A hybrid model for energy-efficient Green Internet of Things enabled intelligent transportation systems using federated learning

Abstract

In the rapidly evolving Internet of Things domain, managing voluminous data streams while ensuring energy efficiency remains a cardinal challenge. Our research introduces a hybrid model designed specifically for an IoT system. This paper sheds light on our unique framework that emphasizes efficient communication and prioritizes energy conservation. The innovative model architecture is lightweight, fostering compatibility with resource-constrained devices and paving the way for personalized federated averaging during training. A crucial highlight of our methodology includes local training using lightweight optimization instead of traditional data transmission, reducing energy overheads considerably. Furthermore, we propose a novel approach for model aggregation using personalized energy-aware averaging. This process iteratively refines a global model by aggregating received updates, drastically reducing the data transfer compared to conventional methods. Lastly, we integrate an energy-aware updates management system, continually monitoring device energy metrics and making adaptive data transmission and model update decisions. The model ensures optimal participation in global updates by consistently monitoring devices’ energy metrics and setting adaptive thresholds. This study leverages the richly annotated Udacity Self-Driving Car Dataset provided by Roboflow to evaluate a novel federated learning model to optimize energy consumption for IoT systems. Our experiments simulate real-world collaborative learning scenarios using the TensorFlow Federated (TFF). Our focus on energy consumption evaluation revealed that our proposed model offers significant energy savings when analyzed based on data communication round time and individual node training duration. A comparative analysis between the proposed and traditional models uncovers substantial improvements in energy efficiency. In our experiments, the proposed approach demonstrated a commendable accuracy of 93.27%. Notably, the local communication time was streamlined to 1.21 s, while the global communication was efficiently clocked at 4.76 s. When compared to traditional methods, these results not only underscore the effectiveness and efficiency of our methodology in the context of IoT systems but also highlight its superior performance.