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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Yuvarani, R. Mahaveerakannan, R. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamilnadu Chennai602105 India

ISBN: (纸本)9798331522667

internet of Things (IoT) devices are growing quickly, which has brought attention to the necessity of strong security measures to safeguard private information and stop illegal access. The performance of two popular cryptographic algorithms, Elliptic Curve Cryptography (ECC) and RSA, is assessed in this study within the framework of QR code-based authentication systems for internet of Things devices. We investigate how to improve the security and scalability of IoT networks by combining these cryptographic techniques with cloud computing. We evaluate the effectiveness, computational overhead, and security features of RSA and ECC using a battery of tests, taking into account variables like resource usage, key sizes, and encryption/decryption speed. According to the results, ECC performs better than RSA in terms of security efficiency. It provides stronger encryption with lower key sizes, which makes it more appropriate for internet of Things devices with limited resources. Furthermore, the cloud integration offers scalability and dependability for extensive IoT environments, greatly improving the authentication process overall. In addition to offering insights into choosing suitable algorithms for safe QR code-based authentication in cloud-integrated IoT devices, this study advances our understanding of cryptographic optimizations in IoT security. © 2025 IEEE.

关键词： Cloud security

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Industrial IoT Smart Manufacturing Systems based on Cloud computing Energy Efficiency Analysis using Machine Learning Algorithms 3

Industrial IoT Smart Manufacturing Systems based on Cloud Co...

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3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Chinnasamy, P. Khan, Patan Feridoz Sasikala, C. Swathi, Mavalluru Kumar, M. Prema Yadav, Ajay Kumar Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering Tamilnadu India Mohan Babu University School of Computing Department of Computer Science and Engineering Andhra Pradesh Tirupati India Dept of Cse Andhra Pradesh Ananthapuramu India Audisankara College of Engineering and Technology Andhra Pradesh India Dept. of Ece Andhra Pradesh Bhimavaram India United Institute of Management Department of Computer Application Prayagraj India

ISBN: (纸本)9798331509675

With open connectivity and emergent computing, the Industrial internet of Things (IIoT) combined with cloud computing resources provides significant breakthroughs in the field of industrial automation. These developments lead to the evolution of supplemental services, which improve automation in the production and industrial sectors. This research proposes novel technique in smart manufacturing system based on cloud computing with IIoT in energy efficiency using machine learning. Here the smart manufacturing process has been analysed based on IIoT with cloud computing network, where the energy efficiency is analysed using virtual machine-based reinforcement markov chain based linear regression. Experimental analysis has been carried out in terms of latency, energy efficiency, task response time, accuracy. proposed technique obtained 97% of Latency, 95% of Energy efficiency, 98% of Accuracy, 94% of Task response time. © 2025 IEEE.

关键词： Smart manufacturing

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20th international conference on Grid, Cloud, and Cluster computing, GCC 2024, 3rd international conference on Quantum Technologies, ICEQT 2024, and 21st international conference on Modeling, Simulation and Visualization Methods, MSV 2024, held as part of the World Congress in Computer science, Computer engineering and Applied computing, CSCE 2024

20th International Conference on Grid, Cloud, and Cluster Co...

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20th international conference on Grid, Cloud, and Cluster computing, GCC 2024, 3rd international conference on Quantum Technologies, ICEQT 2024, and 21st international conference on Modeling, Simulation and Visualization Methods, MSV 2024, held as part of the World Congress in Computer science, Computer engineering and Applied computing, CSCE 2024

ISBN: (纸本)9783031858833

The proceedings contain 20 papers. The special focus in this conference is on Grid, Cloud, and Cluster computing. The topics include: Scientific Workflow Provenance Management: System Requirements and a Reference Architecture;Enhancing Data Security with Decentralized Cloud Storage: An IPFS Approach;learning Robust Observable to Address Noise in Quantum Machine Learning;evaluating Cost-Effective Reconfigurable Hardware for Quantum Simulation;an Efficient Quantum Solver for Multidimensional Partial Differential Equations;explaining the Design of the Quantum Fourier Transform;Optimizing Depth of Quantum Circuit for Generating GHZ States;a Comparative Analysis of Hybrid-Quantum Classical Neural Networks;NRQNN: The Role of Observable Selection in Noise-Resilient Quantum Neural Networks;studying the Impact of Quantum-Specific Hyperparameters on Hybrid Quantum-Classical Neural Networks;Eclipse Qrisp QAOA: Description and Preliminary Comparison with Qiskit Counterparts;federal Cloud computing Adoption Case Study: A Retrospective Analysis as a Precursor to Optimized Quantum Adoption Methodologies;integrating Secure Quantum Digital Signature into Quantum Communications;methodology to Accelerate Federal Agency Adoption of Quantum Technologies;understanding Public Policy Effects on Alcohol-Related Behaviors and Outcomes Using System Dynamics;Performance Investigation of Small UAV Attitude Control Based on Optimized Nonlinear Dynamic Inversion;Prediction of 1st Year Registration Renewal of General-Use JP Domain Names with the Use of Machine Learning;data Clustering and Visualization with Recursive Max k-Cut Algorithm.

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Education and Teaching Research of IoT Embedded Subject under the National Vocational and Technical Skills Standards 6

Education and Teaching Research of IoT Embedded Subject unde...

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Yanglu Wang, Xingchen Yadav, Amit Shah, Abhishek Pratap College of Computer and Software Chengdu Neusoft University Chengdu China College of Engineering IT and Environment Charles Darwin University Darwin Australia School of Management Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331522667

This research study analyzes six key factors in the education and teaching of IoT embedded direction: training objectives (which direction to teach), curriculum system (what to teach), teaching organization (how to teach), faculty team (who teaches), and teaching evaluation (how well the teaching is) under the current status of embedded teaching on the internet of Things (IoT) engineering discipline. Guided by the latest National Occupational Technical Skill Standards for IoT engineering professionals and in accordance with the educational philosophy of Chengdu Neusoft University, here we propose a design for teaching system for IoT embedded talent courses, clarified the key elements and formulated new ideas for the current development of talents in this direction to assist improvement and perfection of related curriculum systems, and to promote the establishment and operation of practical and technological activity platforms. It aims to provide practical references for the construction of professionalism in education and teaching standards of IoT. © 2025 IEEE.

关键词： Curricula

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Reliability-Aware Personalized Deployment of Approximate Computation IoT Applications in Serverless Mobile Edge computing

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2025年第2期44卷 430-443页

作者： Cao, Kun Chen, Mingsong Karnouskos, Stamatis Hu, Shiyan Jinan Univ Coll Informat Sci & Technol Guangzhou 510632 Peoples R China East China Normal Univ Engn Res Ctr Software Hardware Codesign Technol & Minist Educ Shanghai 200062 Peoples R China SAP D-69190 Walldorf Germany Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BJ England

Over the past few years, the integration of mobile edge computing (MEC) and serverless computing, known as serverless MEC (SMEC), has garnered considerable attention. Despite abundant existing works on SMEC exploration, there remains an unaddressed gap in guaranteeing dependable application outputs due to ignoring the threat of both soft and bit errors on SMEC infrastructures. Furthermore, existing works fall short of accommodating the personalized requirements and approximate computation of internet of Things (IoT) applications, thereby resulting in holistic quality-of-service (QoS) degradation of SMEC systems typically provisioned by limited edge resources. In this article, we investigate the reliability-aware personalized deployment of approximate computation IoT applications for QoS maximization in SMEC environments. To this end, we propose a hybrid methodology composed of offline and online optimization phases. At the offline phase, a decomposition-based function placement method is devised to accomplish function-to-server mapping by integrating convex optimization, cross-entropy method, and incremental control techniques. At the online phase, a lightweight reinforcement learning scheme based on proximal policy optimization (PPO) is developed to handle the inherent dynamicity of IoT applications. We also build a simulation platform upon the real-world base station distribution in Shanghai Telecom and the practical cluster trace in the Alibaba open program. Evaluations demonstrate that our hybrid approach boosts the holistic QoS by 63.9% compared with the state-of-the-art peer algorithms.

关键词： Approximate computation personalized internet of Things (IoT) deployment Reliability serverless mobile edge computing (SMEC) Approximate computation personalized internet of Things (IoT) deployment reliability serverless mobile edge computing (SMEC)

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Dynamic Fog Federation Scheme Using Vertex Cover Problem and Shapley Value-Based Approach

Dynamic Fog Federation Scheme Using Vertex Cover Problem and...

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2025 international conference on Computer, Electrical and Communication engineering, ICCECE 2025

作者： Bandopadhyay, Neeladri Ray, Eshna Sharma, Neeraj Swain, Sujata Bandyopadhyay, Anjan Biswas, Mainak Kiit Deemed to be University School of Computer Engineering Bhubaneswar India Lovely Professional University School of Computer Science and Engineering Jalandhar India

ISBN: (纸本)9798331513894

The internet of Vehicles (IoV) has progressed remarkably, leading to a heightened dependence on applications that require low latency and high bandwidth. In light of the growing computational demands, fog computing has surfaced as a vital strategy for offloading tasks from centralized cloud infrastructures. Nevertheless, traditional fog resource allocation techniques face considerable obstacles in dynamic environments defined by vehicle mobility and the diversity of fog nodes. This research introduces a new Dynamic Fog Federation Scheme (DFFS) designed for IoV settings, which integrates the Vertex Cover problem with a resource allocation framework based on the Shapley value. The proposed scheme is analyzed for its efficiency, fairness, and computational complexity, revealing significant enhancements in load balancing and resource utilization within dynamic IoV ecosystems. © 2025 IEEE.

关键词： Fog computing

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E-Waste Management using Deep Learning 6

E-Waste Management using Deep Learning

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6th international conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Kumar, Boddu Manoj Sujitha, Kaduru Abinaya, K. School of Computer Science Engineering with Blockchain Technology Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India School of Computer Science Engineering with Internet of Things Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India School of Computer Science and Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai600119 India

ISBN: (纸本)9798331522667

Using the YOLO v8 model and deep learning approaches, this study explores the field of e-waste management and provides effective item detection. Our research attempts to increase the accuracy and scalability of e-waste detection, simplifying its incorporation into current waste management systems. Through utilizing deep learning, our goal is to transform the way that electronic waste is categorized, from tiny parts to larger appliances, allowing for more efficient as well as environmentally friendly management techniques. When it comes to aiming for higher performance, privacy comes first. Methods of preservation guarantee the accuracy of sensitive data correlated with pictures of e-waste. By means of execution utilizing data anonymization and encryption methods, we ensure compliance with legal requirements while safeguarding against potential privacy threats and ethical issues. The YOLO v8 model achieved an average precision (Map) of 97.1% in detecting e-waste items, demonstrating its effectiveness and scalability for real-world applications. © 2025 IEEE.

关键词： Differential privacy

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Artificial intelligence-inspired comprehensive framework for desertification prediction

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international Journal of Cognitive computing in engineering 2025年 6卷 462-476页

作者： Alsubai, Shtwai Mosleh, Mogeeb A.A. Al-Hadhrami, Suheer A. Bhatia, Munish College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Alkharj Saudi Arabia Faculty of Engineering & Computing University of Science & Technology Aden Yemen Faculty of Engineering and Information Technology Taiz Univeristy Taiz Yemen Department of Computer Engineering College of Engineering Al Mukalla Hadhramout Yemen Department of Computer Applications National Institute of Technology Kurukshetra India

Desertification greatly affects land deterioration, farming efficiency, economic growth, and health, especially in Gulf nations. Climate change has worsened desertification, making developmental issues in the area even more difficult. This research presents an enhanced framework utilizing the internet of Things (IoT) for ongoing monitoring, data gathering, and analysis to evaluate desertification patterns. The framework utilizes Bayesian Belief Networks (BBN) to categorize IoT data, while a low-latency processing method on edge computing platforms enables effective detection of desertification trends. The classified data is subsequently analyzed using an Artificial Neural Network (ANN) optimized with a Genetic Algorithm (GA) for forecasting decisions. Using cloud computing infrastructure, the ANN-GA model examines intricate data connections to forecast desertification risk elements. Moreover, the Autoregressive Integrated Moving Average (ARIMA) model is employed to predict desertification over varied time intervals. Experimental simulations illustrate the effectiveness of the suggested framework, attaining enhanced performance in essential metrics: Temporal Delay (103.68 s), Classification Efficacy—Sensitivity (96.44 %), Precision (95.56 %), Specificity (96.97 %), and F-Measure (96.69 %)—Predictive Efficiency—Accuracy (97.76 %) and Root Mean Square Error (RMSE) (1.95 %)—along with Reliability (93.73 %) and Stability (75 %). The results of classification effectiveness and prediction performance emphasize the framework's ability to detect high-risk zones and predict the severity of desertification. This innovative method improves the comprehension of desertification processes and encourages sustainable land management practices, reducing the socio-economic impacts of desertification and bolstering at-risk ecosystems. The results of the study hold considerable importance for enhancing regional efforts in combating desertification, ensuring food security, and formulatin

关键词： Life on land Climate action Desertification internet of things Artificial neural network

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Smartphone Price Prediction and Category Classification using Machine Learning 3

Smartphone Price Prediction and Category Classification usin...

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3rd international conference on Intelligent Data Communication Technologies and internet of Things, IDCIoT 2025

作者： Agalayil, Helen Terese Radha, D. Kirthika Devi, V.S. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru India

ISBN: (纸本)9798331527549

The rapid evolution of smartphone technology and the diverse range of available models have made selecting a cost-effective mobile phone a complex decision for consumers. Although brand, internal memory, camera quality, battery life, processor speed are all important determinants of purchase decisions, it is frequently unclear how these factors relate to the price. This work explores the application of machine learning algorithms to predict mobile phone prices and their price category classification based on these characteristics, offering insights to aid consumer decision-making. Five classifiers and five regressors were compared using accuracy and time complexity as evaluation metrics and k-NN was found to be the best classifier for the price class classification and LightGBM was found to be the best regressor for price prediction. © 2025 IEEE.

关键词： Smartphones

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An Enhanced Framework for Churn Prediction using Stratifed Bagging and Stacked Multi-Output Random Forests 3

An Enhanced Framework for Churn Prediction using Stratifed B...

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3rd international conference on Intelligent Data Communication Technologies and internet of Things, IDCIoT 2025

作者： Bhardwaj, Anay Radha, D. Kirthika Devi, V.S. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidhyapeetham Bengaluru India Department of Electrical and Electronics Engineering Amrita School of Engineering Amrita Vishwa Vidhyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Customer churn prediction is an important task in customer relationship management because it helps businesses know who is at risk of leaving and retain such at-risk *** and time-efficient churn prediction is essential to identify the factors driving churn, enabling businesses to implement timely and effective retention strategies.A framework is proposed which combines stratified bagging with multi-output Random Forest and stacked feature transformation, to enhance prediction accuracy. This approach balances the dataset, trains a custom Random Forest using stratified bagging, and applies stacked learning using Logistic Regression on transformed features. Tested on real-world data, the model demonstrated 99% accuracy, showing improvements over the classic Random Forest in precision, recall, and prediction time. © 2025 IEEE.

关键词： Sales

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