Correlating the Ambient Conditions and Performance Indicators of the LoRaWAN via Surrogate Gaussian Process-Based Bidirectional LSTM Stacked Autoencoder

Abstract

LoRa’s biggest advantage is its flexibility, which is the ability to increase or decrease data rate and range while decreasing or increasing sensitivity. Whenever propagation conditions change frequently, this function allows the spreading factor to be modified accordingly. Despite their efficiency and scalability, adaptive data rate algorithms ignore and fail to factor in the complex correlation between ambient weather parameters influencing the communication channel desig n. In this research, a Bayesian surrogate Gaussian pr ocess based bidirectional LSTM stacked autoencoder model (BSGP BLSTMSAE) is proposed to estimate the channel perfor-mance indicators such as received signal strength indicator (RSSI) and signal to noise ratio (SNR) and to determine the correlation between the ambient weather conditions and per-formance indicators for the LoRaWAN network. Bayesian op-timization algorithm has been used to optimize the hyper parameters of the developed model. A LoRaWAN experimental multivariate time series dataset has been used for the evalua-tion of the developed model, which upon testing and validation produces high accuracy in predicting the channel performance indicators and ambient conditions of the experimental Lo-RaWAN network. The mean absolu te error of the developed model was around 0.45. Thus, the proposed model can predict the link performance indicators and thereby assist in real time optimization of the transmission parameters to enhance the network performance in LoRaWAN based systems at different ambient conditions