# Context-Aware Pending Interest Table Management Scheme for NDN-Based VANETs
## 4 Current State-of-the-Art Schemes
### Adaptive PIT Management
Two information components are exploited for PEL threshold computation: (i) the maximum number of Interests received on a particular interface, and (ii) the total number of entries in the PIT. In the event of PIT overload and similar Interest rates on all faces, the lifetime threshold is computed based on the average of PITs lifetime in PIT. In the case of PIT overflow and dissimilar Interest rates on all the faces, the face with the maximum number of received Interests is considered. The lifetime threshold is calculated by taking the average of all \(PITE\) lifetimes (received on face f). In the case of network overload, a shorter PEL value, i.e., a minimum of lifetime threshold and PEL, is considered. In [24; 25], application type and scope are not considered while updating PEL. [PERSON] et al. [26] proposed the Highest Lifetime Least Request (HLLR) protocol to optimize the PIT performance in case of high network traffic. In [27], the author proposed a \(PITE\) replacement policy in CCN networks considering a natural disaster application scenario. In [10], the PIT entry replacement scheme is presented, which is based on the Highest Lifetime least Request Policy (HLLR) presented in [26; 27]. Upon receiving the Interest, if the PIT is full, the \(PITE\) with the longest remaining LT and the fewest requests is replaced with a new one. In the event of PIT overflow, the scheme replaces the \(PITE\) for the upcoming Interest with the \(PITE\) with a maximum lifespan and a minimum number of incoming faces. The replacement decision is based on the following components: (i) number of incoming interfaces, and (ii) Interest lifetime. The PIT replacement policy does not consider the time \(PITE\) spent in the PIT as a factor in determining the lowest priority PITe (\(PITE\) in the PIT) to be replaced. It is possible that the \(PITE\) in the PIT which is selected for removal might be close to satisfaction. The scheme may replace the \(PITE\) in the PIT, which might be satisfied in the following few milliseconds. Not considering the time spent on each \(PITE\) can affect PIT utilization. In [10], the \(PITE\) adaptive lifetime policy is also presented, where the PEL is managed as follows. When the PIT is empty, the PEL threshold is set as an average of the value specified in the incoming Interest packet \(InterestLifetime\) field and the default lifetime value. In case the PIT is not overflowed and not empty, the PEL threshold is updated by calculating the average PELbetween the PEL threshold value and the value specified in the incoming Interest packet \(InterestLifetime\) field. PIT is an important player in NDN data retrieval. PIT size is the bottleneck due to the limited opportunities provided by current memory technologies. PEL adjustment is a critical challenge that can have an impact on overall network performance. There are adaptive PIT management schemes that use the following parameters to reduce the lifetime of the \(PITE\) between the consumer node and the last Interest receiving node: hop count [20; 22], Round-Trip Times (RTT) [20; 21; 22], and Interest Satisfaction Rate (ISR) [20]. In VANETs, the hop count, network density, and RTT [20; 21; 22] are unpredictable. Whereas ISR also depends on the underlying caching mechanism's efficiency. Due to the transient communication nature, the time of vehicles' interconnection cannot be predicted. Speed variation makes estimation of propagation delay difficult. To address the broadcast storm problem, receiver-based forwarding schemes introduce delays (waiting time) at each hop. As discussed above, receiver-based schemes introduced delays (wait period) to address congestion. As the packets are delayed at different hops for an unknown amount of time, it is hard to predict forwarding (queuing) delay and RTT. The presented schemes [21; 22] do not consider queuing delay and processing delay in the model. Vehicles are mobile and pass through areas with different densities, topography features, and wireless conditions. This makes the prediction of contention delay difficult.