Energy-Efficient Internet of Things (IoT) Device Communication with Artificial Neural Networks (ANN)
DOI:
https://doi.org/10.70112/ajeat-2026.15.1.4338Keywords:
Artificial Neural Network (ANN), Internet of Things (IoT), Real Time, Energy Efficiency, PowerAbstract
Energy efficiency is the main challenge in the Internet of Things (IoT) paradigm, where devices rely on limited power supplies and operate under fluctuating conditions. Conventional communication protocols such as Wi-Fi, Zigbee, and Bluetooth are not so adaptive and consume excessive energy. An Artificial Neural Network (ANN)-based communication model is proposed in this paper that optimizes device-to-device (D2D) communication by adapting transmission parameters in real time. Implemented on an ESP32 microcontroller with LDR and DHT11 sensors, the system collects environmental data to predict the most energy-efficient communication protocol. A feedforward ANN model was deployed using TensorFlow Lite, and it achieved adaptive protocol switching with an approximate 95% accuracy. The experimented results showed that there was up to 30% of energy saved and also the battery life being extended by over 20% when compared to conventional techniques. The proposed ANN-based approach enhances communication efficiency and sustainability without sacrificing reliable performance on resource-constrained hardware, therefore being suitable for large-scale IoT applications in healthcare, agriculture, and the environment.
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