Fuzzy logic-based VANET routing method to increase the QoS by considering the dynamic nature of vehicles

Abstract

Vehicular ad hoc network usually operates in various challenging situations like frequent topology changes, high vehicular mobility and the wide range of communication networks. Due to this it is very hard to maintain a higher data rate and also to achieve low latency during data communication. To overcome these problems, given the dynamic natures of all the vehicles in a given network in the proposed routing method, we have defined two fundamental parameters to determine the forwarding vehicle. The first parameter, which we developed, we call it "Channel quality factor (CQF)" or 'Z'. The other parameter known as "Communication expiration time" or 'T' together with CQF is used in the present method to determine the forwarding vehicle. Fuzzy logic is also used to optimize various Quality of Service matrices. This proposed routing method involves two main parts; one is for forwarding Vehicle selection in the road based on the fuzzy logic. The second one is Road selection at the Road Junction to select the right path to reach the signal to the destination vehicle. The simulation results show that our proposed method performs well compare to other well-known protocols (MoZo, BRAVE, OFAODV) in terms of the average end to end delay, packet delivery ratio and control packet overhead, given any number of vehicles in a set of streets. While we are comparing with VEFR protocol, our proposed method shows higher performance in terms of average E2E delay and control packet overhead. However, it is interesting to see that VEFR gives ∼ 5% better result than our proposed method when the number of vehicles in the streets are lower. But in the limit, when the number of vehicles reaches close to ∼ 1900 the difference between the proposed method and method in VEFR goes to zero. At last we compare our proposed method with junction based two V2I protocols. In every cases, it shows better result even though we change the speed of the vehicles, beacon interval, channel data rate and transmission region.

Traffic Aware Routing using Multi-Objective Delay Centric Enhanced Artificial Ecosystem-Based Optimization for VANET

“…Subsequently, the consumer phase is performed as shown in Eqs. (7)to (9) whereas the position update of herbivore, carnivore and omnivore is determined according to the random number. After performing the consumer phase, the decomposer phase is enabled based on Eq.…”

“…Moreover, essential services developed by VANETs are hazard International Journal of Intelligent Engineering and Systems, Vol. 16 control systems and emergency handling services [8,9]. Generally, the VANET is a decentralized wireless ad hoc network, so it doesn't have a predefined architecture in the network.…”

Cluster-Based Routing Schema Using Harris Hawks Optimization in the Vehicular Ad Hoc Networks

“…In [27], the authors proposed a fuzzy-logic-based routing scheme for VANETs to improve the QoS parameters. They have defined two important parameters to identify the forwarding vehicle node: channel quality factor (CQF) or "Z" and communication expiration time or "T." This routing scheme includes two main parts: (a) determining the forwarding vehicle node in the road based on the fuzzy logic and (b) road selection at the road junction to choose the right route to reach the data to the destination node.…”

Hierarchical traffic light-aware routing via fuzzy reinforcement learning in software-defined vehicular networks

“…The performance evaluation of our HIFS scheme is discussed in this section in various scenarios compared to the different state-of-the-art methods, including real-time evaluation of road conditions using CP packets (e.g., ITAR-FQ [31]), traffic light aware strategy (e.g., MISR [28]), and SDN-enabled routing scheme (e.g., GLS [16]). Details of these methods for selecting intersections and relay nodes are described in Section 2.…”

“… Transmission of CP packets at intersections such as ITAR-FQ [31] requires updating periodically, which increases latency and overhead, especially at high densities or high traffic loads.  Routing decision-making based on the local knowledge or two-hop neighbor's information due to the restricted view of vehicular topology leads to nodes trapped into the local maxima problem resulting in degradation of data routing efficiency [25][26][27][28][29][30][31][32].  Regardless of road constraints and traffic light conditions, routing efficiency would be restricted.…”