# Integration of Directional Antennas in an RSS Fingerprinting-Based Indoor Localization System
## 6 Conclusions  
The present paper addresses the integration of directional antennas to enhance the performance of receive signal strength (RSS) fingerprinting-based indoor localization systems (ILS). A room-level RSS fingerprinting-based ILS has been developed for research purposes, with a localization engine based on an artificial neural network (ANN) to infer the tag position. The system architecture is based on a sensor network composed of a set of \(K\) sensor readers (SRs), each one connected to an external omnidirectional or directional antenna, which are in charge of measuring the RSS from the beacons transmitted by the tag. A centralized server will infer the tag position in real time from the RSSs gathered by the SRs. A full deployment process of the sensor network is described. A planning phase was previously performed, aided by our own simulation tool, to estimate the number and position/orientation of omnidirectional/directional antennas in order to optimize the system performance. After planning, the physical deployment of the sensor network is chosen, and a site survey process is conducted to generate a radio map with fingerprints that rigorously characterizes the indoor environment. A final calibration phase is used to train the ANNs with the radio map data in order to infer the possible tag positions from the RSS fingerprints. A prototype has been deployed in a real scenario of 432 m\({}^{2}\) used as the experimental testbed. The scenario represents the conditions of typical office environment activity. Different combinations of omnidirectional and directional antennas have been intensively studied to analyze the effect of antenna type to increase the probability of positioning the tag in the true room. A radio map with more than 1000 fingerprints was generated in an efficient way, by the aid of our own software tools developed in C#, Java and MATLAB, allowing generating fingerprints very quickly and cost-effectively. Furthermore, different test pathways performed by different people were used to better characterize and evaluate the system performance during the experiments. In general, the results demonstrate our initial hypothesis, which established that adding directional antennas, suitably oriented and positioned, introduces a new degree of freedom (and novelty), which increases the quality of the RSS fingerprints, ultimately enhancing the room discrimination realized by the ANNs. This way, it has been demonstrated that in most of the configurations evaluated, the localization performance could be improved if appropriate sets of directional and omnidirectional antennas were chosen. A summary of the most successful results allows us to conclude that the performance can be improved in certain rooms using properly-positioned/oriented directional antennas, without affecting the performance in other spatial regions and, therefore, improving the overall system performance. Furthermore, results also demonstrate that using properly-planned configurations combining omnidirectional and directional antennas would allow maintaining good performance while the number of needed antennas is dramatically reduced, therefore saving on economic costs. Future works will be focused on the introduction of new low-cost planar and multi-beam directive antennas. These antennas will be able to sectorize space with three or six narrow beams (low beam width) in order to get better results, without economically jeopardizing the solution and gaining the feasibility of the RSS fingerprinting-based ILSs. This research has been supported by the Ministerio de Economia y Competitividad/Fondo de Europeo de Desarrollo Regional (MINECO/FEDER), project titled \"COoperating IN Intelligence Schemes for Future Internet emerging technologies\" (COINS) (Project Ref. TEC2013-47016-C2-2-R), and the Spanish national project, \"ChAracterization, Evaluation, Planning and IMProvement of Key Technologies for the Future Internet: Knowledge and Transfer\" (CALM), (Project Ref. TEC2010-21405-C02-02).