# Cloud-Based Single-Frequency Snapshot RTK Positioning
## 1 Introduction  
In recent years, applications of Internet of Things (IoT) and Location Based Service (LBS) have gained much greater attention from the industry [1] and research communities [2; 3; 4]. Behind these popular concepts are the fast development of technologies such as Global Navigation Satellite Systems (GNSS) [5], cloud computing and 4G/5G communication. The great pace of these developments brought a number of enabling capabilities; we mention here three main aspects related to the work in this article. First of all, mobile communication modules allow r aw data to be transmitted from the sensor front-end to a remote data center for further processing. The fast pace of the 4G/5G base station deployment has made internet access available for much larger areas with faster speed and lower delay. Secondly, an increasing number of cloud computing platforms have been developed by different service providers; better computation power and greater data storage capabilities are available to individuals or companies with easy access and great flexibility. Finally, an ever increasing number of GNSS reference stations are being deployed worldwide and reference correction data distributed ensuring shorter baseline distances to the GNSS user receivers, which is critical for Real Time Kinematics (RTK) applications [6]. With all the benefits brought by these recent developments, cloud-based snapshot positioning has been proposed aiming at IoT applications such as asset tracking and logistics. The basic work flow was introduced [7] and primary results were evaluated inmany papers [8; 9; 10]. The cloud-based snapshot positioning technique collects a short period of GNSS signal, named as a snapshot, and places the subsequent signal and data processing procedures on the cloud in order to take full advantage of the previously mentioned benefits of cloud computing. This technique has improved the efficiency substantially in terms of power consumption compared to the conventional receivers since the majority of computation burdens are moved to the remote server side. Furthermore, snapshot receivers can enable a duty cycle mechanism to further reduce the power consumed on the user side, that is, switch off the receiver or shift to a sleep mode when there are no positioning needs. On the contrary, conventional receivers have to keep running on board until valid GNSS measurements can be generated before obtaining a RTK solution. This advantage is extremely useful for certain applications such as asset tracking that requires position information only a few times a day. Moreover, this technique also takes full advantage of the high computational capability of the cloud computing infrastructures, so that the complex tasks such as signal acquisition can be performed faster and the time to obtain a solution can be reduced. These advantages of cloud-based snapshot positioning come along with some drawbacks, mainly the need of a communication module for sending snapshots to the cloud and the possible latency of positioning results caused by the data transmission and signal processing delay on the cloud. While the advantages of cloud-based snapshot positioning in power efficiency and computational capabilities have been well addressed by the GNSS industry [1; 8], its positioning accuracy has not yet received adequate attention. In order to improve the positioning accuracy, the authors have developed a new GNSS positioning technique on the basis of cloud-based snapshot positioning, which is called Snapshot RTK (SRTK). In [11], the authors proved theoretically that SRTK is feasible under certain Signal-to-Noise-Ratio (SNR) conditions after experimenting on simulated data. Based on that, we have recently confirmed the feasibility of SRTK using GNSS signals collected from the real world, although our experiments were limited to zero-baseline configurations only [12]. The present article extends this previous work and provides a deeper insight into the strategies used in the measurement generation process.