# An Intelligent Fuzzy-Based Routing Protocol for Vehicular Opportunistic Networks
## 2 Literature Review of Opportunistic Networks
### Related Work  
The surveyed protocols address various challenges in opportunistic and mobile ad hoc networks, with some leveraging fuzzy logic for routing decisions, while others rely on alternative approaches. A summary of these research works is given in Table 1, highlighting their key findings but also the research gaps. In [29], the authors use forward transmission count and message size to make decisions on message prioritization and dropping policies whereas other authors [28] use parameters such as message transmitted count of nodes, message size and time to live to calculate the importance of messages. Both these works aim to optimize delivery probability and reduce latency and network overhead. The results show slight improvement compared to traditional Spray and Wait and Epidemic; however, there is no comparison with different approaches or benchmark protocols like MaxProp. In [30], the authors employ Bayesian inference to simplify the integration of various routing attributes and use the predictions of movement patterns to make informed decisions. They compare their proposed protocol with Epidemic, MaxProp, and Direct Delivery. The results show that they perform better than MaxProp but fall behind Epidemic drastically and therefore lack generalization for diverse types of networks. Machine Learning (ML) classifiers are used in [31] to predict network traffic and determine the likelihood of encountering nodes in a given path. This is achieved by \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref.** & **Approach** & **Objective** & **Key Metrics** & **Research Gaps** \\ \hline
[28] & Fuzzy logic & Educated decisions on message prioritization and dropping policies & Delivery probability, latency, overhead & No comparison with protocols based on other approaches, only to traditional Spray and Wait \\
[29] & Fuzzy logic & Increase delivery ratio and decrease the buffer consumption & Delivery probability, overhead & Lack of comparison with benchmark protocols like MaxProp \\
[30] & Bayesian classifier & Better routing decisions by integrating various routing attributes & Delivery probability, latency & Lack of generalization to diverse network types \\
[31] & ML Classifiers & Informed decisions based on a pre-trained model & Network overhead, latency & Cannot adapt to new data in real-time \\
[32] & Analytical modeling & Improve energy utilization employing an opportunistic strategy & Packet loss, latency, network lifetime & Network overhead due to calculation complexity \\
[33] & Analytical modeling & Consider residual energy and perform informed distance-based forwarding & Delivery probability, latency, network lifetime & Does not update forwarding list dynamically \\
[34] & Analytical modeling & Enhance network lifetime and delivery ratio & Delivery probability, latency, network lifetime & Cannot handle void links dynamically \\
[35] & Conceptual design & Prioritize messages based on the frequency and recentness of interactions between nodes & N/A & Lack of simulation or experimental evaluation \\
[36] & Analytical modeling & Enhance message forwarding efficiency & Delivery probability, latency, overhead, message drop rate, hop count & Low scalability and efficiency in larger networks \\
[37] & Adaptive reinforcement learning & Enhance message delivery by selecting optimal next-hop nodes based on adaptive learning and probabilistic rewards & Delivery probability, latency, overhead & Need only on limited mobility models \\
[38] & Probabilistic modeling & Improve message delivery in opportunistic networks & Delivery probability, latency, overhead & Low scalability in dynamic environments and excessive overhead in large networks \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of research works with regards to the problem solved, approach used, and key metrics. training the models using a dataset and then deploying the model to make informed routing decisions.