In recent years, Low Power Wide Area Networks (LPWAN) have received much attention, due to the rise of the Internet of Things (IoT) and the need to localize devices in these long-range networks, using minimal power consumption. Asset tracking is one of the classic applications of LPWAN localization. However, the more accurate a localization algorithm, the more application potential (e.g. home automation, health care solutions and smart cities) there is to use this algorithm. Therefore, we need advanced technologies and algorithms to improve the accuracy and reliability of LPWAN localization. Although feature-based localization is widely used in indoor environments, we will extend the use of this methodology to outdoor environments. Features are defined as signal characteristics, such as signal strength. The class of feature-based localization can be subdivided into different subclasses. Fingerprinting and ranging are two of the most important techniques in the featurebased class. In this research, we will investigate new and existing algorithms to increase the accuracy and reliability of feature-based localization techniques in LPWAN. A comparative study between the accuracy and reliability of LPWAN technologies (Sigfox, LoRaWAN and NB-IoT) will be made as well.