###### Abstract  
Indoor positioning based on the received signal strength (RSS) of the WiFi signal has become the most popular solution for indoor localization. In order to realize the rapid deployment of indoor localization systems, solutions based on crowdsourcing have been proposed. However, compared to conventional methods, lots of different devices are used in crowdsourcing system and less RSS values are collected by each device. Therefore, the crowdsourced RSS values are more erroneous and can result in significant localization errors. In order to eliminate the signal strength variations across diverse devices, the Linear Regression (LR) algorithm is proposed to solve the device diversity problem in crowdsourcing system. After obtaining the uniform RSS values, a graph-based semi-supervised learning (G-SSL) method is used to exploit the correlation between the RSS values at nearby locations to estimate an optimal RSS value at each location. As a result, the negative effect of the erroneous measurements could be mitigated. Since the AP locations need to be known in G-SSL algorithm, the Compressed Sensing (CS) method is applied to precisely estimate the location of the APs. Based on the location of the APs and a simple signal propagation model, the RSS difference between different locations is calculated and used as an additional constraint to improve the performance of G-SSL. Furthermore, to exploit the sparsity of the weights used in the G-SSL, we use the CS method to reconstruct these weights more accurately and make a further improvement on the performance of the G-SSL. Experimental results show improved results in terms of the smoothness of the radio map and the localization accuracy. Keywords:Indoor localization; crowdsourcing; received signal strength; graph-based semi-supervised learning; linear regression; compressed sensing +
Footnote †: journal: Applied  
With the growing deployment of WiFi access points in indoor environments and the widespread use of mobile devices such as smart phones, WiFi _received signal strength_ (RSS)-based indoor localization methods are getting popular due to their low deployment cost and relatively high localization accuracy. In general, there are two main categories of localization methods that use WiFi RSS readings. The first category comprises those methods that rely on the radio propagation model of the WiFi signal in indoor environments as well as the locations of the WiFi Access Points (AP). Specifically, the RSS readings from different access points are used to estimate the distance of a mobile device from those access points. Then a triangulation method is used to estimate the location of the mobile device. The next category includes those methods that are based on WiFi RSS fingerprints also known as fingerprint-based methods. Originally proposed by [PERSON] et al. [7], various fingerprint-based localization systems have been designed and developed during the last decade [7; 8; 9]. Typically, fingerprint-based methods consist of an offline phase followed by an online phase [7]. In the offline phase, RSS values from different WiFi access points are measured at some known locations throughout the indoor area. These location are referred to as Reference Points (RP) and the measured RSS vector for each RP is called a _fingerprints_. All fingerprints and their corresponding RPs are stored in a database called the _radio map_. In the online phase, a user's position can be estimated by comparing the RSS values measured by the user with the RSS fingerprints stored in the radio map. A disadvantage to the offline phase of the fingerprint-based methods is the required time and labor to collect sufficient number of fingerprints throughout the indoor area. In addition, the RSS value of an AP at a certain location can change over time due to a number of reasons including but not limited to multipath fading, shadowing, moving objects and people [10].