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4th International Conference on Evolutionary computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Arjun Supreeth, N.M. Akhil, K.M. Sunil, Sougandh Department of Computer Science School of Computing Amrita Vishwa Vidyapeetham Mysore570026 India

This paper presents a novel method for enhancing localization accuracy in Wireless Sensor Networks (WSNs) through an improved trilateration approach. Despite advancements in localization techniques, challenges remain in achieving reliable accuracy, particularly in complex environments. The proposed method enhances traditional trilateration by integrating angle of arrival (AoA) measurements, leading to better positioning of sensor nodes. In this study, the aim is to confirm whether the final coordinates of trilateration can be supported by adding residual analysis and AoA observations. Experiments were conducted to assess the localization system's effectiveness. The results showed that residual validation outperformed AoA localization, particularly in noisy outdoor environments, providing more reliable distance estimates even under challenging conditions. This method enhances the trustworthiness of the localization system while also minimizing hardware needs and reducing computational complexity, making it a practical choice for resource-limited WSNs. The AoA verification method achieved average accuracies of 54% for x-coordinates and 55% for y-coordinates. In contrast, incorporating residual analysis improved these figures to 79% for x-coordinates and 80% for y-coordinates. These insights focus on the localization process and demonstrate the value of residual analysis in boosting system performance. © 2025 The Authors. Published by Elsevier B.V.

关键词： Sensor nodes

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Forecasting Bitcoin Price Trends: Integrated Machine Learning with Market Trends 3

Forecasting Bitcoin Price Trends: Integrated Machine Learnin...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Samir Ahamed, S. Radha, D. Kirthika Devi, V.S. Department of Computer Science And Engineering Amrita school of computing Amrita Vishwa Vidhyapeetham Bengaluru India Department of Electrical and Electronics Engineering Amrita school of Engineering Amrita Vishwa Vidhyapeetham Bengaluru India

ISBN: (纸本)9798331527549

Bitcoin price prediction remains a complicated task in financial markets since price fluctuations are characterized by high variability, non-linear and dependent on different trends. The common models of forecasting can be disruptive because they do not readily accommodate the breadth of the data and the dynamics of the marketplace. The paper presents a new approach to predicting Bitcoin price shifts based on state-of-the-art data structures and both machine learning and technical analysis. The formative historical price datasets and large scale features are organized and manipulated in an efficient manner with the aid of data structures as well as the temporal and non-linear trends in prices are captured by ARIMA,Exponential smoothing and Random. Also, trends of moving averages are used to boost the model’s competency to analyze the sentiments of the market. The proposed framework was put through a test on real world Bitcoin price data and it was shown to provide better results as compared to the traditional methods with better accuracy and visualization. Besides that, our method enhances the accuracy of the forecast, while the deployment of the method allows developing a realtime Bitcoin price prediction model that can be useful for traders and investors. The integration of machine learning and technical analysis gives a powerful and reliable tool to forecast the Bitcoins price trends. © 2025 IEEE.

关键词： Prediction models

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A Design and Challenges in Energy Optimizing CR-Wireless Sensor Networks

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Recent Advances in computer science and Communications 2023年第5期16卷 82-92页

作者： Reddy, Pundru Chandra Shaker Sucharitha, Yadala School of Computing and Information Technology REVA University Karnataka Bengaluru India Department of Computer Science and Engineering CMR Institute of Technology TS Hyderabad India

Background: The progress of the Cognitive Radio-Wireless Sensor Network is being influenced by advancements in wireless sensor networks (WSNs), which significantly have unique features of cognitive radio technology (CR-WSN). Enhancing the network lifespan of any network requires better utilization of the available spectrum as well as the selection of a good routing mechanism for transmitting informational data to the base station from the sensor node without data conflict. Aims: Cognitive radio methods play a significant part in achieving this, and when paired with WSNs, the above-mentioned objectives can be met to a large extent. Methods: A unique energy-saving Distance-Based Multi-hop Clustering and Routing (DBMCR) methodology in association with spectrum allocation is proposed as a heterogeneous CR-WSN model. The supplied heterogeneous CR-wireless sensor networks are separated into areas and assigned a different spectrum depending on the distance. Information is sent over a multi-hop connection after dynamic clustering using distance computation. Results: The findings show that the suggested method achieves higher stability and ensures the energy-optimizing CR-WSN. The enhanced scalability can be seen in the First Node Death (FND). Additionally, the improved throughput helps to preserve the residual energy of the network which helps to address the issue of load balancing across nodes. Conclusion: Thus, the result acquired from the above findings shows that the proposed heterogeneous model achieves the enhanced network lifetime and ensures the energy optimizing CR-WSN. © 2023 Bentham science Publishers.

关键词： Cognitive radio

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Ruppert's Delaunay Triangulation Refinement Scheme for Optimal RSUs Placement in Vehicle to Infrastructure Communication Network

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China Communications 2023年第7期20卷 303-320页

作者： Selvakumari P Chinnasamy A Sree Rathna Lakshmi NVS Sheela D Department of Computer Science and Engineering Tagore Engineering CollegeChennai 600048India Department of Data Science and Business Systems School of ComputingSRMIST kattankulathur CampusChennai 603203India Department of Electronics and Communication Engineering Department of ECE at Agni College of TechnologyChennai 603103India Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATSChennai 600124India

Road Side Units(RSUs)are the essential component of vehicular communication for the objective of improving safety and mobility in the road *** are generally deployed at the roadside and more specifically at the intersections in order to collect traffic information from the vehicles and disseminate alarms and messages in emergency situations to the neighborhood vehicles cooperating with the ***,the development of a predominant RSUs placement algorithm for ensuring competent communication in VANETs is a challenging issue due to the hindrance of obstacles like water bodies,trees and *** this paper,Ruppert’s Delaunay Triangulation Refinement Scheme(RDTRS)for optimal RSUs placement is proposed for accurately estimating the optimal number of RSUs that has the possibility of enhancing the area of coverage during data *** RDTRS is proposed by considering the maximum number of factors such as global coverage,intersection popularity,vehicle density and obstacles present in the map for optimal RSUs placement,which is considered as the core improvement over the existing RSUs optimal placement *** is contributed for deploying requisite RSUs with essential transmission range for maximal coverage in the convex map such that each position of the map could be effectively covered by at least one RSU in the presence of *** simulation experiments of the proposed RDTRS are conducted with complex road traffic *** results of this proposed RDTRS confirmed its predominance in reducing the end-to-end delay by 21.32%,packet loss by 9.38%with improved packet delivery rate of 10.68%,compared to the benchmarked schemes.

关键词： road side units(RSUs) ruppert’s delau-nay triangulation refinement area of global coverage optimal RSUs placement

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An Assessment Towards 2D and 3D Human Pose Estimation and its Applications to Activity Recognition: A Review

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SN computer science 2025年第2期6卷 1-24页

作者： Verma, Pratishtha Srivastava, Rajeev Tripathy, Santosh Kumar Department of Computer Engineering National Institute of Technology Haryana Kurukshetra India Computer Science and Engineering IIT BHU Varanasi India Department of Computing Science and Engineering National Institute of Technology Patna Bihar Patna India

Human pose estimation (HPE) from images or video is not only a major issue of computer vision, but also it plays a vital role in many real-world applications. The most challenging problems of human pose estimation are the disparity of poses, disturbing background clutter, different camera views, changes in scene lighting conditions, and occlusion. To solve these issues, a significant amount of research effort has been considered in the past. In this paper, we explore various vision-based activity recognition using 2D and 3D human pose estimations. We explore the conventional system, which includes both statistical and deep learning-based approaches for HPE, and then explore the current progress of each one in comparison to existing ones. At last, we also present various current state-of-the-art human activity recognition (HAR) techniques that use HPE for recognition. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Human activity recognition (HAR) Human body model (HBM) Human pose estimation (HPE)

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PricingTheCloud: A Pricing Estimator for an Informed Cloud-Migration Process 20th

PricingTheCloud: A Pricing Estimator for an Informed Cloud...

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20th International Conference on the Economics of Grids, Clouds, Systems, and Services, GECON 2024

作者： Djouela Kamgang, Ines Raissa Elhabbash, Abdessalam Elkhatib, Yehia Department of Computer Engineering Faculty of Engineering and Technology University of Buea Buea Cameroon School of Computing and Communications Lancaster University Lancaster United Kingdom School of Computing Science University of Glasgow Glasgow United Kingdom

ISBN: (纸本)9783031812255

One of the main challenges facing businesses migrating to the cloud is getting an estimate of their costs in advance. The estimators available to date allow companies to compare the different virtual machine offerings from each operator, but only venture very slightly into estimating the overall cost, which includes operational and network costs. Existing estimators include operational costs in their estimates, but almost no one considers the network cost, which is a complex but far from negligible component. In this paper, we seek to address this issue by proposing a new estimator called PricingTheCloud. It is an estimator that enables companies to have an accurate estimate of their costs in advance. Unlike other estimators, PricingTheCloud considers network costs in the cost estimation. Its evaluation shows an average accuracy of 86.73% for compute costs and 65.44% for network costs in different AWS-to-AWS scenarios as compared to AWS invoices and shows the effectiveness of the proposed estimator compared to three other cloud costs estimators namely, Cloudorado, Holori, and Vantage. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Computational cost

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Harnessing Industry 4.0: A Framework for Integrating AI and Emerging Technologies 6

Harnessing Industry 4.0: A Framework for Integrating AI and ...

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6th International Workshop on Artificial Intelligence and Education, WAIE 2024

作者： Baroud, Shadia Yahya Qureshi, Mohammed Naeem University Malaysia of Computer Science and Engineering Department of Computing Petaling Jaya Malaysia

ISBN: (纸本)9798350378894

The emergence of Industry 4.0 marks a transformative era characterized by advanced technologies. These innovations promise significant advantages, including enhanced productivity, sustainable progress and heightened resilience. However, integrating these technologies poses challenges, notably the need for a skilled workforce proficient in information technology and data analytics. This study proposes a comprehensive framework for integrating Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), Multi-Agent Systems (MAS) and Extended Reality (XR) technologies into educational games (EDs) to improve personalized learning, foster engagement and enhance outcomes as a proof of concept. We employed a mixed-methods approach, combining quantitative and qualitative data to provide a holistic view of the framework's effectiveness. The outcomes indicate that integrating advanced technologies in EDs significantly enhances learning outcomes, supports critical thinking and fosters motivation. However, challenges such as technical complexity, data privacy and ethical considerations. This study discusses these findings' practical implications and offers future research and development recommendations. By addressing the previous challenges, the proposed framework aims to prepare individuals for the digital age, fostering a skilled, resilient and responsible workforce. © 2024 IEEE.

关键词： Adversarial machine learning

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On building automation system security

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High-Confidence computing 2024年第3期4卷 103-122页

作者： Christopher Morales-Gonzalez Matthew Harper Michael Cash Lan Luo Zhen Ling Qun Z.Sun Xinwen Fu Department of Computer Science University of Massachusetts LowellLowell 01854USA Department of Electrical and Computer Engineering University of Central FloridaOrlando 32816USA School of Computer Science and Technology Southeast UniversityMa’anshan 243032China School of Computer Science and Engineering Anhui University of TechnologyNanjing 211189China

Building Automation Systems(BASs)are seeing increased usage in modern society due to the plethora of benefits they provide such as automation for climate control,HVAC systems,entry systems,and lighting *** BASs in use are outdated and suffer from numerous vulnerabilities that stem from the design of the underlying BAS *** this paper,we provide a comprehensive,up-to-date survey on BASs and attacks against seven BAS protocols including BACnet,EnOcean,KNX,LonWorks,Modbus,ZigBee,and *** studies of secure BAS protocols are also presented,covering BACnet Secure Connect,KNX Data Secure,KNX/IP Secure,ModBus/TCP Security,EnOcean High Security and Z-Wave *** and ZigBee do not have security *** point out how these security protocols improve the security of the BAS and what issues remain.A case study is provided which describes a real-world BAS and showcases its vulnerabilities as well as recommendations for improving the security of *** seek to raise awareness to those in academia and industry as well as highlight open problems within BAS security.

关键词： Building automation system BAS protocols Security Attack

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RAPID: Integrating AI and Multi-Agent Systems for Enhanced Traffic Management Framework with YOLOv9

RAPID: Integrating AI and Multi-Agent Systems for Enhanced T...

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2024 International Conference on Innovation and Intelligence for Informatics, computing, and Technologies, 3ICT 2024

作者： Baroud, Shadia Yahya Yahaya, Nor Adnan Department of Computing University Malaysia of Computer Science and Engineering Petaling Jaya Malaysia

ISBN: (纸本)9798331533137

Traffic accidents pose a major risk to human life and have a considerable economic impact worldwide. In this study, we present the RAPID framework, an innovative approach that integrates Artificial Intelligence (AI) and Multi-Agent Systems (MAS) to enhance traffic management. The core of the RAPID framework is the YOLOv9 model, a "state-of-the-art" deep learning (DL) algorithm for real-time object detection and classification, which we employ to detect and classify traffic accidents accurately. The RAPID framework addresses key challenges in modern traffic management, including data heterogeneity, scalability and real-time processing. Our system leverages a multi-layered architecture: data is acquired from various sources, including traffic cameras and CCTV and then transmitted to a cloud data center for storage and processing. Within the cloud infrastructure, the data undergoes pre-processing and is analyzed by the YOLOv9 model. The MAS coordinates various components of the framework, distributing tasks among specialized agents responsible for data acquisition, preprocessing, model training and real-time detection. We evaluated the performance of the RAPID framework using a dataset of 3000 labeled traffic accident images. The YOLOv9 model demonstrated 95.4% accuracy, 93.8% precision, 92.6% recall and 93.2% F1-score. The experimental results indicate that the RAPID framework significantly improves traffic accident detection and response times compared to traditional systems, highlighting its potential for real-world applications. The RAPID framework contributes to the field of intelligent transportation systems (ITS) by providing a robust, scalable and efficient solution for real-time traffic management. ©2024 IEEE.

关键词： Multi agent systems

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Pepper Berries Graded Using Deep Learning Models 9

Pepper Berries Graded Using Deep Learning Models

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Bipin Nair, B.J. Abrav Nanda, K.M. Raghavendra, V. School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science Mysuru India

ISBN: (纸本)9798350377972

The classification of pepper berries is a critical task in agriculture, traditionally graded by manual methods that are prone to inconsistencies, high labor costs, and inefficiencies. This study aims to automate the classification of pepper berries into four distinct grades using advanced deep learning techniques by utilizing various advanced deep learning architectures with a refined DenseNet-151 model. Researchers demonstrate through experiments on a variety of datasets that deep learning approaches significantly improve classification accuracy and efficiency compared with traditional approaches. Due to its effective feature propagation and dense connection the optimized DenseNet-151 model proved to be the best performer obtaining an accuracy of 98%. Furthermore, the models MobileNetV2, Vgg16 and InceptionNet had strong results, achieving 93%, 94% and 94% accuracy while ResNet only managed 87% accuracy. Future work will concentrate on expanding the dataset employing complex techniques of data augmentation and investigating advanced model architectures in order to further improve performance. Validating the practical efficiency of these models and developing precision agriculture and smart agricultural practices will depend greatly on their real-world deployment. © 2024 IEEE.

关键词： Pepper

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