# Context-Aware Pending Interest Table Management Scheme for NDN-Based VANETs
## 5 Context-Aware Pending Information Table Entry Management (CPITEM) Scheme
### Non-Safety PITE Priority
#### 5.2.4. PITE Priority
(**a**) Overall Interest messages dropped as a function of Interest message growth in the network, (**b**) Interest messages related to safety dropped as a function of Interest message growth in the network, (**c**) interest messages related to non-safety dropped as a function of Interest message growth in the network, (**d**) Interest messages related to popular, non-safety content dropped as the number of Interest messages in the network increases (**e**) Interest messages related to non-popular, non-safety content dropped as a function of Interest message growth in the network, (**f**) average interest satisfaction delay. a \(PITE\) if the \(PITE\) does not exist and PIT size is above certain threshold. Furthermore, only vehicles with matching PIT entries forward the Data message. Unsolicited messages are simply dropped. Figure 2 depicts how the CPITEM and HLLR (implemented \(PITE\) replacement scheme) behave as the PIT size grows. The number of overall Interest messages dropped as a function of increase in PIT size is depicted in Figure 2a. Figure 2b depicts the number of safety Interests messages dropped as a function of PIT size. Figure 2c depicts the number of non-popular, non-safety contents dropped as a function PIT size variation. Figure 2d depicts the number of popular Interest messages dropped as a function of PIT size variation. Figure 2e depicts ISD as a function of PIT size growth. Figure 2: Experiment 2: Effect of PIT size. (**a**) Overall Interest messages dropped due to PIT size, (**b**) Interest messages related to safety dropped due to PIT size, (**c**) Interest messages related to non-popular, non-safety dropped due to PIT size, (**d**) Interest messages related to popular, non-safety content dropped due to PIT size, (**e**) average ISD. Figure 2 demonstrates that considering the load on the network in terms of Interest messages, the CPITEM scheme performs efficiently. Considering the static load and by increasing the PIT size, the number of dropped messages reduces both for CPITEM and HLLR schemes. CPITEM intelligently prioritises the safety content over the non-safety content. No safety related content is dropped by CPITEM. Moreover, CPITEM also prioritises popular content over non-popular content. CPITEM results in reduced average ISD compared to HLLR. HLLR is incapable of distinguishing between safety and non-safety content, as well as popular and non-popular non-safety content. ## 7 Conclusions

The Pending Information Table (PIT) is an important component of the NDN content discovery process. Due to the limited capabilities of today's memory technologies, PIT size is a bottleneck. Packet loss and delays are typical in VANETs, resulting in a maximum number of stale \(PITEs\). PIT overflow results in service disruptions as new \(PITEs\) (of incoming interests) cannot be added to PIT. In VANETs, applications under the safety subgroup include time-sensitive applications. The utility of pertinent information is lost if it is not delivered on time. Safety applications demand near-real-time quality of service. In PIT management, the most important challenge is to decide which \(PITE\) to be replaced in the event of PIT overload. Removing the \(PITE\) might disrupt some critical services. The current state-of-the-art PIT management schemes remove PIT entries of low priority to make room for incoming Interest messages PITEs. When computing priority, the current state-of-the-art method ignores context such as application type, time rested in PIT, and content type. In this work, we proposed a Context-aware PIT Entry Management (CPITEM) protocol for NDN-based VANETs. It manages the PIT table by taking into account information components such as content type, \(PITE\) lifetime, \(PITE\) size, time for which \(PITE\) rested in the PIT, and application popularity. To demonstrate that the proposed CPITEM scheme is efficient and effective, a set of simulations are run and compared to a benchmark scheme. Conceptualization, W.U.I.Z.