# Automatic Clustering for Improved Radio Environment Maps in Distributed Applications
This work assumed a single path loss trend for the whole area of interest, as is the case with the majority of the models available in the literature [36]. The assumptions may not provide accurate results, since the behavior of wireless communication is unpredictable. The precise prediction of REM can be a challenging task, especially in large-scale and unstable environments [8]. This is due to several external factors, such as mobility and spectrum interference. Currently, many traditional learning-based models depend on a lengthy training process to predict an accurate REM. However, this can be challenging, since the learning algorithms are often complex, and require a significant amount of training sets [34; 37]. Given these issues, this work proposes a dynamic approach that aims at improving REM construction in distributed applications. In the approach proposed, automatic clustering-based KMC is integrated with the Kriging technique. This integration process is proposed for several reasons. First, KMC is a flexible and efficient clustering algorithm, which enhances the efficiency of the clustering process. This is critical, due to the challenges that are being faced in wireless environments (e.g., mobility, instability) [38]. Second, KMC supports scalability as it reduces the calculation of the pairwise distance among different points in a given dataset. Additionally, KMC does not induce complexity, which is a key concern in most distributed applications, due to the large number of communicating entities. On the other hand, the Kriging technique provides several estimation advantages, including measuring errors and uncertainty in sample results. It also considers direct and dependent relationships. Furthermore, the Kriging technique is not restricted in terms of the range of observations. In fact, combining KMC with the Kriging technique not only improves prediction accuracy, but also promotes the efficiency of RSRP prediction. The overall objective is to improve both the efficiency and accuracy of the construction process of REM. More specifically, we summarize the contributions of this work as follows:  
* An automatic clustering approach using KMC in conjunction with the Kriging technique is proposed. In our proposal, two steps (i.e., clustering using KMC technique and the RSRP prediction using the Kriging technique) are needed to produce more accurate REMs. To the best of our knowledge, this is the first work that uses KMC to improve the accuracy of RSRP prediction. This justifies the novelty of the paper, since combining these two technologies proved to be significant, based on the results provided in this work. The proposed work in this paper also enhances the path loss exponent estimation through the distinction between different environments existing in the area of interest. This is considered to be a key contribution due to the importance of path loss exponent estimation in the field of wireless communication and REM construction. * A comparative study with the proposed approach is performed, in which the Kriging technique, considered with only one cluster, is utilized as a benchmark. Here, the performance of the proposed REM construction technique using KMC is investigated and evaluated with the root mean square error (RMSE) metric. Multiple simulation tests were carried out to prove the superiority of our proposed work. * The proposed approach is evaluated, in regard to its potential as a technique for constructing REMs, using KMC with R\({}^{2}\). This further proved the viability of the approach, because the method measures the variance of dependent variables in relation to independent variables in a dataset. In fact, using both RMSE and R\({}^{2}\) indicated the effectiveness of the proposed REM construction technique. Given the above discussion, Table 1 provides the list of abbreviations to simplify the readability of the paper.