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AI Revolutionizing 5G and Next-Generation Networks 17

AI Revolutionizing 5G and Next-Generation Networks

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17th ieee international Scientific conference on Informatics, INFORMATICS 2024

作者： Pirecisejdiu, Nora Rendevski, Nikola Ristevski, Blagoj University "St. Kliment Ohridski" - Bitola Faculty of Information and Communication Technologies Bitola Macedonia

ISBN: (纸本)9798350387674

The introduction of 5G and beyond (B5G) networks has transformed massively telecommunications resulting in enhanced capacity, speed, and connectivity of mobile communications. The requirements and the complexity of these networks need to be addressed by innovative approaches to effective network management and optimization. This paper analyzes the critical role of Artificial Intelligence (AI) and Machine Learning (ML) in upgrading next-generation mobile networks and provides a comprehensive analysis of how AI and ML are integrated into 5G systems, highlighting the ways these technologies improve security, edge computing, and network management. It also details the evolution of AI, from the onset in symbolic logic to modern neural networks, and its application in network optimization, including computational offloading, beamforming design, and resource management. AI-driven Self-Organizing Networks (SON) provide adaptive network management and predictive maintenance, while AI-powered edge computing deals with the real-time processing demands of applications such as autonomous vehicles and smart cities. Moreover, the paper also explores how AI improves network security by enhancing threat detection and response capabilities. It concludes by highlighting key future directions, including the integration of AI with emerging technologies such as blockchain and advancements in edge computing. The AI and 5G fusion offer a new era of connected intelligence, revolutionizing the way we interact with technology and one another. This convergence is about transforming communication and connectivity, laying the foundations for more innovative applications and more resilient network infrastructure. © 2024 ieee.

关键词： 5G mobile communication systems

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A Soft real-time Memory Request Scheduler for Phase Change Memory systems 27

A Soft Real-time Memory Request Scheduler for Phase Change M...

引用

27th ieee international conference on embedded and real-time computing systems and applications (RTCSA)

作者： Aswathy, N. S. Kapoor, Hemangee K. Sarkar, Arnab IIT Guwahati Dept CSE Gauhati India IIT Kharagpur Adv Technol Dev Ctr ATDC Kharagpur W Bengal India

ISBN: (纸本)9781665441889

Phase Change Memory (PCM) has emerged as a viable alternative to traditional DRAM memories especially in real-time embedded systems, due to their higher density and lower leakage power dissipation. However, PCM comes with its own drawbacks. Although, the performances of DRAM and PCM are comparable for memory reads, PCM is about three times slower in terms of write latency, and suffers from significantly lower write endurance. The high write latency of PCM may be detrimental to delivered QoS and may lead to deadline misses in real-time systems. To circumvent the problem, this paper proposes a novel memory scheduling scheme which employs separate write request buffer in order to prioritize reads over writes. The read requests are scheduled using an urgency based scheduler where urgency depends on allowable response times of tasks. The write requests are serviced when there are no pending reads using a similar urgency based scheduler as used for read requests. Experimental evaluation using standard benchmarks reveal that the proposed scheme is able to achieve better normalized QoS compared to existing scheduling techniques for PCM and comparable access latencies with respect to DRAM.

关键词： Deadlines real-time Reward-aware Scheduling PCM Read/Write latency

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A Proposal for the Smart Classroom Infrastructure using IoT and Artificial Intelligence 48

A Proposal for the Smart Classroom Infrastructure using IoT ...

引用

48th Annual ieee international Computers, Software, and applications conference (COMPSAC) - Digital Development for a Better Future

作者： Martinez-Balleste, Antoni Batista, Edgar Figueroa, Elena Torruella, Gabriela Fretes Llurba, Celia Quiles-Rodriguez, Jose Unciti, Oihane Palau, Ramon Univ Rovira & Virgili Smart Technol Res Grp Dept Comp Engn & Math Tarragona Spain Univ Rovira & Virgili Dept Pedag ARGET Res Grp Tarragona Spain

ISBN: (纸本)9798350376975;9798350376968

With the technological developments of recent years, the concept of the "Smart Classroom" has gained in importance and is increasingly becoming a plausible reality. This refers to a classroom in which real-time data collection facilitates decision-making to improve teaching and learning processes. This article presents a proposal for the smart classroom infrastructure that utilises based on the Internet of Things and Cognitive and Intelligent systems. It consists of a number of perception units that focus on monitoring environmental variables such as light, air quality and noise, as well as variables related to the actors involved (students and teachers), especially those about emotions (which play a crucial role in educational processes). The information extracted from the data is stored and analysed by the classroom agent, which provides teachers with relevant information and suggestions in a timely manner. It is important to further develop our smart classroom infrastructure and test it in real environments to evaluate its potential and benefits in terms of the teaching and learning process, as well as to identify possible drawbacks and obstacles to implementation.

关键词： smart classrooms context-aware systems cognitive systems

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AI Enhanced Anesthesia Dosing: Evaluating ML Models 5

AI Enhanced Anesthesia Dosing: Evaluating ML Models

引用

5th international conference on Emerging systems and Intelligent computing, ESIC 2025

作者： Patangia, Deepshikha Panda, Amrita Dash, Satya Ranjan Barra, Paola Disha, M. Kalinga Institute of Medical Sciences Dept. of Anaesthesiology Bhubaneswar India Kiit University School of Computer Applications India University of Naples 'Parthenope' Department of Science and Technology Italy

ISBN: (纸本)9798331522100

Artificial intelligence (AI) is revolutionizing health-care, particularly in the field of anesthesiology. This paper evaluates the application of AI-enhanced models in optimizing anesthesia dosing to improve patient outcomes and operational efficiency. By integrating machine learning (ML) techniques, we analyze structured and unstructured patient data to predict anesthesia needs, automate drug delivery, and monitor vital signs in real time. The study examines several ML models, such as Support Vector Machines, Decision Trees, and Gradient Boosting, assessing their accuracy in predicting anesthetic requirements. The results show that AI can effectively reduce human error, enhance decision-making, and improve patient safety during perioperative care. The paper discusses the system architecture, data collection methods, and model performance, emphasizing the future potential of AI in automating anesthesia processes. © 2025 ieee.

关键词： Anesthesiology

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MRTOM: Mostly Reliable Totally Ordered Multicast, a Network Primitive to Offload Distributed systems 43

MRTOM: Mostly Reliable Totally Ordered Multicast, a Network ...

引用

43rd ieee international conference on Distributed computing systems (ICDCS)

作者： Liu, Zhang Grunwald, Dirk Izraelevitz, Joseph Wang, Gaukas Ha, Sangtae Univ Colorado Boulder CO 80309 USA

ISBN: (纸本)9798350339864

As datacenters become the new computing platform, integrating server-centric distributed systems into modern network hardware is gaining interest under the diminishing Moore's law. Researchers want to build reusable primitives that can take advantage of modern network hardware and offload common system components of a broad range of applications. In this paper, we present Mostly Reliable Totally Ordered Multicast, a reusable network primitive that can embed reliable group communication into the network. MRTOM is a network-centric approach that handles message replication, ordering, and reliable delivery using a network fast path, freeing server CPUs for application logic. MRTOM can be implemented in the programmable switches and edge interfaces (e.g., SmartNICs), significantly reducing network traffic compared to the existing approaches and improving job finish time amid packet loss. With MRTOM, we were able to accelerate multiple high-performance applications whose fast path can be totally offloaded into the network. For example, a Paxos application, MRTOM-Paxos, achieves > 1,100,000 transactions/secs and 23 mu s minimum latency. A replicated key-value store, MRTOM-KV, also shows significant latency reduction with eBPF/XDP in the Linux Kernel, which is further improved by offloading into SmartNICs.

关键词： programmable network network primitives distributed system offloading

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AI Based Automated Gym Trainer Using Machine Learning 10

AI Based Automated Gym Trainer Using Machine Learning

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10th international conference on Advanced computing and Communication systems, ICACCS 2024

作者： Cholaraja, K. Gururaj, M. Swasthika, V. Gnanasambandam, S.R. Sri Eshwar College of Engineering Department of Computer Science and Business Systems Coimbatore India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384369

The 'Smart Exercise Counter using Computer Vision' is a groundbreaking system that blends cutting-edge computer vision technology with exercise monitoring. In an age where fitness and health are paramount, this innovative solution addresses the limitations of conventional fitness tracking methods by offering real-time, accurate feedback and comprehensive data analysis. By utilizing computer vision algorithms, this system tracks and analyzes the movements of individuals during their exercise routines, enhancing exercise counting, form assessment, and overall workout efficacy. Key components of the system include strategically positioned cameras, real-time image processing, machine learning models for exercise recognition, and biomechanically-informed form analysis. The system not only counts repetitions but also evaluates posture and technique, providing immediate feedback and corrections. Users can access their exercise data and progress through an intuitive mobile app or web dashboard, making it accessible and user-friendly. The applications of this technology extend beyond personal fitness, encompassing healthcare, sports training, and gym facilities. Healthcare professionals can employ it for rehabilitation and therapy, while athletes and coaches can refine training regimens. Gyms can enhance member experiences by offering advanced exercise monitoring. The 'Smart Exercise Counter using Computer Vision' embodies the future of exercise monitoring, where technology aligns with health and fitness goals, offering a promising path towards more accurate, effective, and enjoyable workouts. This abstract encapsulates the transformative potential of computer vision in shaping the way we approach physical exercise and well-being. © 2024 ieee.

关键词： Training Computer vision Accuracy Tracking Medical treatment Machine learning real-time systems Mobile applications Monitoring Sports

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Open Platform for Rapid Prototyping of Smart Environment applications 18

Open Platform for Rapid Prototyping of Smart Environment App...

引用

18th ieee international Wireless Communications and Mobile computing conference (IWCMC)

作者： Malhi, Avleen Apopei, Vlad Bournemouth Univ Poole England Aalto Univ Espoo Finland

ISBN: (纸本)9781665467490

The popularity of home automation has been rapidly increasing in recent years due to higher affordability and simplicity. Today, IoT platforms are vertically oriented and often still closed systems. To enable interoperable interaction with those platforms, we present a home automation system based on open standards enabling IoT peer-to-peer communications. The multiple platforms in multiple IoT applications have been connected using this system. The idea is to enable an open ecosystem by considering standardized communication interfaces to allow real-time data exchanges across platforms and systems. In this work, we perform smart home prototyping and enable device interaction for data sharing and real-time accessibility using any device.

关键词： Internet of things home automation Wireless Sensors AI

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Optimization and Implementation of IoT embedded Software Based on Big Data 3

Optimization and Implementation of IoT Embedded Software Bas...

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3rd international conference on Artificial Intelligence and Computer Information Technology, AICIT 2024

作者： Peng, Yao Wuhan Guanggu Vocational College Wuhan China

ISBN: (纸本)9798350365443

With the rapid growth of Internet of Things (IoT) devices, the explosive increase in data volume poses significant challenges for traditional embedded systems in handling massive data and managing multiple transactions. To address this issue, this project proposes an IoT embedded software optimization and implementation method based on big data technology. The research integrates big data into the IoT architecture to achieve efficient data processing and management. Relevant file systems and drivers were ported into the embedded system to ensure the stability of big data processing and the real-time nature of data handling. Additionally, the study designs a multi-transaction priority management mechanism and a load balancing strategy, effectively enhancing the performance of IoT systems in high-concurrency scenarios. Experimental results show that the optimized embedded system maintains stable data transmission when faced with an increasing number of transactions, demonstrating high stability. The downtime recovery time is reduced to within 0.5 seconds, with no deadlock occurrences. This research provides technical support for the further development of IoT systems. © 2024 ieee.

关键词： Data assimilation

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Hardware Architecture and FPGA Implementation of Low Latency Turbo Encoder for Deep-Space Communication systems 36

Hardware Architecture and FPGA Implementation of Low Latency...

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36th international conference on VLSI Design (VLSID) / 22nd international conference on embedded systems (ES)

作者： Pathak, Meghvern Shresthat, Rahul Indian Inst Thchnol Mandi Sch Comp & Elect Engn Mandi Himachal Prades India

ISBN: (纸本)9798350346787

Venturing deeper into the outer-space has always extended the reaches of our intellect, "the farther we go, the more we know." It has not only fostered our scientific and technological advancements but has also made us love and cherish our cozy abode in this vast universe. The knowledge gained from space missions in the guise of measurement data, is of no use if not accurately communicated back to the Earth. This work, for the first time, proposes a new VLSI architecture of turbo encoder for deep space communication systems that has been designed as per the contemporary standard specifications, recommended by CCSDS. The design has been implemented on FPGA where our turbo encoder, operates at the maximum clock frequency of 27 MHz. In addition, this paper also proposes a novel technique with an implementable architecture for simultaneous puncturing and serialization of the encoded bits, providing a 50% reduction in the overall latency over the conventional puncturing and serialization technique, consequently, aiding to achieve real-time operating capability for the designed turbo encoder system.

关键词： Turbo encoding field programmable gate array (FPGA) puncturing digital VLSI architecture latency serialization

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ArtFL: Exploiting Data Resolution in Federated Learning for Dynamic Runtime Inference via Multi-Scale Training 23

ArtFL: Exploiting Data Resolution in Federated Learning for ...

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23rd ACM/ieee international conference on Information Processing in Sensor Networks, IPSN 2024

作者： Jiang, Siyang Shuai, Xian Xing, Guoliang The Chinese University of Hong Kong Hong Kong SAR Hong Kong

ISBN: (纸本)9798350362015

Federated Learning (FL) has emerged as a prominent paradigm for distributed machine learning, crucial for mission-critical applications such as autonomous driving and smart health. However, existing FL systems have not adequately addressed the dynamic real-time requirements of these applications due to stringent inference deadlines and resource limitations on edge devices. In this paper, we propose ArtFL, a novel federated learning system designed to support dynamic runtime inference through multi-scale training. The key idea of ArtFL is to utilize the data resolution, i.e., frame resolution of videos, as a knob to accommodate dynamic inference latency requirements. Specifically, we initially propose data-utility-based multi-scale training, allowing the trained model to process data of varying resolutions during inference. Subsequently, we introduce an innovative strategy for frame resolution selection in inference, based on the similarity of adjacent frames. Finally, leveraging latency-based dynamic data dropping, we propose a systematic scheme to reduce the overall training time by shortening the waiting time in FL. For evaluation, we build two real-world FL testbeds for smart vehicles and healthcare applications, utilizing a heterogeneous edge platform. Extensive experiments across our testbeds and three public datasets show that ArtFL outperforms state-of-the-art baselines in overall accuracy and system performance up to 36.36% and 47.81%, respectively. A demo video of ArtFL on our smart vehicle testbed is available at https://***/eeK6yRVEG3U, and our code is available at https://***/siyang-jiang/*** CONCEPTS• computing methodologies → Machine learning. © 2024 ieee.

关键词： real time systems

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