ENERGY EFFICIENT CLOCK SYNCHRONIZATION IN IOT USING REINFORCEMENT LEARNING
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Date
2024-04-19
Authors
Nadirkhanova, Aizhuldyz
Assylbek, Damir
Journal Title
Journal ISSN
Volume Title
Publisher
Nazarbayev University School of Engineering and Digital Sciences
Abstract
This project addresses the vital challenge of achieving precise clock
synchronization within the Internet of Things (IoT), a foundational element for the
seamless and efficient operation of interconnected devices. Such synchronization is
indispensable for critical IoT functions like coordinated actions, streamlined
communication, and power management. The project introduces a novel approach
leveraging Reinforcement Learning (RL), specifically the
State-Action-Reward-State-Action (SARSA) algorithm. This method equips devices with
the capability to autonomously learn and anticipate the timing of data transmissions,
fostering self-synchronization without manual intervention or pre-programmed
schedules. It's a significant shift from traditional manual adjustments of clock drift,
accommodating the unique timing characteristics of each device's crystal oscillator.
Small testbeds with ESP32 devices using the ESP-NOW protocol have validated the
approach's adaptability to transmission timing variances, maintaining a high success
rate in data receipt. Furthermore, the project continues to work on incorporating
knowledge transfer techniques and Huffman coding to compress trained data,
facilitating rapid convergence to optimal behavior and fostering an environment where
devices benefit from shared learning experiences.
Description
Keywords
Machine Learning, Reinforcement Learning, Internet of Things, Synchronization, Wireless networks, Synchronization, Wireless networks, Type of access: Open Access
Citation
Assylbek, D., Nadirkhanova, A.(2024). Energy efficient Clock Synchronization in IoT Using Reinforcement Learning. Nazarbayev University School of Engineering and Digital Sciences