# Hybrid Positioning for Smart Spaces: Proposal and Evaluation
## 6 Conclusions  
A holistic positioning framework, which is open to multiple indoor and outdoor technologies and algorithms, is presented in the paper. Smart deployments are especially considered and provide an essential support to improve performance. The system maintains accuracy by selecting the most appropriate solution every time based on the estimated error. The fingerprinting algorithm and model are essential in the system, by providing not only position estimates, but also including extended semantics with information that supports the handover between technologies and algorithms used, and the detection of especial situation such as floor changing or building entering/exiting. Moreover, the advantages of the architecture to hybridize position estimates is showed with a reference development of improving the base proximity algorithm with information coming from nearby fingerprints.  
The actual implementation considers GPS, WiFi, Bluetooth LE and card ID technologies, together with fingerprinting and proximity positioning algorithms. All of them have been used in a reference testbed aimed at presenting a realistic deployment scenario. This involves not only indoor situations, but also outdoor parts surrounding the building and different handover scenarios in which it is necessary to change between different technologies and algorithms to provide the best position estimation. Challenging scenarios when entering and exiting the building, using the lift for floor changes and walking near walls are considered in the tests. The results obtained in the indoor/outdoor scenario present an average real error of 4.62 \(\pm\) 0.31 m. This implies that our system provides good position estimations even under challenging settings.  
It is planned to extend the positioning system with new technologies and algorithms, considering our background gained in the past [26; 27]. The hybridization approach among technologies to compute a merged solution paves the way to find more accurate and cost-efficient solutions for mobile devices. Next steps also include the evolution of our parallel line about reducing computational cost of fingerprinting matching tasks by offloading tasks into the cloud, by including a flexible system with both edge and far cloud support.  
Conceptualization, P.J.F., J.S. and A.F.S.; methodology, P.J.F., J.S. and A.F.S.; software, P.J.F.; validation, P.J.F. and J.S.; formal analysis, P.J.F. and J.S.; investigation, P.J.F. and J.S.; resources, J.S. and A.F.S.; data curation, P.J.F.; writing--original draft preparation, P.J.F.; writing--review and editing, P.J.F. and J.S.; visualization, P.J.F., J.S. and A.F.S.; supervision, J.S. and A.F.S.; project administration, J.S. and A.F.S.; funding acquisition, J.S. and A.F.S. All authors have read and agreed to the published version of the manuscript.  
This work has been supported by the Spanish Ministry of Science, Innovation and Universities, under the Ramon y Cajal Program (Grant No. RYC-2017-23823) and the project PERSEIDES (Grant No. TIN2017-86885-R); and by the European Commission, under the 5G-MOBIX (Grant No. 825496) project.  
The authors would like to thank University of Murcia for letting us use the Faculty of Computer Science building and its facilities during the execution of experiments.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Stats** & **HYBRID** & **GPS** & **NET** & **WIFI** & **BTLE** & **CARD (NFC)** \\ \hline Mean & **4.62** m & 12.71 m & 15.04 m & 8.45 m & 6.53 m & 2.61 m \\ Maximum & **19.23** m & 38.40 m & 40.85 m & 37.56 m & 17.69 m & 4.78 m \\ Minimum & **0.12** m & 0.53 m & 2.53 m & 0.12 m & 0.51 m & 0.65 m \\ Standard deviation & **4.08** m & 9.39 m & 13.19 m & 13.78 m & 3.91 m & 1.63 m \\ Confidence interval 95\% & **0.31** m & 0.99 m & 1.07 m & 1.03 m & 0.45 m & 0.66 m \\ Samples & **664** & 342 & 573 & 671 & 279 & 23 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics obtained from the error values collected for each technology.