# RSS-Based Localization and Mobility Evaluation Using a Single NB-IoT Cell+
## 1 Introduction  
With the number of Internet of Things (IoT) devices that keeps growing rapidly, it has become indispensable to provide a location estimate of these devices in end user applications. For example, the location of mobile air quality sensors is essential when monitoring the air quality in a city. Oftentimes, the devices are battery-equipped and need to last for several years. Therefore, it is of utmost importance to localize them with as little energy as possible. Traditional localization applications use a Global Navigation Satellite System (GNSS) in order to locate or track the position of a mobile device, with an accuracy of up to a few meters. However, satellite-based solutions have their limitations in the context of IoT. First, IoT devices often move between indoor and outdoor scenarios, e.g., an asset lying in a warehouse waiting to be transported. Since satellite signals do not penetrate well through walls, satellite-based solutions are not suitable in this situation. Second, a GNSS receiver consumes a significant amount of energy, reducing the expected battery lifetime from a few years to a few days or weeks, depending on the location update rate. Finally, a GNSS receiver is able to locate itself based on the Time of Flight (ToF) and constellation of available satellites. However, in many IoT applications, the position of the mobile device needs to be known at the network or application side, which consequently requires the deviceto send the estimated GNSS coordinate over a wireless network to the application, consuming even more energy. In contrast to GNSS solutions, Low Power Wide Area Networks (LPWANs) provide an energy-efficient solution to communicate short messages between a mobile device and an end user application. Moreover, the Base Stations (BSs) of an LPWAN network can be used to estimate the location of a Mobile Terminal (MT). While it is unlikely that this approach will be as accurate as GNSS-based approaches, LPWAN localization is further encouraged in privacy sensitive applications. More specifically, sometimes it is not desired to know the exact location (i.e., within a few meters) of a person or a package. For a logistics company, for instance, it is usually sufficient to have a rough location estimate of an asset. As LPWAN technologies often use star topologies, non-cooperative localization (NCL) schemes are mostly considered in LPWAN-based localization. In NCL schemes, end devices cannot directly communicate with other nearby end devices. In contrast, cooperative localization (CL) systems can lead to higher accuracies; however, they do not outweigh the increased device complexity and power consumption, which play a significant role in many LPWAN applications. For the same reason, it is often preferred to perform the localization in the cloud, instead of on the device itself. LPWAN localization systems may be approached using various technologies such as Sigfox, LoRaWAN and Narrowband Internet of Things (Narrowband IoT (NB-IoT)). The focus in this research is on outdoor localization using a public NB-IoT network. An overview of the history of NB-IoT releases, as well as detailed specifications of both physical and data link layers of NB-IoT can be found in [1]. Released in 2016 by the Third Generation Partnership Project (3 GPP), NB-IoT has several advantages over other technologies, making it one of the most popular LPWAN technologies. In contrast to Sigfox and LoRaWAN, the cellular-based NB-IoT technology operates in licensed frequency bands. Consequently, there are less duty cycle limitations and interference effects. Moreover, the use of existing Long Term Evolution (LTE) infrastructure enables providers to efficiently deploy a scalable NB-IoT network. Release 14 of the 3 GPP introduced Observed Time Difference of Arrival (OTDoA) in NB-IoT [2]. This downlink-based localization approach combines the Reference Signal Time Difference (RSTD) measurements between neighboring cells and a reference cell to estimate the location of a mobile User Equipment (UE).