###### Abstract  
In large indoor environments, accurate positioning and tracking of people and autonomous equipment have become essential requirements. The application of increasingly automated moving transportation units in large indoor spaces demands a precise knowledge of their positions, for both efficiency and safety reasons. Moreover, satellite-based Global Positioning System (GPS) signals are likely to be unusable in deep indoor spaces, and technologies like WiFi and Bluetooth are susceptible to signal noise and fading effects. For these reasons, a hybrid approach that employs at least two different signal typologies proved to be more effective, resilient, robust, and accurate in determining localization in indoor environments. This paper proposes an improved hybrid technique that implements fingerprinting-based indoor positioning using Received Signal Strength (RSS) information from available Wireless Local Area Network (WLAN) access points and Wireless Sensor Network (WSN) technology. Six signals were recorded on a regular grid of anchor points covering the research surface. For optimization purposes, appropriate raw signal weighing was applied in accordance with previous research on the same data. The novel approach in this work consisted of performing a virtual tessellation of the considered indoor surface with a regular set of tiles encompassing the whole area. The optimization process was focused on varying the size of the tiles as well as their relative position concerning the signal acquisition grid, with the goal of minimizing the average distance error based on tile identification accuracy. The optimization process was conducted using a standard Quantum Particle Swarm Optimization (QPSO), while the position error estimate for each tile configuration was performed using a 3-layer Multilayer Perceptron (MLP) neural network. These experimental results showed a 16% reduction in the positioning error when a suitable tile configuration was calculated in the optimization process. Our final achieved value of 0.611 m of location incertitude shows a sensible improvement compared to our previous results. quantum particle swarm optimization; indoor localization; fingerprinting; WiFi; wireless sensor network; multilayer perceptron; surface tessellation +
Footnote †: journal: _Algorithms_  
0000-0001-6000-0001-6000]G.S.S. signals, becoming completely useless in many indoor and underground environments. Consequently, GPS becomes an unreliable and ineffective method in scenarios where precise indoor localization is required [10]. In order to overcome these limitations, wireless technologies are being widely implemented for indoor positioning purposes. These techniques include Radio Frequency Identification (RFID) [11; 12], and Wireless Local Area Networks (WLANs) [13; 14; 15], Bluetooth [16], Wireless Sensor Networks (WSNs), among others [17; 18; 19]. Other methods, based on the angle-of-arrival and the time-of-arrival, depend on measurements that require additional hardware and are therefore less common [20; 21]. Thanks to the diffusion of smartphones and the availability of free WiFi in most indoor public spaces, the implementation of many of these communication technologies can be easily and inexpensively undertaken. As a consequence, location-based services are rapidly expanding with several benefits, mainly driven by the common availability of the appropriate hardware, both in terms of signal generation and reception. The most common approach to infer the position of a receiver in a place where many wireless signals are available is based on the Received Signal Strength (RSS), which can be measured without the need to decode any communication feature. Localization is consequently determined by the pattern matching of the intensity of multiple WLAN sources [22].