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The testing framework for vehicular Edge computing and Communications on the Smart Highway 20

The testing framework for Vehicular Edge Computing and Commu...

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IEEE 20th Consumer Communications and Networking conference (CCNC)

作者： verschoor, Thomas Charpentier, vincent Slamnik-Krijestorac, Nina Marquez-Barja, Johann Univ Antwerp Fac Appl Engn Antwerp Belgium Univ Antwerp Fac Appl Engn Fdn Bruno Kessler ImecIDLab Antwerp Belgium Univ Antwerp Fac Appl Engn IMEC IDLab Antwerp Belgium

ISBN: (纸本)9781665497343

vehicular Edge computing (vEC) brings cloud infrastructure to the vehicular edge, resulting in better performances and avoiding network congestions. In this work-in-progress paper, the benefits of edge computing over cloud computing are discussed in a vehicular environment context, and they are leveraged by creating a Cooperative, Connected and Automated Mobility (CCAM) performance measurement framework. This measurement tool can follow vehicles by moving across different devices, enabling measurements on Key Performance Indicators (KPIs) using edge computing. We already used this tool to evaluate latencies of both a stationary and driving vehicle, moving over the Smart Highway testbed in Antwerp, Belgium. When driving, smart-edge-following algorithms can be deployed to choose the nearest Road Side Unit (RSU) using broadcasted Cooperative Awareness Messages (CAMs) of the vehicle. While driving on the Smart Highway, the application monitors important performance metrics such as throughput, latency, packet loss, packet delivery rate and more. We compare short-range vehicular communications technologies on the Smart Highway (ITS-G5 and LTE-v2X PC5) against the cellular. Our preliminary results demonstrate the benefits in terms of latency by using short-range communications technologies in vEC applications. These results validate that moving applications to the edge is truly beneficial, since our results confirmed up to 90% lower latency using ITS-G5, up to 50% using LTE-v2X PC5. Future deployments of 5G in the Smart Highway are planned, which would further improve the performance edge computing technologies.

关键词： Measurement tooling v2X ITS-G5 LTE-v2X PC5 5G LTE-v2X Uu vehicular Networks Edge computing MEC vEC

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vital Sense Analytics—For Maximizing the Performance of Football Players Using Wearables 6th

Vital Sense Analytics—For Maximizing the Performance of Foo...

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6th International conference on Soft computing and Signal Processing, ICSCSP 2023

作者： Thomas Leonid, T. Abilaash, S. Gopal, Adith v. HariBabu, E. Department of Electronics and Communication Engineering KCG College of Technology Chennai600097 India

ISBN: (纸本)9789819986279

Wearable devices are increasingly utilized in the realm of sports, including football, to track players’ performance during training and games. These devices incorporate various sensors such as GPS trackers, Pulse oximeters, Node MCU, and ECG sensors, to capture biometric data, including speed, distance covered, and heart rate. The collected data is then transmitted to a central system for analysis (Ash and Stults-Kolehmainen in Establishing a global standard for wearable devices in sport and fitness: perspectives from the New England chapter of the American College of Sports Medicine Members, 2020 [1]). The analyzed and processed data is fed into a machine learning algorithm, which generates output that is received by an application. The model is trained with 10,000 samples and tested with almost 300 test sets. Multiple algorithms have been used out of which, random forest classifies efficiently with an accuracy of 95%. To evaluate the player with performance metrics, the random forest algorithm has been found to be the best when compared among the other algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

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Employing Collective Intelligence at the IoT Edge for Spatial Decisions 15

Employing Collective Intelligence at the IoT Edge for Spatia...

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15th International conference on Ambient Systems, Networks and Technologies Networks, ANT 2024 / The 7th International conference on Emerging Data and Industry 4.0, EDI40 2024

作者： Spezzano, Giandomenico National Research Council of Italy Via P. Bucci 8-9c Rende87036 Italy

This paper explores the synergies between collective intelligence and edge computing, presenting a novel paradigm that harnesses decentralized processing power for collaborative problem-solving. Edge devices, such as sensors and IoT devices, collect spatial data in real time from their local environments. They can incorporate machine learning algorithms to analyze spatial data, enabling quicker and more context-aware decision-making. Spatial clustering, a pivotal strategy in IoT edge computing, is examined to optimize localized data processing, enhance resource efficiency, and enable real-time analytics in decentralized environments. By leveraging the physical proximity of devices, spatial clustering contributes to the effectiveness and sustainability of IoT deployments at the edge. The paper introduces an innovative approach to adaptive spatial clustering by adopting swarm intelligence, drawing inspiration from the collective behavior of a flock of birds. Building upon the classical flock model of Reynolds, our extended model incorporates movement in a multi-dimensional space and introduces different types of birds. In this context, the birds serve as agents for discovering points with specific characteristics in a multidimensional space. The integration of swarm intelligence into spatial clustering presents a promising avenue for addressing the challenges of decentralized processing in edge computing environments, paving the way for more efficient and responsive IoT deployments. © 2024 Elsevier B.v.. All rights reserved.

关键词： Edge computing

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The Role of Fog Device Density in IoT-Fog-Cloud Systems 19

The Role of Fog Device Density in IoT-Fog-Cloud Systems

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19th International conference on Future Networks and Communications, FNC 2024 / 21st International conference on Mobile Systems and Pervasive computing, MobiSPC 2024 / 14th International conference on Sustainable Energy Information Technology, SEIT 2024

作者： Almulifi, Asma Kurdi, Heba Computer Science Department College of Computer and information science King Saud University Riyadh Saudi Arabia Department of Computer Science Majmaah University College of Science in Zulf Majmaah11952 Saudi Arabia

Fog computing has emerged as an essential technology for enabling real-time, low-latency responses in cloud-based applications within Internet of Things (IoT) systems. This study explored the impact of the number of fog devices (NFDs) on the performance of IoT-fog-cloud systems. Through comparative analysis, two scheduling algorithms—First Come First Serve (FCFS) and Priority—were evaluated across different scales of system deployment. The results indicated that the FCFS algorithm was optimal for systems with fewer NFDs, whereas the Priority algorithm proved advantageous in larger settings. These findings not only establish guidelines for selecting appropriate scheduling strategies based on the scale of fog device deployment but also provide strategic insights for businesses on the optimal distribution of computational resources. This research aids companies in deciding whether to centralize resources in fewer branches with more employees or to decentralize into more branches with fewer employees, thereby optimizing operational efficiency and responsiveness. © 2024 The Authors. Published by Elsevier B.v.

关键词： Fog computing

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AI-Driven HR Optimization Strategies in Finance and Marketing: Methodological Framework and Applications

AI-Driven HR Optimization Strategies in Finance and Marketin...

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2024 International conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Lekshmi, R.S. Mary, v. Sheela Arasuraja, G. Krishnamoorthy, v. Kaliappan, S. Selvameena, R. St.Joseph's College of Engineering Department of Management Studies Tamil Nadu Chennai India Aarupadai Veedu Institute of Technology Vinayaka Mission's Research Foundation Department of Management Studies Tamil Nadu Chennai India Kongu Engineering College Department of Management Studies Perundurai Tamil Nadu Erode India Lovely Professional University Division of Research and Development Punjab Phagwara India Dr.M.G.R Educational and Research Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331543617

The proposed systems that include AI and the IoT present innovative financial and marketing opportunities. AI analytics capabilities and networked devices with the capability to provide real-time data mean the possibility to significantly improve recruitment, employee engagement, performance management, and retention. It combines natural language processing and machine learning algorithms, offering personalized recommendations, and in the vision insights, and in the case of environmental sensors, smart devices, and wearable health monitors, it facilitates accumulation of significant data. This framework will integrate IoT with AI to enhance HR procedures: more engaging, efficient, and enjoyable work environment. Apart from better decision-making and automated occupations, it also advocates for a data-driven approach to HRM. Further, it can result in the formation of intelligent and responsive HR systems that have the potential of improving organizational effectiveness and work productivity. © 2024 IEEE.

关键词： Marketing

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Machine Learning based Secure Cardless Financial Transactions using Multi Factor Authentication 6

Machine Learning based Secure Cardless Financial Transaction...

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6th International conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Prasanth, v.S. Thousifa, Patnam vyshnavi, Sunkesula Yasaswitha, Ketham Parveen Akhther, A. Dept. of ECE Madanapalle Institute Of Technology & Science Madanapalle India Dept. of ECE Sathyabama Institute of Science & Technology Madanapalle India Dept. of CSE Sathyabama Institute of Science & Technology Chennai India

ISBN: (纸本)9798331522667

In this rapidly changing world, ensure secure transactions is more important than ever. This paper discusses how combining artificial intelligence with multi-factor authentication enhances the security for cardless transactions. Machine Learning plays a vital role by automatically detecting and preventing fraud activities, while MFA adds extra layers of security. This paper includes algorithms like Convolutional Neural Networks Algorithm, Advanced Encryption Standards Encryption Algorithm and Cosine Similarity for feature mapping. These layers include using mobile devices and advanced identification methods like facial recognition, making the process secure and user-friendly. Together, these technologies create a robust defense system that not only prevents fraud but also ensures smooth and successful transactions. This combination facilitates the way for future growth of digital finance, providing both safety and convenience. © 2025 IEEE.

关键词： Adversarial machine learning

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Analysis of Gesture based Control System in Infotainment of Hybrid Car using Internet of Things 6

Analysis of Gesture based Control System in Infotainment of ...

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6th International conference on Mobile computing and Sustainable Informatics, ICMCSI 2025

作者： Kumar, vinoth K. Purohit, Reena vidhya, K. Aravinda, K. Purohith, T.A. Aruni Badigar, vinay v. Ghalagi, Nikhita G. Rame, Neiphretuonuo Department of EEE New Horizon College of Engineering Bengaluru India Department of BCA New Horizon College Marathahalli Bengaluru India Department of EEE Velammal Engineering College Chennai India Department of ECE New Horizon College of Engineering Bengaluru India

ISBN: (纸本)9798331522667

Using numerous devices while driving causes drivers to lose focus on the road, contributing to accidents in about one-third of cases. To address this issue, significant research is focused on developing interfaces between humans and machines that enable drivers to control automobile electronics without becoming distracted. This research study presents a complete system that enables control of infotainment systems through hand gestures. The proposed system utilizes the car's roof is equipped with a visible-infrared camera that focuses on the shift stick area and integrates both new and algorithms for computer vision. Using a real car and a car simulator, 23 individuals participated in the testing. According to the findings, people marginally prefer this gesture-based method over the touch-screen interface is similar. © 2025 IEEE.

关键词： Touch screens

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Combinatorial Client-Master Multiagent Deep Reinforcement Learning for Task Offloading in Mobile Edge computing 23

Combinatorial Client-Master Multiagent Deep Reinforcement Le...

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23rd International conference on Autonomous Agents and Multiagent Systems, AAMAS 2024

作者： Gebrekidan, Tesfay Zemuy Stein, Sebastian Norman, Timothy J. University of Southampton Southampton United Kingdom

Deep reinforcement learning (DRL) is gaining popularity in task-offloading problems because it can adapt to dynamic changes and minimize online computational complexity. However, the various types of continuous and discrete resource constraints on user devices (UDs) and mobile edge computing (MEC) servers pose challenges to the design of an efficient DRL-based task-offloading strategy. Existing DRL-based task-offloading algorithms focus on the constraints of the UDs, assuming the availability of enough storage resources on the server. Moreover, existing multiagent DRL (MADRL)-based task-offloading algorithms are homogeneous agents and consider homogeneous constraints as a penalty in their reward function. In this work, we propose a novel combinatorial client-master MADRL (CCM_MADRL) algorithm for task offloading in mobile edge computing (CCM_MADRL_MEC) that allows UDs to decide their resource requirements and the server to make a combinatorial decision based on the UDs' requirements. CCM_MADRL_MEC is the first MADRL approach in task offloading to consider server storage capacity in addition to the constraints of the UDs. By taking advantage of the combinatorial action selection, CCM_MADRL_MEC has shown superior convergence over existing benchmark and heuristic algorithms. © 2024 International Foundation for Autonomous Agents and Multiagent Systems.

关键词： Reinforcement learning

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Revolutionizing Healthcare through IoT and Edge computing: A Evaluation of Optimization Techniques 1

Revolutionizing Healthcare through IoT and Edge Computing: A...

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1st International conference on Cognitive computing and Engineering Education, ICCCEE 2023

作者： Peshane, vanshika Baig, Mirza Moiz Sonekar, Shrikant v. Sawwashere, Supriya JDCOEM Department of Computer Science and Engineering Nagpur India

ISBN: (纸本)9798350332803

The purpose of this evaluation paper is to provide an overview of the current state of the art research on the implementation of optimization techniques in edge computing for smart hospitals, with a focus on the integration of Industry 4.0 concepts, ant colony optimization algorithms, and Internet of Things (IoT) devices. The paper begins by providing a background on the concept of edge computing and its potential in the healthcare industry, followed by a discussion of the current challenges and limitations in implementing edge computing in smart hospitals. The main body of the paper reviews recent studies that have applied optimization techniques, such as ant colony optimization, to various aspects of hospital operations, including resource allocation, patient scheduling, and facility management. The paper also examines the use of IoT devices and sensors to collect real-Time data from the hospital environment, which is then processed by the edge computing system to optimize operations. The review concludes with an assessment of the current state of research in this field, highlighting areas of progress and opportunities for future research. Overall, this paper provides a comprehensive analysis of the use of optimization techniques in edge computing for smart hospitals, and demonstrates the potential of Industry 4.0 concepts, ant colony optimization, and IoT in revolutionizing the healthcare industry. © 2023 IEEE.

关键词： Internet of things

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Performance Evaluation of Supervisory Machine Learning algorithms for Cellular Networks Payload Enhancement in Downlink 14

Performance Evaluation of Supervisory Machine Learning Algor...

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14th International conference on computing Communication and Networking Technologies, ICCCNT 2023

作者： Suresh, B. Sai Prakash Sakuru, K.L.v. National Institute of Technology Warangal Department of Electronics & Communication TS Warangal506004 India

ISBN: (纸本)9798350335095

Payload allocation in downlink cellular networks is vital for efficiently distributing data transmission to users/devices. The base stations (RRH + BBU) task allocates and assigns radio channels, time slots, and other resources to facilitate good data reception by users. The allocation process considers quality of service (QoS), traffic management, and scheduling algorithms. This study aims to optimize resource utilization, mitigate network congestion, and ensure fair and reliable data transmission to users with Supervisory Machine Learning approaches by considering the sizeable cellular traffic data to improve payload efficiency and network performance by dynamic resource allocation for advanced cellular technologies. This complete design is implemented in Google Colabortory (Colab), an open-source cloud platform. In this paper, XBOOST, LINEAR REGRESSION, GRADIENT BOOST, and RANDOM FOREST supervisory machine learning algorithms were compared for resource allocation and time complexity, and the results are presented. © 2023 IEEE.

关键词： Quality of service

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