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AI-Powered IoT: A Survey on Integrating Artificial Intelligence With IoT for Enhanced Security, Efficiency, and Smart Applications

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ieee ACCESS 2025年 13卷 50296-50339页

作者： Menon, U. Ivek Babu Kumaravelu, Vinoth Kumar, C. Vinoth Rammohan, A. Chinnadurai, Sunil Venkatesan, Rajeshkumar Hai, Han Selvaprabhu, Poongundran Univ Modena & Reggio Emilia Dept Engn Enzo Ferrari I-41124 Modena Italy Vellore Inst Technol Sch Elect Engn Dept Commun Engn Vellore 632014 Tamil Nadu India Vellore Inst Technol Automot Res Ctr Vellore 632014 Tamil Nadu India SRM Univ AP Sch Engn & Sci Dept Elect & Commun Engn Guntur 522502 Andhra Pradesh India Donghua Univ Coll Informat Sci & Technol Shanghai 201620 Peoples R China

The Internet of Things (IoT) and artificial intelligence (AI) enabled IoT is a significant paradigm that has been proliferating to new heights in recent years. IoT is a smart technology in which the physical objects or the things that are ubiquitously around us are networked and linked to the internet to deliver new services and enhance efficiency. The primary objective of the IoT is to connect all the physical objects or the things of the world under a common infrastructure, allowing humans to control them and get timely, frequent updates on their status. These things or devices connected to IoT generate, gather and process a massive volume of binary data. This massive volume of data generated from these devices is analyzed and learned by AI algorithms and techniques that aid in providing users with better services. Thus, AI-enabled IoT or artificial IoT (AIoT) is a hybrid technology that merges AI with IoT and is capable of simplifying complicated and strenuous tasks with ease and efficiency. The various machine learning (ML) and deep learning (DL) algorithms in IoT are necessary to ensure the IoT network's improved security and confidentiality. Furthermore, this paper also surveys the various architectures that form the backbone of IoT and AIoT. Moreover, the myriad state-of-the-art ML and DL-based approaches for securing IoT, including detecting anomalies/intrusions, authentication and access control, attack detection and mitigation, preventing distributed denial of service (DDoS) attacks, and analyzing malware in IoT, are also enlightened. In addition, this work also reviews the role of AIoT in optimizing network efficiency, securing IoT infrastructures, and addressing key challenges. Furthermore, it explores cutting-edge technologies like blockchain, 6G-enabled AIoT, federated learning (FL), and hyperdimensional (HD) computing, indicating their potential in advancing IoT and AIoT-driven applications within sectors like healthcare, autonomous systems, and indus

关键词： Internet of Things Artificial intelligence Security Surveys Wireless sensor networks Scalability Explainable AI Computer architecture Wireless fidelity Radiofrequency identification artificial intelligence blockchain machine learning IoT security 6G AIoT

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data-driven Decision Making in Smart Health and Emergency Management

Data-Driven Decision Making in Smart Health and Emergency Ma...

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2025 ieee International Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Sudharson, K. Selvi, K. Ramu, V. Monika, V. Sureshkumar, A. Nagarajan, S. R.M.D. Engineering College Department of Aiml Chennai India S.A. Engineering College Department of Mba Chennai India Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Mba Chennai India Vel Tech Ranga Sanku Arts College Department of Mba Chennai India Vel Tech High Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Mba India

ISBN: (纸本)9798331529833

Phenomena such as smart health and emergency management systems when embedded with data-driven decision-making have become a game-changer in the field of healthcare and an emergency response. In this paper, we investigate the use of advanced data analytics, machine learning algorithms and real-time data processing in improving decision-making activities in these domains. Using data from a diverse set of large datasets such as EHRs, wearables, emergency response systems etc., we hope to build predictive models and decision support systems which can help save lives by predicting the epidemic before it peaks saving valuable time when every minute counts. The research also deals with issues such as data privacy, interoperability and real-time data integration. Early findings suggest large efficiencies and effectiveness gains in health and emergency management, showing the game-changing potential of datadriven solutions in their domains. © 2025 ieee.

关键词： Electronic health record

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Intelligent Algorithm System for Security Governance driven by Big data in Autonomous systems 2

Intelligent Algorithm System for Security Governance Driven ...

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2nd International conference on Machine learning and Autonomous systems, ICMLAS 2025

作者： Wang, Yiran Shanghai University of Political Science and Law Shanghai201500 China

ISBN: (纸本)9798331505745

With the advancement of big data encryption and decryption technology, the role of data in national governance and social governance has become increasingly prominent. In order to effectively deal with the increasingly complex security governance issues, this study proposes the novel big data-driven intelligent algorithm system. The purpose of this study is to optimize the challenges of the network attack identification, protection measures, and encryption processing of information leakage in security governance through real-time monitoring and predictive analysis of data. The core of the designed system includes two modules: network attack detection algorithm and information leakage modeling encryption algorithm. From a methodological perspective, this study proposes a model for predicting abnormal network traffic based on chaos dynamics. This groundbreaking model uses time series analysis to identify and locate potential DDoS attack traffic. At the same time, combined with information entropy theory, a cascade network attack chain analysis model for DDoS attack protection is designed. At the data security level, an encryption algorithm based on chaos theory is used to effectively protect against information leakage. Through experimental verification, it is known that the proposed encryption algorithm can effectively retain the original data during the information encryption and decryption process. At the same time, through encryption algorithm verification, the security and anti-attack ability of information are improved. © 2025 ieee.

关键词： Information leakage

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Trajectory Following controller Design with Physicsbased data-driven Vehicle Dynamics Modelling 3

Trajectory Following Controller Design with Physicsbased Dat...

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3rd International conference on Mechatronics, control and Robotics, ICMCR 2025

作者： Lu, Yong Tian, He Yang, Diange Jiang, Kun Lu, Ziwang Li, Runfeng School of Vehicle and Mobility Tsinghua University Beijing China DIAS Automotive Electronic Systems Co. Ltd Beijing China

ISBN: (纸本)9798331544546

Accurate modeling of vehicle dynamics plays a crucial role in accelerating algorithm development and reducing associated costs. This study presents a trajectory-following controller that integrates physics-based and data-driven approaches for vehicle dynamics modeling. A hybrid model, combining a two-degree-of-freedom (2DOF) physical model with a data-driven long short-term memory (LSTM) framework, is developed to capture vehicle dynamics. Using this hybrid model, the feedforward steering angle is determined by solving an optimization problem. The dataset is specifically designed to align with the operational design domain of advanced driver assistance systems (ADAS). Simulation results indicate that the hybrid model achieves higher accuracy than traditional physics-based models and demonstrates superior generalization compared to purely data-driven models. Furthermore, the findings validate the enhanced performance of the proposed control algorithm. © 2025 ieee.

关键词： Advanced driver assistance systems

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Coordination for Complex Road Conditions at Unsignalized Intersections: A MADDPG Method with Enhanced data Processing 11

Coordination for Complex Road Conditions at Unsignalized Int...

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11th International conference on Vehicle Technology and Intelligent Transport systems, VEHITS 2025

作者： Chen, Ruo Zhu, Yang Su, Hongye Ningbo Innovation Center Zhejiang University Ningbo315100 China College of Control Science and Engineering Zhejiang University Hangzhou310027 China

ISBN: (纸本)9789897587450

In this paper, we use deep reinforcement learning to enable connected and automated vehicles (CAVs) to drive in a intersection with human-driven vehicles. The multi-agent deep deterministic policy gradient (MADDPG) algorithm is improved to be more efficient for data processing, so that it can solve the problem of learning bottlenecks in complex environments, and use sliding control to execute control strategies. Finally, the feasibility of the method is verified in the simulation environment of CARLA. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： data reduction

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RD-OpenMax: Rethinking OpenMax for Robust Realistic Open-Set Recognition

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ieee TRANSACTIONS ON NEURAL NETWORKS AND learning systems 2025年第4期36卷 7565-7579页

作者： Yin, Xiaojie Cao, Bing Hu, Qinghua Wang, Qilong Tianjin Univ Coll Intelligence & Comp Tianjin 300350 Peoples R China Haihe Lab Informat Technol Applicat Innovat Tianjin 300350 Peoples R China

Open-set recognition (OSR) toward a practical open-world setting has attracted increasing research attention in recent years. However, existing OSR settings are either too idealized or focus on specific scenes such as long-tailed distribution and few-shot samples, which fail to capture the complexity of real-world scenarios. In this article, we propose a realistic OSR (ROSR) setting that covers a diverse range of challenging and real-world scenarios, including fine-grained cases with strong semantic correlation and a large number of species, few-shot samples, long-tailed sample distribution, dynamic inputs (e.g., images, spatio-temporal, and multimodal signals) and cross-domain adaptation. In particular, we rethink the simple and basic OpenMax for the ROSR setting and introduce a novel method, regularized discriminative OpenMax (RD-OpenMax), to handle the challenges in the ROSR setting. RD-OpenMax improves upon the basic OpenMax approach by introducing a covariance attention-based covariance pooling (CACP) module as a global aggregation step before the deep architecture's classifier. This module explores rich statistical information on features and provides discriminative distance scores for OpenMax. To address the instability of extreme value theory (EVT) estimation due to insufficient training samples under few-shot and long-tailed scenarios, we propose a regularized EVT (REVT) method based on Monte Carlo sampling to recalibrate the distribution of distance scores. As such, our RD-OpenMax performs a REVT model of distance scores generated by discriminative CACP representations to distinguish known classes and recognize unknown ones effectively and robustly. Extensive experiments are conducted on more than ten visual benchmarks across several scenarios, and the empirical comparisons show that the ROSR setting challenges existing state-of-the-art OSR approaches. Moreover, our RD-OpenMax clearly outperforms its counterparts under the ROSR setting while performing fav

关键词： Semantics Correlation Training Benchmark testing Estimation Birds Weibull distribution Covariance attention-based covariance pooling (CACP) OpenMax realistic open-set recognition (ROSR) regularized extreme value theory (EVT)

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Intelligent Generation of Test Cases for a Parallel Testing System: A Case Study on Railway systems

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ieee INTELLIGENT TRANSPORTATION systems MAGAZINE 2025年第3期17卷 6-17页

作者： Jiao, Yuantao Wang, Jian Li, Runmei Wang, Fei-Yue Zhao, Hongtao Xiong, Gang Beijing Jiaotong Univ Sch Automat & Intelligence Beijing 100044 Peoples R China Chinese Acad Sci Inst Automat Natl Key Lab Multimodal Artificial Intelligence Sy Beijing 100190 Peoples R China China Acad Railway Sci Signal & Commun Res Inst Beijing 100081 Peoples R China

Currently, the testing approach for complex systems relies on manual test case generation, resulting in problems, such as low efficiency and incomplete test case development. To address these challenges and improve the quality of test case generation for complex systems, this article proposes an intelligent method for generating test cases in parallel testing systems. Within the domain of railway systems, characterized by inherent intricacies, the centralized traffic control system serves as a pertinent example. This typical large and intricate railway transport system presents significant challenges, particularly in ensuring safety and conducting comprehensive functional testing. The design of the parallel testing system is detailed using the artificial systems, computational experiments, and parallel execution methodology, where an artificial system is built in a data-driven way that realistically replicates the test environment of a real system. Computational experiments were conducted on a test case library using the bidirectional encoder representations from transformers (BERT) model of natural language processing. BERT's next sentence prediction task was used for the associative learning of test case pairs. Finally, the test case intelligent generation software completes the parallel execution of the final testing task, which can intelligently generate the best relevant test cases based on the results of the computational experiments of the BERT model. This approach serves as a valuable tool for test engineers, enabling them to streamline test case formulations and enhance the efficiency of testing in complex systems.

关键词： Testing Rail transportation Servers Dispatching Software Transportation Manuals

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Intelligent Defenses: Advancing Cybersecurity through Machine learning-driven Malware Detection 14

Intelligent Defenses: Advancing Cybersecurity through Machin...

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

作者： Mittal, Sakshi Rajvanshi, Prateek United States

ISBN: (纸本)9798331531935

The exponential rise in cyberthreats has rendered malware detection a crucial component of cybersecurity. Traditional signature-based detection technologies are insufficient for combating emerging threat vectors, as increasingly complex malware targets sensitive systems. Machine learning techniques have emerged as a viable alternative for the precise detection and classification of malware through data-driven insights. This study presents a comprehensive malware detection system that combines Convolutional Neural Networks (CNN) with sophisticated machine learning (ML) methodologies, including ensemble techniques like Random Forest and Gradient Boosted Decision Trees. To rectify class imbalance and enhance model performance, the framework utilizes extensive preprocessing methods, including SMOTE. The Random Forest achieved the highest accuracy of 97.4%, followed by Gradient Boosted Decision Trees at 96.7%;the CNN demonstrated competitive performance at 95.0%. Dynamic API call sequences were identified as essential elements for precise classification. The proposed technique significantly reduces false negatives, hence ensuring improved protection against malicious software and enhancing recall. This research provides a scalable framework for future malware detection systems and demonstrates how machine learning may enhance cybersecurity protocols. © 2025 ieee.

关键词： Convolutional neural networks

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data-driven E-Waste Management: Leveraging Machine learning Algorithms for Efficient Collection and Recycling systems 1

Data-Driven E-Waste Management: Leveraging Machine Learning ...

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

作者： Varsha, K.S. Mohan, Anju Ramu, Murugan Saveetha School of Law Chennai India

ISBN: (纸本)9798331529246

Quick growth of e-waste poses a substantial risk environment and health concerns;thereby, there is an imminent need for efficient management solutions. This paper explains the utilization of ML-based algorithms in improving e-waste collection and recycling capabilities. It employs superior techniques like CNN and YOLOv8 along with image recognition to identify e-waste in real time. The CNN-VGG model uses deep convolutional layers to extract features in the E-Waste dataset to classify e-wastes. YOLOv8 assists in the location and detection of e-waste objects. The new approach is developed in Python and demonstrates an accuracy of nearly 99%, better than other models such as FrHHGO-based ShCNN and SNN by a margin of 10.44%. This shows how CNN-VGG is good for the classification of e-waste. Adding these ML models into automation systems in recycling helps recycle more material and sustain the environment in e-waste management. The work published here advances more effective, ecologically sound e-waste recycling practice. © 2025 ieee.

关键词： data-driven solutions e-waste management image classification machine learning object detection

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Identifying AC control Patterns From a Massive Longitudinal Log dataset Using Deep Clustering

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ieee ACCESS 2025年 13卷 56195-56209页

作者： Bae, Seunghwan Kim, Donghee Seong, Seokhwan Jang, Jaeyeon Suh, Young-Kyoon Kim, Honghyun Han, Dongwoo Jung, Inuk Kyungpook Natl Univ Sch Comp Sci & Engn Daegu 41566 South Korea Seoul Natl Univ Hosp Biomed Res Inst Seoul 03082 South Korea LG Elect Home Appliance & Air Solut Co Air Solut Res & Dev Lab Changwon Si 51554 Gyeongsangnam D South Korea

An air conditioner is a household appliance and one of the most innovative inventions, primarily used to cool indoor temperatures and lower humidity to provide a comfortable environment. The sale of air conditioners increased continuously, reaching approximately 135 million units annually, with nearly 1.6 billion devices currently in operation. While it is important to devise and develop technologies for improving their energy efficiency, it is even more important to analytically investigate the performance of its main objective, user experience, or comfortableness. Here, we collected a massive set of logs collected in a longitudinal manner during the summer season of the year 2021 from approximately 1.47 million AC devices manufactured by LG Electronics and located in South Korea. Among the many features in the log, the current temperature, target temperature, humidity, energy consumption, and wind strength features were used to identify clusters embedding meaningful time-course feature patterns. We particularly focused on identifying patterns and user groups who either actively or inactively controlled the target temperature since such behavior likely indicates a discomforting air state. As a result, a total of 10 distinctive patterns and 14 representative user groups were searched. Among the 10 patterns, five showed active target temperature control and the other five inactive control. Regardless of control activity, all patterns are highly correlated with the progressing weather conditions during the summer. The feature patterns of the actively controlled group significantly differed from those of the inactively controlled group. Further investigation of the semantic difference between the groups would provide valuable research directions for improving the user experience of AC usage. It may also be used to devise an AI-driven automatic AC controller to minimize user intervention while providing an optimal air state.

关键词： Humidity Temperature measurement Indexes User experience Temperature distribution Object recognition Market research Home appliances Heating systems Focusing Air conditioner big data time series clustering deep learning

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