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End-To-End Encryption Enabled Lightweight Mutual Authentication Scheme for Resource Constrained IoT Network

computers, Materials & Continua 2025年第2期82卷 3223-3249页

作者： Shafi Ullah Haidawati Muhammad Nasir Kushsairy Kadir Akbar Khan Ahsanullah Memon Shanila Azhar Ilyas Khan Muhammad Ashraf Department of Computer Engineering Balochistan University of Information TechnologyEngineering and Management SciencesQuetta87300Pakistan Computer Engineering Section Malaysian Institute of Technology Universiti Kuala LumpurKuala Lumpur50250Malaysia Electrical Engineering Section Universiti Kuala LumpurBritish Malaysian InstituteSalengor53100Malaysia Electrical Engineering Department Mehran UniversityKhairpur-Mirs76062Pakistan Department of Mathematics College of Science Al-ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia

Machine-to-machine (M2M) communication networks consist of resource-constrained autonomous devices, also known as autonomous Internet of things (IoTs) or machine-type communication devices (MTCDs) which act as a backbone for Industrial IoT, smart cities, and other autonomous systems. Due to the limited computing and memory capacity, these devices cannot maintain strong security if conventional security methods are applied such as heavy encryption. This article proposed a novel lightweight mutual authentication scheme including elliptic curve cryptography (ECC) driven end-to-end encryption through curve25519 such as (i): efficient end-to-end encrypted communication with pre-calculation strategy using curve25519;and (ii): elliptic curve Diffie-Hellman (ECDH) based mutual authentication technique through a novel lightweight hash function. The proposed scheme attempts to efficiently counter all known perception layer security threats. Moreover, the pre-calculated key generation strategy resulted in cost-effective encryption with 192-bit curve security. It showed comparative efficiency in key strength, and curve strength compared with similar authentication schemes in terms of computational and memory cost, communication performance and encryption robustness.

关键词： Mutual authentication lightweight end-to-end encryption elliptic curve cryptography industrial internet of things curve25519 machine-to-machine communication

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Automatic text summarization using deep reinforced model coupling contextualized word representation and attention mechanism

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Multimedia Tools and Applications 2024年第1期83卷 733-762页

作者： Aliakbarpour, Hassan Manzuri, Mohammad Taghi Rahmani, Amir Masoud Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Computer Engineering Department Sharif University of Technology Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan

With the rapid and unprecedented growth of textual data in recent years, there is a remarkable need for automatic text summarization models to retrieve useful information from these large numbers of textual documents without human intervention within a reasonable time. Text summarization is commonly performed based on extractive and abstractive paradigms. Although different machine learning and deep learning based methods have been proposed for the task of text summarization during the last decades, they are still in their early steps of development and their potential has yet to be fully explored. Accordingly, a new summarization model is proposed in this paper which takes advantage of both extractive and abstractive text summarization models as a single unified model based on the strategy gradient of reinforcement learning. The proposed model also employs the combination of convolutional neural network and gated recurrent unit in both extraction and abstraction modules besides attention mechanism. Moreover, language models, namely Word2Vec and BERT, are used as the backbone of the proposed model to better express sentence semantics as a word vector. We conducted our experiments on widely-studied text summarization datasets (CNN\Daily Mail and DUC-2004) and according to the empirical results, not only the proposed model achieved higher accuracy compared to both extractive and abstractive summarization models in terms of ROUGE metric but also its generated summaries presented higher saliency and readability based on human evaluation. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Reinforcement learning

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Data Replication Methods in Cloud, Fog, and Edge Computing: A Systematic Literature Review

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Wireless Personal Communications 2024年第1期135卷 531-561页

作者： Karamimirazizi, Fatemeh Jameii, Seyed Mahdi Rahmani, Amir Masoud Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Department of Computer Engineering Shahr-e-Qods Branch Islamic Azad University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan

Nowadays, a large amount of data is being generated and these data are usually stored in distributed environments such as cloud, fog, and edge environments. Data replication, which is commonly used to manage large amounts of data, improves overall accessibility and availability. Due to the importance of data replication, a lot of research has been done on replication methods in these environments. In this paper, a systematic review has been conducted to analyze the existing studies on data replication methods in cloud, fog, edge, and edge-cloud environments. The advantages, disadvantages, and some of the main challenges of the existing methods are explained and a taxonomy of data replication methods is provided. The selected papers are analyzed based on various criteria related to data replication, such as recovery, storage cost, load balancing, replica consistency, security, mobility, and data migration. Finally, the open issues and future research directions are discussed. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Fog

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An Adaptable Algorithm for Optimizing Bus Line Distribution Using the Clustering Method

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Wireless Personal Communications 2024年第4期134卷 2347-2370页

作者： Sheikhi, Fatemeh Rahmani, Amir Masoud Computer Engineering Department Science and Research Branch Islamic Azad University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan

The current fleet of cars requires reevaluation, upgrades, and replacement in response to the expanding human population, the increase in urban traffic volume, and the elevated levels of air pollution attributed to vehicular emissions. In densely populated cities, bus routes effectively compete with private transportation, optimize user accessibility, and provide access to all urban residents. This study proposes a novel method for optimizing bus routes, encompassing four primary processes: preprocessing, site clustering, ridership prediction, and optimization. The clustering process employs the K-means technique to classify available bus stops based on their geographical information. The suggested method utilizes an artificial neural network model to forecast the number of passengers at different locations. Subsequently, the bee colony optimization algorithm is implemented to determine bus frequencies and achieve an optimal distribution of buses across various traffic lines. Results obtained using a real traffic line dataset indicate a 32% increase in traffic fleet capacity and a 22.33% reduction in passenger waiting time upon application of the proposed method. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Data mining

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Lane and Platoon Assignment in Intelligent Transportation System: A Novel Heuristic Approach

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International Journal of Intelligent Transportation Systems Research 2024年第2期22卷 298-315页

作者： Mahini, Hamidreza Asadi, Mehrdad Mahini, Alireza Rahmani, Amir Masoud Fathy, Mahmood Department of Computer Engineering Gorgan Branch Islamic Azad University Gorgan Iran Vrije Universiteit Brussel Brussels Belgium Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Department of Computer Engineering Iran University of Science and Technology Tehran Iran

The right lane and platoon assignment to a vehicle significantly impacts achieving the desired driving goals to improve occupants’ safety or reduce pollution. Preparing an intelligent assistant to address this issue has exposed researchers’ attention, especially in intelligent transportation systems. Implementing such a system is complex and challenging to design, mainly because of conflicting driving goals possibility and the mixed traffic of autonomous and human-driven vehicles. This paper presents a multi-tier computational architecture to utilize the cloud, fog, and edge computational resources and formulate the lane/platoon assignment as an optimization problem subject to safety combined with any other secondary goal. The problem is then solved by the meta-heuristic Genetic Algorithm. By evaluating the proposed method by real data gathering, machine learning, and numerical experiments, the results show that the proposed method satisfies the minimization of overlapping collision areas during vehicles’ lane/platoon changes and harmonizes the traffic in all lanes and each platoon. © The Author(s), under exclusive licence to Intelligent Transportation Systems Japan 2024.

关键词： Genetic algorithms

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UAV detection technology research based on deep learning 7

UAV detection technology research based on deep learning

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7th International Conference on computer Network, Electronic and Automation, ICCNEA 2024

作者： Wang, Yu Bai, Xiaojun Di, Liuhua Zhao, Shifeng School of Computer Science and Engineering Xi’an Technological University Xi’an China East China Section TechnoPro China Wuxi China

In the process of UAV target recognition, the target is small, the feature is not obvious, and the recognition accuracy is low, a UAV target detection method called YOLOv8-UD is proposed based on YOLOv8. This method replaces downsampling layers with SPD convolutions to mitigate fine-grained feature loss issues. It employs a BiFPN structure to get information which is multi-scale, enhancing representation of global semantic message and Improve awareness of small goals. As a result, to target detection, it significantly enhances the correctness and effectiveness. Additionally, to address difficulty of detecting low-resolution small targets, the network structure was modified by adding a detection head, for small target, thereby enhancing capability to detect it. Combining an improved loss function based on Inner-IoU accelerated model convergence and improved detection accuracy of samples at high IoU thresholds. We can tell by experiments that The algorithm proposed in this paper have 0.805 and 0.334 in mAP(0.5) and mAP(0.5:0.95) metrics, respectively. Compared to YOLOv8, improvements of 4.6% and 7% are achieved in mAP(0.5) and mAP (0.5:0.95) metrics. The experiments validate that the proposed way significantly enhances accuracy to UAV small target detection. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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RC structural system control subjected to earthquakes and TMD

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Structural engineering and Mechanics 2024年第2期89卷 213-223页

作者： Shao, Jenchung Noor, M. Nasir Ken, P. Chang, Chuho Wang, R. College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Taiwan College of Maritime National Kaohsiung University of Science and Technology Taiwan Information and Research Center of China 1/9 Road: 2 Block: D Section: 15 Dhaka1216 Bangladesh

This paper proposes a composite design of fuzzy adaptive control scheme based on TMD RC structural system and the gain of two-dimensional fuzzy control is controlled by parameters. Monitoring and learning in LMI then produces performance indicators with a weighting matrix as a function of cost. It allows to control the trade-off between the two efficiencies by adjusting the appropriate weighting matrix. The two-dimensional Boost control model is equivalent to the LMI-constrained multi-objective optimization problem under dual performance criteria. By using the proposed intelligent control model, the fuzzy nonlinear criterion is satisfied. Therefore, the data connection can be further extended. Evaluation of controller performance the proposed controller is compared with other control techniques. This ensures good performance of the control routines used for position and trajectory control in the presence of model uncertainties and external influences. Quantitative verification of the effectiveness of monitoring and control. The purpose of this article is to ensure access to adequate, safe and affordable housing and basic services. Therefore, it is assumed that this goal will be achieved in the near future through the continuous development of artificial intelligence and control theory. Copyright © 2024 Techno-Press, Ltd.

关键词： Fuzzy control

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Revolutionizing Recruitment Management: Optimizing Advanced Digital Solutions for Manpower Planning in Higher Education 12

Revolutionizing Recruitment Management: Optimizing Advanced ...

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12th IEEE Conference on Systems, Process and Control, ICSPC 2024

作者： Andrian, Ahmad Mukafi Khamis, Nor Kamaliah Subbiah, Ahgalya Zuhriyah, Halimatuz Faizah, Safira Management and Science University Faculty of Information Sciences and Engineering Section 13 Selangor Shah Alam40100 Malaysia Jakarta Global University Faculty Engineering and Computer Science Department Informatics Engineering Depok16412 Indonesia

ISBN: (纸本)9798350391398

Effective manpower planning is crucial for any organization, including universities, as it directly influences performance, productivity, and overall success. Inefficient recruitment processes can lead to increased costs, higher turnover rates, and suboptimal utilization of human resources, negatively impacting the institution's ability to achieve its goals and maintain a competitive advantage. The objective of this study is to develop a comprehensive digital solution to streamline and enhance recruitment and manpower planning processes, replacing traditional manual methods. This paper presents the Recruitment and Manpower Planning System, an innovative web-based platform designed using Extreme Programming (XP) methodologies. The system integrates modern programming tools such as Visual Studio Code, Laragon, Laravel 9, and MySQL, and utilizes Unified Modelling Language (UML) for precise system design. A mixed-methods approach, incorporating quantitative data from structured questionnaires administered to 500 users, informed the system's design. Data analysis indicates a 40% reduction in recruitment time, a 35% increase in successful job placements, and a 90% user satisfaction rate. Rigorous testing phases ensured system accuracy and quality. This digital solution not only optimizes recruitment processes but also provides a robust prototype for future advancements in manpower planning systems. © 2024 IEEE.

关键词： Unified Modeling Language

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MapReduce-based Fuzzy C-means Algorithm for Distributed Document Clustering

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Journal of The Institution of Engineers (India): Series B 2022年第1期103卷 131-142页

作者： Sardar, Tanvir H. Ansari, Zahid School of Computer Science & Engineering Jain University Bangalore India Electrical Engineering Section University Polytechnic Aligarh Muslim University Aligarh India

The clustering of big data is a challenging task. The traditional clustering algorithms are inefficient for clustering big data. The recent researches in this field suggest that the traditional clustering algorithms needed to be redesigned for the modern architecture of computing. This wok has proposed a novel MapReduce-based fuzzy C-means algorithm for big document data clustering. The algorithm is extensively experimented with using different sizes of document datasets and executed over the Hadoop cluster of different sizes. The proposed algorithm’s efficiency is compared against serial traditional fuzzy C-means and MapReduce-based K-means algorithms. The proposed design of the fuzzy C-means algorithm is scaled well with the Hadoop platform and documents big datasets and resulted in a performance gain. © 2021, The Institution of Engineers (India).

关键词： MapReduce

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Distributed Big Data Clustering using MapReduce-based Fuzzy C-Medoids

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Journal of The Institution of Engineers (India): Series B 2022年第1期103卷 73-82页

Efficient big data clustering is a requirement for massive data generating in this digitalized connected world. The traditional clustering algorithms do not scale over massively sized and highly unstructured big data. Thus, to obtain efficiency in clustering big data new architecture and programming paradigm is required. In this work, a novel MapReduce-based Fuzzy C-Medoids clustering algorithm is designed and experimented with to cluster big data repository of documents datasets. The performance of the proposed algorithm is experimentally evaluated for different-sized Hadoop cluster sizes and different-sized document datasets. The algorithm is found to be scalable and efficient in performing clustering jobs. © 2021, The Institution of Engineers (India).

关键词： MapReduce

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