# Device-Free Localization and Identification Using Sub-GHz Passive Radio Mapping
## 5 Conclusion and Future Work  
In this paper, we experimentally investigated the use of two RSS-based passive fingerprinting techniques for both localization and identification of stationary human targets: one technique based on Euclidean distance minimization and one based on a naive Bayesian classifier. In particular, we focused on the impact of the number of RF nodes, the use of (multiple) sub-GHz frequencies and the body types of the targets on the resulting accuracy rates. For localization, the relationship between the number of nodes and the resulting localization accuracies tended to follow a known pattern within the broader field of DFL. While decreasing the number of nodes generally led to an increased localization error, this was not always the case as accuracy rates for the same number of nodes could differ significantly depending on node locations. In regards to frequency, we found the differences between the 433 MHz and the 868 MHz band to be quite minor, but combining data from both consistently led to strong improvements in accuracy. Finally, it was shown that the selection of different targets, both for training the fingerprint database and for evaluation, can have a significant impact on localization accuracy as well. When the data from only a single experiment participant was used, instead of from all four for whom training measurements existed, accuracy rates for the naive Bayesian classifier plummeted below those of the Euclidean approach. For identification, the resulting accuracy rates tended to be rather poor. The participant with the lowest height and weight (respectively 159 cm and 55.6 kg) was a major exception to this, however, as they were correctly identified more than 90 % of the time regardless of the fingerprinting technique and (combination of) frequency band(s) that were used. Frequency band selection in general appeared to have little impact on the results, even if 433 MHz and 868 MHz were combined. We also looked at the identification accuracies separately for each location and found that they could vary significantly from each other. When using the Bayesian classifier approach on 433 MHz data, one location had an identification accuracy rate rate of 0.1 while another location had one of 0.83. While we could provide clear answers to our research questions regarding the number of nodes and the use of multiple sub-GHz frequencies in the context of passive fingerprinting-based localization, it is evident that a significant amount of future research needs to be performed in regards to the impact of the body type of a target and passive identification in general. The results provided in this study indicate that some level of differentiation is possible for body type categories that are far apart--in our case, participant 1 versus the other participants. This immediately invites follow-up questions regarding how these categories can be defined and how far apart they need to be from each other. In order to be able to answer them, it will be highly important to investigate which changes in RSS measurements between individuals are actually caused by differences in body type and which are the result of other factors (e.g., the random stationary movements performed by the participants during the training measurements described in this paper). This will require a vastly larger number of human participants compared to the experiments described within this paper. Furthermore, developing a method to identify the RF communication links which are most sensitive to body type differences will be crucial as well. In addition to all of these aspects, much more advanced fingerprinting techniques will need to be investigated. Examples include random forest, support vector machine (SVM) and logistic regression classifiers which have all been used successfully in the context of 2.4 GHz passive radio mapping [15,17]. Furthermore, experiments will need to be performed in a variety of different environments, with targets that are not bound to static locations but move around freely.