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Deadline Constrained Least Laxity Time Based Cloudlet Scheduling

Deadline Constrained Least Laxity Time Based Cloudlet Schedu...

引用

Computing, Semiconductor, Mechatronics, Intelligent Systems and Communications (COSMIC), International Conference on

作者： Lipika Datta Dept. of Computer Science and Engineering College of Engineering and Management Kolaghat West Bengal India

ISBN: (数字)9798331517892

ISBN: (纸本)9798331517908

Users send their requests to cloud service providers, who then deliver the services over networked systems. With a high volume of requests, it is crucial to schedule cloudlets effectively to ensure that most meet their specified deadlines for optimal performance. This paper focuses on calculating Both slack time and laxity time for cloudlets across all virtual machines and scheduling them accordingly. It also considers the rearrangement of cloudlets already assigned to virtual machines to accommodate new cloudlets. The CloudSim simulation environment is utilized to schedule cloudlets by evaluating their slack time and laxity time across various virtual machines. This approach helps in selecting the most suitable virtual machine for executing the cloudlets. By incorporating laxity time into the scheduling decisions, the system becomes more balanced, reduces makespan, and thus enhances overall performance. The proposed method has been shown to reduce task rejection by at least 10% and the makespan by an average of 17% when compared to the EFD algorithm.

关键词： Cloud computing Schedules Mechatronics Processor scheduling Virtual machines Time factors Intelligent systems

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BSCIAM: A Blockchain based Secure Cloud Identity and Access Management Framework

BSCIAM: A Blockchain based Secure Cloud Identity and Access ...

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IEEE Students' Conference on Electrical, Electronics and computer science (SCEECS)

作者： Swatisipra Das Mohammad Sahil Naveen Kumar Pandit Rojalina Priyadarshini Sarada Prasad Gochhayat Postgraduate Dept. of Computer Science Fakir Mohan University Odisha India Dept. of Computer Science and Engineering C.V. Raman Global University Odisha India Dept. of Computer Science and Engineering Indian Institute of Technology Jammu J&K India

Cloud computing enables on-demand computation on remote servers and computers. Thanks to the adaptability and scalability of the infrastructure for data storage, processing, and management. To solve the security problems arising in cloud identity management techniques such as, dependency on a third-party token provider, leakage of user’s details, single point of failure, etc., decentralized cloud identity management systems came into the picture. Blockchain is a decentralized database providing immutability and transparency to recorded transaction data. The current token-based decentralized cloud identity management systems have limitations, including the absence of data access management procedures and a lack of security features. This paper suggests a Blockchain based Secure Cloud Identity and Access Management (BSCIAM) model for secure identity and access management, utilizing encryption and key sharing techniques. The proposed model has been implemented using Ethereum smart contracts for token-based identity management.

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GNN-based Disease Detection and Classification in Cassava Leaf

GNN-based Disease Detection and Classification in Cassava Le...

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IoT Based Control Networks and Intelligent Systems (ICICNIS), International Conference on

作者： M Meenalochini A Amalroshini K L Megha Nandhini C R Sogil Murugan Dept. of Computer Science and Design Kongu Engineering College Erode India

ISBN: (数字)9798331518097

ISBN: (纸本)9798331518103

Cassava, a staple crop for millions of people worldwide, is highly susceptible to various leaf diseases, which can significantly reduce crop yields. Detecting and classifying these diseases at an early stage is crucial for effective disease management and crop protection. In this paper, we propose a novel approach for cassava leaf disease classification by combining Convolutional Neural Networks (CNNs) and Graph Neural Networks (GNNs). While CNNs excel at extracting local features from images, they often fail to capture complex spatial relationships between different regions of the leaf. To address this limitation, we integrate GNNs, which are well-suited for learning from irregular, structured data, such as the vein patterns and disease spread structures found in cassava leaves. By modeling the relationship between different regions of the cassava leaf as a graph G (V, E), where V represents nodes corresponding to superpixel regions of the leaf, and E represents edges capturing spatial relationships, the hybrid CNN-GNN model offers a more comprehensive and accurate classification system. The model is trained and evaluated on a dataset of cassava leaf images covering multiple disease categories, demonstrating superior performance over traditional CNN-based image classification techniques. This approach provides a scalable solution for improving the detection and management of cassava leaf diseases.

关键词： Accuracy Scalability Feature extraction Graph neural networks Real-time systems Convolutional neural networks Protection Intelligent systems Diseases Farming

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Time-Dependent Multi-Objective Trip Planning by Ant Colony Optimization with Route API

Time-Dependent Multi-Objective Trip Planning by Ant Colony O...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Etsushi Saeki Siya Bao Toshinori Takayama Nozomu Togawa Dept. of Computer Science and Communications Engineering Waseda University ZENRIN DataCom

In this paper, we propose a time-dependent multi-objective trip planning using ant colony optimization. Especially, the proposed method deals with time-dependent POI factors by utilizing past-trip records with time stamps and computes time-dependent travel time by utilizing route API. Moreover, we reduced the response time from the route API calls. Compared with two conventional methods, our proposed method provided routes with high time-dependent values. Meanwhile, the number of API calls is reduced by 98.8% on average by introducing the API call reduction.

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Predictive Maintenance of a Metro's Air Compressor 5

Predictive Maintenance of a Metro's Air Compressor

引用

5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Barpute, Jyotsna Vilas Suryawanshi, Shubhangi Kshirsagar, Vanita Bhosale, Digvijay Patil, Pramod Patil, Atharv Dr. D. Y. Patil Institute of Technology Dept. of Artificial Intelligence and Science Pune Pimpri India Dr. D. Y. Patil Institute of Technology Dept. of Mechanical Engineering Pune Pimpri India Dr. D. Y. Patil Institute of Technology Dept. of Computer Engineering Pune Pimpri India

ISBN: (纸本)9798350379945

A Metro air compressor turns power into potential energy by compressing and storing air in a tank. When the pressure reaches its limit, the compressor shuts down, storing the compressed air until it is needed for various uses. Early prediction enables proactive decision-making, which helps to avoid unfavourable outcomes before they occur. It also optimizes resource allocation to ensure efficiency and effectiveness across several processes. The proposed method is a machine learning-based strategy for predictive maintenance of a metro train's air compressor. The MetroPT -3 dataset was used, which includes parameters for pressure, temperature, motor current, air intake valves, and so on. Seven analogue and eight digital signals. The technique entails data preprocessing, anomaly identification using an exclusion forest classifier, training multiple machine learning models, and integrating the chosen model with the rest of the system. Gradient-boosting models outperformed linear models in terms of accuracy, but they were slower to compute. They conclude that predictive maintenance benefits metro compressors and has the potential to revolutionize equipment maintenance in general. © 2024 IEEE.

关键词： Predictive maintenance

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Bidirectional Multi-Step Domain Generalization for Visible-Infrared Person Re-Identification

Bidirectional Multi-Step Domain Generalization for Visible-I...

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IEEE Workshop on Applications of computer Vision (WACV)

作者： Mahdi Alehdaghi Pourya Shamsolmoali Rafael M. O. Cruz Eric Granger Dept. of Systems Engineering ETS LIVIA ILLS Montreal Canada Dept. of Computer Science University of York UK

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

A key challenge in visible-infrared person re-identification (V-I ReID) is training a backbone model capable of effectively addressing the significant discrepancies across modalities. State-of-the-art methods that generate a single intermediate bridging domain are often less effective, as this generated domain may not adequately capture sufficient common discriminant information. This paper introduces Bidirectional Multi-step Domain Generalization (BMDG), a novel approach for unifying feature representations across diverse modalities. BMDG creates multiple virtual intermediate domains by learning and aligning body part features extracted from both I and V modalities. In particular, our method aims to minimize the cross-modal gap in two steps. First, BMDG aligns modalities in the feature space by learning shared and modality-invariant body part prototypes from V and I images. Then, it generalizes the feature representation by applying bidirectional multi-step learning, which progressively refines feature representations in each step and incorporates more prototypes from both modalities. Based on these prototypes, multiple bridging steps enhance the feature representation. Experiments 1 1 Our code is available at: ***/BMDG conducted on V-I ReID datasets indicate that our BMDG approach can outperform state-of-the-art part-based and intermediate generation methods, and can be integrated into other part-based methods to enhance their V-I ReID performance.

关键词： Training computer vision Accuracy Codes Prototypes Feature extraction Identification of persons

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Performance Analysis of Slicing on a 10-node 5G Architecture for Networked Music Performances

Performance Analysis of Slicing on a 10-node 5G Architecture...

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IEEE Symposium on computers and Communications (ISCC)

作者： Luca Turchet Paolo Casari Dept. of Information Engineering and Computer Science University of Trento Trento Italy

ISBN: (数字)9798350354232

ISBN: (纸本)9798350354249

Networked Music Performances (NMPs), where geographically displaced musicians play together over a data network, represent a challenging application for today’s wireless communications. This is due to the stringent constraints on latency, throughput, and reliability that need to be obeyed in order to achieve a satisfactory quality of experience for the musicians. Slicing is a promising feature of 5G networks in the context of NMP applications, as it makes it possible to isolate the networking and computing resources allocated to NMP devices. However, the use of slicing has been scarcely investigated for the NMP context so far. Moreover, previous works focusing on NMPs over 5G involved up to 4 nodes. To bridge these gaps, we study 5G performance in support of NMPs involving an architecture with 10 nodes, both with and without slicing. Specifically, we focused on the assessment of the sole wireless link, as the measurements can be easily transferred to a realistic NMP architecture involving a wide area network. Our results show that, in the slicing condition, latency slightly increased due to the realistically different computing specifications of the MEC servers compared to those of the Core Network servers, whereas reliability slightly improved as expected.

关键词： Wireless communication Wide area networks 5G mobile communication computer network reliability Music computer architecture Throughput Reliability Servers Quality of experience

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Development of QR Code-Guided Autonomous Navigation System for Grape Cultivation Robot in Overhead Trellis Vineyard 26

Development of QR Code-Guided Autonomous Navigation System f...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Fujimoto, Ren Siang Leow, Chee Yajima, Hideaki Makino, Koji Mao, Xiaoyang Nishiizaki, Hiromitsu University of Yamanashi Dept. of Mechatoronics Kofu Japan University of Yamanashi Dept. of Computer Science and Engineering Kofu Japan

ISBN: (纸本)9798331521950

This paper presents a novel autonomous navigation system for grapevine cultivation robots operating in overhead trellis vineyards, a challenging environment characterized by irregular grape cluster distribution and complex 3D structures. The system integrates two complementary navigation approaches: high-precision positioning under target grape bunches using YOLOv8-based object detection of QR codes, and large-scale vineyard navigation using global navigation satellite system (GNSS). Each QR code uniquely identifies a grape bunch and provides access to its associated cultivation data. Experimental evaluations in a simulated vineyard environment demonstrated an 83.3% success rate for object detection-based positioning beneath grape bunches and 90% accuracy for GNSS-based navigation within a 1.0 m radius of the target locations. These results validate the system's effectiveness in automating navigation tasks within overhead trellis vineyards, offering a promising solution to address labor shortages and improve the efficiency of grape cultivation practices. © 2025 IEEE.

关键词： Precision agriculture

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Efficient mmWave Beam Selection using ViTs and GVEC: GPS-based Virtual Environment Capture 14

Efficient mmWave Beam Selection using ViTs and GVEC: GPS-bas...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Gupta, Srajan Saxena, Navrati Roy, Abhishek Ravi, Akshay San Jose State University Dept. of Computer Science San Jose United States Mediatek Inc. Engineering San Jose United States

ISBN: (纸本)9798350335095

Millimeter waves (mmWaves) providing higher bandwidth is used by 5G network technology to achieve higher network capacity and faster data transfer. However, the process of beam sweeping across multiple antenna arrays can be time-consuming and inefficient. Machine learning (ML) models can address this issue by predicting the optimal beam pair based on data from on-vehicle light detection and ranging (LIDAR) and global positioning system (GPS) sensors. This paper proposes a new vision transformer (ViT) ML model for beam selection using GPS and LIDAR data. GPS-based virtual environment capture (GVEC) has been introduced to overcome to overcome noise in the LIDAR data caused by adverse weather conditions. The proposed solution demonstrates improved performance compared to previous approaches when tested on noisy LIDAR data, achieving a 92% accuracy in searching among the top 10 beams. © 2023 IEEE.

关键词： Millimeter waves

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Virtual Field Trip of the Central Heating and Chiller Plant as an Instructional Tool for Thermodynamics Education 53

Virtual Field Trip of the Central Heating and Chiller Plant ...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Ha, Oai Berrier, Seth Oss, Keif University of Wisconsin-Stout Dept. of Engineering and Technology MenomonieWI United States Dept. of Math Stats and Computer Science University of Wisconsin-Stout MenomonieWI United States University of Wisconsin-Stout Dept. of Design MenomonieWI United States

ISBN: (纸本)9798350336429

This work in progress introduces a framework for developing a virtual tour of the central heating and chiller plant (the Plant) at a university in the US Midwest for teaching and learning Thermodynamics at the campus. Traditionally, this theory-heavy and abstract course is taught using textbooks, lectures, problem solving, and some laboratory experiments. The majority of course content lacks the tangible connections between engineering devices and systems in a close real-world application, making the course challenging and uninteresting for many students. In this project, 360-degree panoramic photos and proper authoring software are employed to render an immersive virtual environment of the Plant that students can visit at any time and from anywhere. The virtual field trip would provide students with tangible insights into thermodynamic and heat transfer processes from over 25 main engineering equipment, devices, and systems at the Plant. Among them are boilers, chillers, a steam turbine, heat exchangers, cooling towers, blowers, and economizers. The follow-up quizzes and assessments are designed to contribute to the course learning outcomes, such as an understanding of phase transitions, thermodynamic properties of steam, and thermal efficiencies of engineering devices. The virtual immersive experiences can help students enhance their learning experiences and engagement in the course's materials without the costs and dangers inherent in a physical tour of the Plant facility. A preliminary result of the virtual tour's evaluation with a group of students and faculty is also included to provide inputs to the next phase of the project. © 2023 IEEE.

关键词： 360-degree panoramic images immersive education mechanical engineering education Thermodynamics education virtual field trip

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