# Adjacency-Information-Entropy-Based Cooperative Name Resolution Approach in ICN
###### Abstract
NetInf uses a nested NRS, based on multi-layer DHT to query name mapping records through a combination of global NRS and local NRS. The global NRS is more concerned with the availability and full searchability of name resolution. To support the name resolution requirements of latency-sensitive applications in 5G/B5G scenarios, such as ultra-reliable and low-latency communications (URLLC), it is necessary to provide the deterministic response latency of name resolution. It is the key to the QoS of NRS, and the related research is very challenging. SEANet [11] is a novel ICN architecture based on the identifier-locator separation mode. It uses a combination of a global naming and mapping system (GNMRS) and a local naming and mapping system (LNMRS). GNMRS is a general global information service system, which mainly guarantees the full storage of identifier-locator mapping records of the whole network and also ensures the searchability of name resolution. LNMRS is a distributed autonomous system deployed at the edge of the network near users and provides immediate name resolution service. It divides the resolution region into multiple levels according to the communication latency constraint between name resolution nodes and between the nodes and users, and its hierarchical nested structure can provide users with name resolution service with the upper bound of response latency at different levels. The main principle of the local NRS is to make use of the local characteristics of name resolution requests in the network to provide immediate name resolution responses to nearby users [19]. However, unlike the full searchability service provided by the global NRS, the local NRS cannot guarantee that there will be available addresses in the response messages returned to users, since the name mapping may not be stored in this local name resolution node. A name resolution request in LNMRS [11] is restricted to a corresponding name resolution node to ensure deterministic response latency. This method needs to be improved because the constraint is strict, and the name resolution success rate is not ideal. Once local name resolution fails, users have to access the global NRS, and the QoS will inevitably degrade. In LNMRS, each service level is divided into several name resolution regions according to the latency upper bound. The name resolution node in each region provides name resolution services only for users in this region. In this paper, we explore the benefits of the proximity between name resolution nodes and propose Adjacency-information-entropy-based cooperative name resolution (ACNR) approach. In ACNR, name resolution nodes can use their neighboring name resolution nodes to respond to users' name resolution requests in a parallel processing manner. It expands the name mapping search range within the limit of latency constraint and improves the name resolution success rate. In existing neighbor cooperation approaches, there is a trade-off between neighbor discovery efficiency and the search message overhead. Additionally, the name resolution node cannot know the neighbors' structures well, so it is easy to cause extra cooperation burden for the neighbor, but the overall name resolution benefit is low. ACNR leads into the information entropy of adjacency in neighborhood management, which helps to quantify the system structure more accurately and optimize the discovery and usage strategy of neighborhoods. The main contributions of this paper are as follows:  
1. We designed a neighbor cooperative name resolution approach for NRS in ICN, including discovery, usage, and maintenance of nodes' neighbor relationships, which can fully exploit the potential of name resolution nodes while reserving the deterministic latency characteristic. 2. We extended the concept of adjacency information entropy in networks with transmission latency constraint and applied it to the neighbor discovery and the name resolution cooperation in ACNR, to quantify and use the system structure more accurately. 3.