# Indoor Localization System Using Fingerprinting and Novelty Detection for Evaluation of Confidence
###### Abstract  
Indoor localization systems are used to locate mobile devices inside buildings where traditional solutions, such as the Global Navigation Satellite Systems (GNSS), do not work well due to the lack of direct visibility to the satellites. Fingerprinting is one of the most known solutions for indoor localization. It is based on the Received Signal Strength (RSS) of packets transmitted among mobile devices and anchor nodes. However, RSS values are known to be unstable and noisy due to obstacles and the dynamicity of the scenarios, causing inaccuracies in the position estimations. This instability and noise often cause the system to indicate a location that it is not quite sure is correct, although it is the most likely based on the calculations. This property of RSS can cause algorithms to return a localization with a low confidence level. If we could choose more reliable results, we would have an overall result with better quality. Thus, in our solution, we created a checking phase of the confidence level of the localization result. For this, we use the prediction probability provided by KNN and the novelty detection to discard classifications that are not very reliable and often wrong. In this work, we propose LoFiND (Localization using Fingerprinting and Novelty Detection), a fingerprint-based solution that uses prediction probability and novelty detection to evaluate the confidence of the estimated positions and mitigate inaccuracies caused by RSS in the localization phase. We implemented our solution in a real-world, large-scale school area using Bluetooth-based devices. Our performance evaluation shows considerable improvement in the localization accuracy and stability while discarding only a few, low confidence estimations. indoor localization; positioning system; fingerprinting; bluetooth; machine learning +
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multiple transmission power levels with the RSS more stable at a high transmission power level [11; 12], but it has important practical implications for energy efficiency, resulting in a trade-off between location performance and battery lifetime [13]. RSS is one of the most popular and available information used by localization systems. One of the reasons for its widespread adoption is that it does not require extra equipment and can be found on most devices equipped with wireless communications [14]. The RSS is a measure of the received signal strength of the packets. A common technique for indoor localization systems is fingerprinting, which uses RSS data to construct radio maps, also known as fingerprint databases. In the offline phase, RSS maps are created based on the RSS values obtained at the different points of the area where we want to have the localization service. Then, a classifier uses this information to estimate the position of the nodes in the online phase. A commonly used classifier for this purpose is the k-Nearest Neighbors (kNN), as used by RADAR [15; 16]. The main idea of the fingerprinting technique is based on the assumption that each spatial location indoors can be identified by a single, mostly unique, set of measurable RSSs [4]. However, since the RSS is very susceptible to noise in the environment, RSS often can cause errors in the location estimations. For instance, our main application in this paper aims at providing location information for small, battery-powered devices to be used by students in schools and allowing several interesting applications such as automatic student attendance record-keeping, entry and exit control, and so on. However, in the first versions of our localization system, the instability of the RSS resulted in the students \"jumping\" from room to room, even though they were static. Several techniques are proposed in the literature to deal with the RSS noise [17; 18; 19; 20; 21; 22]. Most of these solutions focus on averaging RSS values or making other transformations in the fingerprint database.