Machine Learning-Driven APPs Recommendation for Energy Optimization in Green Communication and Networking for Connected and Autonomous Vehicles
Yueshen Xu, Jun-Wei Lin, Honghao Gao, Rui Li, Zhiping Jiang, Yuyu Yin, Yinchen Wu
Abstract
With the rapid development of connected and autonomous vehicles (CAVs), a large number of mobile and edge applications (APPs) have been developed and deployed through green communication and networking technology. The problem of high energy consumption during APPs usage becomes serious and in this paper, we propose to optimize energy usage through effective APPs recommendation. Traditional recommendation methods have been developed for years, such as collaborative filtering and latent factor models. But those methods are not designed for APPs recommendation and only focus on the use of historical records. We find that there are hidden relationships in the content and context of APPs in green communication and networking. In this paper, we develop a holistic APPs recommendation framework for CAVs in green communication and networking. The developed framework is driven by machine learning, where we propose two joint matrix factorization models and hidden relationship mining method. The machine learning-driven models can leverage the neglected information and learn latent features in APPs recommendation for CAVs. We used a real-word mobile and edge APPs dataset, performed sufficient experiments and compared our framework with well-known methods. Experimental results show that our framework produces the best performance in all test cases.