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network-Wide Energy-Efficiency Maximization in UAV-Aided IoT networks: Quasi-distributed Deep Reinforcement Learning Approach

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IEEE INTERNET OF THINGS JOURNAL 2025年第11期12卷 15404-15414页

作者： Lee, Seungmin Ban, Tae-Won Lee, Howon Newratek Inc Dept Wi Fi MAC Stand & Software Engn Seoul 06175 South Korea Gyeongsang Natl Univ Dept AI & Informat Engn Jinju 52828 South Korea Ajou Univ Dept Elect & Comp Engn Suwon 16499 Gyeonggi South Korea

In uncrewed aerial vehicle (UAV)-aided Internet of Things (IoT) networks, providing seamless and reliable wireless connectivity to ground devices (GDs) is difficult owing to the short battery lifetimes of UAVs. Hence, we consider a deep reinforcement learning (DRL)-based UAV base station (UAV-BS) control method to maximize the network-wide energy efficiency of UAV-aided IoT networks featuring continuously moving GDs. First, we introduce two centralized DRL approaches;round-robin deep Q-learning (RR-DQL) and selective-k deep Q-learning (SK-DQL), where all UAV-BSs are controlled by a ground control station that collects the status information of UAV-BSs and determines their actions. However, significant signaling overhead and undesired processing latency can occur in these centralized approaches. Hence, we herein propose a quasi-distributed DQL-based UAV-BS control (QD-DQL) method that determines the actions of each agent based on its local information. By performing intensive simulations, we verify the algorithmic robustness and performance excellence of the proposed QD-DQL method based on comparison with several benchmark methods (i.e., RR-DQL, SK-DQL, multiagent Q-learning, and exhaustive search method) while considering the mobility of GDs and the increase in the number of UAV-BSs.

关键词： Q-learning Internet of Things Heuristic algorithms neural networks Autonomous aerial vehicles Three-dimensional displays Optimization Batteries Wireless networks Trajectory Multiagent deep reinforcement learning (DRL) network-wide energy efficiency maximization UAV-base station (BS) UAV Control uncrewed aerial vehicle (UAV)-aided Internet of Things (IoT) network

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Neuromorphic Event-Driven Semantic Communication in Microgrids

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IEEE TRANSACTIONS ON SMART GRID 2024年第5期15卷 4300-4314页

作者： Diao, Xiaoguang Song, Yubo Sahoo, Subham Li, Yuan Wuhan Univ Sch Elect Engn & Automat Wuhan 430072 Peoples R China Aalborg Univ Dept Energy DK-9220 Aalborg Denmark

Synergies between advanced communications, computing and artificial intelligence are unraveling new directions of coordinated operation and resiliency in microgrids. On one hand, coordination among sources is facilitated by distributed, privacy-minded processing at multiple locations, whereas on the other hand, it also creates exogenous data arrival paths for adversaries that can lead to cyber-physical attacks amongst other reliability issues in the communication layer. This long-standing problem necessitates new intrinsic ways of exchanging information between converters through power lines to optimize the system's control performance. Going beyond the existing power and data co-transfer technologies that are limited by efficiency and scalability concerns, this paper proposes neuromorphic learning to implant communicative features using spiking neural networks (SNNs) at each node, which is trained collaboratively in an online manner simply using the power exchanges between the nodes. As opposed to the conventional neuromorphic sensors that operate with spiking signals, we employ an event-driven selective process to collect sparse data for training of SNNs. Finally, its multi-fold effectiveness and reliable performance is validated under simulation conditions with different microgrid topologies and components to establish a new direction in the sense-actuate-compute cycle for power electronic dominated grids and microgrids.

关键词： Microgrids Semantics Neuromorphics Load flow Training Computer architecture Scalability Neuromorphic computing microgrids hierarchical control spiking neural network semantic communication

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Time-Frequency Resolution Analysis for Continuous Human Activity Recognition using Radar networks

Time-Frequency Resolution Analysis for Continuous Human Acti...

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International Workshop on Antenna Technology (iWAT)

作者： Guendel, Ronny G. Fioranelli, Francesco Yarovoy, Alexander Delft Univ Technol Microwave Sensing Signals & Syst MS3 Grp Delft Netherlands

ISBN: (纸本)9798350314762;9798350314755

The effect of different time-frequency (TF) resolution values is analyzed in the context of Human Activity Recognition (HAR) using multiple radars distributed in a network. Specifically, different spectrograms computed with various Short-Time Fourier Transform (STFT) window lengths and Morse wavelet transform are compared as input representation to a Convolutional neural network (CNN), together with a coherent combination of multiple spectrograms. The study emphasizes the importance of selecting appropriate window sizes for TF analysis and for classification, balancing the observation time with the physical duration of the diverse activities, and also avoiding correlation between different data samples that may compromise the generalization ability of the method. The results employing this coherent sensor fusion demonstrate the efficacy of the investigated method, achieving an F1 score of 0.943 on a challenging public dataset containing 9 activities performed by 15 participants.

关键词： Radar signal processing distributed radar deep learning CNN human activity recognition

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distributed Acoustic Sensing Vertical Seismic Profile Data Denoising Based on Multistage Denoising network

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2022年 60卷 1页

作者： Li, Yue Zhang, Man Zhao, Yuxing Wu, Ning Jilin Univ Coll Commun Engn Dept Informat Changchun 130012 Peoples R China

distributed acoustic sensing (DAS) is a new exploration technology widely used to acquire vertical seismic profiles (VSPs). DAS can achieve low-cost and high-density observations, but the signal-to-noise ratio (SNR) of the VSP data collected by DAS is low compared with traditional electrical geophones. Moreover, DAS VSP data cover many types of noise, including random noise, fading noise, checkerboard noise, and long-period noise. These noises bring many difficulties to the imaging and interpretation of DAS VSP data. To solve this problem, we proposed a multistage denoising network (MSDN) to denoise DAS VSP data. MSDN is a progressive denoising network consisting of four stages. MSDN can recover the signal details better than a single-stage denoising network, which is beneficial when processing deep reflection signals. In addition, MSDN combines residual structure and an attention mechanism. The residual structure can prevent the degradation of the deep neural network, while the attention mechanism can make the network focus on effective signals, making network learning more accurate and efficient. Both synthetic data and field data denoising results showed that MSDN could effectively remove various complex noises and restore signals covered by noise. Compared with other denoising methods, our method has improved signal amplitude preservation ability and noise suppression ability.

关键词： Noise reduction Feature extraction Data models Data mining Training Noise measurement Convolution Attention mechanism convolutional neural network (CNN) distributed acoustic sensing (DAS) progressive denoising training set vertical seismic profile (VSP)

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Improved PointNet Classification network Using Clustering Algorithms 16

Improved PointNet Classification Network Using Clustering Al...

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16th International Conference on Cyber-Enabled distributed Computing and Knowledge Discover, CyberC 2024

作者： Xu, Wei Zhang, Jianxun Pan, Yujiang Tianjin University Of Technology And Education Faculty of Information Technology Engineering Tianjin China

ISBN: (纸本)9798331506896

PointNet was a breakthrough in neural networks for directly processing point cloud data, ensuring transformation and input invariance with a simple, efficient structure. However, it neglects the relationships between individual points' features. This paper introduces an improved model, KMeans-PointNet, which combines PointNet with K-Means clustering. By clustering point cloud data and extracting global features from each cluster, the model enhances local feature utilization. Experiments on the ModelNet40 dataset show that KMeans-PointNet improves overall recognition accuracy by 3% and classification accuracy by 2% compared to the original PointNet. © 2024 IEEE.

关键词： 3D point cloud K-Means neural network PointNet

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ADT network: A Novel Nonlinear Method for Decoding Speech Envelopes From EEG Signals

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TRENDS IN HEARING 2024年 28卷 23312165241282872-23312165241282872页

作者： Liu, Ruixiang Liu, Chang Cui, Dan Zhang, Huan Xu, Xinmeng Duan, Yuxin Chao, Yihu Sha, Xianzheng Sun, Limin Ma, Xiulan Li, Shuo Chang, Shijie China Med Univ Sch Intelligent Med Shenyang Peoples R China China Med Univ Shengjing Hosp Shenyang Peoples R China Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Shanghai Peoples R China China Med Univ Sch Life Sci Shenyang Peoples R China

Decoding speech envelopes from electroencephalogram (EEG) signals holds potential as a research tool for objectively assessing auditory processing, which could contribute to future developments in hearing loss diagnosis. However, current methods struggle to meet both high accuracy and interpretability. We propose a deep learning model called the auditory decoding transformer (ADT) network for speech envelope reconstruction from EEG signals to address these issues. The ADT network uses spatio-temporal convolution for feature extraction, followed by a transformer decoder to decode the speech envelopes. Through anticausal masking, the ADT considers only the current and future EEG features to match the natural relationship of speech and EEG. Performance evaluation shows that the ADT network achieves average reconstruction scores of 0.168 and 0.167 on the SparrKULee and DTU datasets, respectively, rivaling those of other nonlinear models. Furthermore, by visualizing the weights of the spatio-temporal convolution layer as time-domain filters and brain topographies, combined with an ablation study of the temporal convolution kernels, we analyze the behavioral patterns of the ADT network in decoding speech envelopes. The results indicate that low- (0.5-8 Hz) and high-frequency (14-32 Hz) EEG signals are more critical for envelope reconstruction and that the active brain regions are primarily distributed bilaterally in the auditory cortex, consistent with previous research. Visualization of attention scores further validated previous research. In summary, the ADT network balances high performance and interpretability, making it a promising tool for studying neural speech envelope tracking.

关键词： electroencephalogram neural envelope tracking deep learning transformer interpretability

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dsODENet: neural ODE and Depthwise Separable Convolution for Domain Adaptation on FPGAs 30

dsODENet: Neural ODE and Depthwise Separable Convolution for...

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30th Euromicro International Conference on Parallel, distributed and network-Based processing (PDP)

作者： Kawakami, Hiroki Watanabe, Hirohisa Sugiura, Keisuke Matsutani, Hiroki Keio Univ Dept ICS Kohoku Ku 3-14-1 Hiyoshi Yokohama Kanagawa 2238522 Japan

ISBN: (纸本)9781665469586

High-performance deep neural network (DNN)-based systems are in high demand in edge environments. Due to its high computational complexity, it is challenging to deploy DNNs on edge devices with strict limitations on computational resources. In this paper, we derive a compact while highly-accurate DNN model, termed dsODENet. by combining recently-proposed parameter reduction techniques: neural ODE (Ordinary Differential Equation) and DSC (Depthwise Separable Convolution). neural ODE exploits a similarity between ResNet and ODE, and shares most of weight parameters among multiple layers, which greatly reduces the memory consumption. We apply dsODENet to a domain adaptation as a practical use case with image classification datasets. We also propose a resource-efficient FPGA-based design for dsODENet, where all the parameters and feature maps except for pre- and post-processing layers can be mapped onto on-chip memories. It is implemented on Xffinx ZCII104 board and evaluated in terms of domain adaptation accuracy, training speed, FPGA resource utilization, and speedup rate compared to a software counterpart. The results demonstrate that dsODENet achieves comparable or slightly better domain adaptation accuracy compared to our baseline neural ODE implementation, while the total parameter size without pre- and post-processing layers is reduced by 54.2% to 79.8%. Our FPGA implementation accelerates the inference speed by 27.9 times.

关键词： Training Convolution neural networks Memory management Ordinary differential equations Software Mathematical models

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Intelligent Task Offloading and Collaborative Computation in Multi-UAV-Enabled Mobile Edge Computing

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China Communications 2022年第4期19卷 244-256页

作者： Jingming Xia Peng Wang Bin Li Zesong Fei Nanjing University of Information Science and Technology Nanjing 210044China Beijing Institute of Technology Beijing 100081China

This article establishes a three-tier mobile edge computing(MEC) network, which takes into account the cooperation between unmanned aerial vehicles(UAVs). In this MEC network, we aim to minimize the processing delay of tasks by jointly optimizing the deployment of UAVs and offloading decisions,while meeting the computing capacity constraint of UAVs. However, the resulting optimization problem is nonconvex, which cannot be solved by general optimization tools in an effective and efficient way. To this end, we propose a two-layer optimization algorithm to tackle the non-convexity of the problem by capitalizing on alternating optimization. In the upper level algorithm, we rely on differential evolution(DE) learning algorithm to solve the deployment of the UAVs. In the lower level algorithm, we exploit distributed deep neural network(DDNN) to generate offloading decisions. Numerical results demonstrate that the two-layer optimization algorithm can effectively obtain the near-optimal deployment of UAVs and offloading strategy with low complexity.

关键词： mobile edge computing multi-UAV collaborative cloud and edge computing deep neural network differential evolution

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An IoT-Based 5G Wireless Sensor network Employs a Secure Routing Methodology Leveraging DCNN processing

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TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES 2024年第12期35卷

作者： Sabri, Yassine Hilmani, Adil Ibnou Zohr Univ Fac Sci Lab Syst Informat Vis LabSIV Agadir Morocco Mohamed V Univ Super Sch Technol Sale Rabat Morocco

The security principles of the fifth generation (5G) are anticipated to include robust cryptography models, information security models, and machine learning (ML) powered Intrusion Detection Systems (IDS) specifically designed for Internet of Things (IoT) based wireless sensor networks (WSNs). Nevertheless, the existing security models fall short in addressing the dynamic network characteristics of WSNs. In this context, the suggested system introduces a secure and collaborative multi-watchdog system through the implementation of deep convolutional neural network (DCNN) and distributed particle filtering evaluation scheme (DPFES). The proposed system utilizes deep learning (DL) techniques to create a dynamic multi-watchdog system that safeguards each sensor node by monitoring its transmissions. Furthermore, the proposed approach includes secure data-centric and node-centric evaluation methods that are crucial for enhancing the security of 5G-based IoT-WSN networks. The network evaluation processes based on DL facilitate the creation of a secure multi-watchdog system within dense IoT-WSN environments. This system enables the deployment of active watchdog IDS agents as needed. The proposed approach includes various components such as a system dynamics model, cooperative watchdog model, Dual Line Minimum Connected Dominating Set (DL-MCDS), and DL-based event analysis procedures. From a technical perspective, the system is driven by the implementation of DPFES, which utilizes particle filtering frameworks to analyze network events and establish a secure 5G environment. The system has been successfully implemented, and its results have been compared with those of other similar works. The performance of the proposed cooperative multi-watchdog system demonstrates a significant improvement of 10%$$ 10\% $$ and 15%$$ 15\% $$ compared to other techniques.

关键词： 5G deep learning IDS IoT multi-watchdog particle analysis security wireless sensor networks

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Learning distributed neural network-Based Beam Codebooks on FPGAs: Adapting to Unevenly distributed Users in mmWave Massive MIMO IoT System with Hardware Acceleration

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IEEE Internet of Things Journal 2025年

作者： Chen, Zhiheng Liu, Pei Xu, Bo Chen, Yun Zhan, Wen Interdonato, Giovanni Buzzi, Stefano Wuhan University of Technology School of Information Engineering Wuhan430070 China Wuhan University of Technology Advanced Engineering Technology Research Institute of Zhongshan City Zhongshan528437 China Nanjing University of Posts and Telecommunications Key Laboratory of Wireless Communications Jiangsu Nanjing210003 China VIAVI Solutions Inc VIAVI Marconi Labs StevenageSG1 2AN United Kingdom Sun Yat-sen University School of Electronics and Communication Engineering Shenzhen518107 China University of Cassino and Southern Lazio Department of Electrical and Information Engineering Cassino03043 Italy Parma43122 Italy

Millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) is one of the most promising technologies from 5G-based Internet of Things (IoT) to future wireless communication-based IoT, which usually relies on beamforming codebooks for data transmission. However, traditional codebooks often consist of numerous narrow beams, which causes substantial training overhead. Although centralized machine learning-based methods can address this issue to some extent, they overlook minority IoT devices scattered across various areas, which is vital for the coverage equity of the environmental adaptive codebook and the optimal average achievable rate. To circumvent the problem, we propose a distributed learning (DL) framework for codebook design in mmWave massive MIMO systems with uneven user distribution. Specifically, the user channel set is first divided into subsets by pre-classification based on the power responses of the featured combining vectors from different subregions. Then, a novel DL architecture processes these subsets, each assigned to different baseband processing boards (BPBs) in building baseband units, alleviating the centralized machine learning burden on the active antenna unit (AAU) or its directly connected BPB. Meanwhile, the current algorithms lack the hardware perspective or only implement the inference stage of the model. Thus, we deploy an FPGA-adapted DL-based codebook training prototype that runs on FPGA, which fully explores the "Backward-While-Forward" strategy for data reuse in the forward and backward passes. Simulation validates the effectiveness of distributed learning. Notably, the FPGA implementation on the embedded-level board outperforms consumer-grade CPU and GPU in terms of both latency and energy efficiency. © 2014 IEEE.

关键词： Integrated circuit design

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