Optimizing data processing for edge-enabled IoT devices using deep learning based heterogeneous data clustering approach

Abstract

- Edge-based Internet of Things devices have transformed smart farming, aiding in efficient data collection and processing for optimal resource utilization and crop yields. However, task scheduling and resource allocation pose significant challenges due to the dynamic nature of agricultural environments. Our research introduces a novel framework that integrates deep reinforcement learning algorithm into an edge-enabled wireless sensor network for multi-objective optimization of the functionality of the Deep Q-Networks (DQNs). This framework extends the traditional Q-learning method to manage large state-action spaces efficiently. It employs a deep neural network to approximate the Q-value function, rather than relying on a Q-table, making it more capable of handling complex problems with high-dimensional state spaces. It forms heterogenous data clusters supports an optimal task scheduling and resource allocation policies, sustains key objectives such as minimal energy consumption, latency, efficient resource utilization, and reduced task completion time. The framework's performance is evaluated in a simulated environment mimicking real-world smart farming applications. Results confirm its superiority in enhancing performance metrics and lowering energy consumption, as opposed to traditional networks.