The impact of user mobility patterns on opportunistic content distribution network

Understanding the impact of mobility on opportunistic network is a challenging problem. This paper focuses on analyzing the impact of specific type of mobility characteristic, namely user mobility patterns. We base our analysis on Opportunistic temporal-Pairing Access Network (OPAN), an opportunistic content distribution framework that utilizes both pairings between nodes and infrastructure-based wireless network. Focusing our study to explore the impact of peak hour traffic (i.e. due to human mobility patterns involving routines and schedules) and hence node density (clustering) on opportunistic content distribution paradigm, we introduce two models based on a rail public transportation model, namely Random Train Model and Peak hour Train Model. Our simulation results show that peak hour traffic increases the downlink traffic (i.e. uses more downlink capacity) in OPAN however provides faster diffusion time for nodes to download content. Our simulation results further suggests that mobility patterns with high node clustering is more beneficial for content distribution in mobile opportunistic networks due to higher chance of forming direct pairing between nodes.