In the recent literature, several Medium access control (MAC)-enabled routing approaches have been created to deliver multi-hop communication and timely dissemination of safety signals over the vehicle network. To optimize data distribution over the VANET, these suggestions employ MAC layer factors such as transfer location allocation, channel status, and collision probability. Moridi et al. (2017) thoroughly examine these VANET cross-layer routing strategies. This section focuses on a solution for multi-hop emergency message transmission [26]. Chen et al. (2018) proposed that Opportunistic broadcast in VANETs (OB-VAN) is one of the oldest means of distributing parcel shipments. This opportunistic routing protocol makes use of a modified 802.11 MAC layer. To choose relay nodes, OB-VAN employs a confirmation mechanism. Select the optimum relay node using the active signaling technique. The node that catches the data packet delivers a brief acknowledgment consisting of a signal burst calculated immediately after the data packet is received according to the distance standard [27, 28].
Wu et al. (2020) described a new Time Division Multiple Access (TDMA)-based routing system for two-way highway warning signals. Nodes are classified as cluster heads (CH) or ordinary vehicles (OV) using this mechanism, which is known as priority-based controlled media access control (PDMAC) (OV). PDMAC has created a three-level priority approach for disseminating warning alerts. The direction-based relay selection is the first layer. The source sends out an inquiry message (REQ) to its neighbors, stating its direction, destination, and other information and reserving all available time slots within itself [29]. Msongaleli et al. (2019) proposed visible light communication-based VANET data transmission. VANET node sends a data packet to another node, and the self-organizing remote vector routing protocol on request relies on a mechanism connected with the on-demand method, which commences the path [30].
Efficient routing protocol for VANET presented by Chinnasamy et al. (2016). This is a one-of-a-kind feature that is not found in other subcategories of routing protocols. It works in both single and multi-mode. The Dynamic Source Routing (DSR) protocol uses source targets and maintains functional paths. These include routing detection and routing services [31]. Aravindhanet al.(2019) proposed an efficient DSR algorithm for data transmission in VANET. Nodes must consider four fundamental data structures conceptually to participate in DSR: buffer transfer, transmission buffer, route cache, and route request table. This is a collection of records of this node's most recently forwarded or derived route request packets. This translates to better connection status routing. When a topology changes, the MPR (top point relay) creates topological information and sends it to the selected node. It is an active protocol [32] that uses a table-driven technique. As the name implies, the protocol loops topological information using an enhanced link status model. This method is also used by Optimized Link State Routing Protocol (OLSR), although to maintain bandwidth, the protocol works in wireless multi-hop settings, enhancing the flow of OLSR messages. GSR routing can be done in two ways.
Srivastava et al. (2020) proposed three VANET routing algorithms to send data to the nearest neighbor to the destination node for greedy transfer. These routing algorithms determine which nodes are adjacent [33]. Routing is the second way the planner inoculates the concept of rubber monkeys. The transmitting node determines the projected location of the receiving node while using the greedy forwarding approach. Masini&Gawas (2019) suggested a new algorithm that uses the selective distance allocation method. IA uses channel transmission features to guide numerous interference currents in a specified direction on the receiving node, reducing interference as a new means of interrupting multiplexed interference [34, 35]. The author suggested a new strategy for dealing with mobile network interference and IA was used to test it. The simulation shows that IA can significantly improve communication speed. Blind IA for cell networks with incomplete Channel State Information (CSI) information has been studied by Verma et al. (2018) [36]. Blind IA, on the other hand, will create a substantial delay in direct communication without complete CSI information, resulting in a relatively significant loss in routing efficiency. To overcome spectrum deficiency, this work developed the CR-VANET protocol, which combines VANET with CR by Yang et al. (2016) [37]. By equipping the car with CR communication equipment, this technology discovers DSRC-free channels, considerably increasing spectrum utilization. Vehicles may now take full advantage of spectral resources thanks to capturing and utilizing the space and time available in CR spectral holes. The FLUTE protocol, introduced in the paper [38], uses broadcast or multicast to achieve reliable communication between the source and target nodes without requiring two-way communication.
It works well with one-way communication systems like the Internet, satellite, and Wi-Fi. Various forward error-correcting algorithms, such as XOR, Reed-Solomon, and Raptor Code, are used in the FLUTE protocol. Bondre et al. (2015) presented the Ad hoc on-demand multi-path distance vector routing (AOMDV) protocol for efficient data transmission in VANET. This approach efficiently leverages location and street map information to deliver warning messages [39]. In this scenario, the vehicle is driving in two modes: warning mode and (ii) normal mode. The vehicle's typical behavior is in the normal position. For eMDR, two methods have been presented. The one for transmitting is different from the one for receiving. Suppose the car is in warning mode while in transfer mode. A message is sent. Whenever the gap between the transmitter and the receiver exceeds the threshold distance, or if the vehicle is in another location, the message is discarded in the receive mode. eMDR works best in metropolitan settings with densely packed automobiles and towers that absorb radio frequencies. Road Traffic Vehicle Response Mechanism RBVT-R as a response protocol has been presented by C. Ksouri et al. (2019) [40]. Create demand-based road routes by connecting road segments. These routes are represented as a sequence of junctions in the data packet header, and intermediate vehicles use these intersections to transmit packets between them spatially.
The primary goal of this protocol is to reduce undesirable results on the receiving node by preventing the transmission of error messages for error events. The calculation is performed by the vehicle that provided the event alert message (EWM), and the calculation certificate is then attached to the EWM. Proof of Work (POW) is a calculated cost to prevent rogue cars from receiving false information. Reactive routing is combined with all available geo-location information and location-based geo-location in the Hybrid Routing Algorithm (HRA) by Wagh et al. (2018). On request, HLAR starts route detection. The route request packet (RREQ) provides the location coordinates of the source and receiver node and searches for the node closest to the receiver node if the source vehicle does not have a path to the destination [41]. RREQ is routed to the neighboring node nearest to the target node if it is available. The RREQ packet will flood all nodes if the source node cannot discover a closer neighbor. Venkatramanan and Ramalingam (2021) proposed various efficient routing protocols using Harris Hawk optimization and the Internet of things developed for wireless network systems. The performance of the conventional algorithm provides better QoS performance [42–47].
Traditional protocol performance suffers significantly in large VANET scenarios that extend large geographic areas and include many vehicles. The main disadvantages of VANET networks are network inefficiency and network instability. Due to fast-moving vehicles, limited bandwidth resources, and lossy radio channel attributes, providing reliable multi-hop communication becomes a more difficult problem in VANET. Establishing vehicle-to-vehicle communication and stable routing is one of the most difficult challenges in VANET. Another issue with VANET is that it is typically limited to local optima. Effectively predict vehicle-to-vehicle link reliability and design dependable routing service protocols to meet the requirements of various QoS applications. The FSR, DSR, and TORA routing protocols did not necessitate path discovery in this scenario, but they could not maintain new data paths, limiting delay and bandwidth consumption. It also fails to enhance the network QoS, resulting in unsatisfactory performance. As a result, to maximize routing efficiency and reliability in VANET, this research work has proposed the TDSRP-DC algorithm.