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Designing blockchain based secure autonomous vehicular internet of things (IoT) architecture with efficient smart contracts

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International Journal of information technology (Singapore) 2025年第2期17卷 1207-1223页

作者： Hemani Singh, Dayashankar Dwivedi, Rajendra Kumar Department of Information Technology and Computer Application Madan Mohan Malaviya University of Technology UP Gorakhpur India

The future of autonomous vehicles is exciting and holds significant potential for transforming transportation. The future is bright, but these vehicles require a high degree of connectivity complex software to function effectively. This connectivity, however, can also create a pathway for cyberattacks. If a hacker gains access to a connected vehicle, they may be able to control its systems, steal data, or cause it to malfunction. Additionally, the software systems might have flaws or vulnerabilities that intruders could take advantage of. The paper describe how blockchain technology can help to create a tamper-proof and transparent system that enables secure sharing of data between the nodes of autonomous vehicular (AV) network. A smart contract is utilized to establish predefined rules and conditions for data sharing and access. The paper proposes a efficient approach for securing the information of AVs and improving the safety and reliability of AV networks. In this proposed work, smart contracts are implemented using solidity language and deployed a blockchain using Ganache, and truffle development framework to create a test system while MATLAB is utilized for analysis. And the system configurations are Intel Core i5 CPU (2.30 GHz and 8 GB RAM). © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer applications and Management 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s);author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

关键词： Autonomous vehicles Blockchain Fan-Vercauteren Smart contracts

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application of adaptive guard channel reservation under hard handoff constraint in wireless cellular network

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International Journal of Wireless and Mobile Computing 2025年第2期28卷 170-185页

作者： Sahu, Promod Kumar Pati, Hemanta Kumar Pradha, Sateesh Kumar Department of Computer Science and Application Utkal University Vani Vihar Odisha Bhubaneswar India Department of Computer Science and Engineering International Institute of Information Technology Bhubaneswar Odisha Bhubaneswar India

Voice is the king of communication in wireless cellular network (WCN). Again, WCNs provide two types of calls, i.e., new call (NC) and handoff call (HC). Generally, HCs have higher priority than NCs because call dropping gives negative impact to users as compared to call blocking. So, to handle this problem some channels are reserved for HCs. If more channels are reserved for HCs, then handoff call dropping probability (HCDP) decreases at the same time as new call blocking probability (NCBP) increases;if fewer channels are reserved for HCs, HCDP increases and NCBP decreases. If channels are reserved for HCs by considering target HCDP then HCDP will be below the target and NCBP will be minimum for that target. So, keeping this in mind in this paper, we propose a mathematical model that estimates optimal number of guard channels (GCs) for HCs by considering target HCDP and keeping NCBP minimum. The mathematical model proposed assume static traffic parameters. However, in reality, the user traffic is dynamic and usually inherited through its testbed implementation. To obtain optimal performance we propose an adaptive GC reservation scheme by considering hard handoff constraint while implementing this model through testbed. Finally, we have verified testbed results with those of the model-based approach applying the parameters of different Global System for Mobile Communication (GSM) standards to evaluate its applicability to real systems. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Testbeds

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Optimal Production Capacity Matching for Blockchain-Enabled Manufacturing Collaboration With the Iterative Double Auction Method

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 550-562页

作者： Ying Chen Feilong Lin Zhongyu Chen Changbing Tang Cailian Chen the Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province Zhejiang Normal University the School of Computer Science and Technology Zhejiang Normal University IEEE the College of Physics and Electronic Information Engineering Zhejiang Normal University the Department of Automation Shanghai Jiao Tong University

The increased demand for personalized customization calls for new production modes to enhance collaborations among a wide range of manufacturing practitioners who unnecessarily trust each other. In this article, a blockchain-enabled manufacturing collaboration framework is proposed, with a focus on the production capacity matching problem for blockchainbased peer-to-peer(P2P) collaboration. First, a digital model of production capacity description is built for trustworthy and transparent sharing over the blockchain. Second, an optimization problem is formulated for P2P production capacity matching with objectives to maximize both social welfare and individual benefits of all participants. Third, a feasible solution based on an iterative double auction mechanism is designed to determine the optimal price and quantity for production capacity matching with a lack of personal information. It facilitates automation of the matching process while protecting users' privacy via blockchainbased smart contracts. Finally, simulation results from the Hyperledger Fabric-based prototype show that the proposed approach increases social welfare by 1.4% compared to the Bayesian game-based approach, makes all participants profitable,and achieves 90% fairness of enterprises.

关键词： Blockchain iterative double auction manufacturing collaboration production capacity matching

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Multimodal emotion recognition model via hybrid model with improved feature level fusion on facial and EEG feature set

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Multimedia Tools and applications 2025年第1期84卷 1-36页

作者： Singh, Pratima Tripathi, Mukesh Kumar Patil, Mithun B. Shivendra Neelakantappa, Madugundu Department of Computer Science and Engineering Galgotias University Gautam Buddh Nagar Uttar Pradesh India Department of Computer Science and Engineering Vardhaman College of Engineering Telangana Hyderabad India Department of Computer Science and Engineering N K Orchid College of Engineering & Technology Maharastra Solapur India Department of Computer Application D. K. College Dumraon Bihar Buxar India Department of Information Technology Vasavi College of Engineering Telangana Hyderabad India

In recent years, academics have placed a high value on multi-modal emotion identification, as well as extensive research has been conducted in the areas of video, text, voice, and physical signal emotion detection. This paper proposes a novel multimodal emotion recognition model that employs a hybrid model with AMIG-based feature fusion on facial and EEG feature sets. The EEG signal and facial image are subjected to preprocessing to remove unwanted background noises with the Butterworth filter and Viola Jones Algorithm, respectively. While considering the pre-processed EEG signal, features such as EWFS-transform, wavelet features, and CSP-based features are extracted. In the proposed EWFS-transform, the window function is modified by using the frequency function and updated STFT. Conversely, the features including SE-AMM-EST-based features;LGXP and GLCM are extracted while considering the preprocessed face image. In the proposed SE-AMM-EST-based features, the mean of shape is updated and covariance in PCA for extracting texture-based features. In order to extract redundant free essential features, AMIG-based feature fusion is proposed. Further, the fused features are fed into a proposed hybrid model that includes LSTM and AML-CNN models. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Textures

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Forensic handwriting analysis: a hybrid classification framework for writer identification in Devanagari script

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Multimedia Tools and applications 2025年 1-12页

作者： Sethi, Monika Kumar, Munish Jindal, M.K. Department of Computer Science and Application Panjab University Chandigarh India P.G. Department of Information Technology Goswami Ganesh Dutta Sanatan Dharma College Chandigarh India Department of Computational Sciences Maharaja Ranjit Singh Punjab Technical University Punjab Bathinda India Department of Computer Science and Applications Panjab University Regional Centre Muktsar India

In a period marked by swift technological progress, the task of identifying writers through handwriting holds significant importance across various domains, including forensic investigation, plagiarism detection, and historical research. The importance of this endeavor has been further magnified by the widespread influence of digitalization. This study employs a hybrid approach that integrates cutting-edge deep learning techniques, specifically Convolutional Neural Networks (CNNs), with traditional machine learning methodologies, such as Support Vector Machines (SVM) and Random Forest (RF). Deep learning techniques are particularly valuable for analyzing extensive handwriting samples because they can automatically discern complex features and patterns from large datasets. In this research, we synthesize predictions from deep learning models and employ machine learning classification for writer identification based on handwriting analysis. Central to our study is a comprehensive dataset of Devanagari characters, collected from 220 unique writers. This dataset has been thoughtfully curated to validate the methodology we have employed. Our hybrid classification approach, combining CNN with SVM and CNN with RF, achieves remarkable accuracy rates of 91.49% and 88.78%, respectively—significantly surpassing the performance of standalone SVM and RF models, which achieved accuracies of 59.52% and 59%, respectively. Through a series of comprehensive experiments, we demonstrate the enhanced effectiveness and precision of our proposed methodology in the domain of Devanagari script writer identification. The findings of this study not only underscore the potential of our methodology but also represent a substantial advancement in the fields of pattern recognition and machine learning technology. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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EPRFL:An Efficient Privacy-Preserving and Robust Federated Learning Scheme for Fog Computing

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China Communications 2025年第4期22卷 202-222页

作者： Ke Zhijie Xie Yong Syed Hamad Shirazi Li Haifeng School of Computer Technology and Application Qinghai UniversityXining 810016China School of Computer and Information Science Qinghai Institute of TechnologyXining 810016China Guangdong Key Laboratory of Blockchain Security Guangzhou UniversityGuangzhou 510006China Department of Information Technology Hazara UniversityBaffa 21110Pakistan Qinghai Provincial Key Laboratory of Big Data in Finance and Artificial Intelligence Application Technology Xining 810016China

Federated learning(FL)is a distributed machine learning paradigm that excels at preserving data privacy when using data from multiple *** combined with Fog Computing,FL offers enhanced capabilities for machine learning applications in the Internet of Things(IoT).However,implementing FL across large-scale distributed fog networks presents significant challenges in maintaining privacy,preventing collusion attacks,and ensuring robust data *** address these challenges,we propose an Efficient Privacy-preserving and Robust Federated Learning(EPRFL)scheme for fog computing ***,we first propose an efficient secure aggregation strategy based on the improved threshold homomorphic encryption algorithm,which is not only resistant to model inference and collusion attacks,but also robust to fog node ***,we design a dynamic gradient filtering method based on cosine similarity to further reduce the communication *** minimize training delays,we develop a dynamic task scheduling strategy based on comprehensive *** analysis demonstrates that EPRFL offers robust security and low *** experimental results indicate that EPRFL outperforms similar strategies in terms of privacy preserving,model performance,and resource efficiency.

关键词： federated learning fog computing internet of things privacy-preserving robustness

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Utilization of hesitant and intuitionistic fuzzy sets (HFS-IFS) in computational intelligence for decision modeling

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International Journal of information technology (Singapore) 2025年 1-8页

作者： Sharma, Suvarna Dangi, Dharmendra Dixit, Dheeraj Kumar Gupta, Rashmi Kumar, Jeetendra Bhagat, Amit Department of Computer Science and Applications Dr. Vishwanath Karad MITWorld Peace University Pune India Information Technology Indian Institute of Information Technology (IIIT) C/O- Maulana Azad National Institute of Technology (MANIT) Bhopal India Computer Science and Engineering Madhav Institute of Technology & Science (MITS) Madhya Pradesh Gwalior India Computer Science and Application Atal Bihari Vajpayee University Chhattisgarh Bilaspur India Computer Applications Maulana Azad National Institute of Technology Bhopal India

This paper focuses on the utilization of hesitant and intuitionistic fuzzy sets (HFS & IFS) in a computational intelligent approach, mainly for decision modelling under complex vague surroundings. Indeed, classical fuzzy sets can be powerful in some applications, but they may not describe the broad range of epistemic imprecision and non-stationary uncertainty involved in making decisions at any given time. HFS & IFS overcome it by providing the ability to quantify degrees of hesitation and membership uncertainty. HFS allows a decision maker to express the multiple reference degrees associated with an option and also show uncertainty about how to assign that precise level of degree or real value. On the other hand, IFS allows for more possibilities of representing ambiguity. In this research, we incorporate the proposed model into a computational intelligent approach for improving decision-making, particularly in the context of multi-criteria decision analysis (MCDA), resource allocation and task completion parameters. The results show that the proposed HFS-IFS method presents improved performance in terms of accuracy and uncertainty treatment across a series of case studies. © Bharati Vidyapeeth's Institute of computer applications and Management 2025.

关键词： Computational Intelligence Decision modeling Hesitant fuzzy set (HFS) Intuitionistic fuzzy set (IFS) MCDA

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Investigating the Impact of Public-Key Infrastructure in information Security Models for Networked applications 5th

Investigating the Impact of Public-Key Infrastructure in Inf...

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5th International Conference on Data Science, Machine Learning and applications, ICDSMLA 2023

作者： Shree, Ritu Saxena, Kumud Mehta, Deepak Pandeya, Megha Department of Computer Science and Application Vivekananda Global University Jaipur India Department of Computer Science Engineering Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India

ISBN: (纸本)9789819780426

Public-key infrastructure (PKI) is poised to play a crucial function in the use of secure data technology systems. With the increasing complexity of these dais’s networks and packages, PKI gives a reliable way to electronically affirm and securely exchange critical records. This paper investigates the effect of PKI on modern-day facts security models for networked applications. An assessment of the literature is conducted to discover the limitations of various PKI‐enabled architectures and packages. The research seems at the usage of cryptography and digital certificates inside the implementation of PKI, and evaluates the different varieties of authentication, authorization, and encryption strategies in shielding networked structures. Furthermore, the paper discusses the advantages and demanding situations associated with the deployment of PKI for distinctive use instances. The findings exhibit that during order for PKI to efficiently defend networked structures, a sound authentication system need to be blended with sturdy and consumer-friendly encryption strategies. Moreover, the paper shows strategies for overcoming the demanding situations related to enforcing PKI in a relaxed and efficient way. Public-key infrastructure (PKI) is an important device in data security fashions for networked packages. PKI is a security architecture that entails the use of public and private interchangeable cryptographic keys to ensure authenticated and comfortable verbal exchange among entities. Through developing a hierarchical accept as true with device, virtual certificate are used to cozy communications and the sender and authenticity and integrity of records. This architecture gives a at ease foundation for at ease dispensed computing across multiple sites located in numerous geographical places, as it enhances confidentiality, integrity, and authentication of exchanged information. In addition, PKI can facilitate cozy statistics sharing, alleviate potential privateness issues,

关键词： Authentication

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Fuzzy Decision-Based Clustering for Efficient Data Aggregation in Mobile UWSNs

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computers, Materials & Continua 2025年第4期83卷 259-279页

作者： Aadil Mushtaq Pandith Manni Kumar Naveen Kumar Nitin Goyal Sachin Ahuja Yonis Gulzar Rashi Rastogi Rupesh Gupta Department of Computer Science and Engineering Lovely Professional UniversityJalandhar144402PunjabIndia Department of Computer Science and Engineering Chandigarh UniversityMohali140413PunjabIndia Salesforce Inc. DallasTX 75201USA Department of Computer Science and Engineering School of Engineering and TechnologyCentral University of HaryanaMahendragarh123031HaryanaIndia Department of Management Information Systems College of Business AdministrationKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Computer Application Sir Chhotu Ram Institute of Engineering and TechnologyCh.Charan Singh UniversityMeerut250001Uttar PradeshIndia Chitkara University Institute of Engineering and Technology Chitkara UniversityRajpura140401PunjabIndia

Underwater wireless sensor networks(UWSNs)rely on data aggregation to streamline routing operations by merging information at intermediate nodes before transmitting it to the ***,many existing data aggregation techniques are designed exclusively for static networks and fail to reflect the dynamic nature of underwater ***,conventional multi-hop data gathering techniques often lead to energy depletion problems near the sink,commonly known as the energy hole ***,cluster-based aggregation methods face significant challenges such as cluster head(CH)failures and collisions within clusters that degrade overall network *** address these limitations,this paper introduces an innovative framework,the Cluster-based Data Aggregation using Fuzzy Decision Model(CDAFDM),tailored for mobile *** proposed method has four main phases:clustering,CH selection,data aggregation,and *** CH selection,a fuzzy decision model is utilized to ensure efficient cluster head selection based on parameters such as residual energy,distance to the sink,and data delivery likelihood,enhancing network stability and energy *** the aggregation phase,CHs transmit a single,consolidated set of non-redundant data to the base station(BS),thereby reducing data duplication and saving *** adapt to the changing network topology,the re-clustering phase periodically updates cluster formations and reselects *** results show that CDAFDM outperforms current protocols such as CAPTAIN(Collection Algorithm for underwater oPTical-AcoustIc sensor Networks),EDDG(Event-Driven Data Gathering),and DCBMEC(Data Collection Based on Mobile Edge Computing)with a packet delivery ratio increase of up to 4%,an energy consumption reduction of 18%,and a data collection latency reduction of 52%.These findings highlight the framework’s potential for reliable and energy-efficient data aggregation mobile UWSNs.

关键词： Clustering data aggregation data collection fuzzy model monitoring UWSN

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Comparative study of IoT- and AI-based computing disease detection approaches

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Data Science and Management 2025年第1期8卷 94-106页

作者： Rhmann, Wasiur Khan, Jalaluddin Khan, Ghufran Ahmad Ashraf, Zubair Pandey, Babita Khan, Mohammad Ahmar Ali, Ashraf Ishrat, Amaan Alghamdi, Abdulrahman Abdullah Ahamad, Bilal Shaik, Mohammad Khaja Department of Computer Application Lovely Professional University Punjab Phagwara144411 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Guntur522502 India School of Computing Science and Engineering VIT Bhopal University Kothrikalan MP Sehore466114 India Babasaheb Bhimrao Ambedkar University Lucknow226025 India College of Commerce and Business Administration Dhofar University Salalah211 Oman Faculty of Computer Studies Arab Open University 18211 Bahrain Department of Computer Application Shri Ramswaroop Memorial University Lucknow225003 India College of Computing and Information Technology Shaqra University Shaqra11961 Saudi Arabia

The emergence of different computing methods such as cloud-, fog-, and edge-based Internet of Things (IoT) systems has provided the opportunity to develop intelligent systems for disease detection. Compared to other machine learning models, deep learning models have gained more attention from the research community, as they have shown better results with a large volume of data compared to shallow learning. However, no comprehensive survey has been conducted on integrated IoT- and computing-based systems that deploy deep learning for disease detection. This study evaluated different machine learning and deep learning algorithms and their hybrid and optimized algorithms for IoT-based disease detection, using the most recent papers on IoT-based disease detection systems that include computing approaches, such as cloud, edge, and fog. Their analysis focused on an IoT deep learning architecture suitable for disease detection. It also recognizes the different factors that require the attention of researchers to develop better IoT disease detection systems. This study can be helpful to researchers interested in developing better IoT-based disease detection and prediction systems based on deep learning using hybrid algorithms. © 2024 Xi'an Jiaotong University

关键词： Adversarial machine learning

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