Smart Edge Devices: Integrating Deep Learning with IoT for Real-Time Electronics Applications

Authors

  • P Jayarekha

Keywords:

Artificial Intelligence, Deep Learning, Edge Computing, Federated Learning, Internet of Things, Machine Learning, Real-Time Processing, Smart Devices, Smart Electronics, TensorFlow Lite, TinyML, Wireless Sensor Networks

Abstract

The integration of deep learning with Internet of Things (IoT) in smart edge devices is revolutionizing real-time electronics applications by enabling enhanced data processing, low-latency decision-making, and improved operational efficiency.  This research explores how deploying deep learning algorithms directly on edge devices—equipped with sensors and connectivity—facilitates the analysis of vast, complex data streams generated in real time from diverse sources. By leveraging advanced AI accelerators, hardware-aware model optimizations, and edge computing architectures, these smart devices can perform inference locally, reducing dependency on cloud infrastructure and minimizing communication latency and bandwidth use. The study further addresses challenges such as resource constraints, energy efficiency, data privacy, and security, proposing adaptive solutions including model compression techniques and trusted execution environments. Use cases such as predictive maintenance in industrial IoT, autonomous control systems, and real-time threat detection demonstrate the practical benefits of this integration. Ultimately, this paper highlights the transformative potential of combining deep learning and IoT at the edge, fostering scalable, responsive, and secure electronics systems that meet the stringent requirements of contemporary real-time applications. This work lays a foundation for advancing AI-enabled IoT deployments across multiple sectors

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References

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Published

2025-05-19

How to Cite

1.
P Jayarekha PJ. Smart Edge Devices: Integrating Deep Learning with IoT for Real-Time Electronics Applications. J Neonatal Surg [Internet]. 2025May19 [cited 2025Sep.11];14(25S):232-7. Available from: https://www.jneonatalsurg.com/index.php/jns/article/view/6096

Issue

Vol. 14 No. 25S (2025): Journal of Neonatal Surgery

Section

Original Article

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