Experimental Parameter Optimization for Adaptive LoRa Modulation in Body-Centric Applications

The relentless expansion of the Internet of Things is fueled by constant innovations in low-power wide-area network technologies. Industry forerunners such as LoRa, SigFox and NB-IoT continuously seek to achieve larger communication ranges while consuming as little energy as possible. These efforts, in turn, facilitate vast performance increases in a wide range of related application areas, such as body-centric communication. For example, recently, LoRa modules have been integrated onto wearable textile antennas, thereby greatly extending the range of the body-centric networks. However, as the resulting communication links need to accommodate mobile users, many nodes will regularly be communicating using suboptimal LoRa modulation parameters as these users move around. Adaptive LoRa modulation aims to solve this by optimizing these parameters in real-time, based on the location of the user and the actual performance of the wireless link. In this contribution, the optimal settings for one of the key LoRa modulation parameters, the spreading factor, are experimentally determined. More specifically, it is shown that only a very limited number of spreading factor options should be used in an adaptive LoRa modulation scheme. The results also yield insight into more general LoRa communication aspects by analyzing packet reception and channel throughput data gathered in an urban environment.