A Knowledge Distillation-Based Transportation System for Sensory Data Sharing Using LoRa

Abstract

Internet of Things (IoT) provides an adequate and effective approach to make an Intelligent Transportation System (ITS). The ITS uses sensory data to identify driver behaviour, transportation mode, pollution level, road health, and so on. The processing of sensory data in ITS requires high computation and storage, which is possible only on high-end machines. Moreover, sharing of sensory data in ITS also consumes colossal energy. In this article, we propose a Knowledge Distillation-based Transportation (KDT) system for sharing the sensory data based on the requirement of users. We use knowledge distillation technique for compressing a large deep neural network model. The compressed model is suitable for processing the data on limited resource devices in ITS. Next, we use Long-Range (LoRa) for sharing the data to the end-users. We also use game model for selecting the suitable deep learning model and LoRa resources, which helps to transfer the data form LoRa nodes to the end-users effectively. We evaluate the performance of KDT system by using different game and deep neural network parameters. © 2001-2012 IEEE.