# Sigfox and LoRaWAN Datasets for Fingerprint Localization in Large Urban and Rural Areas
## 1 Introduction  
The growing importance of the Internet of Things (IoT) creates a rapidly increasing necessity for wide area communication standards that guarantee reliable connectivity between a multitude of IoT devices. For this purpose, researchers have been developing various Low Power Wide Area Network (LPWAN) standards. IoT requires LPWAN standards to support long-range communication and high scalability of end-devices at a low cost. Also, ubiquitous indoor and outdoor connectivity as well as ultra-low power consumption are crucial aspects for reliable, transparent IoT applications that work for years on small batteries [1]. To meet these requirements, numerous measures have to be considered for LPWAN design, such as modulation techniques, network topology, hardware complexity, use of radio frequent spectrum and regulations. In general, a trade-off between these measures and data rate has to be made [2]. Context-awareness is an important aspect of IoT applications. This means that, depending on the application, an IoT device can alter its behavior based on the measurements it has conducted in its environment. In order to create context-awareness for IoT applications, the location of the device hasto be obtained with minimal location error. Currently, Global Navigation Satellite Systems (GNSS) are the most commonly used method to do so. Although GNSS systems provide accurate location estimations, they have a few drawbacks which oppose some of the aforementioned IoT requirements. Firstly, GNSS receivers generally consume a lot of power, which limits the overall battery lifetime significantly. For instance, Global Positioning System (GPS) receivers consume 30-50 mA while obtaining a GPS fix, which can take tens of seconds [3]. Furthermore, the GNSS location data will only be available on the device itself; forwarding the data through wireless communication involves additional power consumption. On the other hand, wireless positioning techniques can be applied to LPWAN communication messages without having to send additional messages which contain location information. Consequently, a device can be located without increasing power consumption. Secondly, GNSS systems tend to lose connectivity in indoor environments. Since many LPWAN standards operate in sub-GHz ISM bands, they can be used outdoor as well as indoor. Of course, GNSS remains a favorable solution for applications which require continuous high accuracy localization. However, many IoT use cases do not require such accurate location estimations and have more interest in long battery life-time. Therefore, wireless positioning based on LPWAN communication is an interesting alternative for long-term, low power localization. Wireless positioning has been a prominent research topic for decades [4; 5; 6]. Many techniques which were developed over the years are still suitable for localization with modern wireless technologies. These techniques estimate the location of a transmitter or receiver by analyzing physical characteristics of the communication link such as Received Signal Strength (RSS), timing information, signal phase, etc. This paper presents datasets which can be used for a RSS localization method called fingerprinting. With this method, a training database of communication messages is built by storing their transmitter location as well as the Received Signal Strength Indicator (RSSI) for all receiving base stations. Afterwards, RSSI measurements of new messages are matched to the fingerprints in the training database to estimate the transmitter's location, e.g., by applying a \(k\)-Nearest-Neighbor (\(k\)NN) analysis, probabilistic methods, support vector machines, decision trees, etc [5]. A main advantage of fingerprinting is that the locations of the base stations do not have to be known. In order to minimize the location estimation error, an extensive site survey has to be conducted to create a complete training database. Therefore, fingerprinting techniques are mostly used in constricted, indoor areas [7; 8].