# Hybrid Positioning for Smart Spaces: Proposal and Evaluation
## 2 Related Work  
Considering several technologies for indoor positioning are quite common in the recent literature, given that indoor spaces are gaining momentum with the relevance of smart buildings. For example, in [3] it is presented a framework where the system accepts new technologies in a modular fashion, but this work lacks full implementation and no evaluation tests are carried out. In addition, due to the age of the proposal (2012), new communication technologies such as Bluetooth LE or NFC are considered. Similar issues are found in the more recent contribution in [4], where NFC for indoor support is included, with the bid drawback of needing an important deployment of NFC tags to approximate indoor positions. Regarding switching between technologies, a handover procedure is also addressed in [5], presenting a similar approach to the one followed in the current paper, which is based on specific border zones that prepare the decision algorithm to select the next and most promising technology. This allows a seamless transition, since technologies like GPS need more time to warm-up. Another handover approach is introduced in [6], but focusing more on the accuracy estimation. Although GPS provides dilution of precision (DOP) information, for the case of Wi-Fi only the amount of available APs is used as accuracy measurement. A simpler approach is presented in [7], in which the handover triggering is performed taking into account the number of visible GPS satellites. In our case, regarding indoor fingerprinting, the accuracy is computed on the basis of how the nearest fingerprints are distributed in the space, once the pattern matching algorithm is performed. According to [8], this accuracy estimation method has a good trade-off between error and computational cost. Dead reckoning (DR) algorithms are very extended in the literature, but here some works that deal with multiple information sources are highlighted. For example, in [9] the DR algorithm is improved by the addition of landmarks to eliminate the cumulative error. No communication technologies are taken into account in this work, limiting the scope of the solution. The work presented in [10] suffers from the same limitation, but at least it proposes using digital maps and Bluetooth LE to perform DR drift corrections. These pedestrian DR proposals do not have the need to train the system, as it is the case of fingerprinting; however, they are not properly prepared to switch between other positioning technologies. Nevertheless, in [11] a handover mechanism similar to ours is considered, but technologies and algorithms are too coupled with the solution and the proposed DR algorithm is based on a good initial location approximation. The solution proposed in [12] uses a promising handover algorithm for switching between indoor and outdoor technologies. This is achieved thanks to the light and magnetic measurements made by the smartphone sensors, selecting GPS technology for outdoors and the pedestrian dead reckoning (PDR) algorithm for indoors. Our approach also offers this differentiation between outdoor and indoor operation, but it is achieved by the addition of extra semantics in fingerprints that easily allow the identification of indoor and outdoor zones. The issue with PDR approaches is the complexity of calculations and the need of continuously compensating cumulative errors. Due to this, PDR is not initially taken into account in our solution, but thanks to its modular design, it can be easily added in the future. This is a distinctive factor of our positioning system. It is considered as an open framework where multiple technologies and algorithms can coexist and contribute to improve accuracy. Moreover, infrastructure-based positioning technologies available in smart spaces, such us card-based authentication, are considered in our approach.