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Digitalized Infrastructure of Smart Homes Using Iot and cloud computing

Digitalized Infrastructure of Smart Homes Using Iot and Clou...

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2023 international conference on Computational Intelligence and Sustainable engineering Solution, CISES 2023

作者： Negi, Prafful Pachouri, Vikrant Pandey, Shweta Chattopadhyay, Saumitra Gehlot, Anita Joshi, Kapil Uttaranchal Institue of Technology Uttaranchal University Dehradun India Uttaranchal University Lcd Division of Research & Innovation Dehradun India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Uttaranchal University Uit Division of Research & Innovation Dehradun India

ISBN: (纸本)9798350323917

Digitalized homes are considered 'smart homes' where all of the appliances and gadgets are automatically managed from anywhere using any networked devices, such as mobile phones. In the current modern age, the idea of 'smart homes' has become important for people's lifestyles. The current study focuses on the inclusion of the Internet of Things (IoT) and cloud computing which provides an enhancement in the structure of smart homes leading to low budget and effective communication within the smart home utilities. Users may get real-time information immediately through a web browser with the help of IoT and cloud computing. Based on the analysis, this study identifies the functionality of the smart equipment equipped in smart homes and their connectivity via cloud servers. Finally, based on the evaluation the challenges faced by the technology have been discussed, and based on that future recommendations have been provided according to the framework generated. © 2023 IEEE.

关键词： cloud computing

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A Deep Learning-based Optimization Model for Advertisement Campaign 5

A Deep Learning-based Optimization Model for Advertisement C...

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5th international conference on Smart Electronics and communication, ICOSEC 2024

作者： Gummadi, Venkata Ramadevi, N. Udayaraju, Pamula Ravulu, Chaitanya Seelam, Dhanunjay Reddy Swamy, Sureddy Venkat Expedia Group Austin United States Srkr Engineering College Depaertment of It Andhra Pradesh India Srm University-AP Department of Cse School of Engineering and Sciences India Rocket Companies MI United States Walmart Global Tech BentonvilleAR72712 United States Ramachandra College of Engineering Department of Cse AP Eluru India

ISBN: (纸本)9798331504403

Non-profit organizations often struggle to fully utilize the $10,000 online Ads grant to enhance their online presence and attract aligned donors due to a lack of expertise in digital marketing. This gap in effective utilization limits their potential reach and undermines their growth and mission impact. This proposal outlines a solution leveraging cloud computing and artificial intelligence (AI) to address this challenge. By integrating advanced cloud-based tools and AIdriven analytics, we can provide these organizations with tailored strategies and automated support to optimize their Google Ads campaigns. This approach will enable non-profits to maximize their advertising budget, refine targeting strategies, and improve engagement with prospective donors, ultimately fostering greater alignment with their causes and driving sustainable growth. © 2024 IEEE.

关键词： Budget control

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DESIGN AND IMPLEMENTATION OF A cloud-BASED ENERGY MONITORING SYSTEM FOR 5G BASE STATIONS 2

DESIGN AND IMPLEMENTATION OF A CLOUD-BASED ENERGY MONITORING...

引用

2nd international conference on Power, communication, computing and Networking Technologies, PCCNT 2024

作者： Xu, Na Shandong University of Engineering and Vocational Technology Jinan China

ISBN: (纸本)9781837242672

This paper presents the design and implementation of a cloud-based energy monitoring system specifically developed for 5G base stations, with a focus on optimizing energy consumption in civil engineering projects. The proposed system integrates Internet of Things (IoT) sensors, Python for data processing, and SPSS for statistical analysis to collect and analyze real-time energy data from base stations. Through this platform, energy consumption patterns are monitored, providing insights to improve energy efficiency and reduce operational costs in construction environments such as bridges and tunnels. Simulations demonstrate that dynamic power adjustment and renewable energy integration strategies can reduce energy consumption by up to 20%. Furthermore, the system’s scalable cloud-based architecture allows it to accommodate the energy management needs of large-scale infrastructure projects. The research findings indicate that the combination of real-time monitoring, statistical analysis, and predictive modeling has significant potential for improving energy efficiency in 5G base station operations, supporting sustainable practices in civil engineering. © The Institution of engineering & Technology 2024.

关键词： Energy utilization

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Energy-Efficient Task Offloading for Edge computing-Based Smart Grid Networks Using Human Urbanization

Energy-Efficient Task Offloading for Edge Computing-Based Sm...

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2023 international conference on Sustainable communication Networks and Application, ICSCNA 2023

作者： Patra, Sudhansu Shekhar Dash, Bibhuti Bhusan Pandey, Trilok Nath Pattanayak, Binod Kumar Tripathy, Abinash Chandrade, Utpal School of Computer Applications Kiit Deemed to Be University Bhubaneswar India School of Computer Science and Engineering Vit Chennai India Iter Soa Deemed to Be University Department of Computer Science and Engineering Bhubaneswar India

ISBN: (纸本)9798350313987

A microgrid is a generating source used to decentralise the centralised power supply in a small community. It produces, distributes, and regulates the community's power. It can balance the load demands of clients with fluctuating power needs since it is adaptable, dependable, and able to deliver uninterrupted electricity. The utility grid is connected to smart Microgrids, which assist the powering industries' requirement for electricity. They rely heavily on contemporary, integrated information and communication technologies (such as edge computing, cloud computing, fog computing, IoT sensors, etc.). Despite the low cost of the services offered by cloud computing, response, processing, and resource management times are issues. As a supplement to cloud computing, edge computing develops, offering services and tackling issues like latency, security, and traffic. The edge servers are used by smart meters to offload their computational tasks. The energy usage is rising as more data is processed in the edge servers. In order to reduce energy usage, this article suggests optimized task scheduling to edge servers using human urbanization optimization algorithm (HUOA). © 2023 IEEE.

关键词： Edge computing Energy optimization Human urbanization optimization Smart grid

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Multimodal Data Trajectory Prediction: A Review 10

Multimodal Data Trajectory Prediction: A Review

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10th IEEE international conference on Cyber Security and cloud computing and 9th IEEE international conference on Edge computing and Scalable cloud, CScloud-EdgeCom 2023

作者： Wang, Xiaoliang Yue, Hao Yang, Qing Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China Guangzhou Maritime University School of Information and Communication Engineering Guangzhou China

ISBN: (纸本)9798350312461

Trajectory prediction refers to predicting the future movement of an object, person, or vehicle based on past motion trajectory information and other relevant environmental information. Traditional trajectory prediction solutions are suitable for simple driving scenarios and only applicable for short-term predictions. With the improvement of computing power and data processing speed, people can analyze and utilize large-scale datasets faster, use more data and more complex algorithms to build models, and therefore better predict future trends and behaviors. In addition, different solutions focus on how to efficiently extract features from different types of information and how to more accurately predict the future trajectory of the target object. Our goal is to analyze the impact of different information on prediction, and to classify and compare deep learning-based trajectory prediction methods for vehicles or surrounding pedestrians based on the same data. According to the type and quantity of inputs, they are divided into five categories, and the use and prediction effect of different information solutions are elaborated in detail. © 2023 IEEE.

关键词： Deep neural networks

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A Novel Framework for AI-Driven, cloud-Integrated Energy-Efficient IoT Solutions in Smart Homes 8

A Novel Framework for AI-Driven, Cloud-Integrated Energy-Eff...

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8th international conference on Electronics, communication and Aerospace Technology, ICECA 2024

作者： Dankan Gowda, V. Samal, Ratidev Reddy, Premkumar Marthanda, A.V.G.A. Billady, Ravikiran Kamath Rajlakshmi, P.V. Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India Centurion University of Technology and Management School of Management Jatni Bhubaneswar752050 India Frisco TX United States Lakireddy Balireddy College of Engineering Department of Eee India Department of Mechanical Engineering Karnataka Nitte Udupi574110 India Kongu Engineering College Department of English Tamilnadu Erode638060 India

ISBN: (纸本)9798350367904

Smart homes are defined with high needs of energy efficiency in home automation to which the IoT technologies are combined with Artificial Intelligence (AI) and the cloud computing system. That is why this research has posed the prospect of a new framework focused on the application of Artificial Intelligence and cloud solutions to manage energy usage in smart homes. The system harnesses data from smart IoT applications, statistical research and self-adjusting control to minimize energy consumption while providing comfort and satisfaction to the user. Based on initial experiments carried out in a smart home test bed, the proposed approach achieved peak hour energy savings of up to 20%. Due to the AI model's prediction accuracy, which was within 5% of actual usage, one could effectively manage smart devices. Furthermore, the cloud-base architecture also empowered the system's scalability and adequate time consumption with the response time below 0.10 seconds for value added tasks and 5 seconds for critical tasks. The proposed strategy allows improving energy efficiency with further transition to smart home concepts while being applied as a long-term decision. © 2024 IEEE.

关键词： Smart homes

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A Critical Study on Hybrid computing and Its Current Influences and Future Challenges 3

A Critical Study on Hybrid Computing and Its Current Influen...

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3rd international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, IITCEE 2025

作者： Praveen, K.V. Joe Prathap, P.M. Beevi, L. Sherin Dani, W. Vinil St. Peter's College of Engg. and Tech. Department of Information Technology Chennai India R.M.D. Engineering College Department of Computer Science and Engineering Kavaraipettai India St. Peter's College of Engg. and Tech. Department of Eee Chennai India

ISBN: (纸本)9798331515911

This research focuses on the evolving field of hybrid computing, which combines traditional computing architectures with advanced computational paradigms like quantum computing, neuromorphic computing, or cloud-edge computing. Hybrid computing involves integrating multiple computing models or systems to leverage their complementary strengths. For example, combining classical computing with quantum or neuromorphic approaches to solve complex problems more efficiently. The goal is to enhance computational power, increase efficiency, and tackle problems that are difficult for traditional computing models alone. Emerging technologies, including quantum computing, machine learning, and AI, are driving the need for hybrid systems. These technologies enable the handling of more complex computations that classical systems struggle with, such as optimization, simulation, and cryptography. The integration of cloud and edge computing is also central to hybrid computing, enabling decentralized computation and processing closer to data sources. Combining different computing paradigms requires seamless communication and integration between systems, which is a significant technical challenge. Main challenging areas are scalability, software and hardware compatibility, energy efficiency and security. There is a need for standard protocols and frameworks to enable smoother integration of various computing systems. New algorithms that can take advantage of hybrid architectures need to be developed. Depend on applications significant advancements in hardware, including quantum processors, neuromorphic chips, and integrated hybrid processors required. The rise of hybrid computing will also bring ethical concerns, such as the impact on jobs, privacy, and inequality in access to advanced technologies. The study concludes by emphasizing the transformative potential of hybrid computing while acknowledging the technical, economic, and societal hurdles that need to be overcome. It highlights

关键词： Quantum computers

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An IoT-based Smart Farming Solution for Sustainable Agriculture: Integrating AI, cloud computing, and 4G communication for Enhanced Productivity

An IoT-based Smart Farming Solution for Sustainable Agricult...

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international conference on Green Energy, computing and Intelligent Technology 2024, GEn-CITy 2024

作者： Afif, Mustahoshin Hossain Ahamed Iqbal, Javid Borshon, Salim Sadman Rashedul Islam Junayed, M.D. Ahamed, Sabbir SystemSage Solutions Sdn Bhd Jalan Ampang Kuala Lumpur Malaysia School of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Darul Ehsan47500 Malaysia Depart of Mechanical Engineering RUET Rajshahi Bangladesh Software Development Department SystemSage Solutions Sdn Bhd Jalan Ampang Kuala Lumpur Malaysia

ISBN: (纸本)9781837242689

This paper presents a comprehensive smart farming solution designed to address the limitations of current systems and the practical needs of modern farmers. Leveraging cutting-edge technologies such as the Internet of Things (IoT), artificial intelligence (AI), and cloud computing, our solution aims to improve agricultural productivity and sustainability. By integrating industrial-grade sensors with a user-friendly software platform, our system facilitates transparent monitoring and control of agricultural conditions at various scales, while also providing tools for efficient farm finance management. The star topology architecture, combined with 4G network communication, ensures system reliability and efficient data transmission. AI integration provides insights and actionable recommendations, allowing farmers to effectively optimize crop yields and resource utilization. Our approach addresses significant shortcomings observed in existing smart agriculture projects, such as outdated communication technology, limited scalability, and inadequate user interfaces for data management. By integrating solar power to power the system and emphasizing user-centric design, our solution promotes environmental sustainability and operational efficiency. © The Institution of engineering & Technology 2024.

关键词： Smart agriculture

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Resource allocation and task scheduling for computing power aware networks

Resource allocation and task scheduling for computing power ...

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2024 international conference on Physics, Photonics, and Optical engineering, ICPPOE 2024

作者： Peng, Guozheng Zhao, Chuanqi Chen, Lingying Wang, Lanruo Beijing100089 China State Grid Shandong Electric Power Research Institute Ji’nan250003 China

ISBN: (数字)9781510689121

ISBN: (纸本)9781510689114

As an important cornerstone of future information and communication technology, the computing power network cleverly integrates cloud resources, third-party computing devices, and various heterogeneous computing resources scattered at the edge of the network through seamless fusion of widely covered network connections. This innovative architecture aims to break the boundaries of traditional computing models and achieve precise scheduling and global optimization of computing resources by building a highly collaborative computing network resource management system. This article proposes a resource allocation and task scheduling algorithm based on deep reinforcement learning (DRL), which simulates the human decision-making process and enables the system to autonomously learn and optimize strategies in a constantly changing computing network environment. This algorithm can not only effectively cope with large-scale and high dynamic computing power demands, but also accurately predict future resource trends, pre allocate resources in advance, and ensure smooth and efficient task execution. The experimental results show that the algorithm performs well in improving the utilization of computing power resources, reducing task execution latency, enhancing system stability and flexibility, and laying a solid foundation for the further development and application promotion of computing power networks. © 2025 SPIE.

关键词： computing power

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Parallel and Batch Multiple Replica Auditing Protocol for Edge computing 21

Parallel and Batch Multiple Replica Auditing Protocol for Ed...

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21st IEEE international Symposium on Parallel and Distributed Processing with Applications, 13th IEEE international conference on Big Data and cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and communications, ISPA/BDcloud/SocialCom/SustainCom 2023

作者： Li, Yi Zheng, Wenying Pandi, Vijayakumar Bhuiyan, Md Zakirul Alam Thamilarasi, C. Nanjing University of Information Science and Technology School of Computer Science Nanjing China Zhejiang Sci-Tech University School of Computer Science and Technology Hangzhou China University College of Engineering Tindivanam Anna University Chennai Department of Computer Science and Engineering Melpakkam India Fordham University Department of Computer and Information Sciences BronxNY United States P.S.V College of Enfgineering and Technoogy Department of Electronics and Communication Engineering Krishnagiri India

ISBN: (纸本)9798350329223

The edge computing architecture exposes data transmission and storage to unauthorized access, compromising data integrity. Users typically employ multiple data backups to ensure data reliability and availability, enhancing data redundancy and recovery capabilities. Nevertheless, verifying the integrity of multiple backup data under edge computing entails computational and communication overhead. In response, we present a parallel and batch multiple replica auditing protocol tailored for edge computing. Contributions include an efficient multiple replica construction method based on RSA, minimizing computational overhead, and a parallel data integrity verification approach that reduces communication overhead. A prototype implementation is evaluated in a real edge computing environment and compared to existing multiple replica auditing approaches. © 2023 IEEE.

关键词： Edge computing

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