## 7 Edge Intelligence and Networking for IoV
### Edge Network Intelligence for IoV
#### 7.3.1 ICN-Based Naming and Resource Discovery for IoV Networks  
Naming is one of the most significant networking aspects in NDN networks, used to identify the contents, as well as other network functionalities, including forwarding,caching, mobility, security, etc. [88]. ICN decouples content from its location and enables it to be easily retrieved, not only from the original server (producer), but also from the in-network caching nodes in the subsequent content request. Furthermore, it also provides interest aggregation for the requests from different consumers for common content, which, in turn, reduces interest packet traffic. ICN naming brings significant help to IoV communications by allowing vehicles to cache content, and also to forward interest, based on the content name instead of the address of the host, as the IoV environment is characterized by rapid changes and short connectivity duration. However, NDN-based naming for IoV is challenging and most existing ICN frameworks assume that consumers are ex ante aware of the service and resource name. The consumer node sends out an interest packet that carries a name that identifies the desired content. However, vehicular users connecting to the network may not be aware of the name of the content or service of interest. A robust content naming design is needed to allow producers to describe precisely what it has and consumers to express which information they need. Existing research attempts to design content names for the IoV are based on specific use cases. In [54], a hierarchical context-aware content naming scheme for NDN-based VANET, that enables naming of both safety and non-safety application contents, is proposed. It exploits the coding scheme to represent most of the content names. It follows the following naming scheme: content type (1 bit, safety or non-safety), content scope (1 bit, local or global), content format (2 bits, 00 for text, 11 for video, 01 for picture, and 10 for audio), application types (4 bits for main type + 4 bits subtype ), to represent 32 different applications, timestamps, GPS locations, content sizes, and content file types. In [89], a naming scheme for a vehicular content-centric network is proposed with the purpose of uniquely identifying the content generated by vehicles using a hierarchical and hash-based naming scheme. The hierarchical naming scheme is partly used for content name prefix aggregation and as an aid for a routing and hash-based naming scheme to help maintain content integrity. In [90], the authors argue that the existing conventional hierarchical or hybrid NDN naming schema are inefficient to extract contents that are transient in nature and present instead a named-based query and command mechanism (NINQ) framework for ICN-based IoT. The authors of [54] present an adaptive content dissemination solution for VANET networks that includes hierarchical context-aware content naming schemes that enable both safety-critical and non-safety applications. To guarantee that the forwarding and processing rules of the packets are correct, [91] proposes a name space analysis framework to model and analyze NDN data planes using header space analysis. Another challenge with NDN naming is the issue of name lookup in a large-scale namespace in the forwarding plane of the content router. Managing FIBs at an NDN router is challenging having long content names or name prefixes. In [92], a method is proposed to compress FIB with the goals of reducing the required router storage space and meeting the demands of routing efficiently  
The authors of [93] propose a neural network-based solution named Pyramid-NN, which builds an index with the aim of reducing memory consumption and false positive rates while increasing the lookup speed. Thus, an adaptive naming mechanism that exploits AI techniques is required to help vehicular nodes to identify available resources and to provide seamless service and resource discovery. For example, a naming technique based on natural language processing (NLP) is proposed in [94].