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Compressive sector selection and channel estimation for optimizing throughput and delay in IEEE 802.11ad WLAN

International Journal of Information technology (Singapore) 2025年第2期17卷 987-998页

作者： Venkatachalam, Ilayaraja Palaniappan, Senthilnathan Ameerjohn, Sirajudeen School of Computer Science and Engineering Vellore Institute of Technology Tamilnadu Vellore India School of Computing Science and Engineering VIT Bhopal University Bhopal-Indore Highway Kothrikalan Madhya Pradesh Sehore 466114 India

To identify the best beam pair in IEEE 802,11ad Wireless Local Area Network (WLAN), it is necessary to evaluate every potential pair's spectrum responsiveness and Signal-to-noise Ratio (SNR). During beam monitoring, nevertheless, the pilot expenditure associated with SNR or channels predictions have been successfully minimized. Therefore, it is crucial to reduce the aided pilot in beam monitoring. In this study, we present a methodology for IEEE 802,11ad WLAN performance and latency optimization called Compressive Sector Selection and Channel Estimation (CSS-CE). 60 GHz transmissions suffer from scarce channel in beams synchronization. Thus, to lower the sample rate, Compressed Sensing (CS) is used. The next step is to recreate the compressed data utilizing Orthogonal Pairing Pursuit (OPP). Because different antenna sectors share similar spatial characteristics, compressive sensing typically probes portions of antenna sector areas. Next, the most suitable region is chosen to meet the additional transmission's latency as well as throughput limitations. The suggested CSS-CE paradigm outperforms the current methods in terms of performance and interruption, according to data from experiments. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Beam tracking Channel estimation Compressive sensing IEEE 802.11ad WLAN Orthogonal matching pursuit

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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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FEDINVERSE: EVALUATING PRIVACY LEAKAGE IN FEDERATED LEARNING 12

FEDINVERSE: EVALUATING PRIVACY LEAKAGE IN FEDERATED LEARNING

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12th International Conference on Learning Representations, ICLR 2024

作者： Wu, Di Bai, Jun Song, Yiliao Chen, Junjun Zhou, Wei Xiang, Yong Sajjanhar, Atul School of Mathematics Physics and Computing University of Southern Queensland Australia School of Information Technology Deakin University Australia The University of Adelaide Australia Computer Center Peking University China School of Science Computing and Engineering Technologies Swinburne University of Technology Australia

Federated Learning (FL) is a distributed machine learning technique where multiple devices (such as smartphones or IoT devices) train a shared global model by using their local data. FL promises better data privacy as the individual data isn't shared with servers or other participants. However, this research uncovers a groundbreaking insight: a model inversion (MI) attacker, who acts as a benign participant, can invert the shared global model and obtain the data belonging to other participants. In such scenarios, distinguishing between attackers and benign participants becomes challenging, leading to severe data-leakage risk in FL. In addition, we found even the most advanced defense approaches could not effectively address this issue. Therefore, it is important to evaluate such data-leakage risks of an FL system before using it. Motivated by that, we propose FedInverse to evaluate whether the FL global model can be inverted by MI attackers. In particular, FedInverse can be optimized by leveraging the Hilbert-Schmidt independence criterion (HSIC) as a regularizer to adjust the diversity of the MI attack generator. We test FedInverse with three typical MI attackers, GMI, KED-MI, and VMI. The experiments show that FedInverse can effectively evaluate the data leakage risk that attackers successfully obtain the data belonging to other participants. The code of this work is available at https://***/Jun-B0518/FedInverse. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Data privacy

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Exploring information hiding in images – problems and measures: a survey

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Multimedia Tools and Applications 2025年 1-63页

作者： Khan, Ahmed Sarfaraz, Aaliya School of Computer Science Taylor’s University Subang Jaya Malaysia School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Selangor Darul Ehsan Bandar Sunway47500 Malaysia

The widespread adoption of digital communication technologies across various domains has led to a significant shift towards digitalization. However, this evolution has also introduced vulnerabilities, including unauthorized access, data manipulation, and fabrication by malicious entities, which severely impact the confidentiality and integrity of information. The proliferation of digital images on the internet has facilitated the use of these media for embedding confidential data in a way that prevents detection or extraction by unauthorized third parties. Despite numerous efforts, existing image steganography and watermarking techniques often lack robustness. This paper proposes several novel strategies to enhance the dynamism and imperceptibility of these techniques, integrating cryptographic methods by manipulating the entropy of encrypted images using minimal pixel value differences and applying unique image transformations. Additionally, the study highlights that implementing multi-layered security mechanisms can protect confidential information from modern statistical steganalysis attacks, thereby preserving both the integrity of hidden data and image quality. A comprehensive review of information hiding, specifically focusing on image watermarking and steganography, is provided. Furthermore, this survey outlines key problem statements and offers quantitative metrics for future research in this area. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Steganography

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Cooperative Jamming for Physical Layer Security Enhancement Using Deep Reinforcement Learning

Cooperative Jamming for Physical Layer Security Enhancement ...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Hoseini, Sayed Amir Bouhafs, Faycal Aboutorab, Neda Sadeghi, Parastoo Hartog, Frank Den School of Systems and Computing Australia School of Engineering and Technology University of New South Wales Canberra Australia

ISBN: (纸本)9798350370218

Wireless data communications are always facing the risk of eavesdropping and interception. Conventional protection solutions which are based on encryption may not always be practical as is the case for wireless IoT networks or may soon become ineffective against quantum computers. In this regard, Physical Layer Security (PLS) presents a promising approach to secure wireless communications through the exploitation of the physical properties of the wireless channel. Cooperative Friendly Jamming (CFJ) is among the PLS techniques that have received attention in recent years. However, finding an optimal transmit power allocation that results in the highest secrecy is a complex problem that becomes more difficult to address as the size of the wireless network increases. In this paper, we propose an optimization approach to achieve CFJ in large Wi-Fi networks by using a Reinforcement Learning Algorithm. Obtained results show that our optimization approach offers better secrecy results and becomes more effective as the network size and the density of Wi-Fi access points increase. © 2023 IEEE.

关键词： Artificial Noise Friendly Jamming Machine Learning Physical-Layer Security Programmable Networks Reinforcement Learning SDN Secrecy

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SMART DRIVING LICENSING SYSTEM AND AUTHORIZATION OF AUTONOMOUS VEHICLE INTEGRATING WIRELESS SENSOR NETWORKS AND IoT

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Telecommunications and Radio engineering (English translation of Elektrosvyaz and Radiotekhnika) 2024年第9期83卷 69-85页

作者： Santhakumar, D. Sudhan, M.B. Prasanna, R. Manikandan, S.P. Prathaban, Banu Priya Kanna, Rajesh R. Saravanan, G. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Chennai Ramapuram India Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electronics and Communication Engineering Sree Sastha Institute of Engineering and Technology Chennai India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Department of Electronics and Communication Engineering Sri Sairam Institute of Technology Tamil Nadu Chennai India

The advent of autonomous vehicles has revolutionized the automotive industry, offering promising advancements in safety, efficiency, and mobility. To integrate these autonomous vehicles into our society seamlessly, it is imperative to establish a robust licensing system that ensures the safe operation of automated vehicles on public roads. This article presents an effective smart Driving Licensing System (SDLS), a digital framework designed to regulate the existing licensing system and authorize licensing for autonomous vehicles. Through a comprehensive assessment process, the system assesses the drivers and the autonomous vehicle’s ability to navigate various road conditions, respond to dynamic environments, and prioritize safety for passengers and other road users. SDLS integrates high-level security through facial and fingerprint verification for identity verification to preserve licensing data. Hacking and unauthorized access to autonomous vehicles is prevented by robust cybersecurity protocols. The key benefits of SDLS include automated data collection, integration of wireless sensor networks (WSN) and Internet of Things (IoT), smart application processing, real-time license checks, cybersecurity measures, automated testing, and digital certification. The proposed system offers a comprehensive approach to safely integrating autonomous vehicles into our transportation ecosystem. The autonomous vehicles are integrated with antennas at rooftops or into bumpers, depending upon the vehicle design which can be connected with the SDLS for signal communication. Furthermore, the SDLS establishes a framework for continuous monitoring and updates, enabling ongoing assessments to keep pace with evolving technology and safety standards. © 2024 by Begell House, Inc.

关键词： Autonomous vehicles

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Comparing Deep Learning Object detection Methods for Real Time Cow Detection 38

Comparing Deep Learning Object detection Methods for Real Ti...

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38th IEEE Region 10 Conference, TENCON 2023

作者： Martha, Gichuki Wambui Waweru Mwangi, Ronald Aramvith, Supavadee Rimiru, Richard Jomo Kenyatta University of Agriculture & Technology Computing Department School of Computing & Information Technology Nairobi Kenya Chulalongkorn University Department of Electrical Engineering Faculty of Engineering Bangkok Thailand

ISBN: (纸本)9798350302196

Deep learning algorithms particularly Convolutional Neural Networks (CNNs) are the state-of-the-art techniques for object detection, classification, segmentation and behaviour classification. These algorithms have extensive application across various domains including agriculture. However, cow identification in dairy farming still relies on methods like direct visual monitoring which are time consuming, costly and inaccurate;or use of invasive contact devices such as sensors which can cause discomfort during attachment or removal. This research compared three deep learning object detection models i.e. YOLOv5, YOLOv7 and YOLOv8, which were selected based on their performance in object detection tasks. We generated cow images from videos captured from a housed dairy cattle barn. The dataset had 11,828 cow images, augmented to depict different illumination conditions and using makesense AI tool, we annotated the images in YOLO format, trained and validated the three models to visualize cow bounding boxes. Our approach demonstrates efficiency of the YOLOv8 model, achieving an accuracy of 94.7% and 93 % before and after data augmentation respectively. YOLOv8 baseline model was finetuned using the Ray Tune library achieving a mAP@O.50 score of 92. 9%. This research makes a significant contribution in the future research direction of the YOLO algorithm and highlights the practical implementation of deep learning models for cow detection, applicable in livestock management. © 2023 IEEE.

关键词： Object detection

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Deep Learning & Computer Vision Integrated Smart Voting System 2

Deep Learning & Computer Vision Integrated Smart Voting Syst...

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2nd IEEE International Conference on Distributed computing and Electrical Circuits and Electronics, ICDCECE 2023

作者： Dube, Sanskruti Venkata Siva Sandeep, Mysore Challagundla, Hamsitha Nikhilesh Chand, Pulaparthi Vellore Institute of Technology School of Computing Science and Engineering Bhopal India Vellore Institute of Technology School of Information Technology Vellore India Vellore Institute of Technology School of Electronics Engineering Vellore India

ISBN: (纸本)9798350347456

In this paper, an online voting method for elections in India is initially suggested. The suggested model has higher security as the voter's raised secure password must be validated prior to the recording of the vote in the major database owned by our nation's Election Commission. The model's additional feature allows the voter to verify that the right candidate or party received their vote. In this arrangement, a voter has the option to cast a ballot from a place other than the one designated for them or from their favorite site. Vote counting will be made in an automated fashion under the proposed approach, saving a significant amount of period and allowing our nation's Commission set for Election to declare the results in a much fast succession. Alongside the password-based authentication, we have also utilized the face-based authentication along with the successful implementation of the OpenCV in addition to the password validation. We describe a model for a web voting entity for India in this application using the above said constituents. When contrasted with the conventional voting system, this version of voting systems seems to be considerably much secure and efficient. The delays and frauds occurring during counting of votes can be easily prevented. © 2023 IEEE.

关键词： Authentication

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A Creative Research in the Field of Urban Fire Detection 5

A Creative Research in the Field of Urban Fire Detection

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5th International Conference on Computer Vision, Image and Deep Learning, CVIDL 2024

作者： Feng, Junming Wang, Ruixiang Xi, Chenyang Peng, Jiayu Harbin Institute of Technology Faculty of Computing Harbin China Harbin Institute of Technology School of Information Science and Engineering Harbin China Harbin Institute of Technology School of Software Harbin China

ISBN: (纸本)9798350373820

As the most common disaster in human daily production and life, fire is very likely to threaten people's life and property safety. In order to avoid fire, this paper designs and implements a fire detection system based on video and YOLOv5 model, and describes the improvement of YOLOv5 compared with the previous generation YOLO model. Collect fire images through the network, use image processing technology to enhance the data, make fire data set and divide the data set by setting aside method. Then the self-made fire data set is used to train the fire detection model based on YOLOv5 model, and the model is improved by adding transfer learning, multi-scale training and other technologies to improve the fire detection rate, and the corresponding weight files are obtained. Finally, the training method of the model and the ability of the model to detect fire are expounded, and the innovative application of the model in the field of fire detection is realized. © 2024 IEEE.

关键词： Fires

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PALCT: vehicle-to-vehicle communication based on pseudonym assignment and encryption scheme using delay minimisation cover tree algorithm

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International Journal of Vehicle Information and Communication Systems 2025年第2期10卷 132-147页

作者： Tandon, Righa Gupta, P.K. Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science and Engineering Jaypee University of Information Technology HP Solan173 234 India Department of Data Science School of Computing Mohan Babu University AP Tirupati517 102 India

Vehicle-to-vehicle communication is one of the new paradigms of networking, which should be secure, fast, and efficient. In this paper, we propose a framework that implements the pseudonym-based authentication scheme in which communication among vehicles is encrypted by using a matrix array symmetric key (MASK), digital signature algorithm (DSA), and intelligent water drop (IWD). The proposed security scheme also ensures the handling of many security attacks like key-guessing, non-repudiation, replay, and modification. In the proposed scheme, to preserve the identity of the vehicle, we have provided the different pseudonyms for each vehicle in the network that ensure secure communication among vehicles. Furthermore, the proposed delay minimisation cover tree algorithm ensures the issue of timed delay during vehicle-to-vehicle communication. In this algorithm, we have used Dijkstra’s algorithm for finding the optimal shortest path during vehicular communication. The results obtained show that the proposed scheme is effective and efficient, as it reduces the time delay by 4% for 140 vehicle nodes and 28.4% for 1000 vehicle nodes. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Encryption algorithms

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