In recent years, researchers at home and abroad have fully studied and optimized routing protocols that meet the communication environment of wireless Ad Hoc networks based on their characteristics. The earliest innovative research on routing technology was mainly concentrated in the mid-1990s. The MANET research team composed of IETF mainly studied the design and standardization of routing protocols for wireless Ad Hoc networks [5]. These protocols can be categorized into reactive, proactive, and hybrid routing protocols. Representative routing protocols include Dynamic Source Routing (DSR) [6], Optimized Link State Routing (OLSR) [7], Destination Sequence Distance Vector (DSDV) [8], and Ad Hoc On-Demand Distance Vector (AODV) [9].

In the DSDV routing protocol, mobile nodes periodically broadcast their routing information to their neighbors. Each node is required to maintain their routing table. The AODV protocol finds routes using the route request packet, and the route is discovered when needed. Research has shown that AODV performs better than DSDV in packet delivery ratio, throughput, and routing overhead. The delay of AODV is greater than DSDV [10]. For real-time applications, AODV performs better than the DSDV routing protocol [11]. In addition, in urban rail transit scenarios, the trackside nodes are fixed, and trains run along the track. Compared to traditional wireless Ad Hoc network nodes, the communication between them is relatively fixed, and it is not necessary to use active routing protocols to obtain the routing tables of all nodes. Therefore, active routing protocols will incur a significant amount of unnecessary overhead in this scenario.

To continuously improve the performance of routing protocols and better adapt to the communication environment, a large number of studies have optimized routing protocols from different perspectives. Compared with the traditional AODV, the proposed improved protocol has advantages in terms of the packet delivery fraction, overhead, and route setup time.

Many researchers have made improvements to classic protocols. Sheng Liu et al. present an optimized protocol, called B-AODV, based on the shortage of routing finding and routing repair of AODV. In B-AODV, first, through a reverse request by sending BRREQ to replace RREP, it reduces the time of routing finding. Second, two hops IP recorded in control messages and the routing table can improve the rate of routing repair and reduce the time of routing finding [12]. Abdalla M. Hanashi et al. propose a new probabilistic approach that dynamically fine-tunes the rebroadcasting probability of a node for routing request packets (RREQs) according to the number of neighbor nodes. Their proposed approach demonstrates better performance than blind flooding, fixed probabilistic, and adjusted flooding approaches [13]. Reference [14] presents a new fitness function (FFn) used in the Genetic Algorithm (GA) to obtain the optimized route from those routes offered by the Ad Hoc On-demand Multipath Distance Vector (AOMDV) routing protocol. These protocols provide an optimization process to select the efficient routes that have the highest fitness values implementing the shortest route and less data traffic. Ref. [15] proposes a new dynamic relationship zone routing protocol (DRZRP) for Ad Hoc networks. In this protocol, each node in the network establishes a neighboring zone with a radius of ρ hops and activates a relationship zone according to the service request frequency and service hotspot condition. 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 the model. AOMDV performs well, but it does not have intelligent decision capabilities to select immediate routes by their source without discovering new routes or selecting alternate routes via intermediate nodes during failure, which diminishes performance and enhances end-to-end delay. To conquer this dilemma, Ref. [25] proposes an AOMDV intelligent decision protocol (AOMDV-ID). AOMDV-ID acts as hybrid protocol (proactive and reactive). Ref. [26] proposes an improved AODV routing protocol based on restricted broadcasting by communication zones for transmitting data in a largescale VANET. Following the maximum grey correlation degree principle, a series of key communication zones is selected to construct a restricted broadcasting area for route discovery in AODV. Simulation results show that, compared with the traditional AODV, the proposed improved protocol has advantages in packet delivery fraction, overhead, and route setup time.