# RSS-Based Localization and Mobility Evaluation Using a Single NB-IoT Cell+
## 2 Related Work  
Oftentimes, the preferred technology of a localization or tracking application highly depends on the requirements. In many IoT applications, energy efficiency, low cost and low complexity are given higher priorities than meter-level localization accuracy [4]. Therefore, LPWAN technologies such as Sigfox, LoRaWAN and NB-IoT are being used to estimate the location of a mobile device in a large-scale outdoor environment with minimal energy consumption [5; 6]. Regardless of the technology, different localization approaches exist. In general, they can be classified into fingerprint-based, range-based, and Angle of Arrival (AoA)-based approaches. In fingerprint-based approaches, a new measurement is matched against earlier collected measurements. For example, [PERSON] et al. use Channel State Information (CSI) of NB-IoT for indoor pattern matching localization [7]. In ranging experiments, the signal strength or timing information from a MT to a BS is translated into an estimated distance using a path loss model [8]. In AoA-based approaches, one estimates the direction of the transceiver with respect to the gateway [9]. Recently, we proposed a multimodal localization framework, enabling to switch to an optimal localization method, as well as location-based seamless handover mechanisms [10]. While the worldwide deployment of public NB-IoT networks continues rapidly, little interest has been shown by operators to roll out the OTDoA feature in their networks. The core concepts of OTDoA, i.e., Narrowband Positioning Reference Signal (NPRS) and RSTD measurements, are very well explained in [11]. The implementation of this time-based localization feature requires highly accurate clocks in the UE and precise synchronization between the eNBs, leading to an increased complexity and cost of both UE and eNBs. For this reason, most performance evaluations of OTDoA-based positioning are carried out in the form of simulation studies [11; 12; 13; 14]. Very recently, the first implementation of an OTDoA-capable NB-IoT chip was presented [15]. Measurements carried out in a laboratory setup result in a Root Mean Square (RMS) error of around 50 m. Important to note is the fact that due to the presence of a location server in an OTDoA-capable network, multiple eNBs can be used to estimate the location of a mobile UE. As an alternative to OTDoA, a 1-bit passive radar was used to reduce the complexity and energy consumption of the localization [16]. However, time-based localization methods are extensively studied in literature and are out of the scope of this paper. Due to the high cost, high complexity and unavailability of OTDoA, RSS-based localization methods are being investigated. In order to improve the accuracy of outdoor RSS fingerprinting, the distance and RSS representation in a Machine Learning (ML) algorithm should be optimized, as suggested in [17]. We carried out this optimization process for Sigfox in [18] and it was further analyzed by [PERSON] et al. in [19]. Using an optimal parameter set in a \(k\) Nearest Neighbors (\(k\)NN) algorithm, a mean urban accuracy of 340 m is achieved. Although the \(k\)NN algorithm might seem very basic, it outperforms other ML algorithms such as Support Vector Machines (SVMs) in terms of localization accuracy and latency [20]. In [3], we conducted the first RSS-based experiments with NB-IoT. In this paper, we will further analyze the accuracy of a simple proximity algorithm, a range-based algorithm using several path loss models and an optimized fingerprint-based algorithm. During the real-world localization experiments in [3], we experienced some mobility issues in the NB-IoT protocol. It was found that only the currently serving cell was reported to the UE, while neighboring cells in range were not reported. Obviously, this behavior is undesirable, since more participating eNBs result in higher localization accuracy [21].