Dynamic Sleep Scheduling for Wireless TP Sensor Transmissions Based on Reinforcement Learning

Tire Pressure (TP)
sensors are an essential component of the
Tire Pressure Monitoring System (TPMS)
for smart vehicles. Each TP sensor is mounted on a wheel for real-time detecting and monitoring abnormal tire conditions to ensure driving safety. In the TPMS system, multiple TP sensors transmit sensing data randomly at a regular period to the TPMS receiver with a special communication module of 433 MHz. Traditionally, the TPMS receiver adopts a power saving mechanism to perform wake-up and sleep operation within a fixed cycle length for sensing data collection to save energy. However, the transmission delay may become worse once the number of TP sensors increases. Therefore, this letter tries to consider such problem and develops a dynamic scheduling approach for the TPMS by balancing the energy efficiency while ensuring data transmission delay. Specifically, the proposed approach exploits a reinforcement learning approach to dynamically control the wake-up and sleep periods of the TPMS receiver based on the designed reward function. Simulation results show that the proposed approach can significantly decrease data transmission delay by
$21.94\%\text{--}64.13\%$
in average and improve energy efficiency by
$5.29\%\text{--} 39.57\%$
in average, as compared with the existing schemes.