HealthFaaS: AI-Based Smart Healthcare System for Heart Patients Using Serverless Computing

IEEE Internet of Things Journal, Год журнала: 2023, Номер 10(21), С. 18469 - 18476

Опубликована: Май 18, 2023

Heart disease is one of the leading causes death worldwide, and with early detection, mortality rates can be reduced. Well-known studies have shown that latest artificial intelligence (AI) used to determine risk heart disease. However, existing did not consider dynamic scalability get best performance from these AI models in case an increasing number users. To solve this problem, we proposed AI-powered smart healthcare framework called HealthFaaS, using Internet Things (IoT) a Serverless Computing environment reduce disease-related deaths prevent financial losses by reducing misdiagnoses. HealthFaaS collects health data users via IoT devices sends it deployed on Google Cloud Platform (GCP)-based serverless computing due its advantages, such as scalability, less operational complexity, pay-as-you-go pricing model. The five different for detection evaluated compared based key parameters, accuracy, precision, recall, $F$ -Score, AUC. Experimental results demonstrate light gradient boosting machine model gives highest success detecting diseases accuracy rate 91.80%. Further, tested terms Quality-of-Service (QoS) throughput latency against non-serverless platform. In addition, also cold start platform which determined amount memory software language makes direct impact latency.

Язык: Английский

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Edge AI for Internet of Energy: Challenges and perspectives

Internet of Things, Год журнала: 2023, Номер 25, С. 101035 - 101035

Опубликована: Дек. 15, 2023

Язык: Английский

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A Comprehensive Survey on Artificial Intelligence Empowered Edge Computing on Consumer Electronics

IEEE Transactions on Consumer Electronics, Год журнала: 2023, Номер 69(4), С. 1023 - 1034

Опубликована: Сен. 21, 2023

The Internet revolution and Moore's Law drove the rapid expansion of connected consumer electronics. As massive data is generated by Things (IoT) devices, edge computing has been developed applied in electronics to provide agile real-time services. In this paper, we an overview artificial intelligence (AI)-empowered We start with background outline key characteristics challenges, which are used evaluate state research smart homes, buildings, healthcare, intelligent diagnosis. evaluation shows that AI-empowered for mainly focused on latency, robustness, reliability over past 5 years. However, explainability load balancing mentioned less have great potential future research. Although a thriving widely area, there still many unsolved problems challenges researchers can address.

Язык: Английский

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RME-GAN: A Learning Framework for Radio Map Estimation Based on Conditional Generative Adversarial Network

IEEE Internet of Things Journal, Год журнала: 2023, Номер 10(20), С. 18016 - 18027

Опубликована: Май 19, 2023

Outdoor radio coverage map estimation is an important tool for network planning and resource management in modern Internet of Things (IoT) cellular systems. A spatially describes signal strength distribution provides information. practical problem to estimate fine-resolution maps from sparse measurements. However, nonuniformly positioned measurements access constraints pose challenges accurate (RME) spectrum many outdoor environments. In this work, we develop a two-phase learning framework RME by integrating well-known propagation model designing conditional generative adversarial (cGAN). We first explore global information extract patterns. Next, focus on the local features shadowing effect order train optimize cGAN. Our experimental results demonstrate efficacy proposed based models observations scenarios.

Язык: Английский

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LLM-Based Edge Intelligence: A Comprehensive Survey on Architectures, Applications, Security and Trustworthiness

IEEE Open Journal of the Communications Society, Год журнала: 2024, Номер 5, С. 5799 - 5856

Опубликована: Янв. 1, 2024

Язык: Английский

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Edge AI for Internet of Medical Things: A literature review

Computers & Electrical Engineering, Год журнала: 2024, Номер 116, С. 109202 - 109202

Опубликована: Март 25, 2024

Язык: Английский

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Real-Time Fire Detection: Integrating Lightweight Deep Learning Models on Drones with Edge Computing

Drones, Год журнала: 2024, Номер 8(9), С. 483 - 483

Опубликована: Сен. 13, 2024

Fire accidents are life-threatening catastrophes leading to losses of life, financial damage, climate change, and ecological destruction. Promptly efficiently detecting extinguishing fires is essential reduce the loss lives damage. This study uses drone, edge computing, artificial intelligence (AI) techniques, presenting novel methods for real-time fire detection. proposed work utilizes a comprehensive dataset 7187 images advanced deep learning models, e.g., Detection Transformer (DETR), Detectron2, You Only Look Once YOLOv8, Autodistill-based knowledge distillation techniques improve model performance. The approach has been implemented with YOLOv8m (medium) as teacher (base) model. distilled (student) frameworks developed employing YOLOv8n (Nano) DETR techniques. attains best performance 95.21% detection accuracy 0.985 F1 score. A powerful hardware setup, including Raspberry Pi 5 microcontroller, camera module 3, DJI F450 custom-built constructed. deployed in setup identification. achieves 89.23% an approximate frame rate 8 conducted live experiments. Integrating drone devices demonstrates system’s effectiveness potential practical applications hazard mitigation.

Язык: Английский

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A collective AI via lifelong learning and sharing at the edge

Nature Machine Intelligence, Год журнала: 2024, Номер 6(3), С. 251 - 264

Опубликована: Март 22, 2024

Язык: Английский

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Towards a standardized framework for AI-assisted, image-based monitoring of nocturnal insects

Philosophical Transactions of the Royal Society B Biological Sciences, Год журнала: 2024, Номер 379(1904)

Опубликована: Май 5, 2024

Automated sensors have potential to standardize and expand the monitoring of insects across globe. As one most scalable fastest developing sensor technologies, we describe a framework for automated, image-based nocturnal insects—from development field deployment workflows data processing publishing. Sensors comprise light attract insects, camera collecting images computer scheduling, storage processing. Metadata is important sampling schedules that balance capture relevant ecological information against power limitations. Large volumes from automated systems necessitate effective We vision approaches detection, tracking classification including models built existing aggregations labelled insect images. Data account inherent biases. advocate explicitly correct bias in species occurrence or abundance estimates resulting imperfect detection individuals present during occasions. propose ten priorities towards step-change vital task face rapid biodiversity loss global threats. This article part theme issue ‘Towards toolkit monitoring’.

Язык: Английский

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Cold Start Latency in Serverless Computing: A Systematic Review, Taxonomy, and Future Directions

ACM Computing Surveys, Год журнала: 2024, Номер 57(3), С. 1 - 36

Опубликована: Окт. 17, 2024

Recently, academics and the corporate sector have paid attention to serverless computing, which enables dynamic scalability an economic model. In users only pay for time they actually use resources, enabling zero scaling optimise cost resource utilisation. However, this approach also introduces cold start problem. Researchers developed various solutions address problem, yet it remains unresolved research area. article, we propose a systematic literature review on latency in computing. Furthermore, create detailed taxonomy of approaches latency, investigate existing techniques reducing frequency. We classified current studies into several categories such as caching application-level optimisation-based solutions, well Artificial Intelligence/Machine Learning-based solutions. Moreover, analyzed impact quality service, explored mitigation methods, datasets, implementation platforms, them based their common characteristics features. Finally, outline open challenges highlight possible future directions.

Язык: Английский

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