# An Intelligent Fuzzy-Based Routing Protocol for Vehicular Opportunistic Networks
## 3 System Model and Design of the Proposed Protocol
### System Model  
The network consists of vehicles acting as nodes that communicate opportunistically when they come within range of each other. Each node is equipped with a finite buffer for storing messages and relies on intermittent contacts for data forwarding. The protocol assumes that nodes can gather contextual information such as buffer occupancy, the angle to a destination node, and the number of unique connections they maintain. Communication is limited by short contact durations and constrained resources, making efficient routing decisions critical.  
Nodes are assumed to move according to realistic vehicular mobility patterns, such as those generated using city traffic models or Random Waypoint mobility. Contact opportunities between nodes are unpredictable and are influenced by factors like road topology, speed, and traffic density. Each node periodically generates messages with specific destinations, and these messages are propagated using the store-carry-forward mechanism. The simulation environment models constraints such as limited buffer capacity, finite bandwidth, and message time-to-live (TTL) to replicate real-world challenges.  
This system model provides a foundation for evaluating the proposed routing protocol under conditions representative of dynamic vehicular networks. It captures the essential elements of OppNets, ensuring that the results are applicable to practical scenarios. The performance of the proposed protocol is evaluated using four key metrics:  
Delivery Probability: It is the ratio of messages successfully delivered to their intended destinations compared to the total number of messages generated. It serves as a critical metric to evaluate the reliability of a routing protocol, particularly in environments with intermittent connectivity, such as OppNets. A higher delivery probability indicates that the protocol effectively leverages network resources and node mobility to ensure message delivery. This metric is essential for comparing the performance of different protocols under varying network conditions.  
Overhead Ratio: It measures the additional resources required to deliver messages, such as the number of redundant message copies or extra transmissions. This metric provides insights into the efficiency of a routing protocol by quantifying the trade-off between ensuring delivery and conserving resources. A lower overhead ratio suggests that the protocol uses fewer resources to achieve similar or better delivery outcomes, which is especially important in resource-constrained environments. High overhead ratios can lead to network congestion and reduced scalability, making this metric vital for assessing protocol efficiency.  
Average Latency: It refers to the mean time it takes for a message to travel from the source to its destination. This metric reflects the timeliness of a routing protocol, highlighting how quickly it can deliver messages in a dynamic network. Low latency is particularly important for time-sensitive applications, such as emergency communication or real-time traffic updates in vehicular networks. By analyzing latency, researchers can evaluate the protocol's ability to minimize delays while maintaining reliability.  
Average Hop Count: It measures the average number of intermediate nodes a message passes through before reaching its destination. It serves as an indicator of routing efficiency, with fewer hops generally implying a more direct and resource-effective route. A lower hop count is desirable as it reduces the likelihood of network congestion caused by multiple transmissions which otherwise can be avoided. This metric also can assess the impact of routing strategies on energy consumption.