to achieve optimal results. The joint optimization problem is modeled as a Markov Decision Process (MDP), enabling the LBDDPG algorithm to systematically address the challenges of workload imbalance and resource inefficiency. The algorithm incorporates a load optimization strategy to balance workload distribution across MEC servers, mitigating disparities caused by uneven vehicle distributions. The reward function is designed to account for both energy consumption and delay, ensuring an optimal trade-off between these critical factors. To enhance training efficiency, a noise-based exploration strategy is employed, preventing ineffective exploration during the early stages. Additionally, constraints such as computational capacity and latency thresholds are embedded to ensure the algorithm's practical applicability. Experimental results demonstrate that the proposed LBDDPG algorithm achieves faster convergence and superior performance in terms of energy consumption and latency compared to other reinforcement learning algorithms. © 1975-2011 IEEE.

关键词： Markov processes

LAORA: Location-Aware Orientation Adjustment for MIMO Magnetic Wireless Charging System

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LAORA: Location-Aware Orientation Adjustment for MIMO Magnet...

IEEE Communications Society Conference on Sensor and Ad Hoc Communications and networks (SECON)

作者： Lingchang Kong Xinyu Wang Hao Zhou Fengyu Zhou Shenyao Jiang Peide Zhu Qi Song Zhi Liu LINKE Lab School of Computer Science and Technology University of Science and Technology of China CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China Department of Computer and Network Engineering The University of Electro-Communications Tokyo Japan School of Mathematics and Statistics Fujian Normal University Fuzhou China Department of Computer Science City University of Hong Kong Hong Kong China

ISBN: (数字)9798331519186

ISBN: (纸本)9798331519193

Wireless power transfer (WPT) systems using magnetic resonant coupling (MRC) have made significant progress recently, leading to various optimization methods in scenarios involving multiple-input multiple-output (MIMO) to improve charging performance. Adjusting the coil orientation of the power transmitter (TX) is a simple but effective method due to the directional nature of magnetic field distribution, but existing approaches often require unnecessary coil rotations. In this study, we introduce a Location-Aware Orientation Adjustment algorithm, known as LAORA, to address these inefficiencies. LAORA focuses on solving the problems of charging devices (RX) localization and location-based optimization. We begin by introducing the concept of Equivalent Impedance Distribution Image (EIDI) and transform the RX localization problem into a combined matching process involving EIDI. In addition, we establish a dynamic simulation framework to predict charging performance using RX-related knowledge, enabling us to obtain optimal TX orientations through reinforcement learning without needing to rotate on mechanical devices physically. We then implement the prototype and conduct extensive experiments. The results show that, compared to other existing orientation adjustment methods, LAORA achieves an average improvement of 186 % while reducing the mechanical rotations by 83.3 %.

关键词： Location awareness Coils Transmitters Magnetic resonance imaging Prototypes Reinforcement learning Wireless power transfer Transforms Sensors Impedance

A Contrastive Pre-training Approach to Learn Discriminative Autoencoder for Dense Retrieval

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arXiv 2022年

作者： Ma, Xinyu Zhang, Ruqing Guo, Jiafeng Fan, Yixing Cheng, Xueqi CAS Key Lab of Network Data Science and Technology ICT CAS University of Chinese Academy of Sciences Beijing China

Dense retrieval (DR) has shown promising results in information retrieval. In essence, DR requires high-quality text representations to support effective search in the representation space. Recent studies have shown that pre-trained autoencoder-based language models with a weak decoder can provide high-quality text representations, boosting the effectiveness and few-shot ability of DR models. However, even a weak autoregressive decoder has the bypass effect on the encoder. More importantly, the discriminative ability of learned representations may be limited since each token is treated equally important in decoding the input texts. To address the above problems, in this paper, we propose a contrastive pre-training approach to learn a discriminative autoencoder with a lightweight multi-layer perception (MLP) decoder. The basic idea is to generate word distributions of input text in a non-autoregressive fashion and pull the word distributions of two masked versions of one text close while pushing away from others. We theoretically show that our contrastive strategy can suppress the common words and highlight the representative words in decoding, leading to discriminative representations. Empirical results show that our method can significantly outperform the state-of-the-art autoencoder-based language models and other pre-trained models for dense retrieval. Copyright © 2022, The Authors. All rights reserved.

关键词： Decoding

Safety Guaranteed Power-Delivered-to-Load Maximization for Magnetic Wireless Power Transfer

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Safety Guaranteed Power-Delivered-to-Load Maximization for M...

IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Wangqiu Zhou Xinyu Wang Hao Zhou Shenyao Jiang Zhi Liu Yusheng Ji LINKE Lab School of Computer Science and Technology University of Science and Technology of China CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China Deqing Alpha Innovation Institute Huzhou China Department of Computer and Network Engineering The University of Electro-Communications Tokyo Japan Information Systems Architecture Science Research Division National Institute of Informatics Tokyo Japan

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Electromagnetic radiation (EMR) safety has always been a critical reason for hindering the development of magneticenabled wireless power transfer technology. People focus on the actual received energy at charging devices while paying attention to their health. Thus, we study this significant problem in this paper, and propose a universal safety guaranteed power-delivered-to-load (PDL) maximization scheme (called SafeGuard). Technically, we first utilize the off-the-shelf electromagnetic simulator to perform the EMR distribution analysis to ensure the universality of the method. Then, we innovatively introduce the concept of multiple importance sampling for achieving efficient EMR safety constraint extraction. Finally, we treat the proposed optimization problem as an optimal boundary point search problem from the perspective of space geometry, and devise a brand-new grid-based multi-constraint parallel processing algorithm to efficiently solve it. We implement a system prototype for SafeGuard, and conduct extensive experiments to evaluate it. The results indicate that our SafeGuard can obviously improve the achieved PDL by up to 1.75× compared with the state-of-the-art baseline while guaranteeing EMR safety. Furthermore, SafeGuard can accelerate the solution process by 29.12× compared with the traditional numerical method to satisfy the fast optimization requirement of wireless charging systems.

关键词： Performance evaluation Monte Carlo methods System performance Inductive charging Prototypes Wireless power transfer Parallel processing

Scattered or Connected? An Optimized Parameter-efficient Tuning Approach for Information Retrieval

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arXiv 2022年

作者： Ma, Xinyu Guo, Jiafeng Zhang, Ruqing Fan, Yixing Cheng, Xueqi CAS Key Lab of Network Data Science and Technology ICT CAS University of Chinese Academy of Sciences Beijing China

Pre-training and fine-tuning have achieved significant advances in the information retrieval (IR). A typical approach is to fine-tune all the parameters of large-scale pre-trained models (PTMs) on downstream tasks. As the model size and the number of tasks increase greatly, such approach becomes less feasible and prohibitively expensive. Recently, a variety of parameter-efficient tuning methods have been proposed in natural language processing (NLP) that only fine-tune a small number of parameters while still attaining strong performance. Yet there has been little effort to explore parameter-efficient tuning for IR. In this work, we first conduct a comprehensive study of existing parameter-efficient tuning methods at both the retrieval and re-ranking stages. Unlike the promising results in NLP, we find that these methods cannot achieve comparable performance to full fine-tuning at both stages when updating less than 1% of the original model parameters. More importantly, we find that the existing methods are just parameter-efficient, but not learning-efficient as they suffer from unstable training and slow convergence. To analyze the underlying reason, we conduct a theoretical analysis and show that the separation of the inserted trainable modules makes the optimization difficult. To alleviate this issue, we propose to inject additional modules alongside the pre-trained models (PTMs) to make the original scattered modules connected. In this way, all the trainable modules can form a pathway to smooth the loss surface and thus help stabilize the training process. Experiments at both retrieval and re-ranking stages show that our method outperforms existing parameter-efficient methods significantly, and achieves comparable or even better performance over full fine-tuning. Copyright © 2022, The Authors. All rights reserved.

关键词： Information retrieval

Polar R-CNN: End-to-End Lane Detection with Fewer Anchors

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arXiv 2024年

作者： Wang, Shengqi Liu, Junmin Cao, Xiangyong Song, Zengjie Sun, Kai School of Mathematics and Statistics Xi'an Jiaotong University Xi'An710049 China School of Computer Science and Technology Ministry of Education Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Xi'An710049 China

Lane detection is a critical and challenging task in autonomous driving, particularly in real-world scenarios where traffic lanes can be slender, lengthy, and often obscured by other vehicles, complicating detection efforts. Existing anchor-based methods typically rely on prior lane anchors to extract features and subsequently refine the location and shape of lanes. While these methods achieve high performance, manually setting prior anchors is cumbersome, and ensuring sufficient coverage across diverse datasets often requires a large amount of dense anchors. Furthermore, the use of Non-Maximum Suppression (NMS) to eliminate redundant predictions complicates real-world deployment and may underperform in complex scenarios. In this paper, we propose Polar R-CNN, an end-to-end anchor-based method for lane detection. By incorporating both local and global polar coordinate systems, Polar R-CNN facilitates flexible anchor proposals and significantly reduces the number of anchors required without compromising performance. Additionally, we introduce a triplet head with heuristic structure that supports NMS-free paradigm, enhancing deployment efficiency and performance in scenarios with dense lanes. Our method achieves competitive results on five popular lane detection benchmarks-Tusimple, CULane, LLAMAS, CurveLanes, and DL-Rail-while maintaining a lightweight design and straightforward structure. Our source code is available at https://***/ShqWW/PolarRCNN. © 2024, CC BY.

关键词： Graph neural networks

Onion: Dependency-Aware Reliable Communication Protocol for Magnetic MIMO WPT System 27

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Onion: Dependency-Aware Reliable Communication Protocol for ...

27th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2021

作者： Liang, Xiaolun Zhou, Hao Zhou, Wangqiu Cui, Xiang Liu, Zhi Li, Xiang-Yang LINKE Lab School of Computer Science and Technology University of Science and Technology of China China The CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China China The University of Electro-Communications Department of Computer and Network Engineering Tokyo182-8585 Japan

ISBN: (纸本)9781665408783

Magnetic wireless power transfer (WPT) has received widespread attention from both academia and industry, and magnetic resonance coupling (MRC) based WPT systems have a longer charging distance to support the scenarios with multiple transmitters (TXs) and multiple receivers (RXs). In such systems, an in-band reliable TX-RX communication protocol is essential to guarantee to charge performance. In this paper, we devise Onion, a dependency-aware in-band communication protocol for MIMO MRC-WPT systems. Technically, we extend the well-known EPCglobal C1G2 protocol in Radio Frequency Identification (RFID) fielded and make it suitable for mutual inductance based communication links in MRC-WPT systems. Furthermore, we craft an innovative onion-style layer-dependency based communication mechanism to utilize the positive impact of the relay phenomenon. We design and implement the Onion prototype and conduct extensive experiments to evaluate it. The experiment results demonstrate the effectiveness of the proposed protocol, which increases the communication success ratio by an average of 40% as compared to the dependency-unaware scheme. © 2021 IEEE.

关键词： Energy transfer

Across-Platform Detection of Malicious Cryptocurrency Transactions via Account Interaction Learning

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arXiv 2024年

作者： Che, Zheng Shen, Meng Tan, Zhehui Du, Hanbiao Zhu, Liehuang Wang, Wei Chen, Ting Zhao, Qinglin Xie, Yong The School of Computer Science Beijing Institute of Technology Beijing100081 China The School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China The Ministry of Education Key Lab for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an710049 China The School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China The School of Computer Science and Engineering Macau University of Science and Technology China The School of Computer and Information Science Qinghai Institute of Technology Xining810016 China

With the rapid evolution of Web3.0, cryptocurrency has become a cornerstone of decentralized finance. While these digital assets enable efficient and borderless financial transactions, their pseudonymous nature has also attracted malicious activities such as money laundering, fraud, and other financial crimes. Effective detection of malicious transactions is crucial to maintaining the security and integrity of the Web 3.0 ecosystem. Existing malicious transaction detection methods rely on large amounts of labeled data and suffer from low generalization. label-efficient and generalizable malicious transaction detection remains a challenging task. In this paper, we propose ShadowEyes, a novel malicious transaction detection method. Specifically, We first propose a generalized graph structure named TxGraph as a representation of malicious transaction, which captures the interaction features of each malicious account and its neighbors. Then we carefully design a data augmentation method tailored to simulate the evolution of malicious transactions to generate positive pairs. To alleviate account label scarcity, we further design a graph contrastive mechanism, which enables ShadowEyes to learn discriminative features effectively from unlabeled data, thereby enhancing its detection capabilities in real-world scenarios. We conduct extensive experiments using public datasets to evaluate the performance of ShadowEyes. The results demonstrate that it outperforms state-of-the-art (SOTA) methods in four typical scenarios. Specifically, in the zero-shot learning scenario, it can achieve an F1 score of 76.98% for identifying gambling transactions, surpassing the SOTA method by 12.05%. In the scenario of across-platform malicious transaction detection, ShadowEyes maintains an F1 score of around 90%, which is 10% higher than the SOTA method. Copyright © 2024, The Authors. All rights reserved.

关键词： Decentralized finance

Time-Frequency Analysis-Based Transient Harmonic Feature Extraction for Load Monitoring

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Time-Frequency Analysis-Based Transient Harmonic Feature Ext...

International Conference on Parallel and Distributed Systems (ICPADS)

作者： Peng Xia Hao Zhou Shenyao Jiang Fan Deng Zhi Liu Xiang-Yang Li LINKE Lab School of Computer Science and Technology University of Science and Technology of China CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China Deqing Alpha Innovation Institute Huzhou Zhejiang China Department of Computer and Network Engineering The University of Electro-Communications Tokyo Japan

Feature extraction is important for non-intrusive appliance load monitoring (NILM), because it includes crucial steps which altogether transform raw signals into distinct features of appliances. Although numerous studies have achieved good results using transient harmonic features, their performances degrade in practical scenarios. In this paper, a time-frequency analysis-based framework for transient harmonic feature extraction is proposed, which takes all factors that would eventually influence NILM system into account. More specffically, the framework proposes a new qualitative transient harmonic feature, along with current retrieval and data augmentation methods. The proposed framework is evaluated on two well-known datasets (i.e., Controlled On/Off Loads Library and Home Equipment laboratory Dataset). As compared with the other state-of-art methods, the proposed framework achieves similar accuracy (i.e., more than 96%) on single-load data, and more than 33% accuracy improvement over multi-load data (e.g., from 59.4% to 93% when six devices are active at the same time).

关键词： Load monitoring Home appliances Time-frequency analysis Transforms Harmonic analysis Feature extraction Libraries