# Fingerprint-Based Localization Approach for WSN Using Machine Learning Models
## 6 Discussion  
The radio frequency (RF) propagation channels in indoor WSN-deployment environments are commonly affected by shadowing due to the obstructions caused by natural and man-made obstacles. Therefore, triangulation-based localization systems offer efficient localization accuracy in outdoor environments (clean space) [36; 37; 38] but usually fail in indoor environments with the existence of walls and obstacles. In general, RSS-based localization systems are easy to deploy, low in cost, and do not require complicated devices to achieve the positioning function. A device-free fingerprinting-based localization system is proposed in this paper with the investigation of adopting tailored ML algorithms to enhance the localization accuracy for indoor environments. Several research works considered RSS-based localization systems as range-based; however, in our approach, the RSS values are collected from stationary sensor nodes with no requirement of attaching additional sensors/devices to each reference node or mobile node [39]. Therefore, the developed system in this paper can be considered a range-free localization approach. In this work, the positioning accuracy was further analyzed for both approaches, triangulation and fingerprinting. Therefore, two different experiments were conducted in the IIRC lab through implementing a triangulation approach to compare the localization accuracy with the obtained accuracy from the developed fingerprinting system. Through real experiments, the average localization error for the triangulation system was around (5.2 m); this refers to the structure of the IIRC lab, where the walls, obstacles, and objects existed. Figure 16 shows the localization accuracy results for 10 test points, conducted in the IIRC lab through deploying both the KNN and triangulation approach. As noticed, the KNN-based localization system achieves better localization accuracy than the traditional triangulation system in indoor environments. On the other hand, there are several RSS-fingerprint datasets available online with diverse size and accuracy. For instance, the UJIINdoorLoc dataset [40], which consists of 21,048 records, covered three buildings, where the RSS values were collected from 520 different wireless access points (WAPs). The provided dataset is efficient for object localization using simulation studies. However, this dataset is inefficient in real experiments, as it requires a large number of reference nodes (WAPs) to be deployed in the area of interest, which is not valid in several localization scenarios. Therefore, this paper presents reliable and various RSS-fingerprint datasets with three different intensity levels of reference points, which are based on four reference nodes (four WAPs) and can benefit both simulation and practical studies. Therefore, as noticed earlier, ML approaches have enhanced the localization accuracy of fingerprinting localization systems. Unlike the developed systems in [25; 27; 28; 32; 33; 34]  
Figure 16: Localization error for KNN and triangulation systems in 10 different testing points. which mainly focused on simulated experiments, we developed a real-time localization system using low-power consumption and low-cost sensor nodes (XBee Series 2 chips), where the developed system was tested in real indoor environments with different types of obstacles. In general, real experiments offer reliable and accurate results for the problems of indoor localization, as walls, obstacles, and dynamic objects affect the process of estimating the location of target nodes. The presented research works in the literature [26; 30; 31] offer reasonable localization accuracy with an average of 1.7-6 m. However, the presented positioning system offers an accurate localization accuracy with 1.4 m, and this proved that the proposed fingerprinting localization system is efficient in indoor environments.