Implementation of a Grey Prediction System Based On Fuzzy Inference for Transmission Power Control in IoT Edge Sensor Nodes

The rapid growth of the Internet of Things has expanded the research and implementation of wireless sensor networks in various application domains. However, the challenges associated with resource-constrained sensor nodes and the need for ultra-low power consumption pose significant problems. One fundamental strategy to address these challenges is transmission power control, which adjusts the transmission power of nodes to optimize network performance and lifetime. While traditional methods have focused on static scenarios, this work presents a novel approach for mobile wireless sensor networks based on a grey-fuzzy-logic transmission power control. The proposed system integrates grey prediction techniques with fuzzy inference to dynamically adapt transmission power levels. Unlike previous simulations, this work focuses on real implementations, considering practical aspects of wireless sensor network deployments and the characteristics of embedded sensor platforms. The objectives of this work are twofold: first, to implement a grey-fuzzy-logic transmission power control on an Internet of Things embedded hardware platform, ensuring compatibility with IEEE 802.15.4 networks, and second, to propose a runtime adaptive link recovery mechanism to enhance the robustness of the adaptive transmission power control in mobile and unstable contexts. Additionally, a multi-hop mobile grey-fuzzy-logic transmission power control strategy is introduced, enabling collaborative transmission power adaptation among sensor nodes. Experimental tests demonstrate the high prediction accuracy of the proposal even in multi-hop scenarios, confirming the system’s scalability. Results also show that the proposed system outperforms other strategies in terms or energy consumption, achieving up to 43 % of gains depending on the scenario.