# A Low-Cost Collaborative Location Scheme with GNSS and RFID for the Internet of Things
## 3 Collaborative Scheme with Global Navigation Satellite System (GNSS) and RFID Location
### Tag-Location Principle
The root mean error (RME) was opted to measure the absolute difference between the true coordinates and the computed coordinates (Equation (5)), where \((x_{\mathrm{r}},y_{\mathrm{r}})\) is the ground truth coordinates of the target object; \((x_{\mathrm{i}},y_{\mathrm{i}})\) is the estimated coordinates; \(n\) is the triangle number; and \(\varepsilon\) represents the RME error. The smaller the \(\varepsilon\) is, the better the performance of the location scheme:

\[\varepsilon\ =\ \frac{1}{n}\sum_{i=1}^{n}\sqrt{\left(x_{\mathrm{r}}-x_{\mathrm{ i}}\right)^{2}+\left(y_{\mathrm{r}}-y_{\mathrm{i}}\right)^{2}}. \tag{5}\]

Figure 5 shows one of the experiments, where the triangle markers indicate the RFID reader trajectory, and the star is the true position of the RFID tag. The largest distance between the reader and the tag is 24.89 m, and the shortest distance between them is 3.23 m. In this experiment, 49 groups of triangles are generated with a different number of triangles in each group. The coordinates are calculated based on Equations (3)-(5). The detailed coordinate information is shown in Table 1. The result show that the average error is 1.3904 m, the max error is 2.8866 m, and the minimum error is 0.0532 m. Compared to the error of 14.1497 m that is tested outdoors when only the GNSS receiver is used (the solid rectangle in Figure 5), the location accuracy is largely improved in our proposed location system. This accuracy can support infrastructure asset management in smart cities. Also, this scheme only requires one RFID reader and one portable GNSS receiver. Therefore, it is practical for infrastructure asset management in large areas. Figure 4: Experimental devices and area. (**a**) Overview of experimental area; (**b**) overview of target object; (**c**) experimental devices; (**d**) detail of target object. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Triangle** & **Triangle** & \(x_{\text{c}}\) **(m)** & \(y_{\text{c}}\) **(m)** & **Root Mean** & **Triangle** & **Triangle** & **x (m)** & \(y_{\text{c}}\) **(m)** & **Root Mean** \\
**Group ID** & **Number** & & & & & & & & & \\ \hline



& - & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The result of the experiment (where \(x_{\text{c}}\) and \(y_{\text{c}}\) denote the coordinates of this paper, for the concise description of the table, the digital prefix number is shown in the table headline). ## 5 Discussion

More than six billion people (about 70% of the world's population) will live in cities by 2050 [38], resulting in a pressing need for urban public infrastructure [39]. IoT increasingly plays an essential role in meeting this fundamental requirement. Gartner indicates the market for IoT will explode and the number of connected devices will reach 21 billion by 2020 [40]. In the era of IoT and smart cities initiatives, a low-cost and easily deployed location scheme is crucial to LBS. In this section, we discuss the effectiveness of the proposed location method by combining GNSS with RFID. We will discuss the cost and deployment aspects of our scheme, compared to existing techniques first. Then, the accuracy of our scheme is evaluated. ### Low-Cost and Easy Deployment

It is clear that the IoT plays a predominant role in localization and tracking capabilities for smart cities. As mentioned in the review of related work, existing location schemes have drawbacks in cost and deployment. Existing RFID-based location schemes incur high costs for smart cities. RFID, GNSS, and GSM have been used in a variety of projects to provide the location of the target, but GNSS as the popular outdoor location method is limited in high-rise buildings, indoor and underground environments due to the weak signal. Methods combining GNSS, GSM and RFID to track vehicles were proposed [33], in which RFID was used in complex environments to overcome the poor signal drawback of GNSS [41]. Reference tags have been proved as a low-cost solution, but the vast number of reference tags means high investment [42].