## 2 Related Work  
WiFi technology has been extensively researched for both communication and indoor positioning systems. However, relying solely on WiFi can result in poor performance due to signal fading and distortions caused by infrastructure and human presence. To address these issues, additional readily available sources of wireless signals, such as magnetic fields, inertial measurement units, and wireless sensor networks (WSN), have been incorporated.  
[PERSON] et al. [28] integrated multiple sensors, including an Inertial Measurement Unit (IMU), a magnetometer, and a WLAN RSSI (RSS Indicator). They proposed a Euclidean matching algorithm to estimate the location based on the WiFi signal. The Robust Extended Kalman Filter algorithm was used to estimate the location based on the IMU sensor. Additionally, an enhanced dynamic time-warping algorithm is used to match the magnetic fingerprint location based on magnetometer measurement. Although multiple sensor systems improved the accuracy of the positioning system, the computation required user mobile processing power to obtain the location information, which might be unfeasible for real-time applications.  
[PERSON] et al. [29] proposed a joint WSN-WiFi approach for improving indoor positioning estimation. The work considered the communication between sensor nodes as well as between the sensor nodes and the receiver to help with user positioning. The detection algorithm considered a grid-based search, with the computation cost increasing as the search location was expanded due to the increment in the number of grids to be considered. [PERSON] et al. [27] also proposed a WSN-WiFi technique based on a grid search that considered the floor tile spacing. This technique estimates the location of the user to be within the respective boundaries of the specific tile's location based on an appropriately trained neural network.  
[PERSON] et al. [30] included WiFi and cellular telephony network RSS signals in a hybrid approach for an indoor positioning algorithm. The fingerprinting database was analyzed by Principal Component Analysis (PCA) with interpolations of the raw data. Subsequently, data were processed by an ANN separately as cellular signals were first used to provide coarse training, which was then improved using the WiFi data. This hybrid approach improved the positioning precision; however, the process was very demanding in terms of computing time and unsuitable for real-time applications.  
The synergy between multiple smartphone sensors was investigated by [PERSON] and [PERSON] [31], who joined the signals of Bluetooth, magnetic field sensors, IMU, and a camera together. Their accomplishments demonstrated the effectiveness of a hybrid approach, in which combined heterogeneous sources of signals provided better indoor positioning results. However, their method required a permanent connection to a central server, and they chose not to use WiFi signals, which were deemed to be variable and unreliable in RSS fingerprinting algorithms.  
This work implements a consolidated hybrid approach as a positioning system by means of combined RSS signals from WiFi and WSNs in order to improve localization accuracy in indoor spaces. The utilization of WiFi-WSN signals does not require any additional computation in end-user devices and can be used for real-time applications. In this work, we apply the results of our previous research on relative signal weighing and MLP architecture optimization, which led to an overall significant reduction of the positioning error on the same dataset [32]. We also utilize a similar algorithm based on standard QPSO, which we integrate with the MLP neural network in order to improve the accuracy of the localization system. The detailed method, which aims to find values of appropriate tile sizes and tessellation corners, is introduced in the next section.