# A Low-Cost Collaborative Location Scheme with GNSS and RFID for the Internet of Things
## 1 Introduction  
The Internet of Things (IoT) equipped with pervasive computing and ubiquitous intelligence is emerging as a novel computing paradigm [1]. The novel technologies characterizing the IoT have advanced real smart environments, enabling livable sensing, real-time monitoring, and powerful intelligence. Relating to intelligence concerns, one key feature is to employ the systems as autonomously as possible [2]. The current trend is to configure these services as location-aware services, called location-based services (LBS), i.e., applications driven by location information, for example, public infrastructure inspection by location in smart cities initiatives. This paper aims to contribute to the localization and tracking capacities of the IoT. The effort to automatically collect location information from public infrastructure has been receiving increased attention in the academic environment and industry, especially in the smart cities era. The exact location of public infrastructure is essential for active monitoring and a rapid information update of the infrastructure, which is helpful for government administrators and urbanplanning authorities. Existing public infrastructure inspection and inventory methods in smart cities include image recognition [3], field survey [4; 5] and light detection and ranging (LiDAR) information extraction [6], which has been widely used in traffic-sign detection [3], street-light pole location [6], tree extraction [7], and so on. For these methods, complex urban environments limit the practice owing to the heavy occupation of the target object and huge workload. Sensing-based radio frequency identification (RFID) technology has the potential ability to overcome this constraint upon location. However, IoT-based location in the large urban area is subject to issues of enormous investment and difficulties in deployment in a complex environment. With some specific application areas, for example, in an emergency scenario, rapid and easy implementation is crucial to rescue services. Therefore, it is essential to deploy devices and collect the location information of objects rapidly and economically. In general, most of the existing RFID-based location methods used on a large-scale and that are emerging have the following drawbacks. First, the deployment cost is expensive, because either every target object has to be mounted in one Global Navigation Satellite System (GNSS) receiver in a GNSS locating system or a number of reference tags in the RFID-based scheme. When applying such systems in public infrastructure asset management, the investment increases linearly with the number of objects. Second, prior knowledge of the target sites is required. For example, a pre-measured fingerprint database for signal matching in a fingerprint solution and the pre-measurement of reference tags' locations deployed at fix places are required. Finally, deployment has been limited mainly by the environment. For example, in an emergency scenario, timely rescue requires a rapid and easy deployment of a locating system. The position of reference tags needs to be carefully chosen for achieving high-level accuracy. Unfortunately, in some situations, such as museums, hardware infrastructure installation is not usually permitted. Therefore, in this paper, taking the aspects described above into consideration, we proposed a new low-cost collaborating location system, supporting the rapid and easy deployment of location tags. In our scheme, one mobile RFID reader mounted on one GNSS receiver simulates reference tags with a known location in the traditional scheme, which dramatically decreases the cost. A weighting algorithm is proposed to achieve high-level accuracy. This low-cost localization scheme has potential applications in infrastructure inspection and inventory on a large scale in smart cities. The rest of our paper is organized as follows. Section 2 presents related works. In Section 3, an overview of our proposed locating system is demonstrated.