# Context-Aware Pending Interest Table Management Scheme for NDN-Based VANETs
## 4 Current State-of-the-Art Schemes
### Adaptive PIT Management  
There exists very limited work related to adaptive PIT management in NDN-based VANETs [20; 21; 22]. [PERSON] et al. [20] proposed an adaptive PEL scheme. Three information components were used: PEL, rate of decay, and ISR. Initially, the default PEL value was used. PEL depends on two constant information components that are input by consumers: PEL and rate of decay. In this work, no technical details were provided related to the computation of these constants. PEL is reduced at each forwarder vehicle in the upstream direction using the exponential decay model. The proposed scheme is impractical because it considers the fixed (static) initial PEL and the rate of decay. [PERSON] et al. [21] proposed a scheme to compute the PEL adaptively at every vehicle, based on the total time duration required to transmit a packet to neighbours in one hop. Three different types of delays are considered: transmission delay, contention delay, and propagation delay. Different VANETs applications have different latency requirements. Additionally, VANETs characteristics make it very difficult to predict Round-Trip Time (RTT). It is believed that the RTT correctly predicts congestion in the end-to-end network path. When considering a wireless network, this belief may be incorrect. Because of channel fading, interference, and mobility, the quality of the radio link in wireless networks can vary greatly over time. [PERSON] et al. in another paper [22] used hop count in the Interest message to limit the number of hops an Interest message can traverse. A Hop-limit-based adaptive PEL (LAPEL) scheme is presented for NDN-based VANETs. It computes a one-hop PEL based on the following parameters: contention window, (ii) back-off period, (iii) transmission delay, (iv) propagation delay. The concept of TTL is exploited for the following purposes: (i) to limit the Interest broadcast scope, and (ii) to estimate the PEL at each node. The decay rate between the consumer node and the last Interest-receiving node is adaptively computed using a logistic model. In a dynamic environment, it is very difficult to accurately estimate the time of the round trip. The schemes presented in [21; 22] do not consider forwarding (queuing) delay and processing delay in the model. Receiver-based schemes introduce delays (wait period) to address the broadcast storm problem (which results in congestion and contention). As the packets are delayed for an unknown amount of time, it is hard to predict position, hop count, and RTT. For example, in [PERSON] et al. [16], the Interest-forwarding vehicle is considered as the one which is farthest from the previous forwarder compared to neighbouring vehicles. The deferred time is calculated based on relative vehicle distance to the content provider/producer and network density. If the vehicle does not overhear the packet during the wait period, it forwards the packet upon timer expiry. In [23], the authors discussed that adaptive PEL schemes must consider the processing delays while computing the PEL. Not considering processing delays might result in the degradation of ISR in some applications. For example, non-trivial computing in NDN-based edge computing will affect the \(PITE\) expiry time, leading to the degradation of ISR. Furthermore, in source-based forwarding schemes [14; 15], the vehicle collects information from the neighbouring vehicles for forwarder selection. Afterwards, rankings of itself and neighbouring vehicles are computed to select the forwarder. This ultimately adds to processing delays. [PERSON] et al. [24; 25] proposed an adaptive PIT management solution for the emergency environment. The proposed scheme used a Smart Interest Lifetime (SIL) for PIT overflow management based on the network load. For network load calculation, an extra field termed face list is maintained in the PIT, which holds the number of Interests per interface.