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An Integrated Framework for Real-Time Analysis and Observability of Wireless Sensor Data Using AWS Edge Service Capabilities 3

An Integrated Framework for Real-Time Analysis and Observabi...

引用

3rd IEEE international conference of Electron Devices Society Kolkata Chapter, EDKCON 2024

作者： Mohan, Saranga Panda, Sunita Department of Electrical Electronics and Communication Engineering Bengaluru Campus India

ISBN: (纸本)9798350374643

Wireless sensor networks generate vast data offering insightful information for applications from industrial monitoring to smart cities. Optimizing energy consumption in these networks is critical for increasing the efficiency and lifespan of these networks. Earlier cloud computing methodology usually faces challenges in bandwidth, latency and reliability. This research work identifies the current gap in the industry and adds edge computing in wireless sensor networks in processing huge data using AWS IoT Greengrass and explores the novel use case of visualizing AWS sensor data with AWS Quick Sight. The implementation of edge computing will reduce network congestion and lessen costs related to infrastructure. This is a key factor which not only improves the performance of IoT applications but also helps in scalability of cloud -based infrastructure. Also, it's imperative that most IoT applications deal with time series data and latency is a pivotal factor. In addition to the proposal of edge computing this work also incorporated a comparative study of edge computing and traditional cloud computing highlighting better latency, efficiency, reliability and scalability. © 2024 IEEE.

关键词： Mobile edge computing

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Optimization of Heterogeneous Task Scheduling in cloud computing

Optimization of Heterogeneous Task Scheduling in Cloud Compu...

引用

2023 international conference on Intelligent Systems for communication, IoT and Security, ICISCoIS 2023

作者： Gawali, Swapnali Sunil Gawali, Mahendra Bhatu Sanjivani College of Engineering Kopargaon Savitribai Phule Pune University Computer Dept Pune MS India Sanjivani College of Engineering Kopargaon Savitribai Phule Pune University. IT Dept Pune MS India

ISBN: (纸本)9798350335835

The World is moving from hard computation devices to soft computation devices. cloud computing technology has performed a keen role since last decade to fulfill this change. Demand for cloud computing resources are increasing in a rapid way for execution of various tasks. So, it is a challenge for scientist, researchers to schedule various upcoming tasks on cloud computing resources such that resources can be used at maximum level without expelling the task. This paper is work to solve this long-lasting challenge, a novel partitioned based scheduling (PBS) that allocates tasks to a particular virtual machine in cloud computing when another one is heavily loaded is focused by this manuscript. We implemented our novel proposed system on cloud computing simulator. Testing of our proposed methodology is compared with a containerized task co-location scheduler (CTCL), Heuristic method resource allocation in cloud computing. The working proposed results show that our novel methodology works better to utilization of cloud computing resources such as Central Processing Unit, RAM and Bandwidth by minimizing finish time and response time. © 2023 IEEE.

关键词： cloud computing

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Secure Student Record Management using Visual Cryptography in Educational Systems 9

Secure Student Record Management using Visual Cryptography i...

引用

9th international conference on communication and Electronics Systems, ICCES 2024

作者： Chinnasamy, P. Deepalakshmi, P. Kiranchandu, E. Gireesh, C. Kiran, G. Priya, D. Divya School of Computing Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Krishnankoil Virudhunagar India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350377972

cloud computing is vital for data transmission and storage in the digital era, highlighting the urgent requirement for strong data security measures. Conventional encryption techniques such as RSA, while dependable, have considerable computational burden and energy requirements owing to their extensive key sizes. This study examines the efficacy of Elliptic Curve Cryptography (ECC) as a superior solution for data security in cloud environments. The suggested method utilizes ECC's reduced key sizes to attain improved computational efficiency and energy saving, all while upholding rigorous encryption standards. A comparison research indicates that ECC surpasses RSA in performance and resource efficiency, rendering it especially appropriate for extensive cloud systems handling significant amounts of sensitive information. This research examines the implementation of the Advanced Encryption Standard (AES) algorithm in Cipher Block Chaining (CBC) mode for image encryption. The AES-CBC technique efficiently safeguards images by transforming them into encrypted formats, so obstructing illegal access. Comparative assessments using alternative encryption methods highlight AES's enhanced security and computational efficacy. Collectively, these findings underscore the capability of ECC and AES to overcome the shortcomings of conventional encryption, providing secure and resource-efficient solutions for contemporary cloud computing issues. © 2024 IEEE.

关键词： cloud security

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AI - Enhanced Hospital Language Interpretation Services using cloud computing for Effective communication 9

AI - Enhanced Hospital Language Interpretation Services usin...

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9th international conference on communication and Electronics Systems, ICCES 2024

作者： Sree Southry, S. Mohankumar, N. Thanalakshmi, S. Devi, R. Aiyshwariva Srinivasan, C. Ganeshbabu, T.R. Sona College of Technology Department of Electronics and Communication Engineering Tamil Nadu Salem India Nagpur Campus Pune India Gojan School of Business and Technology Department of Management Studies Redhills Tamil Nadu Chennai India R.M.K. College of Engineering and Technology Puduvoyal Tamil Nadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India Department of Electronics and Communication Tamil Nadu Namakkal India

ISBN: (纸本)9798350377972

Impaired communication between patients and healthcare professionals due to language problems may hurt the quality of treatment and patient outcomes. The objective is to develop an AI -driven., cloud-based NLP system for real-time., precise language interpretation., enabling seamless interaction in multilingual health care environments. The solution to this problem is a cloud-based language interpretation service for hospitals powered by artificial intelligence (AI). As a real-time translation service., it employs advanced natural language processing (NLP) models trained in situations and vocabulary particular to healthcare. This technology provides quick implementation., smooth interaction with current healthcare systems., and ongoing development via machine learning by using the scalability and flexibility of cloud computing. Both patients and medical staff may benefit from the intuitive interface's support for text-based and speech-based communication. Security measures are put in place to comply with regulations and safeguard patient confidentiality. Automated documenting of translated interactions is made possible by integration with electronic health records (EHR)., which helps with the continuity of treatment. Continuous improvement is guided by monitoring tools that measure system performance and user satisfaction. Enhance patient care and results by bridging language barriers in healthcare by promoting clear and accurate communication. © 2024 IEEE.

关键词： Health care accessibility Language barriers Natural language processing Patient care Real-time translation Resource optimization

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A Novel Machine Learning-Enhanced Swarm Intelligence Algorithm for Cost-Effective cloud Load Balancing

A Novel Machine Learning-Enhanced Swarm Intelligence Algorit...

引用

2024 international conference on Innovative computing, Intelligent communication and Smart Electrical Systems, ICSES 2024

作者： Satheesh, Nimmati Sakthivel, K. Psna College of Engineering and Technology Department of Computer Applications Tamil Nadu Dindigul India K.S Rangasamy College of Technology Department of Computer Science and Business Systems Tamil Nadu Tiruchengode India

ISBN: (纸本)9798331543617

cloud computing refers to the instant accessibility of shared computer resources over the internet on-demand, thereby revolutionizing the way organizations deploy and manage their IT infrastructure. Efficient load balancing in a cloud environment scales up, utilizing resources with optimized cost-effectiveness. This paper introduces a new Machine Learning-guided Improved Swarm Intelligence Algorithm for reliable load balancing in cloud computing. Methodology It spans on three main elements. The first is the data center controller for the management of tasks. VM allocation uses an Improved Beetle Swarm Optimization Algorithm and one-dimensional convolutional probabilistic neural network is used for host load prediction. To propose an approach that looks to optimize the key metrics such as make span, average cost of VM, resource utilization, and value response time through iterative optimization process. The results of the experiments reflect the success of the proposed algorithm, because an improved performance of load balancing is obtained in comparison with those achieved using PSO, Grey Wolf Optimization, and Genetic Algorithm. The convergence diagram displays the iterative optimization process achieved by the proposed algorithm in obtaining a balance among various performance metrics. Overall, the methodology proposed in this work will provide promising facilitation in the increased efficiency, scalability, and cost-effectiveness of environments based on cloud computing with a reliable load balancing. © 2024 IEEE.

关键词： Genetic algorithms

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Optimizing IoT Device Networks with Edge computing to Address Latency and Bandwidth Constraints 5

Optimizing IoT Device Networks with Edge Computing to Addres...

引用

5th international conference on Electronics and Sustainable communication Systems, ICESC 2024

作者： Hussain, Naziya Dankan Gowda, V. Shyamsunder, Chitta Srinivas, Ved Rani, Ridhi Balaji, M. Ips Academy Department of School of Computers Madhya Pradesh Indore India Pune India Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India Thiagarajar School of Management Department of Human Resources Tamil Nadu Madurai India Symbiosis Institute of Business Management Hyderabad Symbiosis International University Pune India Mohan Babu University Department of Ece Andrapradesh Tirupati India

ISBN: (纸本)9798350379945

The enormous interconnected devices or the 'Internet of Things' are one of the main concerns when it comes to dealing with the network latency and bandwidth. Regular cloud computing offerings are limited because of the relatively large latency and the high amount of bandwidth needed to send data to central points. This research study is focusing on the utilization of the edge computing for managing the challenges concerning the IoT device networks. Edge computing is a form of data processing that is done closer where the data is generated thus reducing latency issues and the bandwidth consumed in the system. The novel architecture that has been suggested entails utilization of edge devices where the processing tasks are offloaded from the cloud;descriptions of the simulations that have been run to analyze the proposed architecture as well as the result that show significant enhancement on the latency and bandwidth. The presented results reveal that edge computing can be used to improve the efficiency of the IoT network and its applicability across different domains. © 2024 IEEE.

关键词： Mobile edge computing

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Research on key technologies and application value of private cloud security cloud management platform 6

Research on key technologies and application value of privat...

引用

6th international conference on Intelligent computing, communication, and Devices, ICCD 2023

作者： Min, Xiao Mei, Guo School of Computer and Artificial Intelligence XiangNan University HuNan ChenZhou423000 China Advanced Embedded Computing and Intelligent Medical Systems Hunan Engineering Research Center HuNan ChenZhou423000 China

ISBN: (数字)9781510666269

ISBN: (纸本)9781510666252

Nowadays, with the rapid development and popularization of informatization and intelligence, "cloud computing service"is also born. Among them, "private cloud"is an important part of "cloud computing services". This is also known as the "internal cloud"or "corporate cloud"and plays an important role in the realization of information management within the enterprise. By studying the overall architecture of private cloud security management platform, this paper studies the key technologies of cloud security management platform and the application value of cloud security management platform. It can provide more flexible security solutions for governments and enterprises and provide better security for computer networks. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： cloud security

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cloud based E-Feedback System for Hospitality Industry 2

Cloud based E-Feedback System for Hospitality Industry

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2nd international conference on Sustainable computing and Data communication Systems, ICSCDS 2023

作者： Ramnarayan Gehlot, Anita Singh, Rajesh Kathuria, Samta Chhabra, Gunjan Joshi, Kapil Uttaranchal University Cse Department Dehradun India Uttaranchal University Uit Division of Research & Innovation 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 Uttaranchal Institute of Technology Dehradun India

ISBN: (纸本)9781665491990

The rapid development and quality of services (QoS) for any business is possible through cloud computing. This study has proposed a dynamic model for customer feedback system based on the existing system and cloud computing. It used input from the customer and compute based on the category and predict results. Feedback can be very effective by using the advanced technologies. Hospitality industry play a significant role to improve the services in order to enhance the quality. cloud computing plays a significant role in obtaining real feedback from the customer. © 2023 IEEE.

关键词： cloud computing

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Fog-Driven Heart Attack Prediction from Wearable Edge Devices

Fog-Driven Heart Attack Prediction from Wearable Edge Device...

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2024 international conference on Integrated Circuits, communication, and computing Systems, ICIC3S 2024

作者： Ravindranathan, Manya K. Sendil Vadivu, D. Rajagopalan, Narendran National Institute of Technology Puducherry Department of Computer Science and Engineering Karaikal India

ISBN: (纸本)9798350364088

Cardiovascular diseases, such as heart attacks, are a significant global health concern, responsible for a great deal of annual mortality. The introduction of wearable edge devices with advanced sensors has enabled continuous realtime monitoring of physiological data. This research aims to use fog computing, a decentralized computing approach, to introduce a system for predicting heart attacks based on data from these wearable devices. The system features a custom Deep Learning (DL) model at the fog layer, allowing realtime analysis of health data without relying on centralized cloud processing. The model obtained an accuracy of 90% which is a significant improvement than the traditional models. This work contributes to the field of cloud computing in healthcare, showcasing the potential of fog-based solutions for timely and context-aware heart attack prediction. Successful outcomes could lead to scalable and decentralized healthcare systems, supporting proactive cardiac care and im-nrovina overall patient well-being. © 2024 IEEE.

关键词： Fog

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Enhanced Security for IOMT Devices: Deep learning Techniques for Network Intrusion Detection 5

Enhanced Security for IOMT Devices: Deep learning Techniques...

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5th IEEE international conference on communication, computing and Industry 6.0, C2I6 2024

作者： Gopala Krishnan, D. Diviya, M. Jyoshika Shri, K. Subramanian, M. Amrita School of Computing Department of Computer Science and Engineering Chennai India St Joseph's College of Engineering Department of Mechanical Engineering Chennai India

ISBN: (纸本)9798350379884

The increasing popularity of Internet of Medical Things (IoMT) devices, like wearable sensors, has greatly improved patient care by allowing continuous monitoring and real-time data transfer to the cloud. However, this progress also raises security concerns, as the data sent from these devices can be at risk of tampering or hacking during transmission. Keeping this sensitive medical data safe is crucial for patient safety. To address these issues, a fused CNN and LSTM are developed to check whether the data from wearable IoMT devices has been altered or attacked during transmission to the cloud. The CNN part of the model helps to pull out important features from the data, while the LSTM part looks for patterns to spot any signs of tampering. The model uses two convolutional layers each of which is followed by a MaxPooling layer. Our model, tested on a large dataset, achieved an accuracy of 97.06%, showing its ability to protect the integrity of transmitted medical data. © 2024 IEEE.

关键词： Network intrusion

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