An adaptive spreading factor allocation scheme for mobile LoRa networks: Blind ADR with distributed TDMA scheduling

Nowadays, the rapid proliferation of IoT opens up enormous potential for a broad range of ap-plications in every industry. Thanks to the chirp spread spectrum modulation and high receiver sensitivity, LoRa technology has an enormous potential to achieve long range transmission with low power consumption. The most critical bottleneck of the LoRa is the scalability limitations due to ALOHA-based random channel access for uplink packet transmission. When considering the scalability of LoRa networks, ADR (Adaptive Data Rate) is a key mechanism for the LoRaWAN protocol stack which controls the allocation of network resources by appropriately selecting the transmission parameters of end devices. The existing ADR algorithms have been mostly interested for static networks, where both end-devices and gateway(s) are fixed. Unfortunately, these algorithms do not provide the expected performance when it comes to mobility of end devices or gateway(s). This study introduces a clear method for blind ADR implementation, in which the spreading factor assignment made by end devices themselves. The performance of the proposed method has been validated through MATLAB-based computer simulations in urban, suburban, and rural environments. The obtained results show a promising performance in terms of packet reliability and fairness.