# A Literature Review on Caching Transient Contents in Vehicular Named Data Networking
## 6 Insights and Research Perspectives  
Although some notable examples can be found in the literature, as reviewed in the previous sections, caching and replacement policies for transient contents in VNDN are still largely unexplored and there is room for further enhancements. Since vehicular traffic, applications, mobility conditions and nodes' density are highly heterogeneous, it is expected that there will be not a single reference policy but multiple ones will likely co-exist, depending on the context. As summarized in Table 2, the main parameters impacting caching and replacement are generally related to the content, e.g., lifetime, popularity. However, also node-related attributes can be taken into account, like geographical position and mobility pattern. Moreover, transient contents caching also cover an important role in new and exciting research contexts, like the ones reported below. **Joint caching and processing of transient contents. ** In addition to the storage and communication resources, vehicles are provided with computing facilities that are generally underutilized. The groundbreaking Vehicular Cloud Computing (VCC) paradigm has been conceived to let vehicles offer joint caching and computing services on the road in a cooperative fashion [59]. Many works have already focused on aspects related to autonomous cloud formation and maintenance and optimal tasks allocation in traditional VANETs, while, in Reference [60], NDN has been recognized as a fundamental and effective content distribution architecture for VCC. In addition to data retrieval and in-network caching, NDN can be employed to directly enable in-network processing services [61]. According to such a new revolutionary vision, sometimes referred to as Named Function Networking (NFN) [62], consumers use Interest packets to request a named computing function to be applied over a named content and the network finds a node able to execute the task and return the result. Comprehensive studies evaluating NFN in vehicular environments and considering the joint caching and processing of transient contents, are still missing. The possibility of coupling Mobile Edge Computing (MEC) and VCC via NFN is also an interesting research direction, as outlined in Reference [63]. **Caching transient contents in software defined vehicular networking. ** Software Defined Networking (SDN) is a new network paradigm that improves network management and performance by decoupling the control plane and the data plane. A centralized Controller with a global view of the network implements control plane functions, like optimized routing and security, and then instructs the network nodes. SDN has been applied in the context of vehicular networks to manage the highly dynamic communication and networking resources [64], while the work in Reference [65] has recognized the complementary features of SDN and NDN paradigms. On the one side, NDN can leverage SDN to simplify the network deployment and improve the routing decisions. On the other side, SDN can leverage the two NDN built-in features, stateful data plane and in-network caching, to further optimize the data delivery performance. In the VNDN context, new functions could be introduced at the Controller to optimally coordinate the caching decisions of vehicular nodes. We encourage the community to consider this promising research aspect. **Improved transient content caching with Artificial Intelligence (AI) techniques. ** AI-based algorithms are a powerful means to improve network resource management and orchestration, even in presence of highly dynamic environments, like vehicular networks. By interacting with the environment and collecting feedback, they can improve the learning and forecasting capabilities of nodes while also solving complex optimization problems [66]. AI-based algorithms, if leveraged in the caching strategy, can help in predicting the popularity and the expected cache hit ratio of contents during their residual lifetime.