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Energy-Aware Edge computing Resource Scheduling Method

Energy-Aware Edge Computing Resource Scheduling Method

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2023 international conference on Algorithms, High Performance computing, and Artificial Intelligence, AHPCAI 2023

作者： Zhu, Guangping Li, Qiang Li, Wenjing Lv, Dongdong Guo, Yongshan State Grid Information and Telecommunication Group Co. Ltd Beijing100052 China

ISBN: (纸本)9781510671881

Currently, various energy sources such as electricity, heat, gas, and water in cities are relatively independently supplied, making it difficult to meet the demand for integrated energy utilization in new smart cities. The urban energy supply structure urgently needs to evolve from single transmission to gradual integration and coupling of multiple flows, which highlights the pressing need to enhance capabilities such as accurate perception of energy status data, trustworthy fusion and sharing, and precise energy efficiency services. These capabilities will greatly contribute to the improvement of overall urban energy efficiency and economic structure optimization. This article primarily focuses on the scheduling of computational resources between edge servers and terminal devices. Edge computing reduces data transmission latency and enhances computational agility during the computation offloading process. However, the computational capacity of edge servers is also limited, and in certain specific computation offloading scenarios (such as ultra-dense networks), interference may arise, leading to unexpected transmission delays. Therefore, it is not advisable to offload all tasks for execution on edge servers;some tasks should be handled by the terminal devices (SMD). Despite consuming more energy through local execution, this approach eliminates the need to consider data transmission time, thus significantly boosting task responsiveness. This article aims to design a task offloading scheme that is aware of terminal energy consumption and involves scheduling computational resources between edge servers and terminal devices. © 2023 SPIE. All rights reserved.

关键词： Computation offloading

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QHSL image encryption scheme based on a logistic chaotic system 23

QHSL image encryption scheme based on a logistic chaotic sys...

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23rd IEEE international conference on High Performance computing and Communications, 7th IEEE international conference on data Science and Systems, 19th IEEE international conference on smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC-DSS-smartCity-DependSys 2021

作者： Yan, Fei Li, Mao Li, Nianqiao Iliyasu, Abdullah M. School of Computer Science and Technology Changchun University of Science and Technology Changchun130022 China College of Engineering Prince Sattam Bin Abdulaziz University Al-Kharj11942 Saudi Arabia School of Computing Tokyo Institute of Technology Yokohama226-8502 Japan

ISBN: (纸本)9781665494571

On digital computers, the hue, saturation, and lightness (HSL) image model is especially useful in feature detection, image segmentation, and other operations in robot vision, object recognition, etc. On the quantum computing paradigm, such a model was only recently formulated and many new inroads with it are just beginning. Our study proposes a scheme to secure quantum images encoded using the quantum HSL (QHSL) image model. The proposed QHSL encryption scheme (or simply QHES) utilises logistic mapping to induce the chaotic system at the heart of the QHES strategy. The scheme consists of a quantum logistic chaos unit composed of quantum arithmetic modules to generate chaotic sequences. This is followed by a quantum logistic key generation that is subsequently used to encrypt the HSL components of QHSL images. The proposed scheme exploits quantum parallelism to execute its encryption procedure which leads to significant reduction in computational resources. Results of simulation-based experiments reported suggest potentials of the proposed QHES as an efficient cryptosystem on quantum computers. © 2021 IEEE.

关键词： Heart Image segmentation Quantum computing Computational modeling Feature detection Robot vision systems Parallel processing

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smart Manufacturing with Augmented Reality 2

Smart Manufacturing with Augmented Reality

引用

2nd international Aerospace conference, AeroCON 2022

作者： Sahu, Parimal Balfour, David Collins Aerospace India

Aircraft Manufacturing procedures are very critical which always require skillful engineers who must adhere to various process and procedure during daily work. The challenge is not only to identify right tools and manuals but also to keep track of operator usage data and behavior. With Augmented reality (AR), intelligent tools and analytics, we can provide a new lease of life to first-line assembly engineers. The objective is how AR can help us to reduce rework or scrap with the concept of Industry 4.0 [1] were integrating with cutting edge technologies like machine learning and the internet of things (IoT) to meet the fundamental requirements during manufacturing. smart Manufacturing consists of four major components Cyber-physical systems, IoT, cloud computing and cognitive computing. Our objective is to integrate Augmented reality with IoT sensor to achieve Cyber-physical systems connectivity and fill the gap between the smallest physical assets connected with digital infrastructure. It can help us to increase manufacturing throughput, make tools intelligent, accelerate training, inventory management and most important operator safety. Our AR applications are developed on PTC [9] software platform which can be tablet-based, or headset based, and content can be accessible globally within organisation. Overall, it is not about a technology that lies in the visualization process only, it is about how data are visualized, captured and utilized to make the working environment safe and productive, which makes this technology powerful in manufacturing ecosystem. © 2022 SAE international. All Rights Reserved.

关键词： Internet of things

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2nd international conference on intelligent and Cloud computing, ICICC 2021

2nd International Conference on Intelligent and Cloud Comput...

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2nd international conference on intelligent and Cloud computing, ICICC 2021

ISBN: (纸本)9789811698729

The proceedings contain 56 papers. The special focus in this conference is on intelligent and Cloud computing. The topics include: An Optimized Planning Model for Management of Distributed Microgrid Systems;Hard and Soft Fault Detection Using Cloud Based VANET;a Novel intelligent Street Light Control System Using IoT;enhancing QoS of Wireless Edge Video Distribution using Friend-Nodes;Groundwater Monitoring and Irrigation Scheduling Using WSN;A Soft Coalition Algorithm for Interference Alignment Under Massive MIMO;improved Grasshopper Optimization Algorithm Using Crazy Factor;reliability Estimation Using Fuzzy Failure Rate;A Sine Cosine Learning Algorithm for Performance Improvement of a CPNN Based CCFD Model;cloud analytics: An Outline of Tools and Practices;quality Control Pipeline for Next Generation Sequencing data Analysis;fittest Secret Key Selection Using Genetic Algorithm in Modern Cryptosystem;variable Step Size Firefly Algorithm for Automatic data Clustering;GWO Based Test Sequence Generation and Prioritization;artificial intelligent Approach to Predict the Student Behavior and Performance;graph Based Automatic Keyword Extraction from Odia Text Document;an Attempt for Wordnet Construction for Odia Language;a Deep Learning Approach for Face Mask Detection;A Computational Intelligence Approach Using SMOTE and Deep Neural Network (DNN);Face Mask Detection in Public Places Using Small CNN Models;an Enhanced Fault-Tolerant Load Balancing Process for a Distributed System;LSTM-RNN-Based Automatic Music Generation Algorithm;A KNN-PNN Decisioning Approach for Fault Detection in Photovoltaic Systems;detecting Phishing Websites Using Machine Learning;designing of Financial Time Series Forecasting Model Using Stochastic Algorithm Based Extreme Learning Machine;test Case Generation Using Metamorphic Relationship.

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Air Quality data Integration and Visualization in Urban Environment 12th

Air Quality Data Integration and Visualization in Urban Envi...

引用

12th international conference on Frontiers of intelligent computing: Theory and Applications, FICTA 2024

作者： Sowinski-Mydlarz, Viktor Vassilev, Vassil Gasiorowski, Pawel GATE Institute Sofia University "St. Kliment Ohridski" Sofia Bulgaria Cyber Security Research Centre London Metropolitan University London United Kingdom

ISBN: (纸本)9789819601424

Monitoring air pollution in urban areas is crucial for safeguarding public health and environmental well-being. The proper integration and visualization of air quality data in extensive urban platforms enabling user interaction are important for gaining valuable insights and making informed decisions about measures to mitigate the problem. This paper proposes an air quality platform, bridging the gap between scientific data and the general audience. It promotes an enhanced understanding and prevention of air pollution. A comprehensive data pipeline is designed to efficiently collect, store, and visualize air quality data in Sofia, Bulgaria. Leveraging open-source technologies like QGIS, MongoDB, Flask, and Cesium Ion, the platform integrates data from 23 air quality stations onto a terrain map and 3D building layer. The paper addresses challenges in data processing, ensuring accuracy, managing high volumes, and effectively communicating complex information. The scalability, transparency, and accessibility of the proposed approach offer significant contributions to environmental monitoring. Additionally, integrating triangulation of measurements enhances the platform’s capabilities, facilitating informed decision-making and supporting sustainable urban development initiatives. The paper concludes with insights into future enhancements, including visualization of CFD simulations of airflow in urban context, promising advancements in understanding urban air quality dynamics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Environmental monitoring

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Power Usage Optimization in smart Grids using AI and IoT

Power Usage Optimization in Smart Grids using AI and IoT

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international conference on ent computing and Control Systems (ICICCS)

作者： Vipul Gohil Viraj Vakharia Om Agrawal Rishikesh Asgekar Department of Electronics and Telecommunication Engineering Mukesh Patel School of Technology Management and EngineeringSVKM’s NMIMS University Mumbai India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

This review paper explores the pivotal role of Internet of Things (IoT) technology, particularly through the deployment of smart meters, in addressing the complex challenge of balancing supply and demand within power grids. By collecting and analyzing real-time data on power usage, smart meters provide invaluable insights that enable grid operators to make informed decisions and optimize grid *** paper delves into the various applications of IoT and smart meters in grid management, including demand response programs, peak load forecasting, energy efficiency optimization, and integration of renewable energy sources. It discusses the technical aspects of IoT implementation, such as communication protocols, data analytics and deploying ML models to meet the power demands. This study outlines the importance of wireless sensor networks (WSNs) and how it goes hand in hand with IoT in monitoring, tracking, and sensing various environmental activities [1]. The proposed model incorporates Long Short-Term Memory (LSTM) algorithms to predict power demand accurately which enables efficient power management. Furthermore, the paper evaluates the benefits and challenges associated with the adoption of IoT and smart meters in power grids, highlighting the potential for improved grid reliability, efficiency, optimization, and ***, the objective of this review is to provide an in-depth overview of how IoT and AI-based smart meter technologies optimize power usage, improves grid reliability and promotes energy sustainability. This paper also evaluates the benefits and challenges associated with the adoption of IoT and smart meters in power grids, highlighting the potential for improved grid reliability, efficiency, optimization, and sustainability.

关键词： Wireless sensor networks Reviews smart meters data models smart grids Reliability Internet of Things Sustainable development Optimization Long short term memory

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Internet Activity Forecasting Over 5G Billing data Using Deep Learning Techniques 1

Internet Activity Forecasting Over 5G Billing Data Using Dee...

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1st international conference on intelligent Controller and computing for smart Power, ICICCSP 2022

作者： Tiwari, Vaibhav Pandey, Chandrasen Roy, Diptendu Sinha Nit Department of Computer Science and Engineering Meghalaya India

ISBN: (数字)9781665472586

ISBN: (纸本)9781665472586

Understanding the flexibility of traffic requirements on wireless networks is challenging due to the high density of mobile devices connected to the network. This has made things more difficult given the wide range of devices available and the different types of services they can provide. Internet activity data of 5G billing traffic is an important way to analyze load in a confined area, providing solutions for various 5G infrastructure and applications. In previous decades, deep learning techniques have played a vital role in analyzing such data, and their result consolidates the proof of its veteran performance. The open-source dataset used in this experimentation work is well known as Big data Challenge 2014, which was made publicly available by Telecom of Italia. We evaluate our work with four different networks GRU, LSTM, Bi-directional LSTM and encoder decoder LSTM in which we achieve the lowest mean absolute error in the encoder-decoder CNN-LSTM model with a training loss of 0.0108 and validation loss of 0.0064. © 2022 IEEE.

关键词： Signal encoding

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Efficient Fault Detection in Renewable Energy Systems through IoT and Isolation Forest Algorithm

Efficient Fault Detection in Renewable Energy Systems throug...

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Innovative computing, intelligent Communication and smart Electrical Systems (ICSES), international conference on

作者： Virendra Singh Thakur Ramakrishnan Raman Symbiosis Law School (SLS) Pune & Symbiosis International (Deemed University) Pune Maharashtra India Symbiosis Institute of Business Management Pune & Symbiosis International (Deemed University) Pune Maharashtra India

As renewable energy sources are becoming more integrated into the power grid, ensuring system reliability is crucial. A unique defect detection method for renewable energy systems leveraging the Internet of Things (IoT) and the Isolation Forest algorithm is presented in this research. The integration of IoT devices provides real-time monitoring and data collection from solar panels, wind turbines, and energy storage units. The machine learning Isolation Forest method effectively finds data abnormalities and defects. The Isolation Forest algorithm can correctly detect problems and abnormalities in renewable energy systems, enabling early maintenance and repair. This proactive defect detection technology improves renewable energy’s reliability and efficiency, minimizing downtime and power grid stability. IoT technology and the Isolation Forest algorithm improve problem detection and system performance and creating a more sustainable and cost-effective energy-producing infrastructure. It shows that advanced data analytics and machine learning may make renewable energy systems more reliable and appealing as clean energy sources.

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Mitigating Bus Bunching Through Inter-Vehicle Communication

Mitigating Bus Bunching Through Inter-Vehicle Communication

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Sustainable computing and smart Systems (ICSCSS), international conference on

作者： K Priya N Sai Teja R Dhanush Y Hariharan Department of Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

Bus bunching is a significant issue in public transportation, causing long wait times and overcrowding, which negatively impact riders. Existing static control methods have limited effectiveness in utilizing real-time bus data to address bunching. This study introduces an intelligent messaging system aimed at mitigating bus bunching through continuous intervehicle communications. The system collects and processes real-time location, speed, and delay information from buses across transit networks using a cloud-based analytics engine. By integrating dynamic data streams with historical insights, predictive models appropriate for identifying bunching risk probabilities across routes are developed. Automated directives are then sent to drivers in high-risk areas, prompting them to take corrective actions such as adjusting speeds, holding at specific timepoints, or adapting frequencies to maintain schedules effectively.

关键词： Schedules Time-frequency analysis System performance Collaboration Real-time systems data models Logic

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Two-way recommendation system for intelligent employment of college students based on data mining

Two-way recommendation system for intelligent employment of ...

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international conference on Application of intelligent Systems in Multi-modal Information analytics, MMIA 2020

作者： Zhang, Yifan Liu, Qian Meng, Qingpeng School of Liaoning Institute of Science and Technology Benxi China

ISBN: (纸本)9783030515553

Based on big data and the new era, the information employment service technology revolution in China’s smart universities brought about by high-tech applications such as mobile internet, internet of things and cloud computing has put forward new technological transformation points and demands for the construction of the ideology of information service for smart employment and the mode of employment, work and life of Chinese college students. In order to make the employment of college students and the recruitment of enterprises more efficient, we have designed a "two-way recommendation system for intelligent employment of college students" by means of data mining and other means, so as to achieve the effect of intelligent integration, reorganization and intelligent sharing of high-quality relevant employment resources and improve the efficiency of employment services for college students [1]. © Springer Nature Switzerland AG 2021.

关键词： data mining

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