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International Journal of Creative and Open Research in Engineering and Management

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ISSN: 3108-1754 (Online)
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Volume 02, Issue 7

Published on: July 2026

A REVIEW ON EDGE INTELLIGENCE FOR LOW POWER IOT PLATFORMS

Naik Joshna

Department of ECE

Brindavan College of Engineering Bangalore,India
DOI:https://doi.org/10.55041/ijcope.v2i7.096

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Plagiarism Passed Peer Reviewed Open Access

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Abstract

Edge Machine Learning is becoming essential as billions of IoT devices demand fast, low latency and privacy aware processing. Cloud only computation creates delays, bandwidth issues and security risks, making on device intelligence necessary. This paper reviews lightweight ML models and compression techniques such as Mobile Nets, Squeeze Net, Bonsai, ProtoNN, pruning and quantization that enable AI on low power microcontrollers. It also compares edge architectures including on device inference, edge cloud collaboration and joint computation frameworks like Neurosurgeon and Deep Things. Key wireless technologies such as BLE, ZigBee, LoRa, NB IoT and 5G and privacy methods such as differential privacy are also discussed. A case study using a CNN on an STM32 microcontroller demonstrates practical feasibility. The review highlights future research directions toward efficient, secure and scalable AI enabled IoT systems.

Keywords-Edge Machine Learning, Internet of Things, Embedded  AI,  Lightweight  ML  Models Model Compression.

How to Cite this Paper

Joshna, N. (2026). A Review on Edge Intelligence for Low Power IoT Platforms. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(7). https://doi.org/10.55041/ijcope.v2i7.096

Joshna, Naik. "A Review on Edge Intelligence for Low Power IoT Platforms." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 7, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i7.096.

Joshna, Naik. "A Review on Edge Intelligence for Low Power IoT Platforms." International Journal of Creative and Open Research in Engineering and Management 02, no. 7 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i7.096.

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  • •Published on: Jul 10 2026
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