# A REM Update Methodology Based on Clustering and Random Forest
## 1 Introduction
### Main Contributions  
In this paper, we propose a REM update methodology based on clustering and ML algorithms. In particular, we consider the \(K\)-means algorithm to create \(K\) clusters in the area of interest, and the random forest (RF) algorithm is applied to predict the quality of the wireless signal for each cluster. The proposed REM update can be applied in different scenarios, such as technology industries, where several sensor nodes interact to carry out different tasks, such as maintenance or scheduled programming. Many times, it is difficult to collect measurements in the whole area to evaluate coverage prediction and build the REM because sensor nodes need to move frequently. Similarly, in hospitals, users storing measurements on every floor for coverage prediction may disturb other users, and measurement collecting tasks cannot be done as many people walk around the area. Therefore, the application of REM updating via clustering can reduce time-consuming tasks and optimize network resources. The main contributions of this paper are summarized as follows:  
* We propose an efficient methodology to update a REM based on clustering and RF in a timely manner. In the proposed scheme, the \(K\)-means algorithm is applied to divide the area of interest in \(K\) clusters, where one RF model is deployed per cluster. The REM is constructed to cover every point within the area of interest, where the prediction of the RSSI values for each location is obtained by the corresponding RF model in each cluster.
* The RSSI measurements were collected by a mobile robot, which can reduce the risk of human error because the robot can be programmed to move in a controlled manner. This can help ensure that RSSI measurements are taken at consistent intervals and under consistent conditions, while improving the accuracy and reliability of the measurements. Moreover, mobile robots can operate autonomously, which can save time and resources compared to manual data collection methods.
* In the REM construction, to avoid abrupt changes in the border areas between the clusters, we propose a methodology that utilizes the weighted average of the RF model predictions from the two nearest centroids to determine the RSSI value of the points within the border areas. Moreover, when new measurements are available, only the RF models for clusters that have enough measurement samples are updated.
* We extensively evaluate the proposed scheme for different scenarios, including the presence of obstacles and relocating the AP, and we consider several comparative ML methods, including the case without clusters. Moreover, the computational complexity of the proposed scheme is analyzed along with the comparative schemes.  
The simulation results demonstrated the superior performance of the proposed scheme compared to the baseline methods in effectively adapting to changes in the wireless environment. Moreover, the proposed approach requires only newly collected data in specific sectors to update a large area of interest.  
The rest of this paper is organized as follows. Section 2 describes the measurement methodology. Section 3 presents the proposed REM updating framework. In Section 4, we present comparative models and an evaluation of simulation results. Finally, Section 5 concludes the paper.