# RSS Indoor Localization Based on a Single Access Point
## 2 Related Work
### RTLS Performance Evaluation Techniques  
A typical RTLS evaluation method refers to the development of benchmark standards for the comparison of the performance of different localization schemes [31]. Such schemes usually include environment type categorization, as well as their dynamic behaviour. The main disadvantages of these benchmarks are the complexity and abstract procedures that need to be implemented. More precise methods were presented by researchers in [32; 33], who suggested the enumeration of a number of critical factors that influence the performance of localization platforms. These factors include the number of transmitters, the number of reference measurements, and the signal measurement dynamics. They examined a set of localization mechanisms and evaluated their performance robustness under various configuration settings using two typical types of building environments: an office building and an underground floor-plan. Although they enumerated several critical factors, they did not provide a direct relation between these factors. The difficulty of assessing the performance of different localization systems was also reported in [34]. The performance of radiomaps was also exploited in [35]. In this research, radiomaps were generated from dynamically collected measurements during the offline phase. The KNN algorithm was then employed for positioning during the online phase. In the dynamic data collection process, signals were collected automatically, while collectors were moving along the pre-designed paths. During the conventional data collection process, the measurements were collected statically: first, the service area was divided into pre-designed cells, which usually had a rectangular shape; then, the collectors gathered signals for each cell, until a sufficient number of samples were collected. Although more accurate, this is a much more laborious and cumbersome data collection process. The positioning performance in the case of the dynamic collection process was compared with different grid spacing, and with various K numbers for a KNN algorithm. However, the proposed methodology is algorithm-specific, and the general outcome was that the positioning performance is affected by various parameters that should be thoroughly decided. Authors do not define such critical factors, and they also do not propose a holistic evaluation methodology. In order to assess any positioning platforms which have already been deployed, in a previous study we proposed the application of binomial distribution [36]. To evaluate of the quality of RSS fingerprint databases, we also developed a dedicated correlation algorithm, named the \"Tolerance-Based Normal Probability Distribution (TBNPD)\" [15]. This algorithm calculates the correlation level for every pair of fingerprint entries forming the radiomap, while also considering possible RSS fluctuations (\(RSS_{tol}\)), occurring due to the dynamic nature of the environment. The TBNPD algorithm offers the possibility to assess the uniqueness of each fingerprint entry in a radiomap, prior to its utilization in a Real-Time Locating System (RTLS). For this reason, the TBNPD algorithm is considered suitable and convenient to be used for the assessment of the candidate radiomaps generated in the current paper.