## 2 Related Work  
Channel congestion is a well-recognized problem in vehicular ad hoc networks. Literature approaches propose to mitigate this problem by regulating the network load according  
Figure 1: Beacon packet content. to different parameters such as vehicle density, vehicle speed, and distance between vehicles. In particular, each vehicle monitors and/or calculates the selected parameter(s) around itself and utilize this information to adapt its transmission parameters. In [17], the authors introduce a protocol called adaptive beaconing rate (ABR), which employs fuzzy logic to adjust the frequency of beacon transmissions. The ABR system utilizes vehicle status and the proportion of vehicles moving in the same direction as inputs to regulate the beacon frequency. Similarly, in [18], a fuzzy logic-based protocol that adjusts the beacon interval using inputs such as vehicle speed, number of single-hop neighbors, and carry time is presented. However, both protocols have only been tested in urban environments and do not incorporate channel status in their beacon rate adaptation mechanism. A Joint space-division multiple access and rate control (JSRC) scheme is introduced in [19]. JSRC joins an adaptive rate control scheme with a space-division multiple access (SDMA) approach to alleviate interference between vehicles and solve the hidden-node problem. In JSRC, vehicles must be sufficiently separated to share the same time slot for message transmission. JSRC utilizes carrier-sense multiple access with collision avoidance (CSMA/CA), allowing for multiple message transmissions per road segment. A beacon rate adaption based on fuzzy logic (BRAIN-F) to control the beacon frequency is proposed in [20]. The fuzzy logic system in BRAIN-F operates across two tiers. At Level 1, it takes into account the vehicle's individual velocity and the average directional speed of neighboring vehicles as inputs, generating a traffic density degree as its output, which subsequently becomes an input for Level 2. At Level 2, the fuzzy logic system factors in vehicle traffic density (output from Level 1), location status, and vehicle status to determine the beacon rate degree as the final output. However, the scheme does not incorporate considerations for the channel status. The impact of radio signal shadowing dynamics on the efficiency of beaconing protocols is studied in [21]. An expression for the upper limit of the channel utilization is derived, and it is shown that beaconing should not exceed approximately 60% of the control channel interval. Then, a dynamic beaconing approach (DynB) is proposed. DynB employs two control parameters: \(b_{t}\), representing the proportion of occupied time between \(t-I\) and \(t\), alongside \(N\), denoting the number of neighbors. These variables are utilized to adjust the beacon interval \(I\) towards a target value \(I_{des}\), ensuring that the channel utilization remains below a specified threshold \(b_{des}\). In [22], based on noncooperative game theory, a beacon rate and awareness control method is introduced. In order to ensure fairness among vehicles with similar needs, each vehicle is allocated a beacon rate tailored to its specific requirements. A distributed beacon congestion control scheme is proposed in [23]. This scheme considers link conditions to control beacon activities, which means a higher beacon rate will be assigned to the vehicles that have more neighbors and better conditions of links. In [24], an adaptive beacon generation rate congestion control scheme for urban environments is proposed. In this method, the beacon generation rate is adjusted to the level of vehicle density in the network. A speed-adaptive beaconing method is proposed in [25]. This approach considers the vehicle count when computing the forwarding probability. The study described in [26] introduces a beacon rate control technique based on link conditions. In that approach, nodes with a greater number of neighboring nodes are allocated a higher beacon rate.