# An Active Path-Associated Cache Scheme for Mobile Scenes
## 2 Related Work
### Mobility Caching in ICN Networks  
Although many researchers have designed a new type of ICN network architecture to naturally support mobility, when mobile consumers move to a new location before receiving the requested content, the delay in receiving the requested data will increase. For delay-sensitive applications, reducing this delay is very important, such as streaming media services, online games, conference calls, etc. [8]. The author of [15] believes that the methods to realize its mobility can be divided into the following types: reactive methods [16; 17; 18; 19], durable subscriptions [20], and active (or prefetching) methods [21; 22]. Among them, compared with obtaining data from the original content source, the active caching strategy can significantly reduce the delay in transmitting content when the subscriber changes its location [23]. [PERSON] et al. present a specific neighbor caching way for supporting users' mobility in publish/subscribe networks [24]. As per the methodology, a mobile's memberships are communicated to a subset of agents that are neighbors of the current representative that the mobile is associated with. Their key commitment is the meaning of an objective expense work and an astute strategy for choosing the subset of neighbors. The upside of their proposition is that it decreases the buffering costs since not all neighbor agents cache things while still obtaining the additions of proactive reserving by reserving things in a subset of neighbor agents that the mobile has a high likelihood of associating with. [PERSON] et al. propose a selective neighbor cache (SNC) method to enhance mobility in ICN architecture [15]. The method is based on actively caching information requests and corresponding items to a subset of agents adjacent to the agent currently connected to the mobile device. An important contribution of their paper is defining a target cost function. This function considers the trade-off between the latency and the cache cost and is used to select the appropriate subset of neighbors for user mobility behavior. They investigate the steady-state and transient performance of the proposed scheme. Compared with the case of active caching and no caching in all neighbor agents, the scheme identified and quantified its benefits. In addition, their survey shows how these benefits are affected by latency and cache costs and mobile behavior. In addition, [PERSON] et al. extended the work of [15]. They proposed a distributed active caching method, which uses the user's mobile information to decide where to actively cache data and uses the congestion pricing scheme to use the cache storage [23]. The proposed method is suitable for objects of different sizes and a two-level cache hierarchy. For both cases, the active cache problem is very difficult. Their evaluation results show the influence of various system parameters on the delay gain of the proposed method. Compared with the optimal scheme of Oracle and plane cache structure, this method achieves a robust and good performance. Moreover, [PERSON] et al. propose a caching and prefetching mechanism that can be used in mobile environments [25]. Their idea is to predict disconnected and prefetch content that users may access in the near future. Therefore, when to trigger the cache and prefetch module is an important part of the target. Their paper mainly studies the design and implementation of the disconnection prediction algorithm. This method predicts the time before the terminal is disconnected from the Access Point (AP) by monitoring the Received Signal Strength Indicator (RSSI). They evaluate and compare through experiments. In recent years, research on mobile caching has also focused on how to design new caching strategies in mobile scenarios. [PERSON] et al. propose a caching strategy based on user mobility and content popularity. This caching strategy uses a Markov model to describe user mobility and uses a multi-linear regression model to predict content popularity [26].