# Indoor Localization System Using Fingerprinting and Novelty Detection for Evaluation of Confidence
## 5 Applicability, Strengths, and Weakness of the Proposed System  
Our main motivation for implementing an indoor localization system is to localize students inside a school. However, in the first version of our system, the unstable position estimations resulted in the students jumping from room to room, even though they were  
Figure 12: Results for the evaluation of confidence using the hybrid approach: relationship among the value of the threshold \(\alpha\) (\(x\)-axis), the percentage of accepted estimations (in blue), and (**a**) the final accuracy of the accepted classifications, (**b**) the average localization error, and (**c**) the room accuracy. A compromise is reached having improved localization estimations while increasing the number of accepted predictions.  
Figure 13: Comparing the cumulative distribution function of the localization error using our three approaches, (**a**) Using lower probability 25% (**b**) Using lower probability 45%, and (**c**) Using lower probability 70%.  
actually static. To reduce these jumps and improve our system accuracy and stability, we started looking for solutions to evaluate and improve the confidence of the predictions.  
Thus, we proposed and applied the LocFiND solution, our localization system, which was able to significantly improve the results and stability while keeping the feeling of a real-time system. Our solution has the advantage of choosing the most reliable results, based on the assessment of probability and novelty. This makes the system more resilient since even if we have any communication problem, packet loss, or interference up to a limit, the system can keep working just by discarding the problematic information. For other scenarios, depending on the requirements of the application, our proposed solution can be easily adjusted to have an acceptable percentage of discarded estimations while considerably improving the accuracy and stability of the system.  
However, discarding data causes some problems for evaluating the results since the final amount of results is less than if we used all of the data. In this work, the evaluation was based on accuracy and mean error. Accuracy can be defined as \(a=h/n\), and the average error as \(ae=e/n\), where \(h\) is the number of correct sort results, and \(e\) is the sum of errors associated with the results considered valid by the algorithms, and \(n\) is the number of valid outputs from the algorithm. However, as our algorithm discards some data, the value of \(n\) for them becomes different, making the comparison between them not totally fair. Thus, in most results, we included the percentage of accepted predictions to make it clear that, even though the error decreased, the number of accepted predictions also decreased.  
Finally, it is known that RSS values reported by the devices are somewhat hardware-dependent and can vary from one manufacturer to another. Even though this might affect the performance of the localization systems, in the case of our application, in which we implemented the proposed solution in the school area, this was not an issue since all of the hardware was similar and from the same manufacturer.