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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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Enhancing Security and Privacy of Machine Learning Algorithms in Agricultural Automation 15

Enhancing Security and Privacy of Machine Learning Algorithm...

引用

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

作者： Singh, Amit Kumar Kharbas, Vikas Kumar Vinay Kumar, S.B. Arka Jain University Department of Journalisim & Mass Communication Jharkhand Jamshedpur India Vivekananda Global University Department of Computer Science & Application Jaipur India School of Engineering and Technology Department of Electronics and Communication Engineering Karnataka Bangalore India

ISBN: (纸本)9798350370249

This paper specializes in enhancing the safety and privacy of machine-mastering algorithms in agricultural automation. With the emergence of digital generation in agricultural production, system mastering algorithms have emerged as essential to offer precision control in farm control operations. However, their security and privacy may be compromised due to assault vectors inclusive of opposed inputs, facts leakage assaults, and facts poisoning attacks. Several ability solutions are studied, which include the introduction of statistics anonymization strategies, getting the right of entry to manage mechanisms, cryptographic strategies, and software program and hardware protection tactics. This paper analyses the one-of-a-kind security and privacy enhancement techniques and affords a comprehensive discussion of their effectiveness. Sooner or later, the authors advise a complete solution that encompasses all the security and privacy enhancement approaches to shield the gadget studying algorithms in agricultural automation effectively. © 2024 IEEE.

关键词： Differential privacy

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Structural Connectivity Analysis in Multiple Sclerosis Using Diffusion Tensor Imaging 5th

Structural Connectivity Analysis in Multiple Sclerosis Using...

引用

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

作者： Yadav, Dhyan Chandra Doda, Davendra Kumar Joshi, Apoorva Rengarajan, A. Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Lucknow India Department of Electrical 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

Structural connectivity evaluation (SCA) is a powerful tool for characterizing mind circuitry and assessing nearby structural adjustments in the mind. Diffusion Tensor Imaging (DTI) is a complicated magnetic resonance imaging technique used to determine the structural integrity of white be-counted tissue and to map the fiber pathways connecting excellent mind areas. This method allows for non-invasive exploration of structural connectivity for the motive of bdd5b54adb3c84011c7516ef3ab47e54, the underlying dynamics of mind networks. This paper uses SCA to sing the changes in mind networks due to more than one Sclerosis (MS) by measuring the integrity of white be-counted number pathways. The consequences of the evaluation display that SCA is useful in detecting close-by changes inside the corpus callosum structure that correlate with the development and severity of MS. Furthermore, it is well-known that MS induces adjustments inside the connectivity among wonderful mind regions that appear to be location unique. The findings display that SCA using DTI is a precious device for statistics of the impact of MS on neural networks at a few stages inside the thoughts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Tensors

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Review on Predicting the Landslide Susceptibility using Machine Learning Techniques

Review on Predicting the Landslide Susceptibility using Mach...

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Computational Intelligence, Communication Technology and Networking (CICTN), International Conference on

作者： Abhishek Sinha Shivam Kumar Giri Himanshu Yadav Sanjeev Kumar Prasad Priya Mishra School of Computer Science and Engineering Galgotias University Greater Noida India Department of Computer Application ABES Engineering College Ghaziabad India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

This Landslide susceptibility prediction is a critical task for Alleviating the risks associated with landslide hazards, which have significant socio-economic and environmental impacts. Existing techniques face challenges related to data quality, model coherence, and generalizability across diverse geographical and climatic regions. Additionally, while advanced machine learning (ML) and deep learning (DL) models offer high accuracy, they often require significant computational resources, which limits their practical application in real-time scenarios. In this review, we aim to highlight the strengths, limitations, and potential improvements of machine learning techniques in predicting landslide susceptibility, providing a comprehensive reference for researchers and practitioners in this domain. Furthermore, the role of Explainable AI is examined to address the critical need for interpretable and transparent models in real-world applications. Hybrid ML-DL models outperform traditional methods in handling complex spatial-temporal data, with dynamic factors improving accuracy and reliability. Explainable AI enhances interpretability, while advancements and collaborations address data and generalizability challenges.

关键词： Deep learning Accuracy Reviews Explainable AI Computational modeling Predictive models Terrain factors Data models Real-time systems Reliability

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A Novel Deep Federated Learning-Based and Profit-Driven Service Caching Method 19th

A Novel Deep Federated Learning-Based and Profit-Driven Ser...

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19th EAI International Conference on Collaborative Computing: Networking, applications and Worksharing, CollaborateCom 2023

作者： Ouyang, Zhaobin Xia, Yunni Peng, Qinglan Li, Yin Chen, Peng Wang, Xu College of Computer Science Chongqing University Chongqing China School of Artificial Intelligence Henan University Zhengzhou China Guangzhou Institute of Software Application Technology Guangzhou China School of Computer and Software Engineering Xihua University Chengdu China College of Mechanical and Vehicle Engineering Chongqing University Chongqing China

ISBN: (纸本)9783031545306

Service caching is an emerging solution to addressing massive service request in a distributed environment for supporting rapidly growing services and applications. With the explosive increases in global mobile data traffic, service caching over the edge computing architecture, Mobile edge computing (MEC), emerges for alleviating traffic congestion as well as for optimizing the efficiency of task processing. In this manuscript, we propose a novel profit-driven service caching method based on a federated learning model for service prediction and a deep reinforcement learning mode for yielding caching decisions (FPDRD) in an edge environment. The proposed method is temporal service popularity and user preference-aware. It aims to ensure quality of service (QoS) of delivery of cached service while maximizing the profits of network service providers. Experimental results clearly demonstrate that the FPDRD method outperforms traditional methods in multiple aspects. © ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Traffic congestion

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KVSAgg: Secure Aggregation of Distributed Key-Value Sets 39

KVSAgg: Secure Aggregation of Distributed Key-Value Sets

引用

39th IEEE International Conference on Data engineering, ICDE 2023

作者： Wu, Yuhan Dong, Siyuan Zhou, Yi Zhao, Yikai Fu, Fangcheng Yang, Tong Niu, Chaoyue Wu, Fan Cui, Bin Peking University School of Computer Science National Engineering Laboratory for Big Data Analysis Technology and Application Beijing China Peng Cheng Laboratory Shenzhen China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China

ISBN: (纸本)9798350322279

In global data analysis, the central server needs the global statistic of the user data stored in local clients. In such cases, an Honest-but-Curious central server might put user privacy at risk in trying to collect individual statistics of each user. In response, the secure aggregation provides a solution for calculating global statistics without revealing users' privacy data. However, existing secure aggregation protocols only focus on the data in the form of vectors or common sets, which limits their application scope. We formalize a general problem - key-value set secure aggregation - that not only includes secure vector aggregation and private set union but also supports more applications. To address the proposed problem, we devise our solution (called the KVSAgg framework) that promises satisfactory performance in security, efficiency, and accuracy. Our key technique is a homomorphic transform algorithm (called HyperIBLT) that is not only capable of bidirectionally transforming data between key-value sets and vectors, but also able to transform sum operation of sets to addition of vectors. We implement KVSAgg on both CPU and GPU platforms and perform the evaluation on three use cases including federated learning, distributed data counting, and finding global hot items. Compared with our baselines, KVSAgg simultaneously achieves the best security, efficiency higher by orders of magnitude, and zero-error in nearly all cases. All codes are open-source anonymously. © 2023 IEEE.

关键词： Vectors

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Designing Blockchain Based Consensus Mechanism for Smart Healthcare IoT

Designing Blockchain Based Consensus Mechanism for Smart Hea...

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2023 IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023

作者： Kumari, Trishla Kumar, Rakesh Dwivedi, Rajendra Kumar Madan Mohan Malviya University of Technology Dept. of Computer Science and Engineering Gorakhpur India Madan Mohan Malviya University of Technology Dept. of Information Technology and Computer Application Gorakhpur India

ISBN: (纸本)9781665462631

In today's world, healthcare system is improving day by day by using smart IoT based system. Blockchain based system is totally a decentralized system due to which attackers are unable to access the confidential records easily. Blockchain play an important role in controlling and management of sensitive information in healthcare system and insurance sector. Consensus mechanisms used in blockchain technology to validate whichever block is going to be added next in the blockchain network. In this paper we proposed a hybrid based system which solves trust issues arises in the blockchain. Hybrid consensus mechanism provides reputation according to the activities of blocks in a blockchain network. Four layer architecture is designed in Hybrid model. Five algorithms are used to create, validate, fork handling, reward and punishment scheme and Markle tree is constructed respectively. This model is implemented on Ethereum based platform which shows that HCM model is better than a PoXR model as it consume less memory (50 percent) and consume less CPU(below 2 percent). © 2023 IEEE.

关键词： Blockchain

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Optimizing Solar-Energy Harvesting Communication Systems: An energy allocation scheme for maximizing data transmission 4

Optimizing Solar-Energy Harvesting Communication Systems: An...

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4th International Conference on Internet of Things and Smart City, IoTSC 2024

作者： Li, Shuai Yao, Zhonghao Chen, Shengbo Schoool of Software Henan University Henan Kaifeng China School of Computer Science & Technology Beijing Institute of Technology Beijing China School of Computer and Information Engineering Henan University Henan Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Henan Kaifeng China

ISBN: (纸本)9781510681767

The solar energy harvesting communication system is the basic support layer that is associated with the Internet of Things (IoT). However, the inherent stochasticity of solar energy brings about extensive research on energy allocation in the system. In this paper, we propose an energy allocation algorithm for maximizing the data transfer (MDT) of a solar energy harvesting communication system while jointly considering the practical constraints of battery capacity and link capacity restrictions. We can solve it through the convex optimization problem, but the solution is extremely complicated. This complexity is the result of the coupling of energy allocation problems caused by restricted battery capacity and link capacity. To simplify the solution process, we propose a decoupled energy allocation (DEA) algorithm. Simulation experiments are conducted through real energy harvesting data sets, and the results show that DEA outperforms the other two heuristic schemes. © 2024 SPIE.

关键词： Convex optimization

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Inception and LightGBM-based Model for Poly-Cystic Ovary Syndrome Detection with CNN in Ultra-sound Images 4

Inception and LightGBM-based Model for Poly-Cystic Ovary Syn...

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4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Puri, Shalini Soni, Akanksha Choudhary, Vishal Manipal University Department of Information Technology Rajasthan Jaipur India UIT-RGPV Department of Electronics Communication & Engineering Bhopal India Lovely Professional University Department of Computer Application Punjab India

ISBN: (纸本)9798350354218

Nowadays, a common hormonal condition that affects women of reproductive age and frequently results in infertility is Poly-Cystic ovary syndrome. Recognizing the Poly-Cystic ovary syndrome early is essential for symptom management and mitigating associated health risks. The traditional methods are used for diagnosis, encompassing high androgen levels, anovulation, and Poly-Cystic ovaries observed via ultra-sound. However, the challenging aspect of manually assessing Poly-Cystic ovaries observed via ultra-sound through follicle counting and thickness determination by medical professionals persists. In addition to addressing patient symptoms, clinicians must rely on a combination of biochemical and clinical indicators, lacking standardized diagnostic procedures. To alleviate these diagnostic challenges, this paper presents a hybrid Poly-Cystic Ovary Syndrome Detection model using a convolutional neural network comprising an ensemble inception model and a light gradient-boosting machine on ultra-sound Images. It significantly improves the detection of the Poly-Cystic ovaries observed and achieves an accuracy of 98.4%, surpassing InceptionV3's 96.3%. This study substantiates this performance gain through comprehensive quantitative effectiveness assessment metrics. This study proposes a promising model for accurate and efficient Poly-Cystic ovary syndrome diagnosis, aiding clinicians in providing timely and targeted care to affected individuals. © 2024 IEEE.

关键词： Deep neural networks

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Research and application of Three Protocols Based on AUTOSAR 22

Research and Application of Three Protocols Based on AUTOSAR

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22nd IEEE International Conference on Communication Technology, ICCT 2022

作者： Wang, Ying Gao, Bo Niu, Bo Xi'an Aeronautics Computing Technique Research Institute Avic Laboratory of Computer Application Engineering Xi'an China

ISBN: (纸本)9781665470674

The AUTOSAR (Automotive Open System Architecture) standard includes a set of specifications that describe software architecture, application programming interfaces and methods, in which the layered software architecture enables the development of software components to be carried out independently. The article focuses on the communication in the AUTOSAR architecture, including three buses: the LIN, the J1939 and the FlexRay. Firstly, it introduces the characteristics of these three bus protocols, and then the application of the LIN, the implementation of the J1939 and the key points in the realization of the FlexRay are studied and analyzed in depth. Through its application and software implementation, these three types of bus structures are comprehensively analyzed, which makes the application and implementation of communication in the AUTOSAR architecture more accurate. Especially through the implementation part of this paper, the implementation of communication in this architecture is easier to grasp. © 2022 IEEE.

关键词： Software architecture

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