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Efficient Power Control for UAV Based on Trajectory and Game Theory

Computers, Materials & Continua 2023年第3期74卷 5589-5606页

作者： Fadhil Mukhlif Ashraf Osman Ibrahim Norafida Ithnin Roobaea Alroobaea Majed Alsafyani Information Assurance and Security Research Group(IASRG) School of ComputingFaculty of EngineeringUniversiti Teknologi MalaysiaJohorMalaysia Faculty of Computing and Informatics University Malaysia SabahKota KinabaluMalaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaifSaudi Arabia

Due to the fact that network space is becoming more limited,the implementation of ultra-dense networks(UDNs)has the potential to enhance not only network coverage but also network *** Aerial Vehicle(UAV)communications have recently garnered a lot of attention due to the fact that they are extremely versatile and may be applied to a wide variety of contexts and purposes.A cognitive UAV is proposed as a solution for the Internet of Things ground terminal’s wireless nodes in this *** the IoT system,the UAV is utilised not only to determine how the resources should be distributed but also to provide power to the wireless *** quality of service(QoS)offered by the cognitive node was interpreted as a price-based utility function,which was demonstrated in the form of a non-cooperative game theory in order to maximise customers’net utility *** energyefficient non-cooperative game theory power allocation with pricing strategy abbreviated as(EE-NGPAP)is implemented in this study with two trajectories Spiral and Sigmoidal in order to facilitate effective power management in Internet of Things(IoT)wireless *** has also been demonstrated,theoretically and by the use of simulations,that the Nash equilibrium does exist and that it is one of a *** proposed energy harvesting approach was shown,through simulations,to significantly reduce the typical amount of power thatwas *** is taken into consideration to agree with the objective of 5G *** order to converge to Nash Equilibrium(NE),the method that is advised only needs roughly 4 iterations,which makes it easier to utilise in the real world,where things aren’t always the same.

关键词： UAV spiral&sigmoid trajectory drones IoT game theory energy efficiency 6G

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AI-Enabled STAR-RIS Aided MISO ISAC Secure Communications

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清华大学学报自然科学版（英文版） 2025年第3期30卷 998-1011页

作者： Zhengyu Zhu Mengfei Gong Gangcan Sun Peijia Liu De Mi School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001China National Mobile Communications Research Laboratory Southeast UniversityNanjing 210019China School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001China School of Computing and Digital Technology Birmingham City UniversityBirmingham B55JUUK

A simultaneous transmitting and reflecting reconfigurable intelligent surface(STAR-RIS)aided integrated sensing and communication(ISAC)dual-secure communication system is studied in this *** sensed target and legitimate users(LUs)are situated on the opposite sides of the STAR-RIS,and the energy splitting and time switching protocols are applied in the STAR-RIS,*** long-term average security rate for LUs is maximized by the joint design of the base station(BS)transmit beamforming and receive filter,along with the STAR-RIS transmitting and reflecting coefficients,under guarantying the echo signal-to-noise ratio thresholds and rate constraints for the *** the channel information changes over time,conventional convex optimization techniques cannot provide the optimal performance for the system,and result in excessively high computational complexity in the exploration of the long-term gains for the *** continuity control decisions into account,the deep deterministic policy gradient and soft actor-critic algorithms based on off-policy are applied to address the complex non-convex *** results comprehensively evaluate the performance of the proposed two reinforcement learning algorithms and demonstrate that STAR-RIS is remarkably better than the two benchmarks in the ISAC system.

关键词： secure communications STAR-RIS ISAC reinforcement learning(RL)

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Principled Design of Translation, Scale, and Rotation Invariant Variation Operators for Metaheuristics

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Chinese Journal of Electronics 2023年第1期32卷 111-129页

作者： TIAN Ye ZHANG Xingyi HE Cheng TAN Kay Chen JIN Yaochu Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University School of Artificial Intelligence Anhui University School of Electrical and Electronic Engineering Huazhong University of Science and Technology Department of Computing The Hong Kong Polytechnic University Faculty of Technology Bielefeld University

A large number of metaheuristics have been proposed and shown high performance in solving complex optimization problems. While most variation operators in existing metaheuristics are empirically designed,new operators are automatically designed in this work,which are expected to be search space independent and thus exhibit robust performance on different *** work first investigates the influence of translation invariance, scale invariance, and rotation invariance on the search behavior and performance of some representative operators. This work then deduces the generic form of translation, scale, and rotation invariant operators, and proposes a principled approach for the automated design of operators, which searches for high-performance operators based on the deduced generic form. The experimental results demonstrate that the operators generated by the proposed approach outperform state-of-the-art ones on a variety of problems with complex landscapes and up to 1000decision variables.

关键词： Deep learning Metaheuristics Reinforcement learning Benchmark testing Search problems Robustness Behavioral sciences

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Secure location-aware geocast routing in Internet of Vehicles

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Multimedia Tools and Applications 2025年第18期84卷 19669-19692页

作者： Kumar, Sunil Dohare, Upasana Kumar, Sushil Department of Computer Science and Information Technology Central University of Haryana Mahendragarh India School of Computing Science & Engineering Galgotias University Greater Noida India School of Computer and Systems Sciences Jawaharlal Nehru University New Delhi India

Efficient message dissemination in Vehicular Ad Hoc Networks (VANETs) relies on robust connectivity between neighboring vehicular nodes, yet it is often compromised by malicious intruders. While recent literature proposes various approaches to enhance geocasting performance, security considerations for connectivity are often overlooked. To address this gap, we propose the Secure Location-Aware Cache Agent based Geocast Routing (SL-CAGR) protocol, emphasizing an Intrusion Prevention Technique (IPT) for message integrity preservation and a Connectivity Reliant Packet Forwarding (CRPF) algorithm for improved VANET connectivity. Our protocol introduces a network model for cache agent-based geocast routing on the Internet of Vehicle (IoV) environment, implements IPT using Diffie-Hellman digital signatures, and presents a secure geocast routing strategy focusing on re-caching and cache agent selection. Empirical simulations demonstrate SL-CAGR's superiority in packet delivery ratio, packet loss, duplication, hop count, end-to-end delay, and network load compared to these protocols. Simulation results demonstrate that SL-CAGR outperforms state-of-the-art techniques like CAG, CTRC, and D-Flooding. SL-CAGR stands out by merging intrusion prevention, secure geocast routing, and cache agent selection, ensuring robust security and efficiency in VANETs. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Vehicular ad hoc networks

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A Novel Air Quality Prediction System with Long Term Storage with Hyper Parameter Tuning 7

A Novel Air Quality Prediction System with Long Term Storage...

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7th International Conference on Intelligent computing and Control Systems, ICICCS 2023

作者： Kalaivani, G. Kamalakkannan, S. Kavitha, P. Pallavaram Department of Computer Science School of Computing Sciences Chennai India Pallavaram Department of Information Technology School of Computing Sciences Chennai India

ISBN: (纸本)9798350397253

Air pollution is the most important contributor to a variety of serious health problems as well as weather transformation. Air quality interpreters are needed to design human activity at a particular environmental location and lessen the adverse pollution detrimental impacts. The authors have examined the problem in difficulty of forecasting the classification of pollutant concentrations in the future and have proposed a novel method based on Long Short-Term Memory (LSTM) model. This is a Deep Neural Network (DNN) model that is identified to work well with consecutive prediction difficulties. Through the data obtained, this approach produces a prediction model that reliably forecasts the Air Quality Index (AQI) from Central Pollution Control Board (CPCB) website. LS TM is experimented eight number of times to choose the best possible function using Python's LeakyRelu package to assess hyperparameter optimizations. The comparative analysis of accuracy metrics such as R Squared, Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) are measured for different models. The proposed model discovered R Squared is more than 1, indicating that the hyper parameter tuned model is the best fit model, based on the concurrent experiments. This dataset has the highest prediction accuracy and less prediction loss. © 2023 IEEE.

关键词： Long short-term memory

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A Shallow Convolutional Neural Network Model for Breast Cancer Histopathology Image Classification 11th

A Shallow Convolutional Neural Network Model for Breast Canc...

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11th International Conference on Recent Trends in computing, ICRTC 2022

作者： Saxena, Shweta Shukla, Praveen Kumar Ukalkar, Yash School of Computing Science Engineering VIT Bhopal University Bhopal India School of Computing and Information Technology Manipal University Jaipur Jaipur India

ISBN: (纸本)9789811988240

Identification of malignancy using histopathology image processing is a crucial method for cancer diagnosis. A model to classify images based on deep convolutional neural networks (CNNs) attains a promising performance in the computer vision for a large dataset but creates a computational complexity for a comparatively small histopathology dataset. A shallow CNN model is proposed in this paper, which classifies the histopathology images of breast tissues. The images are classified as either malignant or benign by the model. It is tested on two publicly available, benchmark breast cancer histopathology datasets. According to the result, the proposed model achieves (95.19 ± 8.50) accuracy and requires a less no. of parameters (8, 052,062) for training as compared to the existing deep CNN models implemented on the same protocol. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Neural network models

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Based on Spectral Clustering Dynamic Community Discovery Algorithm Research in Temporal Network

Based on Spectral Clustering Dynamic Community Discovery Alg...

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2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms, PRMVIA 2023

作者： Yang, Yu Long, Yong Gui, Linbin Ma, Jurun Yunnan Vocational Institute of Energy Technology School of Computing and Information Engineering Qujing China

ISBN: (纸本)9798350346596

The study of temporal community discovery is an essential research area in social network analysis. As nodes join or leave social networks, the relationships between nodes are establishing or terminating, which affects community structure changes. Given the social networks discovery algorithm of static community lacks the indispensable historical information of network community nodes, resulting in insufficient network structure analysis and clustering information. Based on the community network evolution division events, the paper extracted the priority for analysis and proposed the SC-DCDA: Spectral Clustering Based Temporal Community Discovery Algorithm. According to experimental observation, the SC-DCDA firstly reduced the dimensionality of high-dimensional data leveraging the method of spectral mapping. Secondly, the improved Fuzzy C-means clustering algorithm was adopted to determine the correlation between nodes in temporal social networks and the communities to be discovered, and finally the community structure analysis was performed according to the evolutionary similarity matrix. The ground truth datasets combined with the typically community discovery algorithm metric Modularity Score experimental verification and performance evaluation. The experimental results show that the algorithm metric is well-suited for the temporal datasets, indicating that the proposed algorithm has achieved several better results in information interaction, clustering effect, and accuracy. © 2023 IEEE.

关键词： Clustering algorithms

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Optimized Deep Learning Mechanism for Intrusion Detection: Leveraging RFE-Based Feature Selection and PCA for Improved Accuracy 5

Optimized Deep Learning Mechanism for Intrusion Detection: L...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Rajkumar, M. Vs, Lakshi Karthik, R. Pavithra, S. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Department of Smart Computing Tamil Nadu India B. Tech-Information Technology School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu India

ISBN: (纸本)9798331530013

With the growing demand for wireless networks, network traffic has surged, particularly involving large-scale data transmission. This results in an increased number of network security threats and the emergence of various cyber-attacks. To overcome these threats, the development of an efficient intrusion detection system (IDS) has become crucial. By leveraging the recent advancements in Artificial intelligence (AI), this project introduces an optimized deep learning mechanism utilizing a prevalent deep learning model, the Deep Neural Network (DNN) algorithm. This proposed method uses feature engineering techniques like feature selection and feature reduction. Given the high dimensionality of the selected datasets, these techniques are employed to prevent over-fitting. Recursive feature elimination performs feature selection followed by Principal Component Analysis for the feature reduction process, thus enhancing the model's efficiency. The system is assessed using two benchmark datasets: KDD and UNSW-NB15. The experimental results indicate that the presented DNN model on the KDD dataset has achieved 92.45% accuracy and 91.35% accuracy on the UNSW-NB15 dataset for intrusion classification. © 2024 IEEE.

关键词： Network intrusion

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Faster than Fast: Accelerating Oriented FAST Feature Detection on Low-end Embedded GPUs

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ACM Transactions on Embedded computing Systems 2025年第3期24卷 1-22页

作者： Chang, Qiong Chen, Xinyuan Li, Xiang Wang, Weimin Miyazaki, Jun School of Computing Institute of Science Tokyo Meguro Tokyo Japan International School of Information Science and Engineering Dalian University of Technology Liaoning Dalian China School of Electronic Science and Engineering Nanjing University Jiangsu Nanjing China

The visual-based SLAM (Simultaneous Localization and Mapping) is a technology widely used in applications such as robotic navigation and virtual reality, which primarily focuses on detecting feature points from visual images to construct an unknown environmental map and simultaneously determines its own location. It usually imposes stringent requirements on hardware power consumption, processing speed, and accuracy. Currently, the ORB (Oriented FAST and Rotated BRIEF)-based SLAM systems have exhibited superior performance in terms of processing speed and robustness. However, they still fall short of meeting the demands for real-time processing on mobile platforms. This limitation is primarily due to the time-consuming Oriented FAST calculations accounting for approximately half of the entire SLAM system. This article presents two methods to accelerate the Oriented FAST feature detection on low-end embedded GPUs. These methods optimize the most time-consuming steps in Oriented FAST feature detection: FAST feature point detection and Harris corner detection, which is achieved by implementing a binary-level encoding strategy to determine candidate points quickly and a separable Harris detection strategy with efficient low-level GPU hardware-specific instructions. Extensive experiments on a Jetson TX2 embedded GPU demonstrate an average speedup of over 7.3 times compared to widely used OpenCV with GPU support. This significant improvement highlights its effectiveness and potential for real-time applications in mobile and resource-constrained environments. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Edge detection

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Overlapping community‐based particle swarm optimization algorithm for influence maximization in social networks

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CAAI Transactions on Intelligence technology 2023年第3期8卷 893-913页

作者： Lei Zhang Yutong Liu Haipeng Yang Fan Cheng Qi Liu Xingyi Zhang Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Laboratory of Intelligent Computing and Signal Processing of Ministry of EducationSchool of Computer Science and TechnologyAnhui UniversityHefeiChina School of Computer Science and Technology University of Science and Technology of ChinaHefeiChina

Influence maximization,whose aim is to maximise the expected number of influenced nodes by selecting a seed set of k influential nodes from a social network,has many applications such as goods advertising and rumour *** the existing influence maximization methods,the community‐based ones can achieve a good balance between effectiveness and ***,this kind of algorithm usually utilise the network community structures by viewing each node as a non‐overlapping *** fact,many nodes in social networks are overlapping ones,which play more important role in influence *** this end,an overlapping community‐based particle swarm opti-mization algorithm named OCPSO for influence maximization in social networks,which can make full use of overlapping nodes,non‐overlapping nodes,and their interactive information is ***,an overlapping community detection algorithm is used to obtain the information of overlapping community structures,based on which three novel evolutionary strategies,such as initialisation,mutation,and local search are designed in OCPSO for better finding influential *** results in terms of influence spread and running time on nine real‐world social networks demonstrate that the proposed OCPSO is competitive and promising comparing to several state‐of‐the‐arts(***,CMA‐IM,CIM,CDH‐SHRINK,CNCG,and CFIN).

关键词： computational intelligence data mining social network

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