What question did this study set out to answer?

This study aims to address the challenges of latency, energy consumption, and data privacy in environmental monitoring using deep learning and IoT technologies.

June 7, 2026International Journal of Advanced Computer Science and ApplicationsOpen Access

DeepEdgeNet: An Edge-Cloud Deep Learning Framework for Efficient Environmental Monitoring in IoT Systems

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Authors

QNQamar H. NaithUniversity of Jeddah

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Implication

Randomized trial evaluates edge-cloud framework for environmental monitoring in IoT systems, indicating improved efficiency and privacy.

Key Points

This study aims to address the challenges of latency, energy consumption, and data privacy in environmental monitoring using deep learning and IoT technologies.
Developed DeepEdgeNet, a distributed deep learning and machine learning framework based on edge computing and federated learning.
Evaluated the framework on six different IoT datasets related to environmental monitoring.
Conducted experiments to measure accuracy, latency, and energy consumption comparing DeepEdgeNet to existing models.
Achieved 94.5% accuracy for the Air Quality dataset and 95.4% accuracy for the EuroSAT dataset.
Reduced mean absolute error (MAE) to 0.28 for drought prediction time-series datasets.
Demonstrated lower inference latency of up to 130 ms and reduced energy consumption compared to six state-of-the-art models.

Cite This Study

Qamar H. Naith (2026) studied this question.

synapsesocial.com/papers/6a250b0e7def13d035e1b196 https://doi.org/10.14569/ijacsa.2026.0170521

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June 7, 2026International Journal of Advanced Computer Science and ApplicationsOpen Access

DeepEdgeNet: An Edge-Cloud Deep Learning Framework for Efficient Environmental Monitoring in IoT Systems

View Full Paper

Ask AI

Bookmark

Authors

QNQamar H. NaithUniversity of Jeddah

Discussion

Member takes

Implication

Randomized trial evaluates edge-cloud framework for environmental monitoring in IoT systems, indicating improved efficiency and privacy.

Key Points

This study aims to address the challenges of latency, energy consumption, and data privacy in environmental monitoring using deep learning and IoT technologies.
Developed DeepEdgeNet, a distributed deep learning and machine learning framework based on edge computing and federated learning.
Evaluated the framework on six different IoT datasets related to environmental monitoring.
Conducted experiments to measure accuracy, latency, and energy consumption comparing DeepEdgeNet to existing models.
Achieved 94.5% accuracy for the Air Quality dataset and 95.4% accuracy for the EuroSAT dataset.
Reduced mean absolute error (MAE) to 0.28 for drought prediction time-series datasets.
Demonstrated lower inference latency of up to 130 ms and reduced energy consumption compared to six state-of-the-art models.

Cite This Study

Qamar H. Naith (2026) studied this question.

synapsesocial.com/papers/6a250b0e7def13d035e1b196 https://doi.org/10.14569/ijacsa.2026.0170521

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