# RSSI and Device Pose Fusion for Fingerprinting-Based Indoor Smartphone Localization Systems
## 5 Discussion  
In smartphone-based localization systems, RSSI can vary due to the device pose of the smartphone. In this study, a novel data level framework was developed and explored using cascaded neural networks to detect the device pose via accelerometer measurements, which we then combined with the RSSI vector, resulting in consistent improvements in localization accuracy, with an average increase of 10.7%. The experimental study, results, and analysis of this paper support the proposed framework in four ways:  
1. The KS test statistics indicate that the device pose could contribute toward the RSSI distributions for the anchors having higher type II errors between zones (and hence a lower true negative rate for a class). This allowed for the prediction of zone ZB to be the zone that would result in the greatest TN rate improvement after the addition of device-pose information, which was the case in the neural network implementation of the framework. 2. The results of the hyperparameter sweep of the neural networks indicates that RSSI-and-device-pose achieved a lower \(Loss_{\text{CE\_TR}}\) with fewer neurons and with less epochs, indicating that a better model was learned when the RSSI vector was augmented with device-pose information
3. The results of the 100 randomized runs of the RSSI-only and RSSI-and-device-pose neural networks indicate that the RSSI-and-device-pose consistently had higher classification accuracy. It should be noted that both these neural networks were equally sized and trained for the same number of epochs with the same training and testing vector dataset (with device-pose information added for the RSSI-and-device-pose neural network). 4. With the framework implemented using other classification algorithms such as kNN and naive Bayes, a consistent increase in accuracy was also seen but at lower rates. _Crucially, in all the experimental runs in this study, the proposed framework not only had a higher average localization accuracy: it **always** had a higher localization accuracy than any other RSSI-only localization run_ (as shown by the entire accuracy histograms shifting to the right in Figure 12 and the min-max range in Table 4). These results indicate that combining the RSSI with the device pose is likely to provide consistent increases in localization accuracy for fingerprinting systems. The significance of this accuracy improvement should also be considered in the context of the entire system:  
1. As mentioned in Section 4.2, the device-pose identification neural network is very small (five neurons). In the cascaded neural network runs on the collected dataset, the average difference in training time was 4.6% between the frameworks, and the average difference in inference time was less than 1 s. Thus, the accuracy improvement achieved came at a very low computational cost and memory overhead given the right selection of algorithm. 2. In fingerprinting localization systems, RSSI-based zone classification is not carried out on the smartphone, as it does not hold the RSSI fingerprint database, but rather on a central system that holds the fingerprint database. If a method such as pedestrian dead reckoning (PDR) is to be used for improving the zoning accuracy, this means that either accelerometer or PDR (heading and velocity) measurements must be communicated to the central system, or the central system must communicate the zone classification to the smartphone for it to combine with the PDR data. In either case, some sort of PDR location/zoning information must be conveyed over a wireless channel that may be intercepted by a malicious actor, posing a significant privacy risk. However, for the proposed framework, it is possible to conduct device-pose identification on the smartphone using a small neural network that has been calibrated for the individual and to send only a standardized device-pose class to the central system. In this case, no zoning or PDR location data are communicated, avoiding the risk of being intercepted.