Computers & Electrical Engineering最新文献

{"title":"Secure lightweight digital twin (DT) technology for seamless wireless communication in vehicular ad hoc network","authors":"M.K. Kishore ,&nbsp;V. Gajendra Kumar ,&nbsp;B. Nancharaiah","doi":"10.1016/j.compeleceng.2025.110291","DOIUrl":"10.1016/j.compeleceng.2025.110291","url":null,"abstract":"<div><div>Security in VANETs (Vehicular Ad Hoc Networks) is crucial due to the inherent vulnerabilities in wireless communication. These networks face several security challenges, including authentication, privacy protection, and secure data transmission. Authentication issues arise from the dynamic nature of vehicular networks, where vehicles need to authenticate each other and entities like roadside units securely. The challenges include GPS spoofing, where attackers manipulate location data to mislead vehicles; Sybil attacks, where a single node illegitimately claims multiple identities to disrupt network operations; and Denial of Service (DoS) attacks, which overwhelm the network, rendering it unavailable for legitimate users. Additionally, key management for secure encryption, replay attacks where previously transmitted messages are maliciously reused, and jamming attacks that disrupt wireless signals are notable threats. To overcome the issue in VANET wireless communication this paper proposed DT-LHBC (Digital Twin Lightweight Hashing Blockchain Cryptography) into VANETs (Vehicular Ad-Hoc Networks) to enhance security and efficiency in vehicular communication systems. DT-LHBC combines advanced cryptographic techniques with digital twin technology to ensure secure data transmission and communication integrity across dynamic vehicular environments. With the implementation of a digital twin technology cryptography model of hashing-based blockchain (LHBC) is integrated to achieve effective wireless communication. Results demonstrate DT-LHBC's effectiveness in mitigating various security threats, including Sybil attacks, Denial of Service (DoS) attacks, and Message Tampering, achieving up to 98 % accuracy in attack detection. Despite challenges in optimizing cryptographic speeds and addressing GPS Spoofing vulnerabilities, DT-LHBC reduces blockchain latency to as low as 12 milliseconds and maintains a high transaction throughput of 500 transactions per second.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110291"},"PeriodicalIF":4.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738038","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}

{"title":"HardSecUAV: A hardware-based mutual authentication protocol for network of drones","authors":"Anubhav Elhence ,&nbsp;Vinay Chamola","doi":"10.1016/j.compeleceng.2025.110286","DOIUrl":"10.1016/j.compeleceng.2025.110286","url":null,"abstract":"<div><div>Unmanned Aerial Vehicle (UAV) networks are increasingly utilized in various applications, yet they face significant security challenges due to their open operational environments and resource constraints. Existing authentication protocols often rely on stored secret keys, making them vulnerable to physical attacks and key compromise. To address this research gap, we propose <em>HardSecUAV</em>, a lightweight and secure authentication protocol that leverages hardware-based security features of the DS28S60 cryptographic coprocessor, including Physically Unclonable Functions (PUFs). Our protocol eliminates the need for secret key storage by generating device-specific secrets on-demand, enhancing resistance to physical attacks. Implementation results demonstrate that <em>HardSecUAV</em> achieves a total computation time of approximately <strong>28.775</strong> <span><math><mrow><mi>μ</mi><mi>s</mi></mrow></math></span> during the authentication phase, suitable for real-time operations. The storage requirement is <strong>1024 bits</strong>, and the communication overhead is <strong>1664 bits</strong>, comparable to existing schemes. Compared to existing protocols, <em>HardSecUAV</em> provides enhanced security features, including mutual authentication, replay attack resistance, forward secrecy, and UAV-to-UAV authentication without dependence on Ground Station(GS), making it a robust solution for securing UAV networks.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110286"},"PeriodicalIF":4.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738042","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}

{"title":"Cross-domain recommender systems via multimodal domain adaptation","authors":"Adamya Shyam ,&nbsp;Ramya Kamani ,&nbsp;Venkateswara Rao Kagita ,&nbsp;Vikas Kumar","doi":"10.1016/j.compeleceng.2025.110300","DOIUrl":"10.1016/j.compeleceng.2025.110300","url":null,"abstract":"<div><div>Collaborative Filtering (CF) has emerged as one of the most prominent implementation strategies for building recommender systems. The key idea is to exploit the usage patterns of individuals to generate personalized recommendations. CF techniques, especially for newly launched platforms, often face a critical issue known as the data sparsity problem, which greatly limits their performance. Cross-domain CF alleviates the problem of data sparsity by finding a common set of entities (users or items) across the domains, which then act as a conduit for knowledge transfer. Nevertheless, most real-world datasets are collected from different domains, so they often lack information about anchor points or reference information for entity alignment. This paper introduces a domain adaptation technique to align the embeddings of entities across domains. Our approach first exploits the available textual and visual information to independently learn a multi-view latent representation for each entity in the auxiliary and target domains. The different representations of the entity are then fused to generate the corresponding unified representation. A domain classifier is then trained to learn the embedding for the domain alignment by fixing the unified features as the anchor points. Experiments on four publicly available benchmark datasets indicate the effectiveness of our proposed approach.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110300"},"PeriodicalIF":4.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738041","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}

{"title":"Data-driven recursive multivariable modeling, operation, and control of active distribution networks with distributed generation","authors":"Diego Feroldi ,&nbsp;Pablo Rullo ,&nbsp;Sair Rodríguez del Portal,&nbsp;Lautaro Braccia ,&nbsp;Patricio Luppi ,&nbsp;David Zumoffen","doi":"10.1016/j.compeleceng.2025.110241","DOIUrl":"10.1016/j.compeleceng.2025.110241","url":null,"abstract":"<div><div>This paper addresses the challenges arising from the increasing integration of distributed generation into active distribution networks (ADNs), focusing on their modeling, operation, and control. Data-driven recursive multivariable modeling, capable of capturing both static and dynamic interactions in real time, has emerged as a promising solution. By utilizing the extensive data generated by modern grid infrastructure, this approach enhances network model accuracy and improves operational efficiency and control strategies. This paper strengthens the connection between Process Systems Engineering (PSE) and power systems, traditionally underexplored in this domain. By integrating PSE principles, particularly data-driven and control allocation methodologies, into the modeling, operation, and control of ADNs, this work optimizes power system performance. Three Recursive Partial Least Squares (RPLS) methodologies—sample-wise, block-wise, and moving-window—are rigorously compared regarding estimation/prediction characteristics and convergence speed. This novel analysis challenges the assumption of instantaneous model adaptation, emphasizing the importance of carefully considering convergence periods for effective monitoring, control, and optimization. The paper proposes and analyzes three control structures integrated into an RPLS-based supervisory strategy for voltage regulation at ADN nodes: (1) decentralized control, (2) control allocation with measurement combination, and (3) optimization-based centralized control. Different integration formats are evaluated based on the controller technology used: (a) simple setpoint updates, (b) full ADN model adaptation to recalculate controller matrices, and (c) full model adaptation for updating the optimization formulation. Simulation results were obtained using the IEEE 33-bus test system. The results reveal a trade-off between the complexity and performance benefits of each control strategy. Although no strategy proves definitively superior, the latter two show more promising overall prospects.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110241"},"PeriodicalIF":4.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738039","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}

{"title":"PrGChain: A privacy-preserving blockchain-enabled energy trading system","authors":"Ahmed-Sami Berkani ,&nbsp;Hamouma Moumen ,&nbsp;Saber Benharzallah ,&nbsp;Mohand Tahar Kechadi ,&nbsp;Ahcène Bounceur","doi":"10.1016/j.compeleceng.2025.110289","DOIUrl":"10.1016/j.compeleceng.2025.110289","url":null,"abstract":"<div><div>The integration of blockchain, Internet of Things devices, and distributed energy resources is revolutionizing peer-to-peer energy trading by enabling decentralized, efficient, and transparent transactions. However, existing solutions face challenges related to privacy, interoperability, and scalability. This paper presents PrGChain, a privacy-preserving blockchain-enabled energy trading framework within the smart grid, incorporating a decentralized ZKOracle to securely connect blockchain networks with off-chain energy data sources.</div><div>The proposed ZKOracle employs zero-knowledge proofs to verify energy data without exposing sensitive information, ensuring compliance with privacy regulations, while leveraging a distributed network of oracle nodes for enhanced reliability and interoperability. To improve security and efficiency, PrGChain utilizes smart contracts, decentralized applications (dApps), and leverages stablecoins to mitigate cryptocurrency volatility.</div><div>Performance evaluations demonstrate that our system achieves improved decentralization and privacy without sacrificing efficiency. This is particularly true when deployed on Layer 2 blockchain networks like Polygon, where transaction latency and costs are significantly reduced.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110289"},"PeriodicalIF":4.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143737773","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}

{"title":"Hierarchical manifold sample envelope transformation model for ensemble classification","authors":"Jie Ma ,&nbsp;Hong Chen ,&nbsp;Yongming Li ,&nbsp;Pin Wang ,&nbsp;Chengyu Liu ,&nbsp;Yinghua Shen ,&nbsp;Witold Pedrycz ,&nbsp;Wei Wang ,&nbsp;Fan Li","doi":"10.1016/j.compeleceng.2025.110252","DOIUrl":"10.1016/j.compeleceng.2025.110252","url":null,"abstract":"<div><div>Ensemble classification is an important branch and research focus in machine learning and pattern recognition. The current main paradigm of ensemble classification algorithms is based on same original samples, resulting in limited diversity between subsets. Therefore, it is particularly important to mine diverse and effective information from the original samples to build a multi-layer samples. However, samples are input into classifier for training one by one, or batch by batch in the existing ensemble algorithms. This ignores the potential value of correlation information among samples during base classifier training. To solve these problems, a new sample transformation model for ensemble classification - Hierarchical Manifold Sample Envelope Transformation Model (HMSET) is proposed. The model consists of three main parts. The first part is manifold sample enveloping model. It extracts local correlation among samples, thereby constructing manifold envelope samples. The second part is hierarchical sample envelope transformation model, which uses a variety of transformation operators and interlayer consistency to mine and constrain the correlation information among samples to enhance the diversity. The third part is two-dimensional fusion mechanism which fuse the final prediction results of the base classifiers. The 19 UCI datasets and several representative algorithms are used for validation. The results show that compared with the original samples, the proposed model improves the diversity of the sample subsets significantly. Compared with related ensemble classification algorithms, the proposed model has significantly better performance of ensemble classification.</div><div>Data and code are available in: <span><span>https://github.com/acceptthisjj/HMSET</span><svg><path></path></svg></span></div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110252"},"PeriodicalIF":4.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738232","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}

{"title":"Joint optimization approach for improving spectrum utilization in CRN with perfect and imperfect sensing","authors":"Sudipta Mallick ,&nbsp;Susmita Das ,&nbsp;Arun Kumar Ray","doi":"10.1016/j.compeleceng.2025.110293","DOIUrl":"10.1016/j.compeleceng.2025.110293","url":null,"abstract":"<div><div>With the extensive use of intelligent wireless applications, the spectrum allocation strategies and quality of service provided in the 5G era cannot support the growing demand. In this regard, cognitive radio has been incorporated into 6G networks as an intelligent wireless paradigm for solving the spectrum underutilization problem by opportunistically giving dynamic spectrum access to utilize the licensed band. From an implementation perspective, better utilization of available spectrum holes requires fast and reliable spectrum sensing techniques. The spectrum sensing performance in cognitive radio depends on selecting an appropriate decision threshold and sensing duration. However, selecting a decision threshold is a critical factor, and it is observed that a fixed decision threshold approach employs poor detection performance and takes a longer sensing duration. Based on the above shortcomings, a dynamic decision threshold-based energy detection approach is proposed to improve the detection performance and sensing ability even in low SNR regions. Moreover, a PU’s state transition model with SU’s possible data transmission cases is investigated and further analyzed through perfect and imperfect sensing scenarios for effective spectrum utilization. Two critical parameters, decision threshold and sensing duration, are jointly optimized to maximize spectrum utilization efficiency. Simulation results demonstrate that the proposed approach has achieved better spectrum utilization efficiency, up to 24% over existing approaches.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110293"},"PeriodicalIF":4.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747244","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}

{"title":"Intelligent management of waste bins in indoor public places based on waste detection and recognition","authors":"Yangke Li,&nbsp;Xinman Zhang","doi":"10.1016/j.compeleceng.2025.110298","DOIUrl":"10.1016/j.compeleceng.2025.110298","url":null,"abstract":"<div><div>Due to the lack of convenient waste bins nearby, many people choose to litter indiscriminately in indoor public places. Dirty floors not only diminish the shopping experience of customers, but also increase potential risks to pedestrian safety. For the intelligent management of waste bins in indoor public places, this paper proposes a novel solution based on automatic waste detection and recognition, which helps to minimize littering and improve the recycling efficiency of indoor waste. This solution mainly uses a deep learning model to detect and recognize waste items, which can effectively record the distribution of waste and provide a basis for the reasonable placement of waste bins. Specifically, we construct a high-quality indoor waste image dataset for waste detection, which can provide an effective data source for model optimization of this task. This dataset is collected from three common public places, including hospitals, supermarkets, and subway stations. At the same time, it contains four waste categories, 4000 color images, and 6968 box-level annotations. In addition, we propose a novel feature decoupling network for indoor waste detection and recognition, which disentangles specific features for different vision tasks. On the one hand, it uses a recognition enhancement module to generate discriminative feature maps with more semantic information. On the other hand, it introduces a detection enhancement module to output rich feature maps with more detail information. As plug-and-play modules, these two modules are suitable for different networks. Relevant experimental results show that our method is competitive with other representative object detection models and can achieve consistent performance improvements on distinct models. In general, our solution provides new insights into indoor waste bin management.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110298"},"PeriodicalIF":4.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738043","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}

{"title":"Evaluating sustainable wind energy sources with multiple criteria decision-making (MCDM) techniques","authors":"Satyabrata Dash ,&nbsp;Sujata Chakravarty ,&nbsp;Nimay Chandra Giri ,&nbsp;Rohit Khargotra","doi":"10.1016/j.compeleceng.2025.110285","DOIUrl":"10.1016/j.compeleceng.2025.110285","url":null,"abstract":"<div><div>Rural regions with complex topographical constraints face significant challenges in implementing sustainable wind energy solutions due to variations in wind resource availability, infrastructure limitations, and policy gaps. To address this issue, this study integrates Multiple Criteria Decision Making (MCDM) techniques to systematically evaluate and prioritize various wind energy alternatives, considering technical, economic, environmental, and social factors. The PAPRIKA (Potentially All Pairwise RanKings of all possible Alternatives) method is employed to rank wind energy systems based on key criteria such as Capacity Factor, Environmental Impact, and Policy Framework. The findings indicate that Onshore Wind Turbines emerge as the most optimal solution (score: 69.9) due to superior energy production and cost-effectiveness (LCOE). Vertical Axis Wind Turbines (66.5) and Hybrid Wind Systems (60.8) follow, demonstrating balanced performance. Offshore Wind Turbines and Wind Farms with storage show promise but face grid integration and policy challenges, while Floating and Micro Wind Turbines rank lowest due to resource constraints. This research underscores the role of MCDM in integrating quantitative and qualitative assessments, providing a structured framework for energy planners and policymakers to make informed decisions. By optimizing wind energy deployment in rural settings, the study contributes to achieving Sustainable Development Goals (SDGs) 7, 9, and 13, fostering a resilient, low-carbon, and inclusive energy transition.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110285"},"PeriodicalIF":4.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143737772","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}

{"title":"Three-phased multi-scale residual-dense modified-U-Net architecture for deep image steganography","authors":"Suyash Sachdeva ,&nbsp;Ujjwal Sharma ,&nbsp;Priyanshu Rajput ,&nbsp;Riya Singhal ,&nbsp;K. Madhu Kiran ,&nbsp;Rohit Dhiman","doi":"10.1016/j.compeleceng.2025.110299","DOIUrl":"10.1016/j.compeleceng.2025.110299","url":null,"abstract":"<div><div>Recent advancements in deep image steganography have shown promise, yet many existing approaches fail to address the inherently lossy nature of neural networks, limiting their effectiveness. To overcome this limitation, we propose a novel architecture that integrates residual dense multi-scale JBs and FEBs within a three-phased U-Net framework, composed of three interconnected sub-networks: the secret encoder, encoder, and decoder. This modular design enhances data encoding and facilitates easy adaptation to different datasets, offering greater flexibility. By incorporating multi-scale processing to minimize information loss and leveraging high inter-connectivity to improve feature hiding. Additionally, a custom loss function, combining seven distinct components, is employed to guide the model’s learning process. The effectiveness of this architecture is demonstrated on CIFAR-10, CIFAR-100, and CelebA datasets, where it achieves an average improvement of 4.76 in peak signal-to-noise ratio (PSNR) and 0.0292 in structural similarity index measure (SSIM), which highlight the potential proposed architecture to elevate the field of image steganography.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110299"},"PeriodicalIF":4.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738040","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}