## 3 System Model  
This section describes the system model used for this study that set the context of the research problem and the followed process. We consider a conceptual communication model for the LP-IoT system in which a signal is transmitted through a wireless channel and received by the receiver IoT device, which can be represented as follows:  
\[y=hx+n, \tag{1}\]  
where \(x\) denotes the transmitted signal, \(h\) represents the wireless channel between the transmitter and receiver, and \(n\) represents the noise added to the system. To visualise the problem more practically, let us consider a downlink IoT system as shown in Figure 1, where the channel \(h\) is represented as a complex number and is denoted as \(h=\alpha+j\beta\). In this downlink LP-IoT system, we intend to compute the LP-IoT wireless channel gain \(h\) in \(dBm\) for LP-IoT networks. For ease of estimation, we take the norm of vector \(h\) that becomes the scalar. Thus, the channel gain is expressed as \(\gamma=h\), where \(\gamma\) indicates the RSSI measured in real-time from the laboratory settings. The system model presented in this paper is based on the data collected from three distinct scenarios established via hardware setup in the Network Engineering Laboratory at Charles Sturt University (CSU). This setup simulates real-world IoT communications using two Waspmote devices for transmitting and receiving signals. The measured RSSI is the primary channel metric across three scenarios representing typical IoT wireless channels under Line of Sight (LoS) and Non-Line of Sight (NLoS) conditions. The first scenario tested stationary devices at a fixed distance of 3 m, both with and without physical barriers, to establish a baseline for signal strength. The second scenario examined the impact of varying the receiver's position within a fixed 3-meter distance by adding common laboratory objects to simulate NLoS conditions. The third scenario explored varying distances between the transmitter and receiver, from 0.2 to 2.9 m, under both LoS and NLoS conditions. The first experiment ran for over three hours in the initial setup, collecting 10,000 samples per condition, while scenarios 2 and 3 were tested for approximately five minutes each, yielding 220 to 260 samples per location due to random packet loss. In total, 43,163 RSSI samples were obtained, and the dataset was thoroughly cleansed to eliminate any empty entries before the model implementation. Next, we consolidated the data collected from the different experimental scenarios into a single spreadsheet. The specific input features in the prepared data spreadsheet are distance, condition, and category. The output against those features is the RSSI. Table 1 shows the random data samples gathered from the consolidated data from three experiment  
Figure 1: A conceptual communication model for LP-IoT system. scenarios. Our prior research in [30] shows the overall data collection and preparation process. Next, we developed two RFR-based models: a Feature-based RFR(F) model and a Sequence-based RFR(S) model utilising two decision trees for each model (as explained in the subsequent Section 4). The RFR(F) model utilises the environment features as input and estimates the channel using the RFR algorithm. The second model, RFR(S), selects the RSSI sequence against the different feature combinations and estimates the channel using RFR. For the models' training, we segregate the dataset into 80% training and 20% testing sets, followed by bootstrapping (the main process of RFR training). The developed models were then compared with our predecessor ANN(F) and ANN(S) models. ANN(F) and ANN(S) models have also been designed previously to estimate wireless channel in LP-IoT devices using ANNs [30]. These models employ the same Featured and Sequenced techniques as the RFR(F) and RFR(S) models. Finally, we deployed developed models on the Raspberry Pi 4 model B to analyse the complexity of the developed models. Figure 2 illustrates the process flow used in this research to estimate the RSSI.