Enhancing Car Tracking Systems With DNN-LoRa

Abstract

This paper outlines a fusion of deep neural network and LoRa technology for car tracking optimization. LoRa's SX1301 gateway (GW) applies the Bayesian game parameter selection (BGPS) approach for switching the transmission power at the network server. At the same time, the car node (CN) uses a hybrid model to change the spreading factor and data rate. By reducing power losses among GWs, BGPS substantially increases the packet success rate (PSR) at the CN. The hybrid model enables adaptive decision-making, resulting in improved tracking precision and reduced latency with efficient energy usage. However, it exhibits a 95.9% PSR with increased latency noted at the lower bandwidth.