# Indoor Localization System Using Fingerprinting and Novelty Detection for Evaluation of Confidence
###### Abstract
However, this noise can affect not only the training data but also the data received in the online phase especially when considering that, in most cases, training is done in an empty building while the online phase occurs in a more crowded, dynamic scenario. Unfortunately, averaging several RSS values is not an option for the online phase of our application since the mobile devices are extremely power-limited and it is not possible to change or recharge their batteries. Thus, we need a solution that also considers the impact of this RSS variation in the online phase without requiring sending additional data packets. The main limitation of the RSS-based localization systems is the RSS fluctuation, which can induce several errors even when using machine learning techniques. This fluctuation can result in classifications/positions with low reliability. Thus, an intuitive step is to detect and discard these classifications to get more reliable results with better quality. Thus, in our proposed solution, we created an additional step called \"Evaluation of Confidence\". For this, we use the prediction probability provided by KNN and the Novelty Detection technique to detect and discard classifications that are not considered reliable and, often, have higher errors. This is done using prediction probability, which is an additional step on the KNN technique based on the distances of the k nearest neighbors. Additionally, a second filter was created that uses the concept of \"novelty\" which uses machine learning techniques to categorize a new classification as \"normal\" (belongs to that class) or \"novelty\" (does not belong to that class). In this work, we propose a new localization system called LocFiND (Localization using Fingerprinting and Novelty Detection), a fingerprint-based solution that uses techniques of novelty detection to evaluate the confidence of the estimated positions in the online phase allowing the system to discard some of the position estimations that would have unacceptable higher errors due to a noisy RSS data that suffered some unexpected degradation from the environment. Three confidence evaluation techniques have been proposed, one simpler and one more rigid, but they discard many positions that can be useful. To try to reduce these losses, a hybrid approach that recovers some of these discarded locations has been proposed as well. For our application and performance evaluation, we used Bluetooth-based anchor nodes and smartwatch-like mobile devices to be used by the students. We implemented our solution in a large-scale, real-world school area and compared its performance to the traditional kNN fingerprint-based solution. Our results show that our solution was able to improve both the localization accuracy and the number of position fixes while discarding noisy RSS data. The main contributions of this paper are as follows:  
1. The use of novelty detection techniques to evaluate the confidence of the predictions. 2. Considering that we have noise in the localization phase and solving this problem without requiring the need for sending extra data packets, which would consume more energy of the mobile devices. 3. Implementing and evaluating the performance of the proposed solution in a real-world, large-scale school application. This paper is organized as follows. Section 2 shows our related work. Section 3 describes our proposed LocFiND solution. In Section 4, we present our methodology and the results of our performance evaluation. In Section 5, we discuss the application of our solution. Finally, Section 6 shows our conclusions and future work.