# Hybrid Positioning for Smart Spaces: Proposal and Evaluation
## 1 Introduction  
In the last decades, boosted by the appearance and proliferation of smartphones, many indoor positioning systems have been developed. The point in common in all of them has been the usage of existing infrastructures and technologies integrated in these devices for multiple purposes, facilitating the deployment and reducing costs. As a result, many positioning systems can coexist in the same terminal implementing hybrid approaches. In addition, when a base technology or algorithm must be chosen, a handover policy schema should be defined to get the best approximation of the real position. Technologies like WiFi, Bluetooth Low Energy (LE), and built-in movement sensors have been widely exploited to implement indoor positioning systems. In recent years, new technologies related to smart spaces and Internet of Things (IoT) have appeared, presenting potential positioning capabilities. This strong relation between indoor positioning and IoT is stated in the work of [PERSON] et al. [1], for instance. Access control systems and facerecognition cameras are two representative examples that take profit of these new technologies. However, existing positioning systems do not exploit these new infrastructures to better estimate the location. Hence, one of our main contributions in this paper is the presentation of a hybrid and modular positioning system that uses, apart from the most common technologies present in smartphones, new technologies available in smart spaces. Proximity and triangulation based algorithms using radio-frequency requires sensing the medium to compute location. Moreover, two-phase positioning algorithms like fingerprinting [2] also requires the creation of a radio-map in a first phase. At this stage a database is created storing reference points linking a determined position with sensed signal strength indicators (RSSIs) from surrounding access points or tags. In a second phase, the fingerprinting algorithm compares the current received RSSI using a matching algorithm to determine the closest reference point. However, the usage of all available technologies in smartphones and smart spaces for positioning purposes is not straightforward, and a proper platform able to integrate measurements and account for the error committed by each technology is necessary. This issue is even more complex when considering power constrains and mobility patterns of smartphones. Our solution is designed to be used as a library to be easily incorporated in different applications and services. Due to the two-phase nature of some algorithms like Wi-Fi fingerprinting or even generic proximity to base stations, a training application has to be developed to prepare the target indoor spaces to let the system work properly. Here it is necessary to register the fingerprints and the installed base stations, such as WiFi access points or Bluetooth tags. In our case, this application has been developed for Android platform, as reference app that integrates and tests our positioning library developed in Java. An important contribution to the solution presented in this paper is the way different positioning technologies integrate to get not only better position estimations, but also a reduction of required computation effort and, hence, energy consumption, prolonging the life of smartphone batteries. This is implemented through an intelligent handover mechanism between technologies or algorithms, which is fed by extra semantics included in the fingerprints. Apart from supporting the position calculation, registered fingerprints indicate the indoor-outdoor transition areas, floor changing locations, and extensible fields to further suggest positioning technologies are not needed. The paper is organized in the following parts. Section 2 presents most prominent related papers in the literature. An small survey of indoor positioning technologies is detailed in Section 3. The proposed architecture of our approach is given in Section 4.