Computers & Electrical Engineering最新文献

Adaptive priority and optimum parameters based relay vehicle selection scheme in vehicular ad hoc networks 基于自适应优先级和最优参数的车载自组网中继车辆选择方案

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-06-25 DOI: 10.1016/j.compeleceng.2025.110521

Virender Kumar , Vivek Srivastava , Nishu Gupta , Jukka Mäkelä

引用次数: 0

Quantum Blockchain for Internet of Things: A systematic review, proposed solutions and challenges 物联网的量子区块链：系统回顾，提出的解决方案和挑战

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-06-25 DOI: 10.1016/j.compeleceng.2025.110524

Diksha Chawla , Saru Kumari , Rajkumar Singh Rathore , Pawan Singh Mehra , Ashok Kumar Das , Neeraj Kumar

{"title":"Quantum Blockchain for Internet of Things: A systematic review, proposed solutions and challenges","authors":"Diksha Chawla , Saru Kumari , Rajkumar Singh Rathore , Pawan Singh Mehra , Ashok Kumar Das , Neeraj Kumar","doi":"10.1016/j.compeleceng.2025.110524","DOIUrl":"10.1016/j.compeleceng.2025.110524","url":null,"abstract":"<div><div>Blockchain and IoT systems have become the focal point of extensive research efforts in academia and industry. Recent studies have honed in on utilizing Blockchain technology to enhance trust management within IoT networks. Despite the potential benefits across various applications, existing blockchain platforms hinge on digital signatures, public key cryptography, Zero-Knowledge Proofs, and hash functions, making them susceptible to attacks facilitated by Quantum computers. Motivated by these vulnerabilities, this survey paper emphasizes the importance of incorporating Quantum Blockchain technology into the IoT landscape to establish trust among IoT devices. Specifically, we provide an overview of Blockchain technology and its security implications in the context of IoT security. The primary goal of this paper is to propose a Novel Quantum Blockchain framework that protects from classical and Quantum computing attacks. For this purpose, this article examines Quantum cryptography for securing an IoT communication framework. Then, we analyze Blockchain and Quantum integration with IoT, including security primitives, recent tools, technologies, problems and solutions. The comparative analysis of classical and Blockchain cryptographic primitives with Quantum-resistant schemes is analyzed in our work. Our research outlines future avenues for exploration aimed at establishing Quantum Blockchain security to ensure secure communication within an IoT-enabled framework.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"126 ","pages":"Article 110524"},"PeriodicalIF":4.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144470597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards a high efficiency implantable electric simulator for programmable biomedical stimulations 面向可编程生物医学刺激的高效植入式电子模拟器

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-06-25 DOI: 10.1016/j.compeleceng.2025.110495

Hany Shaker , Kasem Khalil , Mohamed Abbas , Khalil Yousef

引用次数: 0

ESecMedIR: Efficient and Secure Dual-level Transformer based Medical Image Retrieval Framework in the Cloud ESecMedIR：高效、安全的基于双电平变压器的云医学图像检索框架

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-06-25 DOI: 10.1016/j.compeleceng.2025.110519

Arun Amaithi Rajan , Vetriselvi V. , Mayank Raikwar , Mohamed Fuzail H.

{"title":"ESecMedIR: Efficient and Secure Dual-level Transformer based Medical Image Retrieval Framework in the Cloud","authors":"Arun Amaithi Rajan , Vetriselvi V. , Mayank Raikwar , Mohamed Fuzail H.","doi":"10.1016/j.compeleceng.2025.110519","DOIUrl":"10.1016/j.compeleceng.2025.110519","url":null,"abstract":"<div><div>Critical information within medical images helps in accurate diagnosis and research. Nevertheless, this increases the need for efficient storage and retrieval of information embedded in medical images. Moreover, it poses crucial challenges, such as privacy management and secure retrieval of these images. To address these challenges, this paper proposes an Efficient and Secure Medical Image Storage and Retrieval framework (ESecMedIR) leveraging cloud computing. To address privacy concerns and the challenge of balancing security with retrieval efficiency, ESecMedIR utilizes Dual-level Vision Transformer-based Hashing and Privacy Region Encryption. Sensitive regions in medical images are identified and encrypted using hyperchaos-based encryption, while hashcodes are generated and used in similar image searches for efficient retrieval. The proposed framework is tested on three standard medical datasets of Brain MRI, Chest X-ray, and Kidney CT, demonstrating a 10%–30% improvement in the retrieval accuracy over existing methods, ensuring secure and efficient image storage and retrieval management.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"126 ","pages":"Article 110519"},"PeriodicalIF":4.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144470596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The mobility modeling and the probability density function of the radar cross section for large-scale unmanned aerial vehicle swarms 大型无人机群机动建模及雷达横截面概率密度函数

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-06-25 DOI: 10.1016/j.compeleceng.2025.110484

Mengxia Liu , Shengbo Hu , Qiwei Hu , Tingting Yan , Yanfeng Shi

{"title":"The mobility modeling and the probability density function of the radar cross section for large-scale unmanned aerial vehicle swarms","authors":"Mengxia Liu , Shengbo Hu , Qiwei Hu , Tingting Yan , Yanfeng Shi","doi":"10.1016/j.compeleceng.2025.110484","DOIUrl":"10.1016/j.compeleceng.2025.110484","url":null,"abstract":"<div><div>In recent years, the deployment of unmanned aerial vehicle (UAV) swarms in both military and civilian applications has attracted considerable attention. Radar cross section (RCS) of irregular geometry plays a critical role in target detection and tracking. The characteristics of UAV swarms — specifically their low, slow, and small (LSS) characteristics — combined with their spatially high-density random distribution, pose significant challenges for accurately modeling and computing the RCS of large-scale UAV swarms. To address these challenges, this paper investigates the mobility modeling and the probability density function(pdf) of the RCS for large-scale UAV swarms. Firstly, using the twin-satellites model, the mobility modeling of the adjacent UAVs of the UAV swarm is built. Secondly, the spatial pdf of the RCS of large-scale UAV swarms is given. Finally, the effectiveness and rationality of the proposed models are verified. These findings provide a solid theoretical basis for advancing methodologies in UAV swarms detection and tracking.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"126 ","pages":"Article 110484"},"PeriodicalIF":4.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144470457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LPC-Det: Attention-based lightweight object detector for power line component detection in UAV images LPC-Det：用于无人机图像中电力线成分检测的基于注意力的轻型目标检测器

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-06-25 DOI: 10.1016/j.compeleceng.2025.110476

Seema Choudhary , Sumeet Saurav , Prashant Gidde , Ravi Saini , Sanjay Singh

{"title":"LPC-Det: Attention-based lightweight object detector for power line component detection in UAV images","authors":"Seema Choudhary , Sumeet Saurav , Prashant Gidde , Ravi Saini , Sanjay Singh","doi":"10.1016/j.compeleceng.2025.110476","DOIUrl":"10.1016/j.compeleceng.2025.110476","url":null,"abstract":"<div><div>Lacking timely maintenance of power line infrastructures is a prime cause of power shortages and large-scale blackouts. The current manual inspection method used in power line monitoring is time-consuming, less accurate, expensive, and prone to human error. Thus, there is a requirement for intelligent monitoring of power line infrastructure. Recent advancements in Unmanned Aerial Vehicles (UAVs) and deep learning have opened the area of intelligent power line infrastructure monitoring. However, the diversity of the UAV dataset can hurt the detection accuracy of lightweight object detectors, while the heavier one has a high computational cost. Thus, achieving a suitable trade-off between computational cost and detection accuracy is challenging. To this end, this work presents a lightweight and robust object detector named LPC-Det for power line component detection. The proposed LPC-Det, built on top of the YOLOv7 object detector, uses parameter-efficient attention modules to enhance the detection accuracy without much enhancement in the computation time. We also introduce a custom in-house power line dataset captured using UAV at different power line infrastructure sites in India. The dataset contains 10,968 power line images labeled into five types of components and aims to highlight diversity in power line infrastructure. Evaluated on the newly introduced dataset, the proposed LPC-Det using 640 × 640 input images achieved a remarkable baseline mAP@50 of 90.30%, a 1.7% improvement over the baseline YOLOv7. To further validate the efficacy of the proposed LPC-Det model, we trained and tested it on five public benchmark power line datasets. The proposed model consistently achieved a better mAP on all these datasets with slightly increased model size and parameters, GFLOPs, and inference time than the baseline YOLOv7 object detector.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"126 ","pages":"Article 110476"},"PeriodicalIF":4.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144470458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing cybersecurity in Agriculture 4.0: A high-performance hybrid deep learning-based framework for DDoS attack detection 加强农业4.0中的网络安全：用于DDoS攻击检测的高性能混合深度学习框架

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-06-24 DOI: 10.1016/j.compeleceng.2025.110431

Prabu Kaliyaperumal , Tamilarasi Karuppiah , Rajakumar Perumal , Manikandan Thirumalaisamy , Balamurugan Balusamy , Francesco Benedetto

{"title":"Enhancing cybersecurity in Agriculture 4.0: A high-performance hybrid deep learning-based framework for DDoS attack detection","authors":"Prabu Kaliyaperumal , Tamilarasi Karuppiah , Rajakumar Perumal , Manikandan Thirumalaisamy , Balamurugan Balusamy , Francesco Benedetto","doi":"10.1016/j.compeleceng.2025.110431","DOIUrl":"10.1016/j.compeleceng.2025.110431","url":null,"abstract":"<div><div>Industry 4.0 technologies are transforming agriculture, moving towards Agriculture 4.0: i.e., a new era focused on enhancing productivity and sustainability through advancements such as Internet of Things (IoT), Artificial Intelligence (AI), fog and cloud computing. Devices equipped with IoT technology continuously gather real-time data on soil quality, crop health, and equipment functionality, which is then analyzed via fog and cloud computing to streamline farming operations and improve agricultural efficiency. Although these advancements enhance productivity, they also pose considerable cybersecurity threats, especially in terms of Distributed Denial of Service (DDoS) attacks, which can jeopardize the availability and reliability of essential systems and critical infrastructures. This paper presents a deep learning-driven security framework aimed at mitigating these vulnerabilities in Agriculture 4.0. We propose a hybrid Intrusion Detection System (IDS) integrating a deep-Autoencoder (dAE) for binary classification and a Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) for multiclass clustering. Our framework, exploiting real-world data from the CIC-DDoS2019 dataset to detect DDOS attacks, evaluates autoencoder models alongside HDBSCAN, with each technique tested in three configurations. This combined approach demonstrates effective threat detection and classification capabilities, achieving accuracy levels exceeding 98%, thus enhancing the cybersecurity of agriculture 4.0, promoting robust, data-informed, and efficient farming practices while aligning with Sustainable Development Goals (SDGs) concerning industrial innovation and resilience.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"126 ","pages":"Article 110431"},"PeriodicalIF":4.0,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144365218","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhanced security of data dissemination in blockchain-based peer-to-peer smart energy trading network 增强基于区块链的点对点智能能源交易网络中数据传播的安全性

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-06-24 DOI: 10.1016/j.compeleceng.2025.110523

Muhammad Izzat Aiman Mohamad Zainal ’Asri, Nur Fadhilah Mohd Shari, Amizah Malip

{"title":"Enhanced security of data dissemination in blockchain-based peer-to-peer smart energy trading network","authors":"Muhammad Izzat Aiman Mohamad Zainal ’Asri, Nur Fadhilah Mohd Shari, Amizah Malip","doi":"10.1016/j.compeleceng.2025.110523","DOIUrl":"10.1016/j.compeleceng.2025.110523","url":null,"abstract":"<div><div>A peer-to-peer (P2P) energy trading network provides a platform that enables households to share surplus energy. Unfortunately, the use of digital platform for energy transactions may raise concerns over privacy breaches, energy data misuse and threats to home security if occupancy patterns are exposed. To address these issues, the network must concurrently provide reliable data sharing, protect user’s privacy and accountability for misbehaviors. Our solution begins with the construction of a generic blockchain-enabled P2P energy trading framework that generalizes the critical operations for secure energy transactions. Consequently, this generic framework serves as the foundation for the construction of our proposed consortium blockchain-enabled data dissemination mechanism in the P2P smart energy trading scheme. The adoption of blockchain in the proposed scheme is supported by the implementation of message-linkable group signature scheme and NBFT consensus mechanism to achieve robust security in decentralized energy trading environment. Additionally, continuous double auction mechanism is integrated in this scheme to connect energy buyers and sellers to engage in transactions consistently. The analysis evaluation demonstrates our proposed scheme is robust against adversarial attacks. delivers performance metrics comparable to existing solutions and achieves throughput requirements for practical real-world establishment.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"126 ","pages":"Article 110523"},"PeriodicalIF":4.0,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144365219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Developing adaptive Yolov5-based Telugu handwritten character segmentation and classification framework using Enhanced Chef-Based Optimization Algorithm and Deep Learning Networks 基于改进的chef优化算法和深度学习网络开发基于yolov5的自适应泰卢固手写体字符分割和分类框架

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-06-24 DOI: 10.1016/j.compeleceng.2025.110533

M. Kiran Sastry, S. Aparna

{"title":"Developing adaptive Yolov5-based Telugu handwritten character segmentation and classification framework using Enhanced Chef-Based Optimization Algorithm and Deep Learning Networks","authors":"M. Kiran Sastry, S. Aparna","doi":"10.1016/j.compeleceng.2025.110533","DOIUrl":"10.1016/j.compeleceng.2025.110533","url":null,"abstract":"<div><div>Handwritten character recognition through automated techniques is one of the recent innovations in the industry, as it helps in interpreting historical documents, digital scripts, and large records. Deep learning techniques are effective in recognizing complex image patterns like handwritten Telugu scripts, and however, inherent variability in writing styles, unique characteristics, limited data pose a challenging recognition environment. Defining a robust segmentation and classification tool with intelligent deep-learning techniques is one of the possible solutions for handling the variability and challenges within handwritten character recognition. So, this paper presented an effective Telugu handwritten character segmentation and classification model for handling the challenges in recognition of variable length sequences. Initially, the handwritten images are acquired from online data sources and are inputted into the Adaptive Yolov5 (A-YoloV5) model for the segmentation process. Here, the Enhanced Chef-Based Optimization Algorithm (ECOA) is developed for improving the performance of YoloV5 platform and reduces complexity in training. Then, the Adaptive Yolov5 (A-YoloV5) segments the telugu characters from the input handwritten images. After extracting ROI, the extracted images are sent into the newly developed Convolutional Neural Networks with Residual Attention-based Long Short-Term Memory layer (CNN-RA-LSTM) model for classification of the handwritten images. This network combines the CNN and LSTM networks with added residual layers to effectively extract the sequential features and then categorize the text. The efficacy of the CNN-RA-LSTM and A-Yolov5 model is compared with recent Telugu handwritten character recognition and has resulted in 95.41 % text recognition accuracy.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"126 ","pages":"Article 110533"},"PeriodicalIF":4.0,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144365217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Open problems and challenges in federated learning for IoT: A comprehensive review and strategic guide 物联网联邦学习中的开放性问题和挑战：全面回顾和战略指南

IF 4 3区计算机科学

Computers & Electrical Engineering Pub Date : 2025-06-23 DOI: 10.1016/j.compeleceng.2025.110515

Bidita Sarkar Diba , Jayonto Dutta Plabon , Tasnim Jahin Mowla , Nazneen Nahar , Durjoy Mistry , Sourav Sarker , M.F. Mridha , Jungpil Shin

{"title":"Open problems and challenges in federated learning for IoT: A comprehensive review and strategic guide","authors":"Bidita Sarkar Diba , Jayonto Dutta Plabon , Tasnim Jahin Mowla , Nazneen Nahar , Durjoy Mistry , Sourav Sarker , M.F. Mridha , Jungpil Shin","doi":"10.1016/j.compeleceng.2025.110515","DOIUrl":"10.1016/j.compeleceng.2025.110515","url":null,"abstract":"<div><div>Federated Learning is defined as a decentralized approach to machine learning that enables multiple devices to collaboratively train a shared model while keeping their data localized and private. This paper offers a comprehensive review of FL’s integration with the Internet of Things (IoT), serving as a guidebook for future research directions through 2033. It explores the current state-of-the-art applications of FL within IoT, emphasizing its potential to enhance critical functionalities such as secure data sharing, computational offloading, attack detection, localization, and mobile crowdsensing. The paper identifies key challenges, including resource constraints, communication efficiency, and the need for robust defenses against adversarial attacks, and proposes targeted research initiatives to address these issues. By encouraging interdisciplinary collaboration and the development of innovative algorithmic solutions, this guide outlines a clear roadmap for advancing the integration of FL within IoT, aiming to foster the creation of secure, scalable, and privacy-preserving IoT networks that will underpin the technological landscape of 2033.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"126 ","pages":"Article 110515"},"PeriodicalIF":4.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144344633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0