# Intersection-Based Routing with Fuzzy Multi-Factor Decision for VANETs
## 2 Related Work  
In this section, we briefly review some existing routing protocols in VANETs. Generally speaking, the routing protocols proposed can be mainly divided into three different types: topology-based routing protocol [12; 13; 14], geographic-based routing protocol [15; 16; 17; 18; 19; 20; 21], and cluster-based routing protocols [22; 23; 24]. In this paper, we focus on the geographic-based routing protocol. Geographic routing (GS) requires that not only each vehicle knows its own location, but also the source is aware of the location of the destination. With this information, GS determiners the forwarding route to the destination without knowledge of the network topology. Thus, it is more robust and promising than the topology-based routing protocol in VANETs. As is well-known, the more famous GS must include GPSR (Greedy Perimeter Stateless Routing) [15] and GPCR (Greedy Perimeter Coordinator Routing) [16]. In GPSR, a strategy called Greedy forwarding is used, where the next-hop is selected, which is geographically closest to the destination among all one-hop neighbors. When the strategy fails because of  
Figure 1: The architecture of the IRFMF routing protocol. the local maximum problem that the current node cannot find a neighbor node closer to the destination than itself geographically, a recovery strategy called Perimeter forwarding is used, where the packet is forwarded around the perimeter of a planar graph based on the right-hand rule. The GPCR can be subdivided into intersection-based geographic routing protocol; different from GPSR, GPCR introduces the intersection (junction), and all packets are first forwarded to vehicles that are at the intersection (junction); then, the next hop is determined. However, GPCR does not completely solve the local maximum problem. When it happens, the packet will be backtracked to a junction node, and then another segment is selected based on the right-hand rule. In GSR (Geographical source routing) [25], according to the availability of a map, a global graph with junction nodes as vertices and streets as edges is drawn, and then a Dijkstra shortest path is computed to the destination. The route cannot be guaranteed to be connected because the connectivity between two junctions is not considered. The EGSR (Efficient Geographical Source Routing) [26] is the improvement of the GSR protocol. It introduces an ant-based algorithm to discover the route with optimum network connectivity. The weight of each street segment is calculated according to its connectivity, and the route with the minimum total weight will be determined by the source vehicle. The traffic lights play a vital role in VANETs in urban areas. Another intersection-based routing protocol, STAR (Shortest-Path-Based Traffic-Light-Aware Routing) [17], takes the traffic lights into consideration to decide how the packets should be forwarded. The packets are firstly forwarded to the red light segment, which is connected. Otherwise, the packets are forwarded to the green light segment, which is closer to the destination. GTLQR (Greedy Traffic Light and Queue Aware Routing Protocol) [18] not only considers the traffic lights but also the network congestion. The scheme selects the street with the highest connectivity, which is calculated by considering traffic lights and uneven distribution of vehicles, and the next-hop vehicle is selected by considering the queuing delay, channel quality, and relative distance. In iCAR-II (iCAR-II: Infrastructure-Based Connectivity Aware Routing) [19], a dynamic real-time network topology is constructed based on the infrastructure. The shortest path can be determined based on up-to-date connectivity information. In TAROC (traffic-aware routing protocol with cooperative coverage-oriented information collection method) [20], a cooperative information collection method was proposed. First, a hierarchical information collection scheme is established by information aggregators and information collectors.