A lightweight received signal strength indicator estimation model for low-power internet of things devices in constrained indoor networks

The Internet of Things (IoT) is a revolutionary advancement that automates daily tasks by interacting between digital and physical realms through a network of mostly Low-Power IoT (LP-IoT) devices. For an IoT ecosystem, reliable wireless connectivity is essential to ensure the optimal operation of LP-IoT devices, especially considering their limited resource capacity. This reliability is often achieved through channel estimation, an essential aspect for optimising signal transmission. Considering the importance of reliable channel estimation for constrained IoT devices, we developed two lightweight yet effective channel estimation models based on Random Forest Regressor (RFR). These two models are namely classified as Feature-based RFR(F) and Sequence-based RFR(S) methods and utilise Received Signal Strength Indicator (RSSI) as a fundamental channel metric to enhance efficiency for the reliability of channel estimation in constrained LP-IoT devices. The models’ performance was assessed by comparing them with the state-of-the-art and our previously developed Artificial Neural Network (ANN)-based method. The experimental results show that the RFR(F) method shows approximately 39.62% improvement in Mean Squared Error (MSE) over the Feature-based ANN(F) model and 37.86% advancement over the state-of-the-art. Similarly, the RFR(S) model shows an improvement in MSE of 24.9% compared to the Sequence-based ANN(S) model and an 80.59% improvement compared to the leading existing methods. We also evaluated the lightweight characteristics of our RFR(F) and RFR(S) methods by deploying them on Raspberry Pi 4 Model B to demonstrate their practicality for LP-IoT devices.