## 2 Literature Review
### Topological Approach  
In [8], the authors proposed a new protocol named load balancing maximal minimal nodal residual energy ad hoc on-demand multipath distance vector routing protocol (LBMMRE-AOMDV) which is an extended version of the AOMDV protocol. The protocol consists of two phases. The first generates disjoint link paths and maintains them in case of having one or more path failures. The second phase balances the data load among the generated link-disjoint paths. Through these phases, the protocol evaluates the generated paths to determine the maximal nodal residual energy and the actual number of packets that can be transmitted over that path without depleting the nodes' energy. Results achieve better performance in terms of packet delivery ratio and energy consumption while taking into account the number of dead nodes [9]. However, it suffers from a long end-to-end delay. Thus, the authors recommend using this protocol in applications such as banking and online shopping rather than generic applications. In [10], the authors presented a hybrid protocol that is based on the AODV algorithm using fuzzy logic named An Enhanced Hybrid Routing Protocol in Vehicular Ad-hoc Networks (TIHOO) to restrict the phase of route discovery. This algorithm is efficient and improves performance. However, it is rather a complex algorithm and unrealistic as it obviously consumes energy and therefore reduces the node's lifetime. On the other hand, the authors in [11] focused on reducing energy consumption using FF-AOMDV protocol with dragonfly topology. FF-AOMDV uses the fitness function to find the optimal multipath routing. It has been proved that the FF-AOMDV algorithm produces better results in terms of energy consumption, PDR, throughput, end-to-end delay, and routing overhead ratio when compared to AOMDV and AOMR-LM (Ad-hoc On-Demand Multipath Routing with Life Maximization). However, the fitness function spends a long processing time and therefore enlarges the end-to-end delay. In [12], the authors presented a traffic-aware routing protocol in VANET by introducing multi-objective auto-regressive whale optimization (ARWO) algorithm. ARWO selects the best path from multiple paths by considering multiple objectives such as end-to-end delay, link lifetime, and node distance in the fitness function. However, it suffers from network overload when there is high traffic and congestion on the routes since all the vehicles try to choose the best path to reach the destination. In [13], Fault-Tolerant Disjoint Multipath Distance Vector Routing Algorithm (FD-AOMDV) was introduced. This algorithm finds the shortest path based on the residual energy of the intermediate nodes at the expense of the number of nodes of that selected path and, therefore, the transmission delay. In [14], the authors presented a novel V2V-enabled resource allocation scheme based on cellular vehicle-to-everything (C-V2X) technology to improve the reliability and latency of VANETs. The main challenge with this cellular-based V2V technique is how to allocate spectrum resources and broadcast opportunities properly in the V2V communication, so the network performance can be improved without causing disturbing interference to cellular users. In [15], a hierarchical failure detection mechanism based on the architecture of VANETs was suggested. The failure detector can adapt to the dynamic network conditions and meet the different Quality of Service (QoS) requirements of multiple applications in VANETs. By sharing messages among vehicles, communication link failures can be overcome, and detection accuracy is further improved. The major shortcoming of this mechanism is its routing overhead which increases as the number of detection messages increases. In [16], the author proposed QMR routing protocol, a method based on Q-learning with a multi-objective optimization mechanism. In this protocol, the author tries to take advantage of both proactive and reactive routing protocols to optimize the delay and the efficiency of the network.