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

Deep learning (DL) is currently considered one of the most powerful tools for environmental monitoring. Many environmental variables, such as air quality, climate, water, and energy, are monitored using Internet of Things (IoT) technologies. However, the DL-based environmental monitoring systems heavily depend on the cloud, and hence they suffer from latency, high energy consumption, and data privacy. This study proposes DeepEdgeNet, a distributed DL and Machine Learning (ML) framework for environmental monitoring systems based on edge computing and federated learning. In DeepEdgeNet, the features are extracted at the edge devices, and the model updates are aggregated at the central server without sharing the sensitive data. The proposed framework was evaluated on six IoT datasets collected from various environmental monitoring systems, such as air quality monitoring, household energy consumption, satellite-based land-cover classification, climate and extreme weather analysis, water quality assessment, and drought prediction. Experimental results have shown that the proposed system significantly improves the accuracy of the considered datasets, which are 94.5% for the Air Quality dataset, 95.4% for the EuroSAT dataset, and the mean absolute error (MAE) of time-series datasets is reduced up to 0.28 for drought prediction. Moreover, the proposed system has lower inference latency, up to 130 ms, and energy consumption compared with six state-of-the-art models. Although the edge–cloud environment was simulated using a unified experimental platform, the obtained results demonstrate the effectiveness of DeepEdgeNet for scalable and privacy-preserving IoT-based environmental monitoring applications. Hence, the proposed system is efficient and applicable to IoT-based environmental monitoring systems.

@article{Naith2026,
  title     = {DeepEdgeNet: An Edge-Cloud Deep Learning Framework for Efficient Environmental Monitoring in IoT Systems},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {5},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Qamar H. Naith},
  doi       = {10.14569/IJACSA.2026.0170521},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170521}
}