# An Intelligent Optimized Route-Discovery Model for IoT-Based VANETs
## 2 Related Work  
Generally, IoT-based VANET communication failure factors are classified in terms of transmit power, signal strength, path loss, and frequency [1]. Various detailed surveys have been conducted using different nature-inspired algorithms for routing purposes [13]. Popular algorithms, such as the genetic algorithm (GA), have proved their capability for addressing communication, but the performance of GA lacks optimal route discovery due to its convergence in a local optimal route [1; 9; 14; 15]. The ant colony optimization (ACO) algorithm was proposed in [16] to ensure the resolution of the dynamic routing protocol. However, the proposed algorithm has a better communication process for a lower number of attempts.In addition, ref. [17] proposed a hybrid ACO and PSO to reduce communication delay and reduce fast convergence. Thus, the incorporation of the PSO algorithm helps to maintain diversity among the solutions, which improves the exploration capability of ACO algorithms. An alternative route-discovery algorithm was investigated using the modified lion algorithm (MLA) in [18], and it shows its dominant performance in terms of convergence and routing cost compared to traditional GA. Likewise, ref. [19] proposed a clustering algorithm based on the grey wolf optimization (GWO) algorithm. The clustering capability of GWO helps to reduce the overhead of a complex network. Furthermore, the bee life algorithm was proposed to enhance the quality of service in IoT-based VANET [20]. The author in [21] proposed an improved firefly algorithm for addressing delay and failing communication in multicast routing using the levy distribution. However, the levy distributes the delays when attaining a convergence solution. In [22], the fuzzy bacterial optimization zone-based routing protocol is presented, which finds a short and stable route for communication within a small environment. Nature-inspired optimization algorithms depict efficiency in IoT-based vehicle ad hoc networks. However, the design conversion from continuous search to discrete search space reduces its performance over a dynamic complex network topology. Thus, [23] clearly illustrates the performance of the discrete bat algorithm (BA), firefly algorithm (FA), and PSO, showing their dominance over cuckoo search (CS). Therefore, optimal path discovery in VANET has a greater impact in vehicle information transfer from V2V and V2I communication in both uplink and downlink channels. Furthermore, a new optimization algorithm-based levy flight-based discrete cuckoo search model helps to find an optimal path in [24]. The investigation used the levy flight and random walk technique to enhance the exploration behavior of CSO, which helps in finding a loop-free optimal communication route. The authors in [25] introduced the enhanced harmony search optimization algorithm to configure a routing protocol using roulette wheel selection and tournament selection. Furthermore, the combination of ant colony optimization (ACO) and on-demand distance vector (AODV) is used to reduce congestion in VANET. Thus, the significance of ACO is further incorporated into the routing protocol in [26], and that establishes the optimal path in low-density networks, and demonstrates poor convergence in high-density networks. However, the limitation of optimization algorithms is the maintenance of the optimal solution for the next generation, and increased convergence time when finding the optimal route discovery due to unbalanced exploration, exploitation and selection strategy. Therefore, in IoT-based VANET, communication via uplink and downlink direction is quite complicated due to a robust network topology and that makes the route-discovery process quite difficult [27; 28]. During an emergency, an infrastructure pushes messages to all vehicles, which causes congestion in the network due to the computational complexity in the routing process. However, the routing process falls under the category of an NP-hard problem.