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Smart Cradle System for Enhancing Infant Care and Safety using IoT and Machine Learning 2

Smart Cradle System for Enhancing Infant Care and Safety usi...

引用

2nd International Conference on Self Sustainable Artificial Intelligence systems, ICSSAS 2024

作者： Sharma, Sandeep Kumar Maniyar, Varun Raj, Aditya Mangla, Poorv Rathore, Pramod Singh Manipal University Jaipur Dept of Computer and Communication Engineering Jaipur India Manipal University Jaipur Dept of IoT and Intelligent Systems Jaipur India

ISBN: (纸本)9798350368413

The IoT Smart Cradle System revolutionizes infant care by integrating IoT technology into traditional cradles. Through sensors and connected devices, it gathers real-time data on infant movement and sound, transmitting it to users for remote monitoring. The proposed system's automation features enable remote control of cradle movements and ambient conditions. This innovative solution enhances infant well-being, offers peace of mind to working parents, and ensures a nurturing environment for optimal development. It represents a significant advancement by providing convenient, secure, and customizable care, bridging the gap between technology and tradition for the benefit of both infants and their families. © 2024 IEEE.

关键词： Arduino Uno Baby Cradle Infant Well-being IoT Real-Time Monitoring

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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

2024 IEEE Global communications Conference, GLOBECOM 2024

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

ISBN: (纸本)9798350351255

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. © 2024 IEEE.

关键词： Mobile edge computing

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TOPMG: Trust-Based Crowdsourcing through Multilateral Bargaining Game Theory

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

引用

2024 IEEE Global communications Conference, GLOBECOM 2024

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

ISBN: (纸本)9798350351255

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. © 2024 IEEE.

关键词： Reinforcement learning

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

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

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2024 IEEE Global communications Conference, GLOBECOM 2024

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

ISBN: (纸本)9798350351255

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. © 2024 IEEE.

关键词： Reinforcement learning

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The Effect of Balanced and Imbalanced Dataset on Voice Pathology Detection Using Online Sequential Extreme Learning Machine (OSELM)

The Effect of Balanced and Imbalanced Dataset on Voice Patho...

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2024 IEEE International Conference on Advanced Telecommunication and Networking Technologies, ATNT 2024

作者： Sazihan, N. F. S. Md Latiff, N. M. Abdul Malik, N. N. N. Abd Mashohor, S. Alaghbari, K. Al-Dhief, F.T. Universiti Teknolgi Malaysia Utm Faculty of Electrical Engineering Johor Bahru Malaysia Universiti Putra Malaysia Faculty of Engineering Dept. of Computer and Communication Systems Engineering Serdang Malaysia

ISBN: (纸本)9798350353501

Voice pathology detection is crucial for early diagnosis and treatment of vocal disorders. This research studies the effect of balanced and imbalanced datasets on the accuracy of voice pathology detection using Online Sequential Extreme Learning Machine (OSELM) on the Saarbruecken Voice Database (SVD). The dataset's balance has been identified as having a significant influence on the performance of the machine learning models. In this work, the voice signals from vowel /a/ of pathology and non-pathology classes are taken from SVD. The dataset is divided into two category which are balanced and imbalanced dataset. Then, the features of the voice signals are then extracted using Mel-Frequency Cepstral Coefficient (MFCC) and fed into classifier called OSELM. The result of balanced and imbalanced dataset is assessed for its accuracy. The results showed that the model achieved an accuracy of 66.25% with the balanced dataset compared to 48% for the imbalanced dataset. These findings highlight the importance of addressing dataset balance to enhance the reliability and effectiveness of voice pathology detection systems. © 2024 IEEE.

关键词： Adversarial machine learning

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Applying LSTM for River's Water Level Forecasting as Potential Flood Indication: A Case Study in Greece 15

Applying LSTM for River's Water Level Forecasting as Potenti...

引用

15th International Conference on Information, Intelligence, systems and Applications, IISA 2024

作者： Tziolas, Theodoros Papageorgiou, Elpiniki Papageorgiou, Konstantinos Zardas, Georgios Lekidis, Alexios Panagiotis, Papazoglou University of Thessaly Dept. of Energy Systems Gaiopolis Campus Larisa41500 Greece University of Thessaly Dept. of Electrical and Computer Engineering Volos Greece National and Kapodistrian University of Athens Dept. of Architecture and Computer and Communication Simulation Systems Chalkida Greece

ISBN: (纸本)9798350368833

Floods can have significant life-threatening and economic impacts on human societies. Flood forecasting with data-driven methods is a crucial aspect of disaster risk management, providing valuable insights into potential flood events and allowing for timely mitigation measures. This work illustrates the application of a Long Short-Term Memory (LSTM) neural network for short-term forecasting of a river's water level. The water treatment data used for training the LSTM models were gathered from Sperchios river, located in central Greece. Historical time-series data in a half-hour interval of the river's water level, water velocity, and area precipitation were utilized for the development of the proposed scheme. The developed model is deployed in the backend of a web application for half-hour ahead predictions of the river's water level that are visualized in the frontend. The post-analysis of the deployed forecasting model indicates that it consistently delivers forecasts with minimal error and demonstrates high accuracy in recent flood events. © 2024 IEEE.

关键词： Rivers

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Enhancing Performance in Hybrid Satellite-Terrestrial Networks: A Novel Joint NOMA-NC Approach

Enhancing Performance in Hybrid Satellite-Terrestrial Networ...

引用

2024 IEEE Global communications Conference, GLOBECOM 2024

作者： Karavolos, Michail Moraitis, Nektarios Tsipi, Lefteris Vouyioukas, Demosthenes University of the Aegean Dept. of Information and Communication Systems Engineering Samos83200 Greece National Technical University of Athens School of Electrical and Computer Engineering Zografou15773 Greece University of Piraeus Dept. of Digital Systems Piraeus18534 Greece

ISBN: (纸本)9798350351255

An appealing alternative towards dealing with mobility and coverage challenges in future wireless networks, are hybrid satellite-terrestrial networks (HSTNs). The combination of network coding (NC) and non-orthogonal multiple access (NOMA) techniques, have recently gained popularity due to their performance gains in improving the quality of a wireless transmission and exploiting the accessible spectrum. This paper introduces a joint NOMA-NC approach, integrated to an HSTN comprising a satellite, a terrestrial base station (BS), and multiple ground-based mobile terminals (MTs). The suggested method facilitates a cluster of end-users to be concurrently served through NOMA from a satellite using optimal power allocation, whereas the BS utilizes random linear NC to augment the MTs' reception in the event of errors. The simulation results confirm the effectiveness of the proposed methodology in improving system sum throughput, demonstrating substantial enhancements compared with both OMA with NC, and standalone NOMA and OMA approaches. The resilience of the suggested method is proved, improving the sum throughput even at low power transmissions and in the worst case channel conditions. © 2024 IEEE.

关键词： Network coding

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Comparison of Mel Frequency Cepstral Coefficient (MFCC) and Mel Spectrogram Techniques to Classify Industrial Machine Sound 15

Comparison of Mel Frequency Cepstral Coefficient (MFCC) and ...

引用

15th International Conference on Software, Knowledge, Information Management and Applications, SKIMA 2023

作者： Hafiz, Nur Fatinah Mashohor, Syamsiah Shazril, M.H.S.E.M.A. Rasid, M. Fadlee A. Ali, Azizi Universiti Putra Malaysia Faculty of Engineering Dept. of Computer and Communication Systems Engineering Selangor Serdang43400 Malaysia Selangor Serdang43400 Malaysia

ISBN: (纸本)9798350316551

The utilization of sound recognition to detect the faulty machines are getting the interest to be explored with the technology advancement in IR 4.0. In this paper, we investigated the performance of two acoustic features in representing the sound of a fan in industrial machines, that is acquired from the MIMII dataset. The Mel Frequency Cepstral Coefficient (MFCC) and Mel Spectrogram techniques are explored and compared in this paper. The classification is performed using Convolutional Neural Network (CNN) with the input of images from both techniques. The dataset of the input images are the audio files of 6dB fans sound in industrial machine. The results suggest that Mel Spectrogram is an effective feature for faulty machine sound recognition when used with CNN model. © 2023 IEEE.

关键词： Convolution

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Power and Rate Allocation for Energy-Efficient Rate-Splitting Multiple Access via Deep Q-Learning

Power and Rate Allocation for Energy-Efficient Rate-Splittin...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Diamanti, Maria Kapsalis, Georgios Tsiropoulou, Eirini Eleni Papavassiliou, Symeon Institute of Communication and Computer Systems School of Electrical and Computer Engineering National Technical University of Athens Athens Greece University of New Mexico Dept. of Electrical and Computer Engineering AlbuquerqueNM United States

ISBN: (纸本)9798350370218

Rate-Splitting Multiple Access (RSMA) has recently emerged as an effective technique for increasing network capacity by smartly controlling the tradeoff between decoding and treating interference as noise. In this paper, aligned with the need for sustainable wireless networks, we study the energy-efficient power and rate allocation of the common and private messages in the downlink of a network where rate-splitting is adopted. The corresponding energy efficiency maximization problem is transformed into a multi-agent Deep Reinforcement Learning (DRL) problem, based on which each private stream transmitted in the downlink constitutes a different DRL agent. The formulated DRL problem is solved by using Deep Q-Learning (DQL) algorithm and training a single Deep Q-Network (DQN) from the cumulative experiences gained from the DRL agents and their exploration of the environment, i.e., the exploration of different private-message power allocations. Numerical results obtained via modeling and simulation verify the effectiveness of the proposed DQL algorithm, demonstrating that it concludes solutions that outperform existing approaches from the literature in the achieved energy efficiency. © 2023 IEEE.

关键词： Deep Reinforcement Learning (DRL) Energy Efficiency Maximization Rate-Splitting Multiple Access (RSMA)

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Resource Allocation as a Market: A Case Study on Multi-Server Multi-Model Federated Learning 20

Resource Allocation as a Market: A Case Study on Multi-Serve...

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20th Wireless On-Demand Network systems and Services Conference, WONS 2025

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

ISBN: (纸本)9783903176713

6G networks envision a seamless integration of data and AI into their core operations, marking a new era where distributed client devices actively engage in intelligent processes, supported by a diverse ecosystem of AI service providers. This shift necessitates approaching traditional resource allocation problems from a market perspective, transparently reflecting the gains and losses of clients and providers within the ecosystem. In this paper, we use a multi-server, multi-model Federated Learning (FL) network as a case study to propose a market-based resource allocation framework. The framework models the joint problem of client resource allocation and pricing as a Fisher market, where servers compete to attract clients with private data and local computing resources for distributed model training. The joint problem is formulated as a convex program, aiming to maximize the total server utility related to their model's accuracy, while incorporating clients' total computing resource constraints and servers' budget constraints. An analytical solution to the convex program is derived concluding the so-called Market Equilibrium (ME) point, where market clearance is achieved. The effectiveness of the proposed market-based resource allocation framework is validated through comparisons with established resource allocation and sharing schemes from the literature. © 2025 IFIP.

关键词： Fisher market Market Equilibrium Multi-server multi-model federated learning pricing resource allocation

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