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 (IoT) has expanded the research and implementation of wireless sensor networks (WSNs) in various application domains. However, the challenges associated with resource-constrained sensor nodes and the need for ultralow power consumption pose significant problems. One fundamental strategy to address these challenges is transmission power control (TPC), 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 WSNs based on a grey-fuzzy-logic TPC (Grey-FTPC). 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 WSN deployments and the characteristics of embedded sensor platforms. The objectives of this work are twofold: 1) to implement a Grey-FTPC on an IoT embedded hardware platform, ensuring compatibility with IEEE 802.15.4 networks and 2) to propose a runtime adaptive link recovery mechanism to enhance the robustness of the adaptive TPC in mobile and unstable contexts. Additionally, a multihop mobile Grey-FTPC strategy is introduced, enabling collaborative transmission power adaptation among sensor nodes. Experimental tests demonstrate the high-prediction accuracy of the proposal even in multihop 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.