  The scale of wireless technologies penetration in our daily lives, primarily
triggered by the Internet-of-things (IoT)-based smart cities, is beaconing the
possibilities of novel localization and tracking techniques. Recently,
low-power wide-area network (LPWAN) technologies have emerged as a solution to
offer scalable wireless connectivity for smart city applications. LoRa is one
such technology that provides energy efficiency and wide-area coverage. This
article explores the use of intelligent machine learning techniques, such as
support vector machines, spline models, decision trees, and ensemble learning,
for received signal strength indicator (RSSI)-based ranging in LoRa networks,
on a training dataset collected in two different environments: indoors and
outdoors. The suitable ranging model is then used to experimentally evaluate
the accuracy of localization and tracking using trilateration in the studied
environments. Later, we present the accuracy of LoRa-based positioning system
(LPS) and compare it with the existing ZigBee, WiFi, and Bluetooth-based
solutions. In the end, we discuss the challenges of satellite-independent
tracking systems and propose future directions to improve accuracy and provide
deployment feasibility.
