# Indoor Localization System Using Fingerprinting and Novelty Detection for Evaluation of Confidence
## 6 Conclusions and Future Work  
In this paper, we propose and evaluate the performance of a new approach for indoor localization, the LocFiND (Localization using Fingerprinting and Novelty Detection). In our solution, information received in the online phase will only be accepted after it passes an evaluation of confidence. We then propose three different approaches for this evaluation of confidence. A first using only the probability of classification, another adding novelty detection, and a hybrid approach uses both prediction probability and novelty detection to improve localization accuracy while trying to discard as little information as possible.  
The main contribution of our solution is the possibility of removing unreliable results (which may have been affected by noisy information) in the online phase, while most works in the literature assess the impact of noise in the training or data collection phase. The main concern of our approach is how to discard the fewer possible estimations while increasing the accuracy and stability. Thus, our solution provides an important tradeoff between accuracy and the number of estimations that can be discarded without compromising the system.  
Our results show that we can have less restricted configurations, for a threshold \(\alpha=40\%\), that improves the position accuracy from 36% to 44% and the average localization error from 1.9 m to 1.6 m compared to a traditional kNN, while discarding only 30% of the estimations. On the other hand, we can have more strict configurations, for a threshold \(\alpha=80\%\), that can improve the accuracy to almost 70% and the average localization error to 0.9 m but accepting only 18% of the estimations. In terms of room accuracy, which was the main focus of our application, LocFiND was able to improve the accuracy from 96% to almost 99.5% while discarding only 20% of the estimations for a threshold \(\alpha=90\%\).  
In future work, we intend to propose and analyze other methods for the evaluation of confidence. For instance, the residual value, mainly used in the least square method, could be used to compute a \"distance\" between the new sample and the nearest neighbors in the fingerprint database. We aim at evaluating how this and other measures of discrepancies could be used to improve our solution.  
Another point that we can advance is the problem generated by discarding unreliable information in the localization phase. We aim at defining, given the response time required by the system, how much information we can discard without harming its operation. We can define minimum amounts of data distributed over time as an application requirement, which defines the maximum data discard limit.  
Also in future work, we intend to compare our work to a more robust, state-of-the-art approach, other than KNN. Our proposed solution has some particularities that make it somewhat different from most proposed solutions, mainly the characteristic of discarding unreliable classifications based on novelty detection. Since most known solutions do not discard these classifications, it would be difficult, in this current work, to make the comparison while being able to see the isolated gains obtained by our evaluation of confidence step.  
The authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by the Institute for Innovation, Research, and Scientific and Technological Development of Amazonas (IPDEC), Positivo Technologies and, according to Article 48 of Decree no 6.008/2006, Samsung Electronics of Amazonia Ltda, under the terms of Federal Law no 8.387/1991, through agreement no 003, signed with ICOMP/UFAM.  
The codes and bases used in the experimentation part of this work are available at [[https://github.com/hasm/doc](https://github.com/hasm/doc)]([https://github.com/hasm/doc](https://github.com/hasm/doc)) (accessed on 15 January 2022).  
The authors declare no conflict of interest.