# 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
Virtual reference elimination (VIRE) based on LocAtioN iDentification through the dynaMic Active Rfid Calibration (LANDMARC) system is proposed to replace the physical reference tags with virtual ones, which dramatically decreases the installation cost while keeping similar accuracy [43]. But for smart cities applications in a large area, a large amount of physical reference tags is necessary to generate these virtual reference tags. Figure 5: RFID reader moving trajectory of one experiment. The deployment pattern has different uncertainties for location accuracy. Many deployment patterns such as cell tag and hexagon deployment [44], triangular deployment [45] and key reference tag [46] have been validated in locations, which show that the layout pattern and number of reference tags influence positioning accuracy [47]. Therefore, research conducted spatiotemporal accuracy analysis with different deployment patterns and reader numbers [27]. This increased the deployment time and technological difficulty, especially in an emergency scenario in which time is crucial for decision-making processes. In research on combining RFID and GNSS for location and tracking, complementary location and location-sensing are the two main categories. For the collaborative location, RFID is a complementary method where the GNSS signal is weak or lower, such as indoor or underground environments [48; 41]. In a construction site, the four or more mobile RFID reader-mounted Global Positioning System (GPS) is used to locate the static construction material [49]. However, the topology of readers must be maintained during movement, which is a critical constraint in practice. For the location-sensing model, GNSS responses the location and tracking, while the RFID senses the target object, such as solid waste monitoring [50], logistics management [51], and vehicle location within connected vehicle networks [52]. According to the above analysis, low-cost, easy deployment and immunity to dynamic environment location schemes are necessary for smart cities. In our proposed method, one moving RFID reader-mounted GNSS receiver simulates the reference tags, which massively reduced the cost and project investment. Although the GNSS location error can propagate to tag location accuracy, it should be noted that the location accuracy could be further improved by implementing methods such as dead-reckoning and Kalman filtering [53]. With the wide application of ground-based augmentation systems (GBAS) at a city scale, GNSS location accuracy can be enhanced [54]. Also, the redundant observations method as a difference calculation model can reduce the GNSS error partly. From our experiment results, we can see a single GNSS receiver error is 14.1497 m, while it is only 1.3904 m in our location scheme. Therefore, our method with one mobile RFID reader and one GNSS receiver has ensured the low-cost and easy deployment features with sufficient location accuracy. However, synchronicity between the RFID signal and the GNSS signal relies on a computer, which makes it challenging to achieve higher accuracy, but this can be improved by the hardware signal synchronously. ### Location Determination Methods

Estimating a target coordinate from candidate points must consider the influence factors and computation complexity. The algebraic mean algorithm is a simple and effective method to calculate the right position from candidate points with the same weight for all factors. It has been used in position recognition from the image [55], and spatial co-location analysis [56]. However, for RFID-based location, a signal propagation error still exists in the averaged result. Some literature shows that the relationship between the spatial transmission distance and RFID signal is a power function [57]. Cui used a specified number of readers, the nearest number of reference tags and the distance between reader and tag as the synthetic weight for the location calculation [58]. Although better accuracy is achieved, such a method adds more computation complexity.