Another Prediction Method and Application to Low-Power Wide-Area Networks

The Internet of Things has become increasingly widespread, and low-power wide-area (LPWA) technology has attracted attention as one of its elemental technologies. LPWA technology achieves wide-area communication without consuming a large amount of energy, which facilitates various types of applications for sensing and collecting data. LoRa (Long Range) is a type of LPWA communication technology that uses unlicensed bands. Because it is possible to build a self-managed network with LoRa, many services using LoRa are scattered in the same area without an administrator. As a result, the communication performance of LoRa may be degraded due to unintended radio interference. However, because many LPWA techniques, including LoRa, have a low data rate, it is difficult to gather sufficient control information to avoid the degradation of communication performance. In this chapter, we propose a method for predicting the network congestion state based on Yuragi learning that enables prediction from fluctuating and noisy data by successive Bayesian estimation. Through computer simulation, we demonstrate that the network state can be predicted by our proposed method with a small amount of control information.