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TRUSTCACHE: Trust-based Content Caching in Information-Centric Networks

TRUSTCACHE: Trust-based Content Caching in Information-Centr...

引用

IEEE Conference on Global communications (GLOBECOM)

作者： Sean Tsikteris Aisha B Rahman Md Sadman Siraj Panagiotis Charatsaris Eirini Eleni Tsiropoulou Symeon Papavassiliou Institute of Communication and Computer Systems School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Dept. of Electrical and Computer Engineering University of New Mexico Albuquerque NM USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

The Information-Centric Networking (ICN) paradigm has reshaped the modern network architectures and promises efficient content delivery to the end-users. This paper introduces TRUSTCACHE, a novel framework enabling the content caching within ICNs and focusing on the trust-based ICN selection and optimal cache memory allocation to the Content Providers (CPs). The TRUSTCACHE framework incorporates the Optimistic Q-learning with Upper Confidence Bound reinforcement learning algorithm that enables the CPs to autonomously select ICNs based on their cache memory availability and trust levels. Also, TRUSTCACHE enables the CPs to jointly consider the reliability of the ICNs and their cache memory availability by integrating a novel trust model. Furthermore, TRUSTCACHE leverages the multilateral bargaining principles in order to ensure the optimal cache memory allocation among the CPs, in terms of aligning with their profit margin characteristics. Simulation-based experiments validate TRUSTCACHE’s operational efficiency across diverse CP profit margin profiles and highlight its superiority over alternative models lacking trust-based ICN selection or employing proportional fairness strategies for cache memory allocation.

关键词： Q-learning Cache memory Information-centric networking Focusing Resource management Reliability Global communication Game theory

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TOPMG: Trust-Based Crowdsourcing through Multilateral Bargaining Game Theory

TOPMG: Trust-Based Crowdsourcing through Multilateral Bargai...

引用

IEEE Conference on Global communications (GLOBECOM)

作者： Panagiotis Charatsaris Adedamola Adesokan Aisha B Rahman Eirini Eleni Tsiropoulou Symeon Papavassiliou Institute of Communication and Computer Systems School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Dept. of Electrical and Computer Engineering University of New Mexico Albuquerque NM USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Crowdsourcing plays a critical role in modern information gathering and task execution, yet it faces challenges regarding the task selection and equitable monetary incentives distribution. In this paper, we introduce the TOPMG framework, which addresses these challenges by enabling the workers to select tasks based on their historically experienced monetary incentives and the platforms’ trustworthiness. Specifically, the TOPMG framework utilizes a reinforcement learning approach based on the principles of Optimistic Q-learning with Upper Confidence Bound (OQ-UCB) algorithm, guiding the platform selection process by considering the workers’ monetary incentives, profit, and the platforms’ trustworthiness. Also, the proposed framework introduces a multilateral bargaining game to allocate the platforms’ monetary incentives to the workers by prioritizing their information contribution, fairness, and the platforms’ reputation. Simulation results demonstrate TOPMG’s operational dynamics, scalability, and efficacy, as well as its superiority over existing methodologies.

关键词： Crowdsourcing Q-learning Social networking (online) Simulation Scalability Heuristic algorithms Games Global communication Game theory Faces

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Eggplant Disease Diagnosis Using a Robust Ensemble of Transfer Learning Architectures

Eggplant Disease Diagnosis Using a Robust Ensemble of Transf...

引用

International Conference on Electrical, computer and communication engineering (ECCE)

作者： Md Ismail Hossain Siddiqui Zishad Hossain Limon Shakil Khan Mahbub Alam Khan Hamdadur Rahman Shimanto Krishna Chakroborty Shamim Forhad Abdullah Al-Sakib S M Arafat Rahman Shafiur Rahman Dept. of Engineering/Industrial Management Westcliff University Irvine CA USA Dept. of Computer Science Westcliff University Irvine CA USA Dept. of Business Analytics International American University CA USA Dept. of Management Information System Pacific State University USA Dept. of Management of Information System International American University USA Dept. of Computer Science and Engineering East West University Dhaka Bangladesh Dept. of Electrical and Electronics Engineering American International University Bangladesh Dept. of Information Technology Westcliff University USA Dept. of Software Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Eggplant diseases present major challenges to agricultural productivity, leading to yield losses and threatening farmers livelihoods. Traditional diagnostic methods tend to be resource-intensive, subjective, and often inaccurate, making them unsuitable for large-scale use. This study addresses these shortcomings by proposing an Ensemble Transfer Learning (ETL) approach for precise classification of eggplant diseases. The model combines the strengths of ConvNeXt, EfficientNetV2, NFNet, and RegNet architectures, enhancing generalization and minimizing overfitting. Two datasets containing over 19,800 images were utilized for training and validation, with advanced preprocessing and data augmentation techniques applied to ensure high input data quality and model robustness. To assess performance, evaluation metrics such as accuracy, F1-score, MCC, and PR-AUC were used. The model achieved an impressive classification accuracy of 99.76%, surpassing state-of-the-art methods and demonstrating strong generalization across various conditions. A web-based application was also developed, enabling real-time disease diagnostics through a user-friendly interface. This application provides farmers with actionable insights, promoting sustainable farming practices by reducing crop losses and minimizing the need for chemical interventions. However, its reliance on high-quality labeled images limits its applicability in uncontrolled environments. Future research will aim to integrate more diverse datasets and develop mobile-based solutions to enhance scalability and usability. This study emphasizes the potential of ensemble transfer learning to transform agricultural disease diagnosis and improve crop productivity.

关键词： Productivity Accuracy Transfer learning Crops computer architecture Real-time systems Medical diagnosis Chemicals Diseases Farming

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Neuromorphic computing by means of recurrent spectrum slicing for next generation high baud rate transmission systems

Neuromorphic computing by means of recurrent spectrum slicin...

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LEOS Summer Topical Meetings

作者： Adonis Bogris Kostas Sozos George Sarantoglou Stavros Deligiannidis Charis Mesaritakis Dept. Informatics and Computer Engineering University of West Attica Athens Greece Dept. of Informatics and Computer Engineering University of West Attica Athens Greece Dept. of Information and Communication Systems Engineering University of the Aegean Karlovassi Greece

In this paper we review our recent work in photonic neural networks based on recurrent optical spectrum slicing as potential accelerators for the mitigation of transmission impairments in short reach, high symbol rate...

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JARVIS - Smart AI Support system

JARVIS - Smart AI Support System

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Electrical Power engineering, communication and Computing Technology(ODICON), Odisha International Conference on

作者： V. Ramya Manaswi Meduri Saketh Mandumula Sai Ravi Tejha Atluri Sai Karthikeya Dharani Chandra Prakash Ahmad Alkhayyat Dept. of Computer Science Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Dept. of computer science engineering with specialisation of cybersecurity GITAM university Hyderabad India Dept. of Electronics & Communication Engineering K.G Reddy College of Engineering and Technology Hyderabad Telangana India Dept. of computers Techniques engineering College of technical engineering The Islamic University Najaf Iraq

ISBN: (数字)9798350354379

ISBN: (纸本)9798350354386

The JARVIS AI Support system represents a remarkable fusion of modern technology, blending a sophisticated GUI design, seamless voice control, and inventive features like the captivating “Air Canvas” facilitated by OpenCV. This AI-driven virtual assistant offers users a natural and intuitive experience, allowing them to effortlessly perform tasks such as browsing the web, interacting with a chatbot, and executing dynamic voice- controlled actions. Moreover, the system showcases advanced capabilities including motion detection and facial recognition with an accuracy of 95% in multiple runs. Leveraging the power of computer vision, the Air Canvas feature empowers users to express creativity through fluid hand gestures, while voice commands effortlessly manage diverse tasks. This innovative project presents an approachable way to interact with technology in the world of AI.

关键词： Emotion recognition Video on demand Speech recognition Chatbots Motion detection Internet of Things Web sites Security Artificial intelligence Graphical user interfaces

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Deep Reinforcement Learning Optimized Intelligent Resource Allocation in Active RIS-Integrated TN-NTN Networks

Deep Reinforcement Learning Optimized Intelligent Resource A...

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IEEE Conference on Wireless communications and Networking

作者： Muhammad Ahmed Mohsin Hassan Rizwan Muhammad Jazib Muhammad Iqbal Muhammad Bilal Tabinda Ashraf Muhammad Farhan Khan Jen-Yi Pan Dept. of Electrical Engineering Stanford University USA Dept. of Electrical Engineering University of California Riverside USA Dept. of Electrical Engineering Pakistan Institute of Engineering and Applied Sciences Pakistan Dept. of Communication Engineering National Chung Cheng University Taiwan Dept. of Electrical Engineering University of California Irvine USA School of Computer Science and Information Technology University College Cork Ireland

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

This work explores the deployment of active reconfigurable intelligent surfaces (A-RIS) in integrated terrestrial and non-terrestrial networks (TN-NTN) while utilizing coordinated multipoint non-orthogonal multiple access (CoMP-NOMA). Our system model incorporates a UAV-assisted RIS in coordination with a terrestrial RIS which aims for signal enhancement. We aim to maximize the sum rate for all users in the network using a custom hybrid proximal policy optimization (H-PPO) algorithm by optimizing the UAV trajectory, base station (BS) power allocation factors, active RIS amplification factor, and phase shift matrix. We integrate edge users into NOMA pairs to achieve diversity gain, further enhancing the overall experience for edge users. Exhaustive comparisons are made with passive RIS-assisted networks to demonstrate the superior efficacy of active RIS in terms of energy efficiency, outage probability, and network sum rate.

关键词： NOMA system performance Reconfigurable intelligent surfaces Probability Energy efficiency Trajectory Power system reliability Resource management Optimization Terahertz communications

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Synergia: Device-Edge Server Association for ISAC-assisted Mobile Edge Computing systems

Synergia: Device-Edge Server Association for ISAC-assisted M...

引用

IEEE Conference on Global communications (GLOBECOM)

作者： Panagiotis Charatsaris Arianna Santamaria Penafiel Maria Diamanti Eirini Eleni Tsiropoulou Symeon Papavassiliou Institute of Communication and Computer Systems School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Dept. of Electrical and Computer Engineering University of New Mexico Albuquerque NM USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

The efficient operation of the unified Integrated Sensing and communication (ISAC) – Mobile Edge Computing (MEC) systems is important for enhancing data sensing, communication, and computation processes in next-generation wireless systems. Despite prior research focusing on these systems, little attention has been given to optimizing the device-edge server associations. This paper addresses this gap by introducing the novel two-stage device-edge server association Synergia framework. Firstly, representative utility functions capture the characteristics of the devices and MEC servers by jointly considering their sensing, communication, and computation characteristics. Secondly, the Estimated Synergia framework leverages the Matching Theory to rapidly determine an initial device-server matching by disregarding the devices’ externalities, i.e., the matching decisions of other devices. Thirdly, the Accurate Synergia model refines and improves this matching by using the coalition formation games, while considering the devices’ externalities in optimizing the utilities of both the devices and the MEC servers. Extensive numerical evaluations demonstrate the Synergia’s operational efficiency and scalability, outperforming reinforcement learningbased approaches. Also, a real-world application involving car accident detection validates its applicability.

关键词： Wireless communication Multi-access edge computing Scalability Games Pricing Integrated sensing and communication Numerical models Servers Global communication Next generation networking

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DC and RF Characteristics Study of III-Nitride β-Ga2O3 Nano-HEMT with the variation of Relative Gate Positions

DC and RF Characteristics Study of III-Nitride β-Ga2O3 Nano...

引用

IEEE Electron Devices Technology and Manufacturing Conference (EDTM)

作者： G. Purnachandra Rao Trupti Ranjan Lenka Samadrita Das Hieu Pham Trung Nguyen Dept. of Electronics and Communication Engineering National Institute of Technology Silchar Assam India Dept. of Electrical and Computer Engineering Texas Tech University 910 Boston Avenue Lubbock Texas USA

This article presents an investigation of the DC and RF characteristics of III-Nitride $/ \boldsymbol{\beta}-\mathrm{Ga}_{2} \mathrm{O}_{3}$ nano-HEMT by varying the relative gate positions. Three gate positions are considered for the investigation of various performance factors comprising of DC, RF/THz, and noise characteristics while maintaining a fixed length between source and drain. It is observed that RF and noise characteristics are diminished when the gate is closer to source than its counterparts. The device physics of this hypothesis is investigated in this article.

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Optimizing Latency in Secure Reliable Transport (SRT) Protocol through Machine Learning for Video Streaming Applications

Optimizing Latency in Secure Reliable Transport (SRT) Protoc...

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IEEE International Conference on Electronics, Computing and communication Technologies (CONECCT)

作者： Rahul V Rao Sushmita B N Manjunath S Nayak Akilesh N S Ramya S Shwetha Baliga Jyotirmoy Karjee Electronics and Communication Dept. RVCE Bangalore India Computer Science and Engineering Dept. RVCE Bangalore India Samsung R&D Institute Bangalore India

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939

The increasing demand for live video streaming, especially using protocols like MPEG-TS and UDP/RTP, faces challenges of high latency and bandwidth overhead. Secure Reliable Transport (SRT) protocol, a superior open-source technology, optimizes streaming over unpredictable networks by managing packet loss, jitter, and bandwidth fluctuations, ensuring high-quality video. However, SRT struggles with latency issues due to packet delivery times, clock discrepancies, time drift, and Round-Trip Time (RTT) fluctuations. This paper introduces a machine learning approach to enhance streaming efficiency by predicting the optimal codec (MPEG4, H264, VP8) using K-means clustering to categorize codec groups and K-nearest neighbors algorithm for training the prediction model. This system significantly enhances bandwidth and reduces latency, jitter, and packet loss, achieving a testing accuracy of 98%. The results indicate a substantial improvement in latency optimization through machine learning in the SRT protocol, highlighting potential bandwidth and performance gains.

关键词： Protocols Machine learning algorithms Fluctuations Codecs Packet loss Clustering algorithms Bandwidth

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An Innovative IoT-Driven Smart Streetlight Management system Design 2

An Innovative IoT-Driven Smart Streetlight Management System...

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2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Shermina Justin, Shekaina Thenmozhi, P. George, Jissy Ann Kumar, T. Ananth Ananth, Christo Muscat College Department of Computing Oman Princess Nourah Bint Abdulrahman University Electrical Engineering Department Saudi Arabia Dept. of Electronics and Communication Engineering Chennai600119 India British University of Bahrain College of Ict Bahrain Ifet College of Engineering Computer Science and Engineering Viluppuram India Samarkand State University Uzbekistan Adjunct Faculty Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Chennai India

ISBN: (纸本)9798350364699

This research introduces a cutting-edge approach to optimizing the management of streetlight systems through automation by using IoT. Traditional methods often suffer from inefficiencies, leading to unnecessary energy consumption and maintenance costs. In this study, we propose a novel automated streetlight management system that leverages advanced technologies such as IoT sensors, machine learning algorithms, and smart scheduling mechanisms. By dynamically adjusting lighting levels based on real-time factors such as traffic density, weather conditions, and time of day, our system aims to achieve significant energy savings while ensuring optimal illumination for pedestrians and vehicles. We present the design architecture of our system, along with experimental results demonstrating its effectiveness in various urban environments. Our findings highlight the potential of automated streetlight management systems to enhance sustainability, improve safety, and reduce operational expenses for municipalities and urban planners. © 2024 IEEE.

关键词： Energy utilization

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