# 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
Inspired by the WCL algorithm [36], we applied a power-law based weight (Equation (4)) to increase the location accuracy. A sensibility analysis is conducted for the optimized parameter of \(\lambda\) in Equation (4). We set the \(\lambda\) value from 1 to 20 respectively, comparing the absolute error. Figure 6 shows the results, in which the best result was achieved when \(\lambda\) = 3. Figure 7 shows the absolute errors of the mean algorithm and the distance-weighted mean algorithm. Average accuracy is improved from 2.5095 m to 1.3904 m. For the maximum error group ID (b38), the accuracy has been increased from 4.1518 m to 2.8866 m.

## 6 Conclusions

In this paper, we proposed a novel low-cost and easy deployment locating system scheme, where RFID is combined with GNSS. In our proposed scheme, one mobile RFID reader is equipped with a GNSS receiver to calculate the target tags' locations. To reduce the system's cost, it takes advantage of the GNSS receiver to replace a large number of reference tags used in current RFID-based location systems. Another advantage of our scheme is its rapid and easy deployment, especially in an emergency scenario. To achieve fine-grained localization accuracy, we put forth a distance-based weight algorithm, rather than a simple average algorithm, in this system. The experimental results demonstrate the effectiveness and efficiency of our scheme, which has potential applications for public infrastructure inspection and management in smart cities. Nevertheless, this location scheme has room for further improvement. For example, the hardware signal synchronicity and outlier detection algorithms will optimize the RFID signal and GNSS signal, which will help to improve the location accuracy further. We also plan to expand our location systems to location-based business applications and services in smart cities initiatives. [PERSON] and [PERSON] conceived and designed the experiments; [PERSON], [PERSON], and [PERSON] performed the experiments; [PERSON], [PERSON], and [PERSON] analyzed the data; [PERSON], [PERSON], and [PERSON] wrote the paper. Figure 6: Sensibility analysis of parameter \(\lambda\). Figure 7: Comparison of algebraic mean algorithm and distance weight mean algorithm. **Funding:** This study is jointly supported by the National Natural Science Foundation of China (Grant # 41771412), the Beijing Natural Science Foundation (Grant # 8182015), Beijing Advanced innovation center for future urban design (Grant # UDC2016050100), and the Open Research Fund of Key Laboratory for National Geographic Census and Monitoring, National Administration of Surveying, Mapping and Geoinformation (Grant # 2016 NGCM10). **Acknowledgments:** The authors would like to thank the anonymous reviewers for their valuable comments. **Conflicts of Interest:** The authors declare no conflict of interest. ## References

* [PERSON] et al. (2010) [PERSON]; [PERSON]; [PERSON] The internet of things: A survey. _Comput. Netw._**2010**, _54_, 2787-2805. [CrossRef]
* [PERSON] et al. (2016) [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] An Indoor Location-Aware System for an IoT-Based Smart Museum. _IEEE Internet Things_**2016**, \(3\), 244-253. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON]; [PERSON] Improving Traffic Signs Recognition Based Region Proposal and Deep Neural Networks. In _Intelligent Information and Database Systems_; Springer: Cham, Germany, 2018; pp. 604-613. * [PERSON] (2000) [PERSON] Mobile mapping technology for road network data acquisition. _J. Geospat. Eng._**2000**, \(2\), 1-14. * [PERSON] and [PERSON] (2007) [PERSON]; [PERSON]; [PERSON] Field Survey of Permeable Pavement Surface Infiltration Rates. _[PERSON]. Irigg. Dmin. Eng._**2007**, _133_, 249-255. [CrossRef]
* [PERSON] et al. (2015) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Semiautomated Extraction of Street Light Poles From Mobile LiDAR Point-Clouds. _IEEE Trans. Geosci. Remote Sens._**2015**, _53_, 1374-1386. [CrossRef]
* [PERSON] et al.