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检索条件"任意字段=Conference on Photonic Devices and Algorithms for Computing V"

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Leveraging Adaptive Intelligence in Edge computing for Efficient Data Processing and Task Management 2

Leveraging Adaptive Intelligence in Edge Computing for Effic...

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2nd IEEE International conference on Emerging Research in Computational Science, ICERCS 2024

作者： Isaac, Lydia D Mohanraj, v. Soms, Nisha Sona College of Technology Department of Information Technology Salem India

ISBN: (纸本)9798331534950

The swift progress of Internet of Things (IoT) devices and latency-sensitive submissions has driven the need for efficient data processing and task management in edge computing environments. Traditional edge computing frameworks often face challenges in optimizing resource utilization and handling dynamic workloads. The integration of adaptive intelligence in edge computing has arisen as a vital solution for augmenting data processing and task management efficiency in modern computing environments. This research introduces a novel algorithm, Edge Reinforcement Learning with Context-Aware Tasking (EDREL-CAT), designed to optimize task allocation and resource management at the edge. By utilizing reinforcement learning techniques coupled with context-aware algorithms, the proposed method aims to significantly improve task scheduling and processing efficiency while addressing the constraints inherent in edge computing. An all-inclusive simulation analysis is steered to evaluate the performance of EDREL-CAT against existing algorithms, including traditional scheduling methods and state-of-the-art approaches. The analysis employs various metrics, including task completion time, system throughput, task queue length and fairness index to provide a thorough comparison. The results demonstrate that EDREL-CAT outperforms existing solutions, confirming its potential as an effective tool for enhancing edge computing capabilities. © 2024 IEEE.

关键词： Reinforcement learning

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FazyRv: Closing the Gap between 32-Bit and Bit-Serial RISC-v Cores with a Scalable Implementation 21

FazyRV: Closing the Gap between 32-Bit and Bit-Serial RISC-V...

引用

21st ACM International conference on computing Frontiers (CF)

作者： Kissich, Meinhard Baunach, Marcel Graz Univ Technol Graz Austria

ISBN: (纸本)9798400705977

RISC-v processor cores with a 32-bit internal data path reach a boundary on their minimal size, requiring novel concepts to decrease silicon area and the cost of Internet of Things (IoT) devices. We propose a minimal-area open-source Rv32I RISC-v core targeting the IoT and low-workload applications. Unlike cores with a similarly small area, FazyRv is inherently scalable to a data path width of 1, 2, 4, or 8 bits. FazyRv has manifold variants to achieve the smallest footprint at given performance requirements. This paper provides insight into FazyRv, its verification, and the resource utilization for five Field-Programmable Gate Array (FPGA) architectures. We also compare its performance with similar cores using the Embench benchmark suite. Based on the findings, we analyze and discuss optimization potentials in depth. Although FazyRv is implemented at the register transfer level, we achieve comparable results to hand-optimized cores at the gate level. In an exemplary IoT application, the whole system on chip is implemented in 77 Slices, or 645 Logic Cells for a Xilinx/AMD 7-Series or an iCE40 FPGA, respectively.

关键词： RISC-v Embedded Systems Internet of Things Processor Architecture Digital System Design

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Text Summarization for Light Weight devices

Text Summarization for Light Weight Devices

引用

2025 IEEE International Students' conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Akshatha, K. Praveen, M. Taleki, Shravan Rakshith Yadav, K v Nataraj Revathi, T. Alliance University Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331529833

In this paper, we propose a text summarization method for low-compute devices with limited hardware capabilities like smartphones, tablets and IOT devices. Traditional methods for text summarization face significant challenges in the settings where these devices are used, given their limited computational power and memory. We present optimization algorithms that target reducing computation and memory effort whilst retaining quality in the summarization. Our solution focuses on lightweight, efficient methods to runtime-compress the summaries generated by a powerful but heavy model -in order to allow them to be computed with high mobility and little energy consumption by small devices- for example through techniques of model compression, quantization or similarity detection in datasets- thus achieving tractable abstraction of useful information from digital content [2], enhancing their accessibility experience while taking advantage of relevant mobile behaviors. Our approach enables efficient summarization with high performance and minimal resource costs, as shown by the corresponding experimental results. © 2025 IEEE.

关键词： Edge computing Lightweight devices Model Compression Natural Language Processing Quantization Text Summarization

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Atrial Fibrillation Classification: A Hybrid Approach Integrating Support vector Machines and Feature Selection for Reduced Computational Complexity 3

Atrial Fibrillation Classification: A Hybrid Approach Integr...

引用

3rd International conference on Applied Artificial Intelligence and computing, ICAAIC 2024

作者： Dsouza, vanditha Keerthana, K.v. Francis Densil Raj, v. St. Joseph's University Dept. of Advanced Computing Bangalore India

ISBN: (纸本)9798350375190

Atrial Fibrillation (AFib) is an important cardiac arrhythmia marked by an irregular and frequently accelerated heartbeat which increases the risk of stroke and other cardiovascular complications. Detecting AFib promptly allows for timely intervention along with the treatments like Cardioversion therapy, medications and Surgery or catheter procedures might help mitigate these risks. Wearable devices and mobile health applications make use of single-lead ECG, allowing for immediate identification and response but most of the devices are based on high computation algorithms leading to a rise in the overall cost of devices. The proposed methodology automatically classifies ECG signals into AFib and Normal using Machine Learning algorithm. To increase the diversity in the dataset, this study has considered 5822 ECG signals from two prominent databases, steps like feature engineering based on variable Clustering were used to handle multicollinearity. Different Machine Learning (ML) models were assessed, encompassing Random Forest, Logistic Regression, SvM, and Adaboost. Among these, SvM emerged as the preferred choice due to its outstanding performance, achieving a F1 score of 0.95. The reliability of this evaluation was confirmed through a 10-fold cross validation method and hypertuning the parameters using the Grid Search method. This study can bridge the gap in Atrial Fibrillation diagnosis and potentially revolutionize healthcare by offering trustworthy and accessible diagnostic procedures. © 2024 IEEE.

关键词： Electrocardiograms

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Enhancing IoT Security: A Novel Attack Detection Model for IoT Logs with Machine Learning Techniques 10

Enhancing IoT Security: A Novel Attack Detection Model for I...

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10th International conference on Smart computing and Communication, ICSCC 2024

作者： Jacob, Anu v Issac, Elizabeth Krishna, Athira Mar Athanasius college of Engineering Department of Computer Science Kerala Kothamangalam India

ISBN: (纸本)9798350363104

The Internet of Things (IoT), has completely transformed the way that we interact with the world around us, by interconnecting everyday objects and devices through a common network. An enormous quantity of data is generated by this massive network of IoT devices, that delivers informative data to businesses and organizations. IoT log analysis is an essential procedure that includes gathering, processing, keeping track of, and analyzing logs produced by IoT devices. Important details regarding user activity, system performance, operational status, and possible attacks or problems are all contained in these logs. Through close examination of these logs, people and organizations can strengthen overall security, detect threats, and improve device health. This research suggests an ensemble model to identify different types of attacks in IoT log data by combining Random Forest and Auto encoder algorithms. This system surpasses previous systems in addition to enhancing system performance and achieves an accuracy of 90.6% compared to the other models Recall, accuracy, precision, fI1 score, and other known measures are used in the performance evaluation process. © 2024 IEEE.

关键词： Network intrusion

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Empowering Dairy Farmers IoT Milk Quality Monitoring devices 3

Empowering Dairy Farmers IoT Milk Quality Monitoring Devices

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International conference on Advances in computing, Communication and Applied Informatics (ACCAI)

作者： Manikandababu, C. S. Prakash, Surya P. vaishnavi, v. Siddharth, S. Sri Ramakrishna Engn Coll Dept ECE Coimbatore Tamil Nadu India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

Dairy farming faces challenges with traditional manual milk quality monitoring systems since it's time-consuming and prone to errors. In response, the study presents a novel IoT-based milk quality monitoring system. In real-time, the device uses strategically positioned sensors to monitor essential characteristics like fat content, acidity levels, and microbial contamination. With wireless data transfer and central processing, the system provides timely feedback and alarms, allowing proactive intervention to maintain quality requirements. Machine learning (ML) algorithms improve the system's ability to detect quality abnormalities, resulting in accurate and fast alerts for dairy producers. Compared to manual systems, the results show significant improvements in efficiency and specific quality parameters, including higher accuracy (95%), precision (92%), sensitivity (94%), specificity (96%), lower contamination levels (10 CFU/ml), and improved fat content (3.8%) and acidity (0.15%) measurements. The IoT-based method provides dairy producers a cost-effective, scalable, and dependable solution for optimizing milk production operations and preserving product integrity.

关键词： Dairy farming Milk quality monitoring IoT devices Real-time monitoring Efficiency Cost-effectiveness Decision-making Quality control Industry standards

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IoT-Based Smart Home Healthcare Monitoring System Using Machine Learning algorithms 3

IoT-Based Smart Home Healthcare Monitoring System Using Mach...

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International conference on Advances in computing, Communication and Applied Informatics (ACCAI)

作者： Jamalpur, Bhavana Saseendran, Arun K. vani, v. Divya Raj, vijilius Helena veeramalai, G. GinniNijhawan SR Univ Sch CS & AI Warangal Telangana India Sree Narayana Arts & Sci Coll Dept Econ Kottayam Kerala India Inst Aeronaut Engn Hyderabad India New Horizon Coll Engn Dept Appl Sci Bangalore Karnataka India M Kumarasamy Coll Engn Dept Math Karur India Lovely Profess Univ Phagwara India

ISBN: (纸本)9798350389432;9798350389449;9798350389456

The purpose of this study is to demonstrate an Internet of Things (IoT)-based smart home healthcare monitoring system that is run by machine literacy algorithms. An opportunity to update healthcare monitoring has arisen as a result of the spread of Internet of Things (IoT) devices. This is especially true when it comes to monitoring patients within the comfort of their own homes. To gather real-time data on vital signs and the conditioning of diurnal life from cases, our proposed system makes use of Internet of Things detectors. In addition, machine learning algorithms are utilized to analyze this data, which enables the implementation of proven and forward-thinking healthcare monitoring. The system can adapt to the activities of individual cases by continuously learning, which allows it to identify anomalies and provide predictions about implicit health problems. The incorporation of machine literacy not only improves the precision of health monitoring but also makes it easier to provide early intervention and preventative care, which ultimately leads to the resolution of patient difficulties and a reduction in the expenses of healthcare. This investigation makes a crucial contribution to the rapidly developing field of Internet of Things (IoT)-)-enabled healthcare systems, illustrating the potential for technology to transform conventional approaches to the delivery of healthcare.

关键词： IoT Smart Home Healthcare Monitoring Machine Learning

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DRL-Based Optimization Algorithm for Wireless Powered IoT Network 1

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23rd International conference on algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Zhu, Mingjie Zhang, Shubin Chi, Kaikai Zhejiang Univ Technol Hangzhou Peoples R China

ISBN: (数字)9789819708086

ISBN: (纸本)9789819708079;9789819708086

The extensive applications of Internet of Things (IoT) bring development and prosperity to multiple industries and greatly influent all aspects of human lives, which rely on the timely computation and communication. However, the limited battery and computing capacity of the common IoT units cannot satisfy the need of processing computation intensive and time sensitive tasks. As a result, the combination of wireless power transmission (WPT) and mobile edge computing (MEC) provides a promising approach of dealing with aforementioned problems. The Hybrid Access Point (HAP) transmits radio frequency (RF) energy to provide power transmission for wireless devices (WDs), and processes tasks offloaded from WDs with the equipped edge computing servers (ECSs). In this paper, we consider a wireless powered mobile edge computing network containing an HAP, multiple WDs. By jointly optimizing energy transmission time duration, partial offloading decisions and transmission power allocation strategy, we obtain the maximum of the sum computation rate (SCR). Firstly, we formulate this as a non-convex problem which is hard to address. Secondly, we decompose the problem into a top-problem of optimizing the energy transmission time duration and a sub-problem of optimizing the partial offloading decisions and transmission power allocation strategy. Finally, we design a DRL-based offloading algorithm, which applies a DNN network with exploring and updating strategy, to address the top-problem and propose an effective optimizing algorithm to address the sub-problem. Extensive numerical results reveal that the proposed algorithm reaches near-optimal SCR and greatly reduces time latency and complexity compared to benchmark algorithms.

关键词： Mobile-edge computing wireless power transfer reinforcement learning partial offloading resource allocation

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Scalable Federated Learning for Fingerprint Recognition Algorithm 22

Scalable Federated Learning for Fingerprint Recognition Algo...

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IEEE 22nd International conference on Trust, Security and Privacy in computing and Communications (TrustCom) / BigDataSE conference / CSE conference / EUC conference / ISCI conference

作者： Wang, Chenzhuo Lu, Yanrong vasilakos, Athanasios v. Civil Aviat Univ China Sch Comp Sci & Technol Tianjin Peoples R China Civil Aviat Univ China Sch Safety Sci & Engn Tianjin Peoples R China Univ Agder Ctr AI Res Grimstad Norway

ISBN: (纸本)9798350381993;9798350382006

Fingerprint recognition technology is widely used in various terminal devices and serves as a powerful and effective method for authentication. Existing research relies on centralized training models based on datasets, overlooking the privacy and heterogeneity of the data itself, resulting in the leakage of user information and decreased recognition accuracy. In order to solve the problem of data security and privacy protection, this paper proposes a federated learning-based architecture called FedFR(Federated Learning-Fingerprint Recognition). The parameters from each endpoint are iteratively aggregated through federated learning to improve the performance of the global model under privacy constraints. Moreover, to solve the client-side unfairness issue in traditional federated learning caused by randomly selecting aggregation weights, a client selection method based on reservoir sampling is proposed, increasing the diversity of data distribution. Using the real-world databses, the effectiveness of FedFR is compared and analyzed through simulation experiments. The results show that FedFR exhibits good performance in terms of privacy protection levels, evaluation accuracy, and scalability. Distinct from traditional fingerprint recognition algorithms, FedFR improves the security and scalability of the model from the data source, providing a reference for the application of federated learning in biometric technology.

关键词： fingerprint recognition federated learning sparse representation reservoir sampling privacy protection

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FastSR-NeRF: Improving NeRF Efficiency on Consumer devices with A Simple Super-Resolution Pipeline

FastSR-NeRF: Improving NeRF Efficiency on Consumer Devices w...

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IEEE/CvF Winter conference on Applications of Computer vision (WACv)

作者： Lin, Chien-Yu Fu, Qichen Merth, Thomas Yang, Karren Ranjan, Anurag Univ Washington Seattle WA 98195 USA Apple Inc Cupertino CA USA

ISBN: (纸本)9798350318920;9798350318937

Super-resolution (SR) techniques have recently been proposed to upscale the outputs of neural radiance fields (NeRF) and generate high-quality images with enhanced inference speeds. However, existing NeRF+SR methods increase training overhead by using extra input features, loss functions, and/or expensive training procedures such as knowledge distillation. In this paper, we aim to leverage SR for efficiency gains without costly training or architectural changes. Specifically, we build a simple NeRF+SR pipeline that directly combines existing modules, and we propose a lightweight augmentation technique, random patch sampling, for training. Compared to existing NeRF+SR methods, our pipeline mitigates the SR computing overhead and can be trained up to 23x faster, making it feasible to run on consumer devices such as the Apple MacBook. Experiments show our pipeline can upscale NeRF outputs by 2-4x while maintaining high quality, increasing inference speeds by up to 18x on an NvIDIA v100 GPU and 12.8x on an M1 Pro chip. We conclude that SR can be a simple but effective technique for improving the efficiency of NeRF models for consumer devices.

关键词： 3D computer vision algorithms Applications Applications Smartphones / end user devices virtual / augmented reality

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