# Adjacency-Information-Entropy-Based Cooperative Name Resolution Approach in ICN
## 4 Proposed Approach
### Latency Neighbor Maintenance
Let the set of all name resolution nodes in an NRS be \(S_{MN}\), and for one of the \(MN\), the latency neighbors obtained by an algorithm are denoted as the set \(L\Gamma^{\prime}(MN)\), the set of its true full latency neighbors is denoted as \(L\Gamma(MN)\), then the overall average latency neighbor recall ratio is defined in Equation (14):

\[Average_{recall}=\frac{1}{|S_{MN}|}\sum_{MN\in S_{MN}}\frac{|L\Gamma^{\prime}( MN)\cap L\Gamma(MN)|}{|L\Gamma(MN)|} \tag{14}\]

As shown in Figure 2, we used different algorithms to discover latency neighbors for the nodes and computed their respective average latency neighbor recall ratios. The experiments first use the GM algorithm to compute all the latency neighbors for each name resolution node, using this latency neighbor set of each name resolution node as a benchmark, so the average latency neighbor recall of the GM algorithm is always 100%. Figure 1(a) shows the experimental results of the latency neighbor recall ratio of each algorithm for different neighbor finding rounds at a network size of 1500. It can be seen that the latency neighbor recall ratio of each algorithm increases as the number of neighbor discovery rounds increases, and the degree of improvement becomes less pronounced when the number of rounds increases to a certain level, indicating that there is marginal utility in the number of neighbor discovery rounds. The PF algorithm, due to sending discovery requests for neighbors at each network node to all directly connected nodes, finds the latency neighbors most complete when the number of neighbor discovery rounds is large enough. The RW algorithm has blindness in finding latency neighbors and has the worst performance. The LG algorithm chooses the nearest node to forward the latency neighbor discovery message, which is thought to increase the neighbor discovery probability, and

the results show that it can be useful compared with RW. However, it is not comprehensive to judge the local structure information only based on the node's degree. Greedy methods often limit the discovery information to local parts and fail to spread the neighbor discovery information through a key node with a low degree in the structure. Unlike the other algorithms, ACNR achieves a high recall rate with fewer neighbor discovery rounds and consistently achieves better performance, which is attributed to the initialization work performed by taking full advantage of the latency constraint structure of LNMRS itself. We also investigate the trend of the performance of each algorithm under different size networks. As shown in Figure 2b, where the network size has almost no effect on the PF algorithm, and the LG and RW methods have a significant decreasing trend in performance as the network size increases, probably because the difference in node degrees in the BA scale-free network model becomes large as the network size increases, and the RW and LG methods are closely related to node degrees lead to the decrease. In contrast, the ILN value is not limited to node degrees, and it quantifies the structural characteristic of the nodes relatively more accurately while taking into account the latency, so the latency neighbor recall ratio decreases slowly. The number of MNs varies across the levels, with the lower levels having the largest number of MNs and occupying the largest proportion of the overall latency neighbor recall ratio. Figure 3 illustrates the average latency neighbor recall ratio for the LNMRS at level 2. It can be seen that in the higher levels, the resolution domain ranges are larger, the average distances between MNs are relatively farther, latency neighbor discovery becomes difficult, and more discovery rounds are required for each algorithm to achieve a stable latency neighbor recall ratio. In comparison, ACNR is more obvious at higher levels in terms of the advantage of discovery effectiveness.