# A Literature Review on Caching Transient Contents in Vehicular Named Data Networking
## 4 Transient Contents Caching in NDN
### Caching Policies in IoT and Sensor Networks  
In Reference [42], [PERSON] et al., present a detailed experimentation of NDN performance in a smart campus in the presence of transient IoT contents. They find that caching is still extremely beneficial, especially in presence of multi-hop lossy channels and constrained sources. [PERSON] et al., in Reference [43], defines a probabilistic caching strategy for Internet content routers that trades off between IoT data lifetime and multihop communications. As also found in previous works on vehicular networks [23], AoI increases with the distance between consumer and producer; therefore, the freshness is maximum when the data is directly fetched from its producer and decreases when the packet moves away from it. The caching decision tries to balance freshness and communication costs. But freshness is not the only crucial parameter to consider when dealing with transient contents. In particular, sensors are the principal sources of transient contents, which are generated as time series of readings, e.g., humidity, temperature, and CO\({}_{2}\) emissions. Being usually battery-powered devices, energy efficiency becomes a major concern. Indeed, on the one side, caching generally reduces the network energy consumption and the load on the producers, since queries can be resolved closer to the consumers. On the other side, a caching operation incurs a certain energy consumption on the node, which has to access his memory resource and transmits the information on behalf of the original source. As a result, in the presence of constrained devices, caching could be selectively performed or even completely inhibited. In Reference [44], sensors autonomously decide whether to cache the received packets, according to a composite probabilistic decision metric that takes into account the freshness of the item, the current battery level and the free storage space. The higher the freshness, the battery level and the free storage space, the higher the caching probability. Vice versa, the probability decreases. A cooperative strategy is instead proposed in Reference [45], where the constrained devices share among each other the load of answering to the content requests. Each node in the network periodically broadcasts its data to the neighbors, which cache them. As a result, some devices can sleep, while the others remain active and provide the contents. Fortunately, vehicles do not have strict energy constraints and they can easily collect data from neighbor sensors. Of course, although they may leverage consistent storage resources, the number of cacheable contents is generally higher than the available storage space and some selective caching decisions should be performed. A content parameter that traditionally is taken into account to filter the caching decision is the content popularity: reasonably, the caching gain can be maximized if the more requested contents are cached. The work in Reference [46] designs an autonomous probabilistic caching strategy for IoT contents that jointly considers lifetime and popularity parameters. The FreshnessPeriod carried in the NDN Data packets is used by the potential cache to estimate if packets are long-lived and short-lived, while the rate of received requests is used to distinguish popular and non-popular contents. The strategy assumes that non-popular contents are never cached (i.e., the caching probability is set to zero), while popular long-lived contents are always cached (i.e., the caching probability is set to one). Finally, popular short-lived contents are cached with a probability that is directly proportional to the freshness of the content. The same parameters and caching logic are used in Reference [47], but the proposed mechanism is cooperative and depends on the position of the caching nodes, which are classified into three main categories. Root nodes are directly connected to the IoT sources, edge nodes are directly connected to the consumers, and the remaining ones are middle-level nodes.