# Proactive Caching at the Edge Leveraging Influential User Detection in Cellular D2D Networks
## 1 Introduction  
Generalised shifts in the world of mobile networks have been driven by the unprecedented growth in mobile data traffic. The exponentially growing number of connected devices and the unquenchable third for better mobile broadband experience is also a driving force behind this evolution. The story of 5G is no more different as it is envisioned to provide a gigabit experience and virtually zero latency while withstanding an expected 500-fold increase in mobile video traffic over the next ten years. It is predicted that 75% of global mobile data will be video content in which 6.7% will be machine-to-machine (M2M) communication. Such massive growth of multimedia traffic was further fueled by social media feeds, such as Facebook and Twitter (representing 15% of the traffic [1]). This trend is undoubtedly going to stress the capacity of core networks, wireless links and mobile backhauls to their limits eventually leading to poor quality-of-experience (QoE). In order to cope with this issue, a 5G network is gearing up with various technologies including device-to-device (D2D) communication [2], Massive MIMO (Multiple-input multiple-output) [3; 4], Device-centric architecture [5], Small Cells, Caching, mmWave communication [6] all orchestrated by self organizing networks (SON) [7]. Among them, caching through D2D mode of communication is being considered a promising technology aimed for reducing backhaul load and minimizing latency [5]. The key idea of D2D enabled caching is to store the popular content of the network at the user end. The key benefit here is that it brings likely to be accessed content as close as possible to the users. This approach can reduce latency substantially, a key challenging requirement in 5G [8]. It can also increase the effective throughput and reduce the backhaul load [9]. The three key research questions in designing an optimal caching enabled next generation Radio Access Network (RAN) are: _Where to cache? How to cache?_ and _What to cache?_ Where to cache focuses on the problem of caching the content over the network in such a way that it reduces the network traffic. How to cache focuses on multiple ways of content caching, and what to cache focuses on the problem of finding content having the highest probabilities to be used in the near future. In order to address these questions, various attempts have been made by the researchers such as [9; 10; 11; 12; 13]. For a detailed review of caching in RAN, the reader is referred to [14; 15]. However, to the best of the authors' knowledge, this is a first study that investigates the potential of the leveraging detection of influential users via centrality measure in cellular networks and then uses that detection for identifying what to cache and pro-actively caching those likely to be accessing content using D2D communications. Online Social Networks, one of the key sources of mobile data explosion, are known to be scale-free networks, which follow power-law distribution. For example, the Twitter network has millions of users and an enormous flow of information is published daily. There are small number of Twitter users who have a large number of followers while there is a huge number of users who have a small number of followers. In this paper, our key idea to design and evaluate a novel proactive yet low complexity caching solution is as follows. It is highly probable that content accessed or generated by influential users will become popular due to their relatively higher following and connectivity. Furthermore, popular content is expected to be in demand in the same community i.e., the community of the influential user first and then spread slowly in the remaining network. The mapping structure, information diffusion and influence phenomena of the social networks can thus be exploited for proactively caching those content which are expected to be highly demanded in the near future, for reducing the backhaul traffic load [16].