Enabling pandemic‐resilient healthcare: Narrowband Internet of Things and edge intelligence for real‐time monitoring

Islam, Md Motaharul; Hasan, Mohammad Kamrul; Islam, Shayla; Balfaqih, Mohammed; Alzahrani, Ahmed Ibrahim; Alalwan, Nasser; Safie, Nurhizam; Bhuiyan, Zaheed Ahmed; Thakkar, Rahul; Ghazal, Taher M.

doi:10.1049/cit2.12314

Cited by 5 publications

(1 citation statement)

References 33 publications

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“…Joint computation enhances system efficiency, scalability, and flexibility, making it useful for applications that require a balance between local processing and centralized resources, such as smart cities, intelligent transportation systems, or distributed surveillance networks. In this study, they introduced an innovative architectural framework to connect smart monitoring robotic devices in healthcare facilities [87]. Their framework consists of three layers: the IoT node layer, the edge layer, and the cloud layer.…”

Section: Edge-cloud Joint Computationmentioning

confidence: 99%

A Survey of Machine Learning in Edge Computing: Techniques, Frameworks, Applications, Issues, and Research Directions

Jouini,

Sethom,

Namoun

et al. 2024

Technologies

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Internet of Things (IoT) devices often operate with limited resources while interacting with users and their environment, generating a wealth of data. Machine learning models interpret such sensor data, enabling accurate predictions and informed decisions. However, the sheer volume of data from billions of devices can overwhelm networks, making traditional cloud data processing inefficient for IoT applications. This paper presents a comprehensive survey of recent advances in models, architectures, hardware, and design requirements for deploying machine learning on low-resource devices at the edge and in cloud networks. Prominent IoT devices tailored to integrate edge intelligence include Raspberry Pi, NVIDIA’s Jetson, Arduino Nano 33 BLE Sense, STM32 Microcontrollers, SparkFun Edge, Google Coral Dev Board, and Beaglebone AI. These devices are boosted with custom AI frameworks, such as TensorFlow Lite, OpenEI, Core ML, Caffe2, and MXNet, to empower ML and DL tasks (e.g., object detection and gesture recognition). Both traditional machine learning (e.g., random forest, logistic regression) and deep learning methods (e.g., ResNet-50, YOLOv4, LSTM) are deployed on devices, distributed edge, and distributed cloud computing. Moreover, we analyzed 1000 recent publications on “ML in IoT” from IEEE Xplore using support vector machine, random forest, and decision tree classifiers to identify emerging topics and application domains. Hot topics included big data, cloud, edge, multimedia, security, privacy, QoS, and activity recognition, while critical domains included industry, healthcare, agriculture, transportation, smart homes and cities, and assisted living. The major challenges hindering the implementation of edge machine learning include encrypting sensitive user data for security and privacy on edge devices, efficiently managing resources of edge nodes through distributed learning architectures, and balancing the energy limitations of edge devices and the energy demands of machine learning.

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Section: Edge-cloud Joint Computationmentioning

confidence: 99%

A Survey of Machine Learning in Edge Computing: Techniques, Frameworks, Applications, Issues, and Research Directions

Jouini,

Sethom,

Namoun

et al. 2024

Technologies

View full text Add to dashboard Cite

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A Smart Coherent Security Model (SCSM) using Intelligent Optimization and Ensemble Deep Learning Mechanisms for Healthcare-IoT Networks

Venkatesan,

Ramakrishnan

2024

2024 Control Instrumentation System Conference (CISCON)

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Towards Improved Threat Mitigation in Digital Environments: A Comprehensive Framework for Cybersecurity Enhancement

Balisane,

Egho-Promise,

Lyada

et al. 2024

Int. J. Res. Granthaalayah

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In today's digital landscape, cybersecurity has become a critical concern due to the increasing sophistication of cyber threats. Traditional cybersecurity measures are often inadequate against evolving attacks, necessitating the development of comprehensive and adaptive threat mitigation frameworks. This study aims to address this gap by proposing a robust cybersecurity framework that integrates advanced technologies such as artificial intelligence (AI), machine learning (ML), and blockchain to enhance threat detection, response, and recovery capabilities. The framework adopts a layered defense mechanism, real-time monitoring, and proactive threat hunting to provide a holistic approach to cybersecurity. By examining current methodologies and identifying their limitations, this research highlights the necessity for enhanced threat mitigation strategies. Through a mixed-methods approach involving online surveys and literature review, the study develops a flexible, scalable, and adaptive framework capable of countering sophisticated cyber threats. Key recommendations include adopting advanced technologies, continuous training, enhancing threat intelligence sharing, implementing a layered defense strategy, and conducting regular security audits. This comprehensive framework aims to improve organizational resilience, ensuring the safety and integrity of digital environments in the face of an ever-evolving cyber threat landscape.

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Enabling pandemic‐resilient healthcare: Narrowband Internet of Things and edge intelligence for real‐time monitoring

Cited by 5 publications

References 33 publications

A Survey of Machine Learning in Edge Computing: Techniques, Frameworks, Applications, Issues, and Research Directions

A Survey of Machine Learning in Edge Computing: Techniques, Frameworks, Applications, Issues, and Research Directions

A Smart Coherent Security Model (SCSM) using Intelligent Optimization and Ensemble Deep Learning Mechanisms for Healthcare-IoT Networks

Towards Improved Threat Mitigation in Digital Environments: A Comprehensive Framework for Cybersecurity Enhancement

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