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AoI Minimization for Air-Ground Integrated Sensing and Communication Networks with Jamming Attack

IEEE Transactions on Vehicular Technology 2025年

作者： Mei, Hao Zhang, Haixia Zhou, Xiaotian Wang, Jing Shandong University School of Control Science and Engineering Shandong Jinan250061 China Shandong Key Laboratory of Intelligent Communication and Sensing-Computing Integration Shandong Jinan250061 China Shandong University School of Information Science and Engineering Shandong Qingdao266237 China

Air-ground integrated sensing and communication (AG-ISAC) networks utilize unmanned aerial vehicles (UAVs) equipped with ISAC units as aerial platforms in next-generation wireless systems, which can sense ground status information to the base stations (BSs) for further decision-making. Given that effective decisions rely on timely sensed data, the Age of Information (AoI) serves as a metric that precisely reflects the timeliness and effectiveness of the sensing and communication (S&C) process. However, recent research has paid little attention to AoI minimization in AG-ISAC networks, particularly in the presence of malicious attackers attempting to interfere S&C operations. To address this research gap, this paper considers AG-ISAC networks with S&C jamming attacks, a mission ISAC UAV senses tasks and transmits the sensed data to the BS, while a mobile UAV attacker sends jamming signals to degrade S&C performance and increase AoI. To solve this, we jointly design the trajectory and S&C transmit power of the mission UAV to mitigate S&C performance degradation to minimize the AoI. Nonetheless, the NP-hard and dynamic long-term nature of the formulated problem poses significant challenges. To tackle this troublesome problem, we develop an efficient solution using the deep reinforcement learning (DRL) technique. Specifically, we propose a twin-delayed deep deterministic policy gradient (TD3)-based trajectory design and power control algorithm (TD3-TP). In addition to this, we incorporate a reward-shaping mechanism to speed up the convergence of the algorithm. Simulation results demonstrate our algorithm achieves the fastest training speed and minimum AoI under attacks relative to four benchmark methods. © 1967-2012 IEEE.

关键词： Jamming

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Energy Aware Metaheuristic Optimization with Location Aided Routing Protocol for MANET

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Computers, Materials & Continua 2022年第4期71卷 1567-1580页

作者： E.Ahila Devi K.C.Ramya K.Sathesh Kumar Sultan Ahmad Seifedine Kadry Hyung Ju Park Byeong-Gwon Kang Department of Instrumentation and Control Engineering St.Joseph’s College of EngineeringChennai600119India Department of Electrical and Electronics Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India School of Computing Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharj11942Saudi Arabia Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Department of ICT Convergence Soonchunhyang UniversityAsan31538Korea

A mobile ad hoc network(MANET)involves a group of wireless mobile nodes which create an impermanent network with no central authority and *** nodes in the MANET are highly mobile and it results in adequate network topology,link loss,and increase the re-initialization of the route discovery *** planning in MANET is a multi-hop communication process due to the restricted transmission range of the *** aided routing(LAR)is one of the effective routing protocols in MANET which suffers from the issue of high energy *** few research works have focused on resolving energy consumption problem in LAR,energy efficiency still remains a major design *** this aspect,this study introduces an energy aware metaheuristic optimization with LAR(EAMO-LAR)protocol for *** EAMO-LAR protocol makes use of manta ray foraging optimization algorithm(MRFO)to help the searching process for the individual solution to be passed to the LAR *** fitness value of the created solutions is determined next to pass the solutions to the objective *** MRFO algorithm is incorporated into the LAR protocol in the EAMO-LAR protocol to reduce the desired energy *** ensure the improved routing efficiency of the proposed EAMO-LAR protocol,a series of simulations take *** resultant experimental values pointed out the supreme outcome of the EAMO-LAR protocol over the recently compared *** resultant values demonstrated that the EAMO-LAR protocol has accomplished effectual results over the other existing techniques.

关键词： MANET routing lar protocol energy efficiency mrfo algorithm metaheuristics

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A Smarter Pavlovian Dog with Optically Modulated Associative Learning in an Organic Ferroelectric Neuromem

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Research 2021年第1期2021卷 1148-1157页

作者： Mengjiao Pei Changjin Wan Qiong Chang Jianhang Guo Sai Jiang Bowen Zhang Xinran Wang Yi Shi Yun Li National Laboratory of Solid-State Microstructures School of Electronic Science and EngineeringCollaborative Innovation Center of Advanced MicrostructuresNanjing UniversityNanjing 210093China School of Computing Tokyo Institute of TechnologyTokyo 152-8550Japan School of Microelectronics and Control Engineering Changzhou UniversityChangzhou 213164China

Associative learning is a critical learning principle uniting discrete ideas and percepts to improve individuals’***,enabling high tunability of the association processes as in biological counterparts and thus integration of multiple signals from the environment,ideally in a single device,is ***,we fabricate an organic ferroelectric neuromem capable of monadically implementing optically modulated associative *** approach couples the photogating effect at the interface with ferroelectric polarization switching,enabling highly tunable optical modulation of charge *** device acts as a smarter Pavlovian dog exhibiting adjustable associative learning with the training cycles tuned from thirteen to *** particular,we obtain a large output difference(>10^(3)),which is very similar to the all-or-nothing biological sensory/motor neuron spiking with decrementless *** proof-of-concept demonstrations,photoferroelectric coupling-based applications in cryptography and logic gates are achieved in a single device,indicating compatibility with biological and digital data processing.

关键词： ferroelectric associative proof

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Hierarchical Multiview Top-k Pooling with Deep-Q-Networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2985-2996页

作者： Li, Zhi-Peng Su, Hai-Long Wu, Yong- Zhang, Qin-Hu Yuan, Chang-An Gribova, Valeriya Filaretov, Vladimir Fedorovich Huang, De-Shuang Eastern Institute of Technology Zhejiang Ningbo315201 China University of Science and Technology of China School of Life Sciences Anhui Hefei230026 China Tongji University Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Shanghai201804 China Guangxi Academy of Sciences Institute of Big Data and Intelligent Computing Research Center Nanning530007 China Far Eastern Branch of the Russian Academy of Sciences Institute of Automation and Control Processes Vladivostok690041 Russia

Graph neural networks (GNNs) are extensions of deep neural networks to graph-structured data. It has already attracted widespread attention for various tasks such as node classification and link prediction. Existing research focuses more on graph convolutional neural networks (GCNs). However, it is usually overlooked that graph pooling can obtain graph representations by summarizing and down-sampling node information. Meanwhile, existing graph pooling methods mainly use top-k for node selection, but most of them consider only single view information when scoring nodes, and the k values in top-k are usually selected empirically. This work proposes the hierarchical multiview top-k pooling (HMTPool) with deep-Q-networks, which scores nodes taking into account multiview information (considering graph structure and features) and does not rely on the empirical adaptive selection of the best k value. HMTPool is a two-stage process. It first uses a variant GCN and multilayer perceptron to score the nodes from structural and feature perspectives, respectively and then performs fusion operations on the multiview information scores of the nodes. In addition, to select the optimal pooling ratio of top-k, we propose a deep-Q-network-based top-k (DTop-k) node selection method, which can adaptively select the best pooling ratio without prior knowledge. Experimental results on six TUDatasets and two Benchmarking GNNs datasets demonstrate the effectiveness of our proposed approach. © 2020 IEEE.

关键词： Convolution

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Research on Intelligent Visual Detection Methods for Human Physiological Parameters 1

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8th International Conference on Life System Modeling and Simulation, LSMS 2024 and 8th International Conference on Intelligent computing for Sustainable Energy and Environment, ICSEE 2024

作者： Wu, Chenggang Wan, Zilin Shao, Rui Lin, Pengcheng Zhang, Kun School of Electrical Engineering and Automation Nantong University Nantong China Nantong Key Laboratory of Intelligent Control and Intelligent Computing Nantong Institute of Technology Jiangsu Nantong China

ISBN: (数字)9789819603138

ISBN: (纸本)9789819603121

In recent years, the incidence and mortality rates of cardiovascular diseases have been increasing year by year, prompting greater attention to the monitoring of physiological parameters in daily life. This paper proposes a deep physiological parameter detection method based on Image Photoplethysmography (IPPG) technology, overcoming the discomfort of contact-based measurements and the limitation of measuring single physiological indices through intelligent visual means. A lightweight attention mechanism combined with the BHCNN model is designed to automatically extract waveform features of IPPG signals for estimating blood pressure and heart rate in the human body. The mean absolute errors (MAE) of the obtained systolic pressure, diastolic pressure, and heart rate are 6.60, 6.22, and 7.15 respectively, with standard deviations (STD) of 3.22, 2.61, and 3.98. The data consistency of both indices exceeds 95%, demonstrating high accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Blood pressure

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IRS-Aided Mobile Edge computing for Mine IoT Networks using Deep Reinforcement Learning

IRS-Aided Mobile Edge Computing for Mine IoT Networks using ...

引用

2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Zhang, Peng Min, Minghui Xiao, Jiayang Li, Shiyin Zhang, Hongliang China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Xuzhou First People's Hospital Xuzhou221116 China Ministry of Education School of CyberScience and Engineering Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan430072 China Peking University School of Electronics Beijing100871 China

ISBN: (纸本)9798350345384

Recent years have witnessed a growing interest in implementing mobile edge computing (MEC) into mine Internet of Things (IoT) networks to enable real-time monitoring and safety management. However, the spatial blockage and limited transmitting power pose challenges to signal coverage and efficient computation offloading. To address these issues, we propose incorporating intelligent reflecting surfaces (IRSs) in the MEC system for underground mines (MMEC) to resolve wireless coverage problems in mine communication scenarios and enhance the channel gains of offloading links. Given the time-varying and complex channel conditions in mines and the multiple coupled variables involved in IRSs' reflecting coefficient matrix, we utilize Deep Reinforcement Learning (DRL) to acquire the optimal IRS-assisted computation offloading strategy in dynamic mine environments. Specifically, we propose a Deep Deterministic Policy Gradient (DDPG)-based IRS-aided computation offloading scheme to jointly optimize the IRS elements' reflection coefficients and the computation offloading rate. Simulation results demonstrate that introducing an IRS into the MMEC system yields outstanding benefits and significantly exceeds benchmarks in enhancing energy efficiency and decreasing computational latency. © 2023 IEEE.

关键词： Reinforcement learning

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Performance Analysis of Quaternion vs Euler-based Approaches in Reduced INS/GNSS Fusion Techniques

Performance Analysis of Quaternion vs Euler-based Approaches...

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2023 International Telecommunications Conference, ITC-Egypt 2023

作者： Tarek, Maher Zahran, Shady Radi, Ahmed Nafea, Sherif F Oda, Eyad S. Hafez, Alaa El-Din Sayed Technical Researches and Development Center Control and Navigation Departement Cairo Egypt Arab Academy for Science Technology and Maritime Transport College of Computing and Information Technology Cairo Egypt Technical Researches Center Cairo Egypt Suez Canal University Electrical Engineering Department Cairo Egypt Alexandria University Electrical Engineering Affiliate Instructor Cairo Egypt

ISBN: (纸本)9798350326062

Unmanned vehicle navigation using various combination of sensors in different fields, civilian or military, has always known to be a challenging and nontrivial task. Bayesian filter algorithms are usually being used for combining and optimally processing measurements provided by such sensors, considering the available knowledge such as the uncertainty in measurements. Since the performance of any navigation system mainly depends on the efficiency of the chosen filter algorithm associated with the utilized sensors' quality, the choice of suitable filtering approach (according to the demand application) can be considered as a critical and essential designing point. In this paper, the performance of two different attitudes representation filtering approaches (Quaternion and Euler angles-based) is investigated, when applied in Multiplicative Extended Kalman Filter (MEKF) design. The overall performance and accuracy of both approaches were analyzed and compared during various GNSS signal outages for a self-driving cars application in straight and curved trajectory paths. Multiple in-filed experiments were conducted using a special setup that includes a MEMS-based SPATIAL Advanced Navigation INS/GNSS system where the output of both approaches was compared to the ground truth of a utilized reference navigation system. The comparison includes 1) Position, Velocity and Attitude (PVA) estimation accuracy, 2) processing time and computational complexity. Results concluded that each approach has its own merits in demerits where the Quaternion-based approach outperforms the Euler one in terms of PVA navigation solution estimation accuracy. On the other hand, a faster calculation for the Euler-based approach associated with lower computational complexity was significantly noticeable. © 2023 IEEE.

关键词： Inertial navigation systems

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Optimizing Human Activity Recognition Using Stacked CNN with Ensemble Learning

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Procedia Computer Science 2025年 258卷 1598-1607页

作者： Divya Yadav Deepika Rani Om Parakash Verma Department of Mathematics and Computing Dr B R Ambedkar National Institute of Technology Jalandhar Jalandhar Punjab India Department of Instrumentation and Control Engineering Dr B R Ambedkar National Institute of Technology Jalandhar Jalandhar Punjab India

In recent times, Human Activity Recognition (HAR) plays a crucial role in numerous artificial intelligence applications, including surveillance, healthcare, and security. However, the primary challenges in accurately identifying human activities from video data include occlusion, low resolution, data scarcity, and high computational costs. The main objective of this study is to enhance both the recognition accuracy and computational efficiency. This study presents a computationally efficient HAR model that leverages deep learning and machine learning algorithms for feature extraction and classification tasks. The proposed model consists of three fine-tuned convolution neural networks: VGG16, VGG19, and MobileNetV2 for feature extraction, combined with four classifiers, Support Vector Machine (SVM), Random Forest (RF), Multilayer Perceptron (MLP), and Fuzzy classifier. The performance of the proposed model was evaluated using three benchmark datasets: KTH, YouTube11, and Peliculas. The proposed method demonstrated exceptional performance of 99.60% and 99.50% accuracy on the KTH and Peliculas dataset using MobileNet and SVM. Additionally, the proposed method achieved a superior performance of 99.80% on the YouTube11 dataset with a combination of VGG16 and SVM. Finally, the performance of the proposed model was compared and validated against previous models.

关键词： Human Activity Recognition (HAR) Convolution Neural Network (CNN) Feature Extraction Classification

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Learning to Learn Transferable Generative Attack for Person Re-Identification

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IEEE Transactions on Image Processing 2025年 PP卷 PP页

作者： Bian, Yuan Liu, Min Wang, Xueping Ma, Yunfeng Wang, Yaonan Hunan University National Engineering Research Center of Robot Visual Perception and Control Technology College of Electrical and Information Engineering Hunan Changsha China Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Changsha China

Deep learning-based person re-identification (reid) models are widely employed in surveillance systems and inevitably inherit the vulnerability of deep networks to adversarial attacks. Existing attacks merely consider cross-dataset and cross-model transferability, ignoring the cross-test capability to perturb models trained in different domains. To powerfully examine the robustness of real-world re-id models, the Meta Transferable Generative Attack (MTGA) method is proposed, which adopts meta-learning optimization to promote the generative attacker producing highly transferable adversarial examples by learning comprehensively simulated transfer-based crossmodel&dataset&test black-box meta attack tasks. Specifically, cross-model&dataset black-box attack tasks are first mimicked by selecting different re-id models and datasets for meta-train and meta-test attack processes. As different models may focus on different feature regions, the Perturbation Random Erasing module is further devised to prevent the attacker from learning to only corrupt model-specific features. To boost the attacker learning to possess cross-test transferability, the Normalization Mix strategy is introduced to imitate diverse feature embedding spaces by mixing multi-domain statistics of target models. Extensive experiments show the superiority of MTGA, especially in cross-model&dataset and cross-model&dataset&test attacks, our MTGA outperforms the SOTA methods by 20.0% and 11.3% on mean mAP drop rate, respectively. © 1992-2012 IEEE.

关键词： Generative adversarial networks

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A practical PID variable stiffness control and its enhancement for compliant force-tracking interactions with unknown environments

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Science China(Technological Sciences) 2023年第10期66卷 2882-2896页

作者： ZHANG Xin ZHOU Hao LIU JinGuo JU ZhaoJie LENG YuQuan YANG ChenGuang State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China University of Chinese Academy of Sciences Beijing 100049China School of Computing University of PortsmouthPortsmouth PO13HEUK Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy EngineeringSouthern University of Science and TechnologyShenzhen 518055China Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and EngineeringSouth China University of TechnologyGuangzhou 510640China

Compliant interaction control is a key technology for robots performing contact-rich manipulation *** design of the compliant controller needs to consider the robot hardware because complex control algorithms may not be compatible with the hardware performance,especially for some industrial robots with low bandwidth *** paper focuses on effective and easy-to-use compliant control algorithms for position/velocity-controlled *** by human arm stiffness adaptation behavior,a novel variable target stiffness(NVTS)admittance control strategy is proposed for adaptive force tracking,in which a proportional integral derivative(PID)variable stiffness law is designed to update the stiffness coefficient of the admittance function by the force and position ***,its stability and force-tracking capability are theoretically *** addition,an impact compensator(Impc)is integrated into the NVTS controller to enhance its disturbance-suppression capability when the robot is subjected to strong vibration disturbances in complicated surface polishing *** proposed controllers are validated through four groups of experimental tests using different robots and the corresponding results demonstrate that they have high-accuracy tracking capability and strong adaptability in unknown environments.

关键词： compliant interaction control force tracking PID variable stiffness impact compensator unknown environments

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