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An Intelligent Privacy Protection Scheme for Efficient Edge Computation Offloading in IoV

Chinese Journal of Electronics 2024年第4期33卷 910-919页

作者： Liang YAO Xiaolong XU Wanchun DOU Muhammad Bilal School of Software Nanjing University of Information Science and Technology State Key Laboratory for Novel Software Technology Nanjing University Department of Computer and Electronics Systems Engineering Hankuk University of Foreign Studies

As a pivotal enabler of intelligent transportation system(ITS), Internet of vehicles(Io V) has aroused extensive attention from academia and industry. The exponential growth of computation-intensive, latency-sensitive,and privacy-aware vehicular applications in Io V result in the transformation from cloud computing to edge computing,which enables tasks to be offloaded to edge nodes(ENs) closer to vehicles for efficient execution. In ITS environment,however, due to dynamic and stochastic computation offloading requests, it is challenging to efficiently orchestrate offloading decisions for application requirements. How to accomplish complex computation offloading of vehicles while ensuring data privacy remains challenging. In this paper, we propose an intelligent computation offloading with privacy protection scheme, named COPP. In particular, an Advanced Encryption Standard-based encryption method is utilized to implement privacy protection. Furthermore, an online offloading scheme is proposed to find optimal offloading policies. Finally, experimental results demonstrate that COPP significantly outperforms benchmark schemes in the performance of both delay and energy consumption.

关键词： Industries Privacy Energy consumption Transportation Computational efficiency Encryption Protection

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IPatch:a remote adversarial patch

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Cybersecurity 2023年第4期6卷 144-162页

作者： Yisroel Mirsky Ben-Gurion University Department of Software and Information Systems EngineeringBeershebaIsrael

Applications such as autonomous vehicles and medical screening use deep learning models to localize and identify hundreds of objects in a single *** the past,it has been shown how an attacker can fool these models by placing an adversarial patch within a ***,these patches must be placed in the target location and do not explicitly alter the semantics elsewhere in the *** this paper,we introduce a new type of adversarial patch which alters a model’s perception of an image’s *** patches can be placed anywhere within an image to change the classification or semantics of locations far from the *** call this new class of adversarial examples‘remote adversarial patches’(RAP).We implement our own RAP called IPatch and perform an in-depth analysis on without pixel clipping on image segmentation RAP attacks using five state-of-the-art architectures with eight different encoders on the CamVid street view ***,we demonstrate that the attack can be extended to object recognition models with preliminary results on the popular YOLOv3 *** found that the patch can change the classification of a remote target region with a success rate of up to 93%on average.

关键词： patch image hundreds

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Demand side management with wireless channel impact in IoT-enabled smart grid system

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Digital Communications and Networks 2025年第2期11卷 493-504页

作者： Md.Farhad Hossain Kumudu S.Munasinghe Nishant Jagannath Khandakar Tanvir Ahmed Md.Nabid Hasan Ibrahim Elgendi Abbas Jamalipour Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology School of Information Technology and Systems University of Canberra School of Electrical and Information Engineering The University of Sydney

Demand Side Management(DSM) is a vital issue in smart grids, given the time-varying user demand for electricity and power generation cost over a day. On the other hand, wireless communications with ubiquitous connectivity and low latency have emerged as a suitable option for smart grid. The design of any DSM system using a wireless network must consider the wireless link impairments, which is missing in existing literature. In this paper, we propose a DSM system using a Real-Time Pricing(RTP) mechanism and a wireless Neighborhood Area Network(NAN) with data transfer uncertainty. A Zigbee-based Internet of Things(IoT) model is considered for the communication infrastructure of the NAN. A sample NAN employing XBee and Raspberry Pi modules is also implemented in real-world settings to evaluate its reliability in transferring smart grid data over a wireless link. The proposed DSM system determines the optimal price corresponding to the optimum system welfare based on the two-way wireless communications among users, decision-makers, and energy providers. A novel cost function is adopted to reduce the impact of changes in user numbers on electricity prices. Simulation results indicate that the proposed system benefits users and energy providers. Furthermore, experimental results demonstrate that the success rate of data transfer significantly varies over the implemented wireless NAN, which can substantially impact the performance of the proposed DSM system. Further simulations are then carried out to quantify and analyze the impact of wireless communications on the electricity price, user welfare, and provider welfare.

关键词： Smart grid Real time pricing Demand side management Wireless communications Zigbee

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DeepCancer: deep learning for brain tumor detection-based application system

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Neural Computing and Applications 2025年第7期37卷 5577-5596页

作者： AlShowarah, Suleyman A. Department of Software Engineering Faculty of Information Technology Mutah University Al-Karak Jordan

A brain tumor is the abnormal cells that growth in the brain, and it is considered as one of the most dangerous diseases that lead to the cause of death. Diagnosis at early is important for increasing the survival rate from the brain tumors. Specialists can identify the tumors manually, but it is very time and effort consuming, and are subject to human error, especially when dealing with large amounts of images. The automatic identification algorithms-based applications can facilitate the process. This study aimed to investigate the possibility of detecting brain cancer based on images using Deep Learning (DL) techniques and statistical operations. The features were extracted using two models of Convolutional Neural Network (CNN), (VGG-19 and AlexNet), then they were used to generate new datasets for statistical operations. CNN is used to extract features with distinct details from brain MRI images. The data were trained in three different training–testing data splitting ratios. Then, the features were classified based on the KNN, RF, and SVM to find the best accuracy of brain MRI image. At the end, the obtained classification accuracy was in favor of statistical operations especially for Large-Value, and Merge between features using KNN (99.1) and SVM (99.1). The features that extracted used in this study can provide high influence on the classification accuracy. The results across all three training–testing data splitting ratios were almost similar, and this approves that the brain cancer can be identified with high accuracy even if the training dataset sizes were minimal. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Convolutional neural networks

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Design of a ML-based trust prediction model using intelligent TrustBoxes in challenged networks

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International Journal of systems, Control and Communications 2024年第3期15卷 209-234页

作者： Barai, Smritikona Kundu, Anindita Bhaumik, Parama Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India Department of Software Systems School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu India Department of Information Technology Jadavpur University Kolkata India

Challenged networks (CNs) contain resource-constrained nodes deployed in regions where human intervention is difficult. Opportunistic networks (OppNets) are CNs with no predefined source-to-destination paths. Due to their inherent properties, CNs and OppNets are highly susceptible to black-hole (BH) attacks, resulting in degraded packet-delivery ratio. In this work, an ML-based trust prediction model (MLTPM) is proposed to identify potential BH nodes in OppNets. MLTPM uses a novel function to calculate the total-trust-value (TTV) of each node. Intelligent TrustBoxes are introduced in the network to identify possible BH nodes, using TTV, along with five more node-behaviour features. TrustBoxes reduce the computational overhead of the resource-constrained nodes. Three simulated scenarios are compared - no detection, non-ML-based detection, and MLTPM, each using epidemic, prophet, and spray-and-wait routing protocols. MLTPM performs best with spray-and-wait, exhibiting about 25.21% and 80% mean improvement in delivery-ratio and dropped-message numbers respectively, compared to non-ML-based detection. An overall 12.62% improvement in delivery-ratio and 26.7% improvement in dropped messages is observed using MLTPM, compared to the above-mentioned scenarios. © 2024 Inderscience Enterprises Ltd.

关键词： HTTP

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Ensemble Approach Combining Deep Residual Networks and BiGRU with Attention Mechanism for Classification of Heart Arrhythmias

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Computers, Materials & Continua 2024年第7期80卷 341-359页

作者： Batyrkhan Omarov Meirzhan Baikuvekov Daniyar Sultan Nurzhan Mukazhanov Madina Suleimenova Maigul Zhekambayeva Department of Information Systems Al-Farabi Kazakh National UniversityAlmaty050040Kazakhstan Department of Mathematical and Computer Modeling International Information Technology UniversityAlmaty050040Kazakhstan Department of Software Engineering Satbayev UniversityAlmaty050013Kazakhstan

This research introduces an innovative ensemble approach,combining Deep Residual Networks(ResNets)and Bidirectional Gated Recurrent Units(BiGRU),augmented with an Attention Mechanism,for the classification of heart *** escalating prevalence of cardiovascular diseases necessitates advanced diagnostic tools to enhance accuracy and *** model leverages the deep hierarchical feature extraction capabilities of ResNets,which are adept at identifying intricate patterns within electrocardiogram(ECG)data,while BiGRU layers capture the temporal dynamics essential for understanding the sequential nature of ECG *** integration of an Attention Mechanism refines the model’s focus on critical segments of ECG data,ensuring a nuanced analysis that highlights the most informative features for arrhythmia *** on a comprehensive dataset of 12-lead ECG recordings,our ensemble model demonstrates superior performance in distinguishing between various types of arrhythmias,with an accuracy of 98.4%,a precision of 98.1%,a recall of 98%,and an F-score of 98%.This novel combination of convolutional and recurrent neural networks,supplemented by attention-driven mechanisms,advances automated ECG analysis,contributing significantly to healthcare’s machine learning applications and presenting a step forward in developing non-invasive,efficient,and reliable tools for early diagnosis and management of heart diseases.

关键词： CNN BiGRU ensemble deep learning ECG arrhythmia heart disease

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An Experimental Study on Dynamic Lifelong Learning with GPT for Mitigating Catastrophic Forgetting in Aspect-Based Sentiment Analysis

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IEEE Access 2025年 13卷 90316-90332页

作者： Huang, Huang Asemi, Adeleh Universiti Malaya Faculty of Computer Science Information Technology Department of Software Engineering Kuala Lumpur Malaysia

GPT is widely recognized as one of the most versatile and powerful large language models, excelling across diverse domains. However, its significant computational demands often render it economically unfeasible for individuals and small businesses, underscoring the need for efficient, domainspecific alternatives. In Aspect-Based Sentiment Analysis (ABSA), existing models are typically optimized for single domains, facing challenges in performing effectively across multiple domains. A key issue, known as "catastrophic forgetting," arises when models trained on one domain lose previously learned knowledge upon exposure to new domain data. This leads to two significant problems: limited cross-domain generalization and difficulty in retaining prior knowledge while learning domain-specific information. To address these challenges, we introduce the Dynamic Lifelong Learning Aspect-Based Sentiment Analysis GPT (DllaGPT), a model designed to handle multiple domains while mitigating catastrophic forgetting. Leveraging datasets from four ABSA domains—Laptops, Restaurants, Tweets, and Finance—this study fine-tunes a pretrained GPT model from HuggingFace sequentially across domains. DllaGPT employs a mechanism to retain real data from earlier domains during new domain training, effectively preserving prior knowledge. Experimental results highlight that DllaGPT achieves an average accuracy of 0.85 and a Backward Transfer (BWT) score of -0.09 across the four domains, showcasing its high accuracy and robust lifelong learning capabilities. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Contrastive Learning

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Robust steganographic approach using generative adversarial network and compressive autoencoder

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Multimedia Tools and Applications 2024年 1-38页

作者： Qasaimeh, Malik Qtaish, Alaa Abu Aljawarneh, Shadi Department of Computer Information Systems Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan Department of Software Engineering co-joint with the Department of Cyber Security Faculty of Computer and Information Technology Jordan University of Science and Technology Irbid Jordan

Nowadays, social media applications and websites have become a crucial part of people’s lives;for sharing their moments, contacting their families and friends, or even for their jobs. However, the fact that these valuable data are transferred via the Internet and open channels, which are vulnerable to attacks and espionage, requires using defense methods to improve the security of data transmission. Recently, the progress of Deep Learning (DL) inspired researchers to use it with security methods such as steganography, the art of hiding secret data in unrelated content. DL and steganography combined enhanced the hiding properties, especially in the Coverless Image Steganography (CIS) methods. In this paper, we propose a Compressive Coverless Image Steganography (CCIS) model, which is a generated-based CIS, to improve the capacity and robustness of Steganography. This model uses the compressive autoencoder to compress the message, thus increasing the capacity, and uses the Generative Adversarial Network (GAN) to generate a stego image from the compressed vector, thus enhancing concealment ability, besides using a regression model and optimization method to extract the data. Another version was designed to enable sending binary messages by replacing the compressive autoencoder network with a robust mapping rule. Experiments show that the proposed model could improve the characteristics of Steganography. Furthermore, the proposed model could extract binary messages with 256 bits from attacked stego images with 99% recovery accuracy. Thus, capacity and robustness were enhanced using our model with both images and text as secret messages. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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System for Alcohol Detection and Drowsiness Monitoring Based on Face and Drowsiness Recognition Model

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Journal of Institute of Control, Robotics and systems 2025年第4期31卷 343-350页

作者： Jang, Soyoung Lee, Yeon Woo Jung, Yuna An, Eun Hyeon Lee, Jaeho Department of Information & Communication Technology Convergence Engineering Duksung University Korea Republic of Department of Software Duksung Universitys Korea Republic of

This study introduces an integrated real-time monitoring system to enhance driver safety. The system incorporates facial recognition, alcohol detection, and drowsiness monitoring to comprehensively analyze the driver’s condition and provide appropriate responses. For facial recognition, it utilizes Google’s MediaPipe AI framework and a Siamese network model. Alcohol detection is achieved through real-time measurement of the driver’s alcohol level using an MQ3 sensor. Drowsiness monitoring is accomplished through a combination of blink detection using dlib and OpenCV with head-rotation techniques to assess driver fatigue in real time. The proposed system offers customized information to drivers via Android and web interfaces. This research demonstrates the potential of systems that analyze driver safety and respond accordingly in real time. © ICROS 2025.

关键词： Face recognition

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A mathematical multi-objective optimization model and metaheuristic algorithm for effective advertising in the social internet of things

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Neural Computing and Applications 2025年第6期37卷 4797-4821页

作者： Molaei, Reza Rahsepar Fard, Kheirollah Bouyer, Asgarali Department of Computer Engineering and Information Technology University of Qom Qom Iran Department of Software Engineering Azarbaijan Shahid Madani University Tabriz Iran Department of Software Engineering Faculty of Engineering and Natural Science Istinye University Istanbul Turkey

The integration of social networks with the Internet of Things (IoT) has been explored in recent research, giving rise to the Social Internet of Things (SIoT). One promising application of SIoT is viral marketing, which plays a critical role in modern advertising. However, current solutions proposed for viral marketing are often impractical in real-world scenarios, and there has been limited investigation into viral marketing within the context of SIoT. To address this issue, we present a multi-objective integer linear programming model named ILP-ASoT that facilitates advertising through SIoT. Our approach introduces two realistic parameters: the node sociality rate and the node appropriateness rate as an advertising destination. By balancing the cost of selecting a seed set with the number of users who receive the advertisement, our model offers an appropriate trade-off. Since this is proven as an NP-hard problem, A PSO-based metaheuristic algorithm is proposed to provide near-optimal solutions for advertising in SIoT, called PSO-ASoT. Through simulations, we show that our PSO-ASoT algorithm outperforms state-of-the-art algorithms in several metrics, such as influence spread, advertising cost, and duplicated advertising rate. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Heuristic algorithms

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