# Retracted: W-GPCR Routing Method for Vehicular Ad Hoc Networks
## 2 Materials and Methods
### Greedy Forwarding Strategy Based on Weight Selection
#### 2.1.1 Influence of Node Movement Direction on Routing Performance
It can be seen that node K will be selected as the next-hop node, and the repair strategy selected by the weight can plan the routing path of A-> B-> C-> K-> J-> H. The packet is sent to the target node. ### Restricted Forwarding Strategy

When a node is forwarding a data packet, it finds that it cannot find the next-hop node for data packet delivery within its signal coverage. This is called the routing hole phenomenon. Routing holes seriously affect normal communication between nodes. In order to avoid communication interruption caused by this phenomenon, a restricted forwarding strategy is proposed. When caught in a routing hole, the node returns the data packet to the intersection node on the street where it reroutes, looking for other paths to deliver the data packet to the target node. In order to avoid rerouting, the data packet is delivered again to the street trapped in the routing hole, and the routing weight calculation formula under the routing hole is proposed:

\[N_{n}=\sin(\overrightarrow{l_{in}},\overrightarrow{l_{iv}}) \tag{3}\]

where \(\overrightarrow{l_{in}}\) is a vector representation of the intersection node pointing to the next-hop node; and \(\overrightarrow{l_{iv}}\) is a vector representation of the intersection node pointing to the node trapped in the routing hole. The value \(\sin(\overrightarrow{l_{in}},\overrightarrow{l_{iv}})\) is used to determine whether the direction of packet delivery during rerouting tends to be in the direction of the routing hole. The larger the value of \(\sin(\overrightarrow{l_{in}},\overrightarrow{l_{iv}})\), the less likely the next-hop node selected by the rerouting will fall into the routing hole again. \(N_{n}\) is the weight value of the next-hop node under the restricted forwarding strategy. By calculating the weights of all neighbor nodes within the signal coverage of the intersection node, the neighbor node with the largest weight is selected as the next-hop node for packet delivery. Figure 4: Schematic diagram of the repair strategy for weight selection. As shown in Figure 5, the source node O sends a data packet to the target node L. According to the greedy forwarding strategy, when a data packet is delivered to node D, it falls into a routing hole and cannot find the next-hop node to forward the data packet within its signal coverage. At this time, according to the solution proposed in this article, the data packet will fall back to the intersection node C, and node C will reroute to find other paths to deliver the data packet. According to the weight calculation formula in this paper, \(N_{E}<N_{F}\) is obtained, so node F will be selected as the next-hop node. Node E will not be selected as the next-hop node, which avoids the dilemma of getting into the empty junction again. After the data packet is delivered to node F, a potential routing path (O\(\cdot>\) C-\(\cdot>\) F-\(>\) G-\(>\) H-\(>\) I-\(>\) J-\(>\) K-\(>\) L) will also be established, which largely guarantees the reliability of normal communication between nodes. ### Simulation Model

In this paper, traffic simulation software (SUMO) [21] and discrete network simulation software (NS3) were used to simulate the GPSR, GPCR, and W-GPCR routing protocols in different scenarios. The number of nodes and the source-destination logarithm can evaluate the performance of each routing protocol. A map of Tianyuan District, Zhuzhou City, Hunan Province was exported from the OpenStreetMap website, as shown in Figure 6a. It was converted into a road file using the netconvert plug-in of SUMO, from which a simulation area of \(1000\times 1000\) m, and a road traffic model of nine intersections and 12 bidirectional lanes, was selected, as shown in Figure 6b. The initial position of the vehicle was randomly distributed, and the movement of the vehicle on the street was limited by the vehicle following model (i.e., the Krauss model). Different network conditions were simulated using five different network node numbers: 20, 40, 60, 80, and 100 nodes.