Upper Confidence Bound Based Communication Parameters Selection to Improve Scalability of LoRa@FIIT Communication

Number of embedded devices connected to the Internet is rapidly increasing, especially in the era of the Internet of Things (IoT). The growing number of IoT devices communicating wirelessly causes a communication-parameters selection problem, implying the increasing number of communication collisions. Multiple factors of IoT networks signify this problem, such as inability of communication-channel listening prior to the transmission (due to longer distances), energy constrains (due to inability of powering devices from the grid), or limitation of duty cycle and high interference (due to usage of an unlicensed band in communication technologies). This article is focused on alleviating this problem in LoRa networks, which is one of the most promising technologies for long-range and low-power communication. We utilize the existing LoRa@FIIT protocol to achieve ultra energy-efficient communication. In the proposed architecture, the scalability of the LoRa network is increased by modifying the communication-parameters selection algorithm. The original adaptive data rate algorithm is efficient in static environments. However, when considering mobile nodes, upper confidence bound algorithm can provide more energy-efficient communication, as shown in this work. The end nodes are partially independent from the network server, able to modify their communication parameters on their own. The adaptation to network changes is thus faster. Also, by ensuring the quality of service mechanism at each node in the infrastructure, the application domain of the proposed architecture is widened. The simulation-based experimental results showed a significantly reduced number of collisions for mobile nodes, which reduces the channel congestion and the wasted energy by retransmissions.