A neural networks based caching scheme for mobile edge networks: poster abstract

Zhou Qin, Yikun Xian, Desheng Zhang

Abstract: Mobile edge networks are pervasive now due to the ubiquitous 4G networks and coming 5G networks, broad edge computing applications are enabled in the meantime, such as mobile bus WiFi. In this paper, we focus on the caching problem in the mobile edge networks and use bus WiFi as an example to further investigate. Mobile bus WiFi is a newly emerged service in modern cities, which provides convenience for citizens and gains certain benefits for operators via commercial advertisements and other services. While the vital challenge for the bus WiFi industry is the high cost of cellular traffic considering the massive number of users and longtime running hours of the bus system. To tackle this, we investigate the caching problem in a nation-scale bus WiFi network deployed in 22 cities of China with 34,377 WiFi devices. We then delve a fundamental question, i.e., how can historical visiting records help us predict future visiting events and further save the cellular traffic by caching. In detail, we propose a Deep Neural Networks (DNN) based method by considering bus WiFi users' historical visits to cached contents to save cellular traffic data for WiFi providers. We implement our method via the city-scale bus WiFi data and compare with a series of state-of-the-art models, the results show that our method achieves the best performance.