# Secure Indoor Localization Based on Extracting Trusted Fingerprint
## 2 Related Work  
Understanding RSSI data analysis is crucial to indoor localization algorithms. Gaussian or log-normal distributions are currently used to simulate the RSSI randomness. Large-scale measurements [24] show that the RSSI histogram fits the Gaussian distribution. In addition, smartphones are smaller and have less computing power than laptops, so smartphones are more sensitive to environmental changes when collecting RSSI for indoor location applications. This stimulated our analysis and research on RSSI and improved the indoor localization accuracy of smartphones. A basic requirement of fingerprint recognition is that the fingerprint must be accurate and updated in time. [PERSON] et al. [25] proposed a new fingerprinting training method, which can set the fingerprint database when the user enters an environment without a prior database. Previous databases can also be updated by collecting information from surrounding users. Both historical data and new data have been applied to update the database, and reduce training costs. The authors in [26; 27] studied autonomous crowdsourcing methods to train and update WiFi databases. For crowdsourcing databases, the accuracy of the fingerprint database is crucial to estimating locations. In [28], the authors improved fingerprinting performance by allowing users to interact with the system. However, these methods require active collaboration with users who may not be willing or likely to make mistakes. Many scholars have done much research on the factors that affect the position of fingerprints. [PERSON] [29] used several analytical models and empirical multi-wall radio propagation models to study the impact of the following key parameters on indoor location: RSSI bias, number of access points and granularity of the grid. [PERSON] and [PERSON] [30] made some important observations about the estimation uncertainty and the dependence of various factors. The observed factors include the signal variance, the number of APs and the distance between APs and signal propagation constants. [PERSON] et al. [31] introduced RSSI analysis of variance, indicating that different users' orientations at the reference point affect the RSSI captured by the device. The result is that the relationship between the true distance of two fingerprints and the RSSI distance is poor. One of the reasons for the localization error is comparing fingerprints with imperfectly suitable reference fingerprints. In order to localize in the underground environment, [PERSON] [32] and others proposed a target method of underground localization based on distance constraints. RSSI technology is used to measure distance, but the radial error is large because the algorithm contains approximation. In [33], in order to improve the localization accuracy of wireless sensor networks in underground tunnels, an anchor node selection mechanism was proposed. The mechanism is based on the tunnel's three-dimensional spatial characteristics and the choice of wireless communication frequency: firstly, sorting from high to low based on RSSI values; secondly, selecting four anchor nodes with higher RSSI by Gaussian elimination. The algorithm selects the anchor nodes according to the RSSI level, but when there are fewer anchor nodes, the unstable anchor nodes may still be selected. [PERSON] et al. [34] studied the time-varying phenomenon of RSSI. The authors point out that environmental impacts, such as interference and movement of people or mobile nodes, cause time-dependent fluctuations in RSSI and severely reduce the performance of the localization system. A discussion of sample correlations was proposed in [35], and the authors used these highly correlated RSSI samples in their localization system. However, in the RSSI time series, there are no detailed reports to study the stationarity or correlation of time-varying phenomena. In addition, over time, signals from APs may disappear due to the intermittent degradation of the radio channel.