# Time Segmentation-Based Hybrid Caching in 5G-ICN Bearer Network
## 3 Path Segmentation-Based Hybrid Caching
### Caching in Low Liquidity Period  
The slow movement speed and small number of mobile users, which cause low frequency, fewer types of requests, and stable user interest, are the distinguishing characteristics of the low liquidity period. Therefore, we concentrate on the acceptance and freshness of user-requested content. We will utilize these variables as inputs while calculating the cache value. Paper [41] proposes content freshness according to mobile characteristics and consider content popularity at the same time. It designs a new path-associated active caching scheme to shorten the time delay of users obtaining content. However, the strategy of selecting only one migration node proposed by [41] is not suitable for large-scale base stations. Figure 3: The process of selecting a cache node. The approach in Section 3.3 is no longer relevant. To make cache decisions, we opt to compute the node cache value. All nodes on the message return path whose cache values satisfy the specifications can cache by establishing a threshold. Still, all that is required for our algorithm to finish is one traversal round. We innovatively consider all nodes on the path in low liquidity period by adding a third parameter for calculating the cache value: the distance between the user and the node. Formula (2) shows the calculation of cache value. Due to the magnitude bias of different data, we use the minimum-maximum method to standardize the data [42], i.e., \(N\left(x\right)\) function. \[\text{Cache}_{value}=\ \omega_{1}\times N(Hop)+\omega_{2}\times N(Freshness)+ \ \omega_{3}\times N(Popularity)\] (2) The definition and initialization of the three parameters are as follows: * Hop: because of the ICNoIP model, we can directly use the time-to-live field (TTL) to calculate the data transmission distance (hop). We initialize it as the hop between the hitting node and the ICN-GW, and record it by hop_count field mentioned in Section 3.2. * Freshness: the freshness of the current packet is the timestamp when the content hits. On the return path, we first compare the timestamp in the packet with the earliest timestamp in the current cache node freshness list. The result of the operation is the final freshness of the content in that node. If the earliest timestamp of the freshness list does not exist in the node, the final freshness is set to zero. We record it by time_stamp field mentioned in Section 3.2, which is initialized to the time the hit occurred. * Popularity: we define content popularity in a cache node as the number of content visits in that node. We initialize popularity to the popularity of that content on hit nodes, and record it by popularity field mentioned in Section 3.2. Algorithm 3 shows the specific method, after the content is obtained from the data source, hop_count, popularity and time_stamp fields will be initialized. If the current cache value is higher than the threshold, nodes will cache on the return path. As shown in Figure 4, if the threshold is set to 1, then Nodes 1 and Node 2 with cache values higher than 1 on the return path are selected as cache points. ```
Input: Request package (Rpt) Output: Operation Statement 1: get Data package (Dpt) from Hit_Node 2: Dpthop_count = getTTL (Rpt.Hit_Node, Rpt.Request_Node) 3: Dpt_Popularity = Hit_Node.Popularity 4: Dpt_time_stamp = time () 5:if next hop is request_node then 6: return 7:else 8: move to next hop 9: freshness = Calculate (Dpt.time_stamp, Current_Node. time_stamp) 10: Cache\({}_{value}\) = Calculate (Dpt.hop_count, Dpt.Popularity, freshness) 11:if Cache\({}_{value}>\) Current_Node.maxvalue then 12: node.is_cache = True 13:else 14: node.is_cache = False 15:endif 16: go back to 5 17:endif
```

**Algorithm 3**Cache strategy in low liquidity period

## 4 Simulation and Results

In this part, we will simulate the proposed TSBC. First, the setting of simulation environment is introduced, and then the simulation results are given. The results show the characteristics of our mechanism and its performance under different real network topologies.