## 2 Related Works
DRZRP establishes proactive routing for the neighboring zone and relationship zone of the node, and the relationship zone of the node can be dynamically maintained, including initialization, relationship zone activation, and relationship zone inactivation. It matches the service relationship among nodes in the network and has comprehensive performance advantages in communication overhead and routing request delay, which improves the quality of the network service. Ref. [16] proposes an improved routing protocol, called L-AODV, which collects the link quality information during the broadcasting progress in network. According to the link quality information included in the arrived RREQ packets, the destination nodes choose the path after evaluating the performance by computing path cost. Experimental results show that the L-AODV routing protocol is improved in packet delivery rate. Ref. [17] presents an AODV with end-to-end reliability (AODV-EER). The main idea of the proposed modification in AODV is to find the route with the lowest drop rate from source to destination. It also proposes a backward route entry mechanism in order to initiate repair action after primary route breaks. The experiments clearly indicate that the proposed protocol performs better than the traditional AODV protocol. Ref. [18] proposes a new modified AODV routing protocol, called EGBB-AODV, where the RREQ mechanism uses a grid-based broadcast (EGBB), which considerably reduces the number of rebroadcasted RREQ packets, and hence improves the performance of the routing protocol. A simulation study shows that EGBB-AODV outperforms AODV in terms of end-to-end delay, delivery ratio, and consumption power. Ref. [19] proposes a new link lifetime prediction method for greedy and contention-based routing. The evaluation of the proposed method is conducted via the use of a stability-based greedy routing algorithm, which selects the next hop node having the highest link lifetime. Ref. [20] proposes a link-quality- and topology-quality-based routing protocol that uses the residual link lifetime of neighbors as the metric of link quality and uses the relationship of the distance between the intermediate node and the tie line of the source-destination as the metric of topology quality. By combining the two metrics to set the forwarding probability, the proposed protocol can not only avoid frequent route disconnect, but also restricts the propagation range of RREQ. The proposed protocol can significantly reduce routing overhead, as well as increase the packet delivery ratio and decrease end-to-end delay, thus improving routing performance. Many of the reactive protocols use a route searching mechanism where a route request is flooded in the network. Ref. [21] investigates this search procedure and tries to reduce it to a limited region with the aid of location information. Different search regions, applied both in a static and an adaptive way, are investigated, greatly reducing routing overhead. Ref. [22] proposes a new probabilistic protocol called Dichotomic AODV. It aims to reduce the number of messages in RREQ using a new probabilistic and dichotomic protocol for the discovery of the destination. This protocol significantly reduces the number of RREQ packets transmitted during route-discovery operations. Ref. [23] presents a scheme called dynamic hybrid routing (DHR). The basic idea is to configure several routing policies in advance and then dynamically rebalance traffic by applying different preconfigured routing policies to react to traffic fluctuations. Most of the above studies are based on mobile Ad Hoc networks, but there are few studies on practical application scenarios. Ref. [24] utilizes network simulator 3 to apply AODV, DSDV, and OLSR routing on V2V nodes. The evaluation criteria for the comparison of these routing protocols include the use of QoS (Quality of Service) parameters such as PLR, packet overhead, and throughput. The results of the simulation demonstrate that AODV is the most optimal method among AODV, OLSR, and DSDV for our model.