## 5 Conclusions  
In this paper, we provided a smartphone precise point positioning model based on the BDS PPP-B2b orbit and clock corrections, the mixed-frequency GNSS observations, and the hybrid weight function based on the C/N\({}_{0}\) and the satellite elevation angle. The purpose of introducing the BDS PPP-B2b orbit and clock corrections instead of these IGS real-time \begin{table}
\begin{tabular}{c c c c} \hline
**Positioning Modes** & **East (m)** & **North (m)** & **Up (m)** \\ \hline GPS-PPP-b2b-MF-Com & 0.68 & 0.65 & 3.58 \\ GB-PPP-b2b-SF-Ele & 2.76 & 3.06 & 5.21 \\ GB-PPP-b2b-SF-CN\({}_{0}\) & 0.78 & 0.98 & 3.87 \\ GB-PPP-b2b-SF-Com & 0.70 & 0.83 & 3.29 \\ GB-PPP-b2b-MF-Com & 0.61 & 0.55 & 3.16 \\ GB-PPP-ULT-MF-Com & 0.65 & 0.88 & 1.14 \\ GB-PPP-FIN-MF-Com & 0.60 & 0.79 & 0.81 \\ \hline \end{tabular}
\end{table} 
Table 5: Positioning RMS of dynamic PPP accuracy for MI8 based on different weight functions. products into the smartphone positioning system is to ensure the possibility of smartphones' real-time high-accuracy positioning in China and its surrounding areas. This is because there is no communication limitation for the BDS PPP-B2b service, while the IGS real-time service is limited by the internet network connections. The hybrid weight's function is to overcome the impacts of both the C/N\({}_{0}\) and satellite elevation angle-dependent weight models on the parameter estimation accuracy. According to the static and dynamic experiments, we know that the positioning accuracy of a smartphone in real time can be upgraded visibly by using the presented PPP model compared to existing positioning methods (the built-in single point positioning and the conventional PPP). According to the statistics in the static experiment, the positioning accuracy of the presented smartphone PPP model can provide a positioning accuracy of 0.51 m, 0.67 m, and 0.71 m in the east, north, and vertical components in terms of RMS values, which brings about 67.55%, 72.18%, and 77.01% positioning accuracy enhancements compared to the PPP based on the satellite elevation angle-dependent weight model. In the dynamic experiment, the positioning accuracies of the presented smartphone PPP based on the PPP-B2b service are 0.61 m, 0.55 m, and 3.16 m in the three directions, and are even higher in the horizontal component than in the IGS final precise product-based PPP. In general, by introducing the hybrid weight function, about 5.13%, 18.00%, and 15.15% positioning improvements and about 71.10%, 72.53%, and 53.93% can be obtained while comparing with the solutions from the C/N\({}_{0}\)-dependent PPP and the satellite-elevation-angle-dependent PPP, respectively. Furthermore, after introducing the mixed-frequency measurements into the hybrid weight function-dependent smartphone PPP, further 14.63%, 17.99%, and 9.21% positioning improvements can be obtained. Finally, by integrating the mixed-frequency PPP model and the hybrid weight function in a smartphone PPP, roughly 76.64% and 59.84% positioning enhancements in the horizontal and vertical components can be obtained compared to the results from the conventional smartphone positioning model. Considering that smartphone users focus more on the horizontal positioning than the vertical, the smartphone PPP model presented in this paper theoretically can meet the high-accuracy requirement for most IoT applications at present. This work has proven the potential application of a smartphone PPP based on the BDS PPP-B2b service, mixed-frequency observations, and the hybrid weight function. However, it still cannot overcome the impact of GNSS satellite signal-blocking. To address this, our work in the future is to introduce inertial measurement units, geomagnetic data, barometer data, etc., into such a PPP model. In addition to the BDS PPP-B2b service used in this paper, our work will also explore the application of services like Galileo HAS in the precise positioning of smartphones. Conceptualization, Z.G. and Z.W. ; data curation, Z.W. and S.L. ; formal analysis, Z.W. and S.L.