A Framework for Fleet Management using IoT and Predictive Analytics

The integration of IoT (Internet of Things) technology in vehicles has become increasingly imperative in today’s rapidly evolving automotive landscape. IoT empowers vehicles to become more intelligent, transforming them into interconnected nodes capable of real-time data exchange. This capability is essential for enhancing road safety, as IoT-enabled vehicles can communicate with one another. In an era of data-driven decision-making, IoT in vehicles is not just a convenience but a necessity for safer, more efficient, and environmentally responsible transportation. In cosmopolitan cities like Bengaluru, the city’s roads, once renowned for their tree-lined beauty, now find themselves choked with a relentless surge of vehicles. There is heavy traffic throughout the city with the air quality deteriorating in the city. Despite efforts to expand infrastructure and promote public transportation, Bengaluru’s traffic remains a formidable challenge. This work proposes a simple framework for an IoT enabled visualization and predictive analytics in the context of fleet management in Bengaluru’s scenario. This architecture could be further enhanced using various other sensors and machine learning models. This framework was implemented, simulated and analyzed using MATLAB and ThingSpeak application.