## 1 Introduction  
The Internet of Things (IoT) technology has revolutionised regular living activities into automated operations by facilitating the interconnectedness between the physical and digital world [1]. In such interconnected environments, reliable communication is crucial, particularly for constrained Low-Power IoT (LP-IoT) devices. Channel estimation is performed to ensure reliable wireless communication by optimising signal strength and enhancing the efficiency of such devices in an interconnected ecosystem. The primary metric for wireless channel estimation in LP-IoT networks is the Received Signal Strength Indicator (RSSI). To recognise the pattern of the RSSI, researchers have conducted numerous studies using RSSI and enhanced the accuracy of signal transmission for such LP-IoT networks [2; 3; 4]. Recent research has increasingly recognised Machine Learning (ML) as a potential method for enhancing channel estimation accuracy [5]. The studies in [6; 7] have demonstrated the capability of ML in predicting channel characteristics. A further study in [8] illustrated the effectiveness of Supervised ML (SML) in RSSI-based indoor localisation for IoT applications, underscoring the benefits of ML over traditional estimation methods. Deep Learning (DL), as an advanced ML paradigm, has also emerged as a noteworthy research area of exploration in wireless channel estimation [9]. The DL-based methods utilise Artificial Neural Networks (ANNs) for their model development [10]. A notable contribution, presenting the theoretical significance of DL for single-input multiple-output (SIMO) systems, can be seen in [11]. This method reduced the Minimum Mean-Squared Error (MMSE) in different settings, even without pre-existing channel data. This study also highlights DL's potential to outperform traditional methods. In the realm of indoor positioning, the study in [12] developed an accurate localisation model utilising Recurrent Neural Networks (RNNs), particularly Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) variations for WiFi fingerprinting. This study affirmed the enhanced localisation accuracy of RNN models over traditional methods. While DL-based solutions have provided promising results in IoT channel estimation [13], they also pose challenges related to high computational demands. Thus, these approaches require further improvement to balance accuracy enhancement with computational efficiency in channel estimation tasks. Building upon the promising implementation of ML-based channel estimation, researchers are increasingly exploring simpler ML algorithms to streamline the complexity associated with ANN-based models. A standout in this field is the Random Forest (RF) algorithm [14]. RF is an ML approach that enhances prediction accuracy and robustness by integrating numerous decision trees. It is capable of handling complex datasets without requiring extensive preprocessing [15]. Its efficiency and lightweight nature make RF particularly suitable for deployment in the resource-limited settings of IoT devices. A study by [16] effectively demonstrates the implementation of the Random Forest algorithm for predicting RSSI values. However, it lacks details on the number of trees used, the data collection process, and the deployment strategy. These gaps highlight the need for a more comprehensive study of RF-based RSSI prediction. By leveraging the motivations and challenges related to RF-based channel estimation for IoT networks [17; 18], this paper implements a variant of the RF algorithm called Random Forest Regressor (RFR) to estimate RSSI as a channel metric in indoor LP-IoT networks. The following are the main contributions of this research:  
* Implemented an RFR-based channel estimation method utilising Feature-based and Sequence-based strategies, demonstrating enhanced accuracy and efficiency for LP-IoT channel estimation. * Conducted a comparative analysis against existing research and ANN-based methods, highlighting the substantial improvement in estimation error and training and testing time.