# A REM Update Methodology Based on Clustering and Random Forest
## 1 Introduction
### Related Work  
For indoor localization systems, mobile crowd sensing has become a popular method for updating radio maps [5; 6; 7; 8; 9; 10; 11; 12]. In [5; 6; 7], the authors propose an approach where mobile users generate reports at their current locations to update the radio maps. The radio map is based on a database, and the predicted location for a query point is obtained by using the nearest neighbors. The method to update the radio map is based on simply appending the new fingerprints into the database and removing those that are older than a certain period. In [8], the authors use an integrity check algorithm to determine whether to update the radio map used for indoor localization. In this method, several new fingerprints are accumulated before updating the received signal strength indicator (RSSI) value at a particular location. The update is performed by using the average value of the accumulated points to update the database. In [9; 10], the authors propose a method for adapting radio maps used for indoor localization to changes in the environment. The proposed method utilizes data gathered from typical wireless users' devices that were stationary at certain locations. RSSI values received from several reference locations are used to update the mapping of the RSSI value to a particular location. In [11], the decision of whether to update the radio map for each reference point is made by a periodic adaptive estimate algorithm. The authors represent the radio map using a matrix expression, and the update process involves updating the fingerprints in the radio map with the average of valid RSSI measurements collected from users. However, the success of the previous approaches heavily depends on the location and behavior of mobile users to gather new measurements, and their proposed algorithms require collecting new data for each location to update the predictive relationship between the RSS value and corresponding positions. In [12], the authors propose an updating method for signal maps based on Bayesian compressive sensing (BCS). Several crowdsourced samples are first mapped to the nearest reference point, and the BCS-based approach computes the signal change for each reference point based on the correlation between the new samples and reference points. In the aforementioned works, the radio map construction is based on collecting data and organizing it into a database, and the methodology to update the radio is focused on indoor localization approaches, where new measurements in each of the reference locations are required to correctly update the radio map. Unlike the previously mentioned methods, our proposed method only requires newly collected data in specific sectors to update a large area of interest. Additionally, the prediction of RSSI values is performed using powerful ML algorithms. The authors in [13] propose a scheme for REM updates based on hypothesis testing, which is used to decide whether to update the REM each time. In [14], the authors propose a REM update mechanism based on Siamese neural networks to determine the level of similarity between an already-constructed REM and a new REM. However, previous work only considered a simple average of collected measurements to construct the REM. In [15], the authors propose a REM update scheme based on clustering and Gaussian process regression (GPR). They manually collected RSSI data with a smartphone in particular areas of the experiment room, and they use the collected dataset to predict the RSSI at specific reference points by using GPR with clustering. However, the objective of the GPR-based scheme is the RSSI prediction of only reference points to later be used for indoor localization, where a complete prediction of the whole area of interest is not obtained. Moreover, the authors only consider scenarios by varying the number of training samples and do not analyze the errors under changes in the wireless conditions such as in the presence of new obstacles or under the relocation of the AP.