## 3 Overview of Fuzzy Logic System
### Fuzzy Logic Application in VANETs  
Fuzzy logic has been employed in VANETs to improve decision-making processes and mitigate communication and computation delays. VANET applications leveraging fuzzy logic include:  
* Routing algorithms;
* Broadcasting;
* Contention window size adjustment;
* Clustering control;
* Data aggregation;  
Figure 2: Fuzzy logic system structure. * Localization;
* Trust management. In [32], the authors proposed an innovative intelligent geographic routing scheme that uses fuzzy logic system to determine the ideal node to forward the packet. The fuzzy logic system uses direction, distance, achievable throughput, and link quality to generate a weight value that determines the optimal forwarding node. In [33], another geographic routing protocol is proposed which utilizes fuzzy logic to make packet forwarding decisions. It uses direction and distance as input to the fuzzy logic system. A routing method, GRVAD (geographic routing method based on velocity, angle, and density), leveraging the fuzzy logic system is introduced in [34]. This method uses relative velocity, angle between nodes, and neighbor node density as inputs for fuzzy logic. In order to store data in vehicular ad hoc networks, a fuzzy logic-based transferring data protocol is proposed in [35]. Considering throughput, vehicle velocity, and bandwidth efficiency, the fuzzy logic system manages the next data carrier node selection (short-term evaluation), while using a Q-learning algorithm to achieve long-term efficiency. In [36], a sender-oriented broadcasting scheme based on fuzzy logic is proposed. The fuzzy logic system uses vehicle velocity, antenna height and the number of neighboring vehicles heading in the same direction as inputs. Thus, the fuzzy logic system selects the backbone node to forward data packets. A fuzzy logic-assisted cross-layer receiver-oriented broadcast scheme is proposed in [37]. In that scheme, based on locally calculated mobility and coverage factors, the proposed fuzzy logic system checks the qualification of each receiving vehicle to rebroadcast the message. Also, to avoid packet collision and increase the delivery rate, considering the local density and distribution of vehicles, a fuzzy logic-based system is used to perform contention window adjustment at the MAC layer. The authors in [38] introduce a solution employing a fuzzy logic system at the MAC and network layers to effectively broadcast safety messages. The proposed rule-based model enhances the contention window (CW) and relay selection process for adaptation to diverse traffic conditions. In [39], a cluster head selection scheme that utilizes fuzzy logic is introduced. A node is selected as a cluster head based on criteria related to each candidate node: received signal strength, speed of vehicle, vehicle location, spectrum price, reachability, and stability of node. In [40], a structure-free aggregation scheme for VANETs is presented. This scheme uses fuzzy logic to make aggregation decisions relying on an extensible and resilient set of criteria. Fuzzy logic rules are utilized to base aggregation decisions upon qualitative metrics including induced quality loss caused by aggregation. Moreover, a fuzzy logic-based scheme for localization in VANETs has been proposed in [41]. This scheme combines a weighted centroid localization (WCL) with fuzzy logic. Two parameters are considered as fuzzy logic inputs, the distance between sender and receiver and the heading information. Beacons' (periodic) messages are utilized to exchange status information such as ID, location, and velocity. The fuzzy logic module generates the output in the form of a weight value. Using WCL, a weight value is assigned to each neighboring vehicle. Finally, the vehicle location is estimated using the neighboring vehicles' weighted coordinates. To facilitate trust management within VANETs, ref. [42] introduces a fuzzy trust model.