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Area of Science:

  • Intelligent Transportation Systems
  • Wireless Sensor Networks
  • Machine Learning Applications

Background:

  • Roadside units (RSUs) are crucial for traffic safety via Internet of Things (IoT) and vehicle-to-infrastructure (V2I) communication.
  • RSU power and network quality are critical for reliable data transmission.
  • Advancements in lithium-ion batteries necessitate improved battery management systems (BMS) for health monitoring.

Purpose of the Study:

  • To evaluate the impact of Received Signal Strength Indication (RSSI) and transmission frequency on RSU battery current consumption.
  • To develop and compare machine learning models for predicting RSU battery power usage.
  • To enhance the dependability and operational lifespan of battery-powered RSUs.

Main Methods:

  • Collected data on static, battery-based RSUs using Global System for Mobile Communications (GSM)/General Packet Radio Services (GPRS).
  • Assessed the influence of RSSI and varying transmission frequencies on current draw.
  • Implemented and compared Random Forest (RF) and Support Vector Machine (SVM) machine learning models to predict battery consumption.

Main Results:

  • Random Forest (RF) model demonstrated superior performance in predicting battery current consumption.
  • RF achieved a coefficient of determination (R2) of 98%, outperforming SVM's R2 of 94%.
  • The study successfully predicted RSU battery current consumption based on RSSI and transmission frequency.

Conclusions:

  • Accurate battery health forecasting is essential for RSU dependability and operational duration.
  • The proposed ML approach aids road managers in making informed decisions regarding battery maintenance.
  • The methodology is adaptable for other remote wireless sensor network (WSN) and IoT applications.