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Real-Time Monitoring and Control Systems Using IoT-Integrated Wireless Sensor Networks-Leveraging Machine Learning Algorithms for Enhanced Performance and Efficiency

Real-Time Monitoring and Control Systems Using IoT-Integrate...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Swaminathan, Subbiah Poongavanam, N. Arun, M. Pushparani, S. Muthukrishnan, A. Johri, Prashant Rajalakshmi Engineering College Department of Information Technology Tamil Nadu Chennai India Saveetha University Department of Computer Science and Engineering Chennai602105 India Panimalar Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Meenakshi College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai600 078 India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Avadi Chennai India School of Computer Application and Technology Galgotias University India

ISBN: (纸本)9798331542344

This paper introduces a developed real-Time monitoring and control system using Internet of Things (IoT) incorporated Wireless Sensor Networks (WSNs) supported by machines learning algorithms. The system focuses at meeting core issues and questions in terms of productivity, reliability and timeliness within evolving settings. The current paper contributes to the ongoing research and development in the area of IoT and WSN to enable, always-on real-Time data gathering from a variety of discrete sensors which is invaluable for various applications including industrial, environmental and smart city applications. The heart of the system is based on the idea of utilising machine learning methods to acquire and elaborate sensor signals. More specifically, the deep learning models that are used include Long Short-Term Memory (LSTM) networks as well as Reinforcement Learning (RL) algorithms for predictive analytics, as well as anomaly detection. The work also entails a comparison of the system performance based on energy consumption and other factors such as latency, packet delivery ratio and even the accuracy of the predictions made by the system. The findings suggest improved utilisation within substantial ranges;decreased energy use by 30 per cent or more. 7%, latency decrease by 33. Although achieving this was accompanied by an increased data packet loss rate to 3%, the overall packet delivery ratio was boosted to 98%. Additionally, the system established overall accuracy rate of 95% with negligible error percentage. In conclusion, this research proves the applicability of incorporating the IoT and the advanced machine learning methods in the real-Time monitoring and control systems development to overcome cases of low efficiency and reduced performance in numerous real-world applications. © 2024 IEEE.

关键词： Predictive analytics

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GIRP: Energy-Efficient QoS-Oriented Microservice Resource Provisioning via Multi-Objective Multi-Task Reinforcement Learning

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IEEE Transactions on Mobile Computing 2025年第7期24卷 5793-5807页

作者： Yuan, Honggang Wang, Ting Fu, Min Shi, Yuanming East China Normal University MoE Engineering Research Center of Software/Hardware Co-design Technology and Application Shanghai Key Laboratory of Trustworthy Computing Shanghai200062 China National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore ShanghaiTech University School of Information Science and Technology Shanghai201210 China

Microservice architecture has revolutionized web service development by facilitating loosely coupled and independently developable components distributed as containers or virtual machines. While existing studies emphasize end-to-end latency, this paper investigates energy-efficient quality-of-service (QoS)-oriented microservice provisioning, focusing on both QoS satisfaction and power consumption (PC) conservation. We propose the Green and Intelligent Resource Provision (GIRP) architecture, integrating a data-driven energy-latency-aware resource allocation and scheduling manager to balance latency and PC. To reconcile the trade-offs involved, a dual-objective optimization problem is formulated to minimize latency and energy use by selecting proper servers, allocating CPU cores, and determining service replicas. To address challenges with discrete variables, dual objectives, and implicit mappings, we leverage a model-free deep deterministic policy gradient-based reinforcement learning algorithm. Specifically, we develop a multi-task agent via the Multi-gate Mixture-of-Experts model to simultaneously make two separate actions regarding CPU core numbers and service replica numbers, followed by a single-task agent to determine service scheduling. Extensive experiments on the DeathStarBenchmark testbed validate GIRP's effectiveness, demonstrating approximately 52% resource savings and a 43% reduction in PC compared to leading methods like Sinan, Firm, and heuristic-based algorithms. These results highlight GIRP's capability to optimize microservice orchestration by balancing end-to-end latency and power efficiency. © 2025 IEEE. All rights reserved.

关键词： Quality of service

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Investigating Domain Adaptation for Medical Image Classification 5th

Investigating Domain Adaptation for Medical Image Classifica...

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

作者： Agarwal, Ankita Seth, Jyoti Sharma, Ambrish Kumar Karthikeyan, M.P. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Computer Science and Engineering Vivekananda Global University Jaipur India Master of Computer Application Noida Institute of Engineering and Technology Uttar Pradesh Greater Noida India Karnataka Bangalore India

ISBN: (纸本)9789819780426

Area version goals bridge the space between supply and target domain names by remodeling statistics from the source domain to the target area without providing categorized goal facts. Medical image domain version is a critical trouble in scientific imaging obligations, consisting of most cancers prognosis, because it lets in for the switch of understanding throughout particular medical imaging modalities, obligations, and affected person cohorts. It can help address record distribution shifts and make undertaking medical photo classes more dependable and accurate. In this examination, we look into several area model techniques and benchmark their performance in the venture of the medical photo category. We examine supervised and unsupervised methods, as well as deep studying-based methods. The results of our experiments offer insights into the performance of numerous area model techniques for clinical picture class that could assist in deciding the pleasant method to use in any given medical imaging task. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Medical imaging

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Advancing Intrusion Detection Systems Innovative Approaches for Prevention and Response

Advancing Intrusion Detection Systems Innovative Approaches ...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Reddy, M. Siva Swetha Krishna, B.S.S. Murali Asha, V. Nijhawan, Ginni Rana, Ajay Jabbar, Ahmed Sabah Abed Al-Zahra Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India Mlr Institute Of Technology Department Of Computer Science And Engineering Telangana Hyderabad India Computer Application New Horizon College Of Engineering Bangalore India Lovely Professional University Phagwara India Amity School Of Engineering And Technology Amity University Greater Noida India The Islamic University Medical Laboratory Technology Department Najaf Iraq

ISBN: (纸本)9798350366846

A group intruder detection system built on K-Nearest Neighbors, Decision Trees, Neural Networks, Support Vector Machines, and Random Forests is shown in this study. A thorough study on ablation shows how the algorithms work together to make breach detection better. We compare how well the suggested method works in terms of memory, F1 score, false negative rate, accuracy, and detection time. Bar charts, line graphs, pie charts, stacked bar graphs, area plots, and scatter plots all show that the new way is better than the old ones. Tables are also used to organize data. An ensemble takes the best features of Random Forests, Support Vector Machines, Neural Networks, Decision Trees, and K-Nearest Neighbors and puts them all together in a single package. Broad powers make the suggested method better at solving cybersecurity problems across a range of review factors. The ablation study shows why each method is important and how they work together to make an adaptive intruder detection system. In a world where safety is always changing, the proposed answer is scalable, flexible, easy to set up, and cheap. © 2024 IEEE.

关键词： Nearest neighbor search

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A high-efficient triboelectric-electromagnetic hybrid nanogenerator for vibration energy harvesting and wireless monitoring

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science China(Information sciences) 2022年第4期65卷 229-237页

作者： Jian HE Xueming FAN Dongyang ZHAO Min CUI Bing HAN Xiaojuan HOU Xiujian CHOU Science and Technology on Electronic Test and Measurement Laboratory North University of China College of Mechatronic Engineering North University of China Beijing Institute of Computer Application China North Industries Group Corporation Limited

Scavenging mechanical vibration energy by combining triboelectric nanogenerators(TENGs) and electromagnetic generators(EMGs) is a renewable and low-carbon way to generate electric power. In this paper, we fabricate a cylindrical structure hybrid nanogenerator based on spring structure to effectively scavenge vibration energy. The introduction of spring structure not only enhances the space utilization, but also supports the moving magnet. Owing to its innovative structure, the instantaneous output power of TENG and EMG are 0.88 mW at a load of 10 MΩ and 216.7 mW at a load of 60 Ω, respectively. With the aids of a power management circuit, the hybrid nanogenerator can provide continuous power for two hygrothermographs connected in series. In addition, a self-powered wireless acceleration sensing monitoring system(SWAM) is developed to measure the acceleration signals of vibration sources. This study provides a novel guideline for harvesting mechanical vibration energy.

关键词： mechanical vibration energy spring structure triboelectric electromagnetic wireless monitoring system

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Design of a Blind Guidance System Based on RealSense and the Improved YOLOv5 Algorithm 38th

Design of a Blind Guidance System Based on RealSense and the...

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38th CCF National Conference of computer applications, CCF NCCA 2023

作者： Zhang, Zhao Shen, Xiaobin Ge, Jing Zha, Yingying Liu, Lisai Liu, Sheng College of Computer Science and Technology Huaibei Normal University Huaibei235000 China Anui Engineering Research Center for Intelligent Computing and Application on Cognitive Behavior Huaibei235000 China

ISBN: (纸本)9789819987634

To assist blind people in travelling and solve the problems of high hardware costs, insufficient portability of blind assistance devices, and vulnerability to environmental impacts, a modified YOLOv5 algorithm (YOLOv5-CM) based on ground plane segmentation and Euclidean clustering algorithms is proposed and applied to a blind guidance system based on RealSense D435. This proposed algorithm adds coordinate attention and uses MobileNetv3 as the backbone network for YOLOv5s to extract the main features. Compared with those of the YOLOv5 model, the mAP was improved by 0.7%, the model size was decreased by 79.1%, and the number of parameters was reduced by 80.3%. In this study, the YOLOv5-CM algorithm was applied to design a blind guidance system. When the system detects a traffic light, the RealSense D435 is used to obtain depth images to detect and measure the distance from the pedestrian traffic light. The system provides warnings based on the measured distance. When the system does not detect a traffic light, it utilizes the ground plane segmentation and Euclidean clustering algorithms to obtain the location and distance of obstacles. This information is conveyed to the blind through voice guidance, which aids navigation. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Clustering algorithms

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Energy-Proficient Cluster Enrichment in Wireless Sensor Networks via Categorized Fuzzy Clustering and Multi-Hop Routing Optimization

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SN computer science 2025年第1期6卷 1-12页

作者： Shakya, Harish Kumar Chandanan, Amit Kumar Subbalakshmi, Chatti Khambra, Geetanjli Ansari, Mohd Shahnawaz Kushwaha, Ajay Roy, Vandana Artificial Intelligence and Machine Learning Manipal University Jaipur Rajasthan Jaipur India Department of Computer Science and Engineering Guru Ghasidas Vishwavidyalaya (A Central University) Chhattisgarh Bilaspur India Department of Computer Science and Engineerings Guru Nanak Instructions Technical Campus (Autonomous) Ibrahimpatnam Ranga Reddy District Telagana State Hyderabad 501506 India Department of Computer Science and Application The Bhopal School of Social Sciences (BSSS) College M.P. Bhopal India Department of Computer Science and Engineering Eklavya University Damoh MP Damoh India Computer Science and Engineering Rungta College of Engineering and Technology Bhilai India Department of Electronics Communication Gyan Ganga Institute of Technology and Sciences MP Jabalpur India

The WSN's lifetime can be extended by practicing energy saving. By using the appropriate routing mechanism when executing clustering, it can be done successfully. For improved energy conservation, clustering, and routing work together. Clustering lowers duplicate data transmission, while superior routing enables energy-saving travel along the quickest route to the destination. Nodes can be grouped via clustering, and each group will have CH. CH provides data to the BS either directly or via the intermediary nodes. By reducing the workload of resource-limited nodes, clustering chooses CH from resource-rich nodes. The entire network needs to undergo re-clustering in the traditional WSN clustering approach. The suggested protocol creates an energy-efficient cluster head selection approach applying FIS in hierarchical WSNs, which in turn increases sensing network lifetime, power consumption rate, and network communication performance. The proposed FBUCA and Modified minimum spanning tree with fuzzy logic can efficiently route data packets. The load-balancing among clusters and decreased energy usage demonstrated by the proposed model in MATLAB R2016 simulations are key to increasing throughput and extending the network's lifetime. The proposed approach takes into account six of the aforementioned use scenarios. The experimental results confirmed the adaptability and sturdiness of the proposed method, showing that it works well with a large number of WSNs. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： CH FBUCA Fuzzy logic WSN

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Fog Computing in IoT: Architecture and applications 9

Fog Computing in IoT: Architecture and Applications

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9th International Conference on science, Technology, engineering and Mathematics, ICONSTEM 2024

作者： Agrawal, Anurag Vijay Shashi Gowridurga, A. Suresh Balakrishnan, T. Shaikh, Anwar Ahamed Kalaipriya, V. J. P. Institute of Engineering And Technology Department of Electronics And Communication Engineering Uttar Pradesh Meerut250001 India Ccs University Department of Computer Application Meerut250004 India Velammal Institute of Technology Department of Computer Science And Engineering Tamil Nadu 601204 India Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences Department of Computer Science And Engineering Tamil Nadu Thandalam602105 India KoneruLakshmaiah Education Foundation Department of Computer Science And Engineering Andhra Pradesh Vaddeswaram522502 India Rmk College of Engineering And Technology Department of Computer Science And Engineering Tamil Nadu Puduvoyal601206 India

ISBN: (纸本)9798350365092

This exploration paper explores the paradigm of Fog Computing as a transformative armature in the Internet of effects(IoT) geography. Fog Computing represents a decentralized approach, positioning computing coffers closer to the edge of the network, thereby addressing quiescence, bandwidth, and data sequestration challenges current in traditional polycentric IoT infrastructures. The paper provides an in-depth analysis of Fog Computing's armature, expounding its crucial factors similar to fog bumps, middleware, and edge bias. likewise, it investigates the different operations of Fog Computing in IoT, ranging from realtime data analytics, effective resource operation, to enhanced security and sequestration measures. By bridging the gap between edge bias and centralised pall waiters, Fog Computing emerges as a vital result, fostering the elaboration of scalable, effective, and responsive IoT ecosystems. This exploration contributes precious perceptivity to the architectural nuances and practical operations, paving the way for the integration of Fog Computing as a foundation in the ever-expanding realm of IoT technologies. © 2024 IEEE.

关键词： Fog computing

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Real-Time Connectivity with the Industrial Internet of Things 15

Real-Time Connectivity with the Industrial Internet of Thing...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Jayakumar, Sujayaraj Samuel Kumar, Ashwini Kharbas, Vikas Kumar School of Sciences Department of Forensic Science Karnataka Bangalore India Arka Jain University Department of Mechnical Engineering Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Application Jaipur India

ISBN: (纸本)9798350370249

The economic net of things (Eliot) is an emerging era permitting the relationship and optimization of devices, machines, and belongings in industrial settings via statistics switch and exchange. Real-time connectivity with the Iota has revolutionized how business systems perform and has opened the door for full-size upgrades in productiveness, performance, and protection. With actual-time connectivity, commercial operations were capable of advantage near real-time perception into gadget performance, surroundings conditions, and the behavior of machines and property. It interprets into improved situational cognizance, higher decision-making main to optimized operational processes, and stepped forward usual operational visibility. Furthermore, actual-time connectivity with the Eliot is likewise allowing superior information analytics capabilities, taking into account the improvement of predictive analytics fashions, optimization of plant operations, and the gathering of critical information factors. All of those traits have improved the efficiency, scalability, and reliability of business operations and networks, permitting companies to higher-utilize their assets and become greater efficient. © 2024 IEEE.

关键词： Predictive analytics

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AI for Disaster Management: Predictive Models and Response Strategies

AI for Disaster Management: Predictive Models and Response S...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Shameem, A. Saroja, Devupalli Hotkar, Damodar S. Nayak, Bijaya Bijeta Pandey, Anand Tripathi, Sachin Amet University Amet Business School India Department of Computer Science and Engineering Visakhapatnam India R.T.E. Society's Rural Engineering College Department of Computer Science and Engineering Karnataka Hulkoti582205 India Kiit Deemed to Be University School of Mechanical Engineering Odisha Bhubaneswar India Sharda University Computer Science and Application U.P Greater Noida India Wathoda Symbiosis Law School Nagpur Maharashtra Nagpur India

ISBN: (纸本)9798331508685

The application of artificial intelligence (AI) in managing severe weather events has become more necessary in recent years owing to their increased frequency. Disaster management is required to control and avoid such occurrences. To successfully aid in better and quicker reactions to catastrophes, AI is widely employed in prediction and disaster preparation, damage mitigation and minimization, and rescue phases. To determine how AI technology can be used to lessen the damaging impacts of different disasters, this article looks into the potential for connecting artificial intelligence methods based on technological innovation. The study potentially contributes in designing an effective framework for disaster management based on the predictive accuracy and response time. © 2025 IEEE.

关键词： Disaster prevention

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