Position Discrimination of a 2.4 GHz IEEE 802.15.4 RF Mobile Source Inside-Outside a Vehicle

Thanks to the recent advancements in the automotive industry, in smart city infrastructure and in electronics miniaturization, low-power wireless sensors are becoming a reference sensing technology connecting the internet of things (IoT) with the conventional world. This study provides an empirical solution to the modern radio location problem of inside-outside position discrimination for a mobile radio frequency (RF) source. The solution is delivered by a detection system that is fully enclosed inside a modern vehicle cabin, whereas the RF ranging is based solely on the received signal strength indicator (RSSI) and the individual sensor’s directivity achieved through shielding. The RF detection system is provided through a low-power wireless sensing network as a complete 2.4 GHz IEEE 802.15.4 solution, anticipating the future integration of this technology in the next generation of smartphones. The RSSI fingerprinting database, which is derived from empirical outdoor measurements for a range up to 5 m, delivers a consistent performance inside the highly RF-reflective vehicle cabin by exploiting the sensor position and directivity, focused on the front of each seat to avoid future human interference. Moreover, a theoretical propagation model based on Friis’ transmission equation constructed on system parameters shows a high correlation with the RSSI fingerprinting experimental model, supporting the consistency of the empirical model, and demonstrating a similar high inside-outside discrimination. The decision algorithm logics used for inside-outside discrimination illustrate a strong example for sensor group decision based on two spatial thresholds: maximum detection range for outside discrimination and the cabin width for inside discrimination. This study’s location system design creates exploitation possibilities beyond the vehicle environment. Various applications that require complete sensor encasement, such as road flushed traffic sensors or underground systems for parking space occupancy detection may benefit from this work’s findings.