# An Intelligent Optimized Route-Discovery Model for IoT-Based VANETs
###### Abstract  
Intelligent Transportation system are becoming an interesting research area, after Internet of Things (IoT)-based sensors have been effectively incorporated in vehicular ad hoc networks (VANETs). The optimal route discovery in a VANET plays a vital role in establishing reliable communication in uplink and downlink direction. Thus, efficient optimal path discovery without a loop-free route makes network communication more efficient. Therefore, this challenge is addressed by nature-inspired optimization algorithms because of their simplicity and flexibility for solving different kinds of optimization problems. NIOAs are copied from natural phenomena and fall under the category of metaheuristic search algorithms. Optimization problems in route discovery are intriguing because the primary objective is to find an optimal arrangement, ordering, or selection process. Therefore, many researchers have proposed different kinds of optimization algorithm to maintain the balance between intensification and diversification. To tackle this problem, we proposed a novel Java macaque algorithm based on the genetic and social behavior of Java macaque monkeys. The behavior model mimicked from the Java macaque monkey maintains well-balanced exploration and exploitation in the search process. The experimentation outcome depicts the efficiency of the proposed Java macaque algorithm compared to existing algorithms such as discrete cuckoo search optimization (DCSO) algorithm, grey wolf optimizer (GWO), particle swarm optimization (PSO), and genetic algorithm (GA). intelligent route discovery; IoT-based VANET; autonomous vehicle; energy efficiency; java macaque algorithm +
Footnote †: journal: _articles  
modern IoT-based sensors play a vital role in identifying location and speed, and authenticating a vehicle via on-board sensors. Effective routing can also consider scenarios such as road surface conditions [3; 4], traffic conditions [1; 5] and information transfer, among other nodes. Route optimization is a major hurdle in the VANET deployment model, because of its highly volatile network topology. Consequently, the development of a robust communication route has become a major problem for VANET implementation, and this serves as a motivation for the development of a novel method of approaching this problem. Nature-inspired optimization algorithms (NIOAs) are effectively used in VANETs to find the optimal routes for communication. The optimization algorithms are designed well to solve large-scale optimization problems. However, the multi-dimensional search space made of multiple parameters limits the existing methods to determining the optimal routing path for communication. Thus, information sent via a non-optimal route may have a delay in communication. Consequently, optimization algorithms incorporate hybrid search strategies for solving the multi-dimensional search space. Eventually, major optimization algorithms have attained dominance in solving continuous optimization problems. However, frequent route updating in VANETs (i.e., discrete search space) has created a vital demand to evolve reliable and robust route optimization algorithms. Most discrete optimization problems fall under the categories of NP-hard or NP-complete [6]. However, the optimization algorithm faces many hurdles when solving typical discrete optimization problems [7; 8]. The problem space of a discrete optimization problem is represented as the set of all feasible solutions that satisfy the constraint and the fitness function that maps each element to the problem space. Thus, the discrete or combinatorial optimization problem searches for the optimal solution from a set of feasible solutions [6; 9; 10]. Thus, the existing optimization algorithms may be trapped with local optimal routes due to the robust nature of vehicle movement.