# An Intelligent Fuzzy-Based Routing Protocol for Vehicular Opportunistic Networks
## 2 Literature Review of Opportunistic Networks
### Related Work
While a viable solution for routing in opportunistic network, their approach is not realistic as it cannot give good results in real-time scenarios. Other studies such as [32; 33; 34] propose mathematical models to optimize routing. Although they target wireless sensor networks and their subsets as their objective is to develop energy-efficient protocols, the protocols are valid for OpSpNets where energy usage is a constraint. Each of them has its merits and demerits, which are summarized in Table 1.  
In [35], the authors propose a protocol for opportunistic IoT networks which considers forwarding factor with acknowledgment. The goal is to maintain a high delivery rate while reducing duplicate messages to save bandwidth. It improves message routing by prioritizing nodes based on their interaction frequency and recentness. However, its initial performance suffers due to limited early interactions, causing delays in message delivery. Although the authors have explained it theoretically, the approach lacks simulation or experimental evaluations. The authors in [36] introduce a trust-based protocol which uses two types of trust values--direct trust, based on the interactions between nodes, and indirect trust, derived from recommendations provided by other nodes. By combining these trust values, their proposed protocol evaluates the reputation of potential relay nodes to select the most reliable and efficient ones for forwarding messages. The protocol demonstrates improvements in key performance metrics like average latency, message drop rate, and hop count compared to other existing protocols. However, its effectiveness is influenced by the configuration of simulation parameters, and it requires further optimization to ensure scalability and efficiency in larger networks. In [37], the authors propose an adaptive learning protocol for routing in opportunistic networks, utilizing a weighted double Q-estimator and a probability-based reward strategy to select optimal nodes for forwarding messages. Simulation results show that proposed protocols outperform benchmark protocols such as Double Q-Learning Routing (DQLR), QLR, RLPRoPHET, and Epidemic in terms of delivery ratio, delay, and overhead. The protocol is evaluated using the ONE simulator. A potential drawback is the lack of an established best value for the balancing factor between single and double estimators, which could impact performance optimization in some scenarios. In [38], the authors introduce a probabilistic routing model for opportunistic networks which can be used in areas characterized by intermittent connectivity. The model uses historical data about how often devices meet to make smarter decisions about routing messages. Simulations in the ONE simulator using the DakNet scenario show that the model performs better than existing protocols like Epidemic and Spray and Wait, offering higher delivery success rates, reduced overhead, and acceptable delays. However, a drawback is that the model's performance gain diminishes under certain conditions, such as excessive message replication in highly congested networks, which can lead to increased processing time and reduced efficiency. Across the surveyed systems, research gaps persist in optimizing latency, reducing hop count, and handling mobility in rapidly changing or complex network scenarios.