## 7 Edge Intelligence and Networking for IoV
### Intelligent Edge Caching for IoV  
The contents of IoV applications, either safety or non-safety, are likely to be transient, characterized by a limited lifespan and to become invalid after a certain period of time. Information, such as traffic conditions of certain road segments, will change after a few minutes after being reported by the source (producer) [79]. Thus, these transient contents have an impact on in-networking caching and forwarding decisions for NDN-based networks.  
A comprehensive literature review on caching transient contents in vehicular NDN is provided in [80].  
Caching improves the distribution and availability of contents and services in ICN [14]. It enhances the network performance by storing contents or services that are frequently used, to improve retrieval time and throughput, thus guaranteeing the quality of user experience (QoE). Cache management is one of the essential elements of NDN. Routing and forwarding are supported since nodes can cache content and interests. Content-caching issues include cache placement and cache replacement. Cache placement refers to which content and where to cache, while cache replacement refers to how long to cache in order to maximize the long-term average hit ratio. Traditional cache replacements include FIFO, LRU, and LFU. Content caching is challenging because of the dynamic and high mobility nature of IoV networks, content growth and sporadic demand of content, delay-sensitive applications, and limited cache capacity.  
In IoV networks, caching can be performed on vehicles, on RSUs, on BSs, or jointly. For example, RSU caching minimizes the latency of retrieving contents and increases the cache hit ratio. Quick response time and storage efficiency are the two main factors to be considered for the cache management scheme. Caching popular content near the user improves efficiency.  
Several cache management methods have been proposed to improve the efficiency of NDN. In the vehicular context they include: mobility-aware caching, delay-aware caching, proactive caching, probabilistic caching, and cooperative caching. For example, The authors of [81] proposed a limited domain cache (LdC), which simultaneously considers cache hit rate and the popularity of contents factors.  
Recently, learning-based approaches have been used to design and optimize edge caching. In the following, we discuss representative studies that employ intelligent caching for IoV. The authors of [82] propose a cooperative edge caching framework which aims to exploit cooperation among BSs, RSUs, and connected vehicles. In [83], joint task caching and computational offloading for vehicular edge computing are proposed.  
Performance evaluation and applicability analysis of different machine learning models for caching and routing have been addressed in [84]. Content popularity and request stream correlation are factors that affect cache performance and routing.  
In [85], a deep-learning-based content caching framework, DeepCache, is proposed. The popularity of the content is predicted using an LSTM-based model. Similarly, [86] uses content popularity and user location to create an intelligent cache for content-centric networks. The approach is that of an optimization problem to determine which type of content should be cached in which router at a given time, thus classifying it as either normal or popular.  
Content replacement is also an important issue in content caching. In [87], a dueling deep-q-network-based delay-aware cache update policy for fog radio access is employed and compared with the traditional caching replacement policies, i.e, FIFO, LRU, and LFU. The paper considers user mobility and time-varying channel states to improve the performance of the caching strategy.