# Advanced Optimization Algorithm Combining a Fuzzy Inference System for Vehicular Communications
## 2 System Models
### Sugeno Fuzzy Inference Systems  
The Sugeno fuzzy inference systems offer several key characteristics and advantages that make it particularly well-suited for vehicular communication applications. In the context of computational efficiency, Sugeno fuzzy inference systems are known for their computational efficiency, which is crucial in the fast-paced environment of vehicular networks. Unlike Mamdani systems that use the centroid method for defuzzification, Sugeno systems employ weighted average or weighted sum defuzzification methods. This approach significantly reduces computational complexity, allowing for quicker decision-making in vehicular communication scenarios where rapid responses are essential. In the context of mathematical representation, one of the strengths of Sugeno fuzzy systems is their ability to be represented mathematically as a set of IF-THEN rules. These rules have fuzzy antecedents and crisp consequents, providing a clear and structured way to model complex vehicular network behaviors. This mathematical formulation allows for easier integration with other analytical tools and optimization techniques commonly used in vehicular network design. When discussing adaptability to dynamic environments, vehicular networks are inherently dynamic, with rapidly changing conditions. Sugeno fuzzy inference systems excel in such environments due to their compatibility with adaptive techniques. This adaptability allows for the system to adjust its parameters in real-time based on changing network conditions, traffic patterns, and communication requirements [24]. In the field of handling uncertainty, one of the primary advantages of Sugeno fuzzy systems in vehicular communication is their ability to effectively handle uncertainty and vagueness. Vehicular networks often deal with imprecise or incomplete information due to factors like varying signal strengths, changing vehicle densities, and unpredictable traffic patterns. Fuzzy logic allows for the system to make robust decisions even in the face of such uncertainties. In the context of the integration of multiple attributes, Sugeno fuzzy inference systems can seamlessly integrate multiple network attributes to make routing decisions. This capability is particularly valuable in vehicular networks where various Quality-of-Service (QoS) requirements need to be met simultaneously. By considering factors such as link quality, vehicle speed, and network congestion, Sugeno systems can make more holistic and effective routing decisions. In the field of improved performance metrics, Fuzzy-based approaches using Sugeno systems have demonstrated superior performance in several key areas of vehicular and sensor networks. These improvements include reduced energy consumption, decreased overhead, and extended network lifetime. By optimizing these critical parameters, Sugeno fuzzy systems contribute to more efficient and sustainable vehicular communication networks [25]. Sugeno fuzzy systems have the unique ability to represent routing decisions in a linguistic form that closely corresponds to human reasoning processes. This feature makes the system more intuitive to design and interpret, allowing for network engineers to implement complex decision-making processes in a more natural and understandable manner [26]. In the face of environmental uncertainties and fluctuations in network parameters, fuzzy-based approaches using Sugeno systems have shown remarkable robustness. This resilience is crucial in vehicular networks where conditions can change rapidly and unpredictably. Fuzzy logic-based systems, including Sugeno systems, can be effectively used to achieve load balancing in vehicular networks. By considering multiple factors such as network congestion, vehicle density, and available resources, these systems can distribute network traffic more evenly, leading to improved overall network performance. Sugeno fuzzy systems allow for the consideration of multiple factors in decision-making processes.