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IoT Security in the Quantum Era: State of the Art and Open Challenges

IoT Security in the Quantum Era: State of the Art and Open C...

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Innovative Research in Applied Science, Engineering and Technology (IRASET),conference on

作者： Moussa Diop Papis Ndiaye Doudou Dione Idy Diop Departements of Computer Sciences Polytechnic Institute (ESP) Cheikh Anta Diop University (UCAD) Dakar Senegal Departements of Computer Sciences Assane Seck University of Ziguinchor (UASZ) Ziguinchor Senegal

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

The Internet of Things (IoT) has grown rapidly, transforming various aspects of daily life into intelligent systems capable of collecting and exchanging data in real time. At the same time, advances in quantum computing have given rise to powerful quantum computers capable of processing complex tasks at unprecedented speed. This disparity between the limited resources of IoT devices and the capabilities of quantum computers poses significant security challenges. Quantum computers introduce new vulnerabilities that threaten IoT systems, particularly in critical applications such as connected healthcare, industrial networks, smart infrastructure, and secure embedded systems. This study reviews the existing literature on post-quantum cryptography (PQC) applied to IoT devices. It analyzes the characteristics and security requirements of IoT environments, current security technologies, and unresolved issues. It highlights the growing threat of post-quantum attacks and the need to adapt security strategies. Finally, the study explores post-quantum code-based cryptosystems as solutions to enhance the security of resource-constrained IoT applications and ensure their resilience against future challenges.

关键词： Computers Quantum computing Reviews Medical services real-time systems Error correction codes Internet of Things Cryptography Intelligent systems Resilience

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AI-Driven Dynamic Workload Balancing for real-time applications on Cloud Infrastructure

AI-Driven Dynamic Workload Balancing for Real-Time Applicati...

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Integrated Circuits and Communication systems (ICICACS), ieee international conference on

作者： Saravanakumar Baskaran Sathyanarayanan Sayiram Jagadeesh Vedula Rajkumar Muthusamy Nasmin Jiwani T. Kiruthiga Microsoft Corporation Seattle USA IBM San Jose USA Digital Transformation Lead Houston USA NTT Data Irving USA Deptartment of Information Technology University of The Cumberlands Williamsburg USA Department of ECE Vetri Vinayaha College of Engineering and Technology Trichy India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

The specification provides a new, resource-efficient method of Dynamic Workload Balancing in AI-driven real-time applications over Cloud Infrastructure. The real-time application keeps processing data at high speeds, and it is too difficult to get or make this type of arrangement using our traditional cloud setups as the system employs artificial intelligence methods to responsively reallocate resources between virtual machines by the required Quality of Service (QoS) levels and capacity requirements. It efficiently schedules jobs to available resources and delivers results promptly, which is necessary for real-time applications like video processing or stock trading. It uses machine learning models based on historical data to predict the resources required for upcoming tasks. These predictions are subsequently utilized to smartly assign resources across VMs, factoring in network latencies and inter-dependence. It allows the system to modify queues in real-time according to changes in workload patterns, performing a dynamic load balancing.

关键词： Cloud computing Quality of service Machine learning Streaming media Dynamic scheduling real-time systems Virtual machines time factors Integrated circuit modeling Load modeling

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Personalized Deep Brain Stimulation Parameter Optimization using Deep Learning with Dimensionality Reduction and Data Augmentation 1

Personalized Deep Brain Stimulation Parameter Optimization u...

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1st international conference on Intelligent systems and Computational Networks, ICISCN 2025

作者： Parimala, S. Iram, Faiza Kishore, Gudipudi Sivaraman, R. Srimathi, S. Jegajothi, B. Srm Institute of Science and Technology Department of Computer Science and Applications Vadapalani India Sr University School of Cs&ai Hasanparthy India University College of Arts and Science Krishna University Department of Biosciences and Biotechnology Machilipatnam India Dwaraka Doss Goverdhan Doss Vaishnav College Department of Mathematics Arumbakkam Chennai India Saveetha School of Engineering Simats Department of Bio Technology Chennai India Srs Tech Solutions Chennai India

ISBN: (纸本)9798331529246

Deep Brain Stimulation (DBS) is a recognized treatment for managing motor symptoms associated with various movement disorders. However, traditional DBS settings often rely on fixed parameters that do not address individual patient needs, leading to suboptimal outcomes and increased clinical visits. This research introduces a novel hybrid approach that employs Convolutional Neural Networks (CNN) combined with Reinforcement Learning (RL) to dynamically optimize DBS parameters using real-time patient feedback. The proposed model significantly enhances symptom management, achieving an 85% improvement compared to the 65% improvement seen with conventional DBS settings. Additionally, the time to reach optimal stimulation settings was reduced from twelve hours to five, and annual clinical visits decreased from five to two. These findings underscore the potential of integrating machine learning techniques into DBS therapy, paving the way for more personalized and effective treatment strategies for movement disorders, ultimately revolutionizing patient-centered care in this field. © 2025 ieee.

关键词： Deep reinforcement learning

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Comprehensive Comparative Analysis of Food Classification Using YOLOv8n, VGG19, and InceptionV3

Comprehensive Comparative Analysis of Food Classification Us...

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Emerging Smart computing and Informatics (ESCI), conference on

作者： Mohit Sapat Sarthak Wadegaonkar Sarthak Patankar Pratibha Mahajan Department of Computer Science Engineering Vishwakarma University Pune India

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

Food classification plays a vital role in areas such as food recognition, nutritional tracking, and culinary exploration. This study evaluates the effectiveness of three deep learning models—YOLOv8n, VGG19, and InceptionV3—utilizing the In-dian Food Classification Dataset and Food-11. Metrics like accuracy, precision, recall, and F1 score indicate that InceptionV3 consistently delivers the highest accuracy, whereas YOLOv8n stands out for its real-time efficiency, making it well-suited for mobile or embedded systems. VGG19 provides a balanced performance but is limited by its larger model size and higher computational requirements. Statistical analysis (p ¡ 0.05) shows significant differences among the models, with confusion matrices revealing difficulties in classifying visually similar foods. The study suggests using InceptionV3 for tasks requiring precision, YOLOv8n for applications where speed is critical, and VGG19 for scenarios needing a balanced approach, while also proposing future research in cross-cultural food recognition, AI-driven food interaction, and personalized nutrition systems.

关键词： Deep learning Training Measurement Analytical models Accuracy embedded systems Statistical analysis Computational modeling real-time systems Informatics

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Optimizing Network Intrusion Detection systems for IoT: Insights from Ensemble and Neural Network Models 3

Optimizing Network Intrusion Detection Systems for IoT: Insi...

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3rd ieee international conference on Industrial Electronics: Developments and applications, ICIDeA 2025

作者： Teja, Chintakunta Pranay Karthik, P Murali Reddy, Kalam Santhosh Rout, Saroja Kumar Kumar, Kottu Santosh Thullimilli, Srisaiteja Department of Information Technology Hyderabad India Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798331533205

The rapid increase in IoT devices has resulted in a significant rise in network traffic, creating new challenges in maintaining strong cybersecurity. To effectively combat both known and unknown cyber threats, such as DoS attacks, advanced network intrusion detection systems (NIDS) in IoT environments must ensure real-time and thorough detection. This study investigates the effectiveness of various machine learning and deep learning models in identifying DoS attacks using the TON_IoT dataset, specifically focusing on its network traffic component. The employed models included ensemble classifiers like the Extra Trees Classifier and Gradient Boosting Classifier, as well as various neural network architectures, including the Multi-Layer Perceptron (MLP) Classifier, Convolutional Neural Networks (CNN), Deep Neural Networks (DNN), and Artificial Neural Networks (ANN). The experimental findings reveal that the Gradient Boosting Classifier achieved the highest performance with a flawless accuracy of 100%, while both the Extra Trees Classifier and MLP Classifier obtained commendable outcomes with accuracies of 90%. The CNN also performed well, achieving an accuracy of 95% due to its high precision and recall metrics. Conversely, both the DNN and ANN struggled with the complexity of the dataset, resulting in lower accuracies of 68% and 38%, respectively. In summary, the findings indicate that ensemble learning techniques, particularly the Gradient Boosting Classifier, are effective in detecting DoS attacks and contribute to the development of robust NIDS tailored for IoT contexts. Thus, this research adds valuable perspectives on creating efficient intrusion detection systems to address the distinctive security challenges commonly faced by IoT networks. © 2025 ieee.

关键词： Network intrusion

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Coding Agents: A Comprehensive Survey of Automated Bug Fixing systems and Benchmarks 14

Coding Agents: A Comprehensive Survey of Automated Bug Fixin...

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14th ieee international conference on Communication systems and Network Technologies, CSNT 2025

作者： Puvvadi, Meghana Arava, Sai Kumar Santoria, Adarsh Chennupati, Sesha Sai Prasanna Puvvadi, Harsha Vardhan Adobe Systems Inference Platform San Jose United States Adobe Systems San Jose United States IIT Mandi Dept. of Electrical Engineering Mandi India Rippling San Jose United States Shell India Markets Pvt. Ltd. Bangalore India

ISBN: (纸本)9798331531935

One of the trickiest problems in software engineering is automating software issue fixes, which calls for a thorough comprehension of contextual relationships, code semantics, and dynamic debugging techniques. The development of automatic program repair (APR) is examined in this survey, which traces a path from early template and constraint-based approaches to more recent developments powered by large language models (LLMs). Three main paradigms are compared here: retrieval-augmented approaches that integrate external knowledge sources, agent-based systems that use multi-agent frameworks, and agentless systems that use simplified repair pipelines. real-world benchmarks that mimic actual engineering workflows and repository-level difficulties, such as SWE-bench, CODEAGENT-BENCH, and CodeRAG-Bench, are used to assess these cutting-edge technologies. This study demonstrates how agentic, agentless, and retrieval- augmented systems use LLMs to achieve previously unheard- of precision and scalability by following the shift from localized, single-file solutions to solving complicated, multi-file, and repository-wide difficulties. According to our findings, while complex agent architectures have potential, straightforward test- time scaling frequently produces better outcomes, especially when paired with containerized environments that allow for parallel exploration. Additionally, the survey looks at industrial applications, emphasizing effective connections with quality assurance and DevOps procedures. In order to further the development of more resilient and flexible APR frameworks that blend in perfectly with contemporary software engineering practices, we conclude by highlighting important issues in context handling and validation and suggesting future research directions in improved contextual models, human-AI collaboration, and multi-modal debugging systems. © 2025 ieee.

关键词： Software design

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Modular Robotic Controllers with Ad-Hoc ESP32 Wireless Mesh Network and embedded AES Security 2nd

Modular Robotic Controllers with Ad-Hoc ESP32 Wireless Mesh ...

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2nd international conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Fang, Yisu Hafizh, Hadyan Ateeq, Muhammad Tan, Andrew Huey Ping School of Internet of Things Xi’an Jiaotong-Liverpool University Taicang China School of Engineering and Technology Sunway University Bandar Sunway Malaysia School of Intelligent Manufacturing Ecosystem Xi’an Jiaotong-Liverpool University Taicang China

ISBN: (纸本)9789819639489

This paper presents an innovative decentralized robotic control system utilizing an ESP32-based wireless mesh network (WMN). Building upon the "painlessMesh" library for network management, this study enables JSON-formatted data exchange in peer-to-peer and broadcast modes. A key contribution is our implementation of a security layer incorporating ECDH key exchange and AES128 encryption, which we thoroughly analyze across various node configurations. The WMN is seamlessly integrated with the Robot Operating System (ROS) via a custom serial bridge, combining the mesh network’s real-time, distributed control capabilities with ROS’s advanced computational resources. This architecture creates a flexible, scalable platform for sophisticated robotic applications. Our study rigorously evaluates system performance, balancing network topology, security, and responsiveness. The results provide critical insights for deploying secure, decentralized mesh networks in real-world robotic applications, particularly in areas such as swarm robotics and robot-infrastructure coordination, while maintaining the cost-effectiveness and energy efficiency of ESP32-based nodes. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Mesh generation

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28th international Symposium on Database Engineered applications, IDEAS 2024

28th International Symposium on Database Engineered Applicat...

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28th international Symposium on Database Engineered applications, IDEAS 2024

ISBN: (纸本)9783031834714

The proceedings contain 26 papers. The special focus in this conference is on Database Engineered applications. The topics include: Enhanced Classification of embedded System Vulnerabilities Using Ensemble Embedding and BiLSTM Networks;predicting Dyskinetic Events Through Verified Multivariate time Series Classification;R2-LGBM: Sales Informative Prediction System in E-commerce Application Using Ensemble Classifier;comparing Related Languages with a Fuzzy Morphism Matching Algorithm;Enhancing LLM Code Generation Using Natural Language Processing in the Context of Machine Learning;LaSSI: Logical, Structural, and Semantic Text Interpretation;clustEm4Ano: Clustering Text Embeddings of Nominal Textual Attributes for Microdata Anonymization;about Relationships in Data Lakes;from Theory to Practice of Multidimensional Big Data Analytics over Big Healthcare Data: A real-Life Case Study;a Database Engineered System for Big Data Analytics on Tornado Climatology;siren Federate: Bridging the Gap Between Document and Relational Data systems for Efficient Exploratory Graph Analysis;outlier-Weighted Traffic Flow Prediction Using Online Autoencoders;Speculative Query Support for RDBMS with Flexible Query Order and Benefit Verification;made to Measure: Towards Approximability of Query Evaluation Engines;DGGS-Based Continuous Trajectory Similarity Comparison;predictive Modeling of Key Performance Indicators for Greenhouse Gas Emission Reduction Using Machine Learning;validation Without Rules: A Data Integration Case Study;fairness in Group Recommender systems Using Variational Autoencoders;a Supervised Contrastive Learning Framework for Aspect-Based Recommendations;multi-attribute Bias Mitigation in Recommender systems;UpLIF: An Updatable Self-tuning Learned Index Framework;scheduling of Intermittent Query Processing;monitoring Environmental Factors for Optimal Health and Welfare of Broiler Chickens Using Sensor Technology in Southwest Nigeria;river Levels Affecting Firefighter Inte

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SUVAD: Semantic Understanding Based Video Anomaly Detection Using MLLM

SUVAD: Semantic Understanding Based Video Anomaly Detection ...

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2025 ieee international conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Gao, Shibo Yang, Peipei Huang, Linlin Beijing Jiaotong University China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China

ISBN: (纸本)9798350368741

Video anomaly detection (VAD) aims at detecting anomalous events in videos. Most existing VAD methods distinguish anomalies by learning visual features of the video, which usually face several challenges in real-world applications. First, these methods are mostly scene-dependent, whose performances degrade obviously once the scene changes. Second, these methods are incapable of giving explanations to the detected anomalies. Third, these methods cannot adjust definitions of normal or abnormal events during test time without retraining the model. One important reason for the drawbacks is that these visual-based methods mainly detect anomalies by fitting visual patterns rather than semantically understanding the events in videos. In this paper, we propose a training-free method named Semantic Understanding based Video Anomaly Detection (SUVAD) using multi-modal large language model (MLLM). By exploiting MLLMs to generate detailed texture descriptions for videos, SUVAD achieves semantic video understanding, and then detects anomalies directly by large language models. We also designed several techniques to mitigate the hallucination problem of MLLMs. Compared to the methods based on visual features, SUVAD obtains obviously better scene generalization, anomaly interpretability, and the ability of flexible adjustment of anomaly definitions. We evaluate our method on five mainstream datasets. The results show that SUVAD achieves the best performance among all the training-free methods. © 2025 ieee.

关键词： Multi-modal Large-language Model Training-free Video Anomaly Detection

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Leveraging IoT and Machine Learning for Intelligent Water Quality Monitoring systems

Leveraging IoT and Machine Learning for Intelligent Water Qu...

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2025 international conference on Intelligent Control, computing and Communications, IC3 2025

作者： Baranwal, Gaurav Singh, Kaushtubh Gupta, Ayush Tiwari, Harsh N. Gupta, Tukur Ajay Kumar Garg Enginnering College Department of Electronics and communication Engineering Ghaziabad India

ISBN: (纸本)9798331527495

Water quality monitoring is a crucial aspect of public health, particularly in mitigating the risks associated with waterborne diseases and ensuring safe drinking water. Traditional monitoring methods are often labor-intensive, costly, and lack real-time capabilities, making them unsuitable for widespread and rural deployment. This paper examines the integration of advanced technologies, specifically the Internet of Things (IoT) and Machine Learning (ML), to design a sophisticated, low-cost water quality monitoring system. Leveraging an array of sensors, including pH, turbidity, temperature, total dissolved solids (TDS), and dissolved oxygen (DO), combined with the advanced embedded processor, the system ensures seamless data acquisition and processing. IoT enables continuous remote monitoring, while ML algorithms provide predictive analytics and intelligent anomaly detection to identify potential contaminants and trends. These value-driven solutions underscore the transformative potential of advanced technologies in revolutionizing water quality assessment. This review article carefully reviews the available literature on water quality assessment in order to lay out an assortment of conclusions on the concerns, problems, challenges, and research gaps that have emerged during the last few years. Research gaps are identified by thoroughly examining the previously suggested methodologies. The study presented in this article primarily identified the problems with the accuracy of the models, the generation of data collection mechanisms, and the types of data that were used in the suggested frameworks. Subsequently, research avenues towards smart water quality monitoring are offered by examining key concepts crucial to the expansion of this field of research. © 2025 ieee.

关键词： Damage detection

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