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2024 international conference on Computational Intelligence and computing Applications, ICCICA 2024

作者： Raj, Naman Sinha, Amisha Bharddwaj, Shubhansh Yadav, Anup Lal Chandigarh University Department Of Cse Mohali India

ISBN: (纸本)9798350306446

Energy management in smart homes is one of the many facets of daily living that have been changed by the integration of Artificial Intelligence (AI) and the Internet of Things (iot). This study investigates the design and execution of an artificial intelligence (AI) driven system for iot-enabled smart home energy optimization. This study's main goal is to provide a framework that uses AI algorithms and Internet of Things sensors to efficiently control and optimize energy use. The system attempts to ensure user comfort and convenience while optimizing energy efficiency through real-time data collecting and analysis. The suggested system's architecture is covered in the paper. It comprises Internet of Things (iot) devices placed throughout the smart home to track the energy usage patterns of various appliances and gadgets. Artificial intelligence (AI) technologies, such as machine learning and deep learning models, then evaluate and analyze this data in order to forecast energy demands and optimize usage appropriately. The study also discusses the difficulties in putting such a system into practice, such as interoperability, security, and data privacy concerns. In order to guarantee the smooth integration and functioning of the AI-powered energy optimization system, strategies and solutions for addressing these issues are also put forth. The efficacy and performance of the suggested system are assessed using simulations and real-world trials, showing notable gains in cost and energy efficiency over conventional methods. © 2024 IEEE.

关键词： Internet of things

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smart Hospitality Review: Using iot and machine learning to Its Most Value in the Hotel Industry

Smart Hospitality Review: Using IoT and Machine Learning to ...

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2024 IEEE international conference on Automation and Computation, AUTOCOM 2024

作者： Kaur, Amandeep Goyal, Sonali Batra, Neera University Department of Cse Mullana India

ISBN: (纸本)9798350382723

The study uses the Internet of Things (iot) and machine learning (ML) to analyse how a hotel industry is changing. This study investigates how machine learning (ML) algorithms improve demand forecasting, predictive analytics and customized guest experiences, with an emphasis on technical advances. Simultaneously, the incorporation of iot devices is reviewed for its potential to establish smart and networked hotel settings, promoting energy conservation and real-time monitoring. The combination of machine learning and the internet of things are essential for transforming operational strategies, supporting data-driven decision-making and providing guests with the highest possible degree of individualization. The paper addresses ethical issues and difficulties with data security and privacy while providing effective case stories. In order to help stakeholders navigate the innovative journey towards smarter, more efficient and guest-centric hospitality services, this assessment offers insights into the present state of ML and iot usage within the hotel sector. © 2024 IEEE.

关键词： Internet of things

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Proceedings of international conference on Circuit Power and computing Technologies, ICCPCT 2024

Proceedings of International Conference on Circuit Power and...

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7th international conference on Circuit Power and computing Technologies, ICCPCT 2024

ISBN: (纸本)9798350372816

The proceedings contain 315 papers. The topics discussed include: cloud-based storage solutions for facial recognition attendance systems;machine learning approaches for proactive phishing attack detection;iot device attacks, security and certification;integrated renewable energy system with ANN based MPPT for efficient power generation, wireless transmission, and energy storage in EV applications;accelerating sustainability through leveraging machine learning to analyze CSR spending in the Indian automobile industry;DDSRF theory for power quality in UPFC applications;towards a greener future: harnessing green computing to reduce ICT carbon emissions;innovative waste management: the efficiency of iot-enabled smart bins;cataracts detection using convolution neural network;and performance enhancement of machine learning algorithms for predicting stroke.

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smart Agriculture: Leveraging iot and machine learning in Wireless Sensor Networks for Precision Farming

Smart Agriculture: Leveraging IoT and Machine Learning in Wi...

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2024 international conference on Innovative computing, Intelligent Communication and smart Electrical Systems, ICSES 2024

作者： Sheela, M. Sahaya Leonid, T. Thomas Aravindhraj, N. Ravi, S. Giri, Pallavi Amer, Ayman Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Ece Chennai India Kcg College of Technology Department of Cse Chennai India Kongu Engineering College Department of Cse Erode India Department of Vlsi Microelectronics Chennai India Laxminarayan Innovation Technological University Department of Civil Engineering Nagpur India Zarqa University Department of Energy Engineer Zarqa Jordan College of Engineering University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

This research explores the challenge of incorporating iot with machine learning (ML) applications to Precision Agriculture, particularly the use of wireless sensors for irrigation control. Information from sensors that monitor key parameters including temperature, humidity, moisture, and water levels are used to forecast water requirements and control pumps. Support vector machine (SVM), Decision tree (DT), Random Forest (RF) and Naïve Bayes (NB) techniques are adopted for the analysis to find out their accuracy levels. It is observed from all the above-performed models that among all, the SVM has the maximum prediction rate of the given dataset and is slightly outperformed by only the DT and RF. The confusion matrix which points to the misclassification patterns of each model gives an insight into identifying the best ML approach for use. The convergence of iot and ML guarantees flexibility and responsiveness when controlling and designing agricultural systems. It is this approach that has the potential to enhance water resources management as well as address issues precipitated by climate change and growing worldwide food demand. © 2024 IEEE.

关键词： smart agriculture

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Differential privacy model for blockchain based smart home architecture

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FUTURE GENERATION COMPUTER SYSTEMS-THE international JOURNAL OF ESCIENCE 2024年 150卷 49-63页

作者： Qashlan, Amjad Nanda, Priyadarsi Mohanty, Manoranjan Univ Jeddah UJ Coll Comp Sci & Engn Jeddah Saudi Arabia Univ Technol Sydney UTS Fac Engn & IT FEIT Sydney NSW 2007 Australia Univ Technol Sydney UTS Fac Sci Sydney NSW 2007 Australia

Secure and private communications using the Internet of Things (iot) pose several challenges for smart home systems. In particular, data collected from iot devices comprise sensitive personal information such as biomedical data, financial data, and location and activity data. Recent research looks into the use of blockchain in smart home systems, protecting the privacy of the data in use. Such solutions need to address the issue of privacy using a formal and mathematical model for data privacy due to the vulnerability associated with privacy-preserving blockchain networks. In the present paper, our approach aims to provide a privacy-preserving data aggregation mechanism in the context of smart Homes that agree to contribute their data to a cloud server using machine learning to improve services for home users. We propose the use of differential privacy, a powerful concept in privacy preserving schemes to provide formal assurances about how much information is leaked using a privacy budget. The main purpose of using such a privacy-preserving scheme is to limit what can be inferred about individual training data from the model. Our techniques use a R ' enyi differential privacy (RDP) machine learning scheme and are based on a variant of the stochastic gradient descent function. The performance of our proposed framework is evaluated using three public datasets: UNSW-NB15, NSL-KDD, and ToN-iot datasets. Our findings show that differential private models can provide privacy protection against attackers by sacrificing a substantial amount of model utility. Therefore, we propose an empirical value of epsilon, that can optimally balances utility and privacy for the current smart home scenario datasets.(c) 2023 Elsevier B.V. All rights reserved.

关键词： smart home Blockchain Differential privacy Membership inference attack Internet of Things Edge computing

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A Detailed Analysis of Cloud Storage and Key Management Techniques in iot Driven smart Grids 7

A Detailed Analysis of Cloud Storage and Key Management Tech...

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7th international conference on Circuit Power and computing Technologies, ICCPCT 2024

作者： Sivaprasad Manivannan, I. Roobert, A. Andrew Muthukumaran, N. Rohini College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu India Francis Xavier Engineering College Department of Electronics and Communication Engineering Tirunelveli627003 India Sri Eshwar College of Engineering Centre for Computational Imaging and Machine Vision Department of Electronics and Communication Engineering Tamil Nadu Coimbatore641202 India

ISBN: (纸本)9798350372816

The Internet of Things (iot) technology has revolutionized smart grid settings, enhancing efficiency and enabling advanced data analytics. However, the integration of iot devices within smart grids introduces significant challenges related to data storage and security. This survey provides a comprehensive review of the various classifiers and optimization algorithms employed to enhance cloud storage solutions and key management systems within smart grid environments. Moreover, it symmetrically examines different classifiers, like machine learning (ML) and Deep learning (DL) methods, which are designed for managing cryptographic keys, focusing on their ability to secure communications, authenticate devices, and ensure data integrity in the presence of cyber threats. Additionally, it categorizes and evaluates a wide range of optimization methods, including evolutionary algorithms, particle swarm optimization (PSO), gray wolf optimization (GWO) and firefly algorithm (FFA) based techniques for optimizing the classifiers, highlighting their applicability in optimizing storage capacities, data retrieval speeds, and cost-effectiveness. By synthesizing recent advancements and comparing the efficacy of these techniques, the survey identifies the best individuals and emerging trends that promise to strengthen the resilience and efficiency of iot-enabled smart grids. The findings promote the critical role of integrated classifiers and optimization techniques in achieving scalable, secure, and reliable smart grid infrastructures. © 2024 IEEE.

关键词： Data integrity

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Distributed Flexible Job Shop Scheduling through Deploying Fog and Edge computing in smart Factories Using Dual Deep Q Networks

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Mobile Networks and Applications 2024年第3期29卷 886-904页

作者： Lin, Chun-Cheng Peng, Yi-Chun Chen, Zhen-Yin Annie Fan, Yu-Hong Chin, Hui-Hsin Department of Industrial Engineering and Management National Yang Ming Chiao Tung University Hsinchu300 Taiwan International Master Program of Smart Manufacturing and Applied Information Science National Chin-Yi University of Technology Taichung411 Taiwan Department of Information Technology Overseas Chinese University Taichung407 Taiwan

Flexible job shop scheduling (FJSP) has garnered enormous attention within the realm of smart manufacturing, where, beyond job sequencing, the selection of machines holds considerable importance. As smart factories progress with the Internet of things (iot) and cyber-physical systems (CPS), scheduling methodologies are advancing towards intelligent decentralization. However, with the expansion of factories, conventional cloud computing struggles to manage the substantial influx of data. To tackle this issue, this work incorporates a fog computing and edge computing framework into the distributed FJSP workstations. In this framework, the workstations each of which consists of multiple machines are categorized based on the different nature of the accommodated machines, and operate independently to reduce unnecessary information transmission, in which each machine is equipped with edge computing capacity. The fusion of fog computing and edge computing allows for the offloading of computational tasks from cloud computing, effectively reducing latency. While previous solutions for FJSP have predominantly relied on linear programming or metaheuristic algorithms, this work proposed a novel distributed approach based on a dual deep Q networks (dual DQN) architecture, integrating deep learning (DL) with reinforcement learning (RL). Within the cloud center, the initial neural network determines the machine selection rules for fog computing, while the secondary neural network decides the job dispatching rules for edge computing devices. Edge computing devices execute the schedule and provide feedback to the cloud, which refines the results through an iterative training process, so that to minimize the makespan. The experimental findings indicate that employing dual DQNs outperforms the methods of utilizing only one single machine selection rule. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Reinforcement learning

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DDoS Attack Detection System for iot Enabled smart City Applications with Correlation Analysis 4

DDoS Attack Detection System for IoT Enabled Smart City Appl...

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4th international conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Nirosha, Veeramachaneni Hemamalini, V. Srm Institute of Science and Technology Department of Networking and Communication Kattankulathur India

ISBN: (纸本)9798350343670

The topic of cybersecurity remains a significant concern for industries involved in digital operations, as seen by the recurring increase in security incidents. The increasing use of Internet of Things (iot) devices in various settings such as homes, offices, transportation, and healthcare has resulted in a corresponding rise in the frequency of malicious assaults. machine learning (ML) is frequently utilized for the purpose of detecting attacks on iot devices. This study presents a novel approach to cybersecurity-focused network traffic correlation analysis and the detection of Distributed Denial of Service (DDoS) attacks using artificial intelligence techniques in the context of an iot-enabled smart city, with the aim of promoting sustainability. A traffic analysis was conducted by utilizing a wireshark network analyser tool, which effectively improves data transmission. The experimental investigation was conducted to evaluate various performance metrics, including accuracy, precision, recall, attack detection rate and time. The proposed technique achieved anaccuracy rate of 96%, precision and recall rates are 93.6% and 91% respectively. © 2024 IEEE.

关键词： correlation analysis Cyber security DDoS attack smart city

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Future Internet Architecture for iot Frameworks Implementing the Synapse Algorithm for Intelligent Energy Management in smart Home Automation

Future Internet Architecture for IoT Frameworks Implementing...

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2024 international conference on Innovative computing, Intelligent Communication and smart Electrical Systems, ICSES 2024

作者： Shalini, M. Nagarajan, K.K. Prince Shri Venkateshwara Padmavathy Engineering College Electronics and Communication Engineering Chennai India Sri Sivasubramaniya Nadar College of Engineering Chennai India

ISBN: (纸本)9798331543617

This paper proposes a novel Internet architecture for iot frameworks that integrates the Synapse Algorithm for intelligent energy management in smart homes. Current iot energy management systems often face challenges such as inefficient energy consumption, poor adaptability to user preferences, and limited scalability in managing multiple interconnected devices. These issues are exacerbated by the inability to process large datasets effectively and in real-time. To address these challenges, the proposed framework, Intelligent Energy Management using the Synapse Algorithm (IEM-SA), offers a sophisticated solution. The framework leverages the Synapse Algorithm to optimize energy consumption by analysing data from interconnected devices in real- time, learning user behaviour patterns, and dynamically adjusting energy usage based on predicted demand. This not only enhances the system's energy efficiency but also enables seamless control and adaptability across different environments and devices. The proposed method improves energy efficiency, scalability, and adaptability within smart home environments. By integrating real-time data processing and machine learning, it supports the seamless operation of multiple iot devices while significantly reducing energy waste. Findings indicate that the IEM-SA framework outperforms existing methods in terms of energy savings, system responsiveness, and scalability. Through simulation results, the proposed framework demonstrates its potential to optimize energy usage while improving the overall smart home automation experience. © 2024 IEEE.

关键词： smart homes

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Proceedings - 2024 international conference on Emerging Innovations and Advanced computing, INNOCOMP 2024

Proceedings - 2024 International Conference on Emerging Inno...

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2024 international conference on Emerging Innovations and Advanced computing, INNOCOMP 2024

ISBN: (纸本)9798350376470

The proceedings contain 113 papers. The topics discussed include: enhancing K-means clustering with data-driven initialization and adaptive distance measures;integrating iot and AI for automated crop with smart urban farming;mango fruit defect detection using MobileNetV2;comparative evaluation of mango fruit diseases classification using machine learning;a review of technology in supervising the migration management for refugees with special reference to artificial intelligence and fog computing;a comprehensive review of deep learning techniques for image and video analysis;a comparative analysis of diverse classification techniques in machine learning for predicting poker hands;and geohazard characterization in upper Bhagirathi Basin: GIS computation for landslide susceptibility using frequency ratio modeling.

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