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Bringing intelligence to Edge/Fog in Internet of Things‐based healthcare applications: Machine learning/deep learning‐based use cases

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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A Machine Learning Framework for Edge Computing to Improve Prediction Accuracy in Mobile Health Monitoring

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Telesurgery Robot Based on 5G Tactile Internet

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An E-health system for monitoring elderly health based on Internet of Things and Fog computing

inferredcites →https://doi.org/10.1016/c2019-0-00113-6openalex-snapshot

Intelligent Data Security Solutions for e-Health Applications

inferredcites →https://doi.org/10.1016/j.comnet.2017.05.018openalex-snapshot

An architecture and protocol for smart continuous eHealth monitoring using 5G

inferredcites →https://doi.org/10.1016/j.compeleceng.2018.02.047openalex-snapshot

Towards energy-aware fog-enabled cloud of things for healthcare

inferredcites →https://doi.org/10.1016/j.compeleceng.2018.07.041openalex-snapshot

Averaged one-dependence estimators on edge devices for smart pregnancy data analysis

inferredcites →https://doi.org/10.1016/j.dcan.2018.10.008openalex-snapshot

Enabling intelligence in fog computing to achieve energy and latency reduction

inferredcites →https://doi.org/10.1016/j.future.2017.04.036openalex-snapshot

Towards fog-driven IoT eHealth: Promises and challenges of IoT in medicine and healthcare

inferredcites →https://doi.org/10.1016/j.future.2019.02.055openalex-snapshot

Performance evaluation of a Fog-assisted IoT solution for e-Health applications

inferredcites →https://doi.org/10.1016/j.ijmedinf.2016.09.014openalex-snapshot

A machine learning-based framework to identify type 2 diabetes through electronic health records

inferredcites →https://doi.org/10.1016/j.ins.2020.05.070openalex-snapshot

Fall detection in older adults with mobile IoT devices and machine learning in the cloud and on the edge

inferredcites →https://doi.org/10.1016/j.jksuci.2021.11.019openalex-snapshot

A review on TinyML: State-of-the-art and prospects

inferredcites →https://doi.org/10.1016/j.micpro.2019.102938openalex-snapshot

Towards collaborative intelligent IoT eHealth: From device to fog, and cloud

inferredcites →https://doi.org/10.1016/j.patrec.2020.05.016openalex-snapshot

Trends in IoT based solutions for health care: Moving AI to the edge

inferredcites →https://doi.org/10.1056/nejmp1006602openalex-snapshot

New Technology and Health Care Costs — The Case of Robot-Assisted Surgery

inferredcites →https://doi.org/10.1109/access.2020.2997831openalex-snapshot

Edge-Cloud Computing and Artificial Intelligence in Internet of Medical Things: Architecture, Technology and Application

inferredcites →https://doi.org/10.1109/acssc.2015.7421170openalex-snapshot

Implementation of Fog computing for reliable E-health applications

inferredcites →https://doi.org/10.1109/camad50429.2020.9209293openalex-snapshot

On the relief of Phantom Limp Pain using Augmented Reality and Edge Computing

inferredcites →https://doi.org/10.1109/ccece.2019.8861806openalex-snapshot

Deep Learning: Edge-Cloud Data Analytics for IoT

inferredcites →https://doi.org/10.1109/comst.2018.2849509openalex-snapshot

Survey on Multi-Access Edge Computing for Internet of Things Realization

inferredcites →https://doi.org/10.1109/comst.2020.2970550openalex-snapshot

Convergence of Edge Computing and Deep Learning: A Comprehensive Survey

inferredcites →https://doi.org/10.1109/cvpr.2018.00943openalex-snapshot

TieNet: Text-Image Embedding Network for Common Thorax Disease Classification and Reporting in Chest X-Rays

inferredcites →https://doi.org/10.1109/fmec.2019.8795362openalex-snapshot

Machine Learning on Mobile: An On-device Inference App for Skin Cancer Detection

inferredcites →https://doi.org/10.1109/giots.2017.8016213openalex-snapshot

Comparison of edge computing implementations: Fog computing, cloudlet and mobile edge computing

inferredcites →https://doi.org/10.1109/icce46568.2020.9043116openalex-snapshot

ECG Beat Classification on Edge Device

inferredcites →https://doi.org/10.1109/icip.2018.8451379openalex-snapshot

Brain Tumor Type Classification via Capsule Networks

inferredcites →https://doi.org/10.1109/iiswc.2016.7581275openalex-snapshot

Fathom: reference workloads for modern deep learning methods

inferredcites →https://doi.org/10.1109/jiot.2019.2918405openalex-snapshot

A Cloudlet Platform With Virtual Sensors for Smart Edge Computing

inferredcites →https://doi.org/10.1109/jproc.2019.2918951openalex-snapshot

Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing

inferredcites →https://doi.org/10.1109/jssc.2013.2253226openalex-snapshot

A Low-Power Processor With Configurable Embedded Machine-Learning Accelerators for High-Order and Adaptive Analysis of Medical-Sensor Signals

inferredcites →https://doi.org/10.1109/ojcoms.2024.3386872openalex-snapshot

Explainable AI for 6G Use Cases: Technical Aspects and Research Challenges

inferredcites →https://doi.org/10.1109/rbme.2020.3013489openalex-snapshot

Secure and Robust Machine Learning for Healthcare: A Survey

inferredcites →https://doi.org/10.1109/sera.2016.7516127openalex-snapshot

Efficient genetic K-Means clustering for health care knowledge discovery

inferredcites →https://doi.org/10.1109/tbc.2019.2901402openalex-snapshot

Enable Advanced QoS-Aware Network Slicing in 5G Networks for Slice-Based Media Use Cases

inferredcites →https://doi.org/10.1109/tcbb.2016.2520933openalex-snapshot

Emerging Security Mechanisms for Medical Cyber Physical Systems

inferredcites →https://doi.org/10.1109/waina.2016.52openalex-snapshot

Cloudlet Softwarization for Pervasive Healthcare

inferredcites →https://doi.org/10.1109/wf-iot.2018.8355116openalex-snapshot

Extending the battery lifetime of wearable sensors with embedded machine learning

inferredcites →https://doi.org/10.1145/3278576.3278597openalex-snapshot

Empowering healthcare IoT systems with hierarchical edge-based deep learning

inferredcites →https://doi.org/10.1186/s13638-019-1498-xopenalex-snapshot

Q-learning-enabled channel access in next-generation dense wireless networks for IoT-based eHealth systems

inferredcites →https://doi.org/10.1609/aaai.v32i1.11902openalex-snapshot

Context-Aware Symptom Checking for Disease Diagnosis Using Hierarchical Reinforcement Learning

inferredcites →https://doi.org/10.3389/fncom.2019.00031openalex-snapshot

The Application of Unsupervised Clustering Methods to Alzheimer’s Disease

inferredcites →https://doi.org/10.3390/app10248934openalex-snapshot

Efficient Learning of Healthcare Data from IoT Devices by Edge Convolution Neural Networks

inferredcites →https://doi.org/10.3390/electronics11010139openalex-snapshot

Low-Power On-Chip Implementation of Enhanced SVM Algorithm for Sensors Fusion-Based Activity Classification in Lightweighted Edge Devices

inferredcites →https://doi.org/10.3390/s20092533openalex-snapshot

Edge Machine Learning for AI-Enabled IoT Devices: A Review

inferredcites →https://doi.org/10.3390/s21155218openalex-snapshot

LPWAN and Embedded Machine Learning as Enablers for the Next Generation of Wearable Devices

inferredcites →https://doi.org/10.4108/eai.7-9-2017.153063openalex-snapshot

Designing Wearable Sensing Platforms for Healthcare in a Residential Environment

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Mass personalization product service system (MP-PSS) driven by industrial intelligence: transformation, implementation, and application

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inferred← citeshttps://doi.org/10.1007/s40031-024-01060-9openalex-snapshot

Sybil Attack Detection for Secure IoT-Based Smart Healthcare Environments

inferred← citeshttps://doi.org/10.1007/s42979-024-03381-4openalex-snapshot

Neural Networks Implementations on FPGA for Biomedical Applications: A Review

inferred← citeshttps://doi.org/10.1109/gcwkshps68340.2025.11591090openalex-snapshot

Multi-layered Defense Strategy for Privacy-Aware Federated Learning on Distributed Greenhouse Data

inferred← citeshttps://doi.org/10.1109/iccad66269.2025.11240961openalex-snapshot

Invited Paper: Mindful AI for Pervasive Health and Wellbeing (PHW)

inferred← citeshttps://doi.org/10.1109/icidea59866.2023.10295269openalex-snapshot

An intelligent optimization framework to predict the vulnerable range of tumor cells using Internet of things

inferred← citeshttps://doi.org/10.1109/icmlas64557.2025.10968462openalex-snapshot

AI-Driven Patient Monitoring in Smart Hospitals: A Survey on Edge Computing Integration and Applications

inferred← citeshttps://doi.org/10.14569/ijacsa.2023.01409104openalex-snapshot

Machine Learning for Smart Cities: A Comprehensive Review of Applications and Opportunities

inferred← citeshttps://doi.org/10.21203/rs.3.rs-8550340/v1openalex-snapshot

Adaptive Hardware/Software Co-Design for Medical Image Processing: From Systematic Review to Adaptive Framework Development

inferred← citeshttps://doi.org/10.31893/multirev.2025ss0122openalex-snapshot

A detailed review of cloud security models for iot environments: Challenges, solutions, and innovative

inferred← citeshttps://doi.org/10.3390/s24247965openalex-snapshot

Improvement of an Edge-IoT Architecture Driven by Artificial Intelligence for Smart-Health Chronic Disease Management

inferred← citeshttps://doi.org/10.3934/math.2024002openalex-snapshot

Inverse chi-square-based flamingo search optimization with machine learning-based security solution for Internet of Things edge devices

inferred← citeshttps://doi.org/10.47392/irjash.2023.042openalex-snapshot

IoT- Edge Deep Learning EHealth Monitoring System

inferred← citeshttps://doi.org/10.7717/peerj-cs.1778openalex-snapshot

A comprehensive secure system enabling healthcare 5.0 using federated learning, intrusion detection and blockchain

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