###### Abstract
Nevertheless, even the hybrid technology of a WiFi and WLAN combined approach for indoor positioning algorithms can lead to unavoidable incertitude, mainly driven by signal instability and fading, reflection on structures, the presence of people, and possibly autonomous trolleys [23; 24]. In this work, the localization approach was based on the fingerprinting matching process from both WLAN and WSN signals as the basic scheme of positioning determination. The two technologies complemented each other in terms of spatial behavior, as WiFi signals penetrate walls, while WLAN presents a shorter range with a stronger gradient [25]. While a pattern-matching approach is classified as a deterministic algorithm [26], the spatial distributions of the signal intensities are strongly non-linear in the whole research space and thus require the implementation of methods such as neural networks and heuristic optimization techniques. Consequently, an Artificial Neural Network (ANN) approach guarantees robustness against noise and interference, which are among the major factors affecting the accuracy of indoor positioning systems. In this work, we extended the concept of surface tessellation, in which we subdivided the research area into identical, discrete rectangular tiles. This approach was based on the idea that most of the time, a coarse location precision was sufficient to infer the actual position of a receiver and its carrier. In our previous work on research area tessellation [27], square tiles of 1 m side were considered as proof of concept. The published results, together with the improvements obtained by signal weighing and an appropriate ANN architecture, encouraged us to further investigate the tessellation technique for indoor positioning. The core contributions of this paper are listed as follows:  
1. To extend previously published results on the same dataset by joining our signal optimization results and the concept of tessellation of the search space. 2. To develop an optimization algorithm by investigating a suitable tessellation of the research space to reduce the overall error in the Multilayer Perceptron positioning algorithm. 3. To introduce a heuristic approach for the determination of regular space meshes in general positioning algorithms. With this fact in mind, a Quantum Particle Swarm Optimization (QPSO) algorithm was integrated into the positioning neural network application. The remaining sections of the paper are organized as: Section 2 gives a brief description of the related work, including our previous analysis of the studied dataset. Section 3 presents in more detail the approach and techniques we used in this research. Here, particular attention is given to the QPSO process and to the improvement of the MLP application in space tessellation, which was used in our previously published works. The following part of this paper presents the results of this study, with references to the optimization of the MLP and the performance of our proposed QPSO algorithm. The achievements of our optimization process are presented and compared with results both from the literature and from the authors' previous research. Finally, the conclusions of this work are briefly discussed, and projects and ideas for future research are introduced.