An Intelligent Optimized Route-Discovery Model for IoT-Based VANETs

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 upli...

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Veröffentlicht in:Processes Jg. 9; H. 12; S. 2171
Hauptverfasser: Karunanidy, Dinesh, Ramalingam, Rajakumar, Dumka, Ankur, Singh, Rajesh, Alsukayti, Ibrahim, Anand, Divya, Hamam, Habib, Ibrahim, Muhammad
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.12.2021
Schlagworte:
Algorithms
Communication
Experimentation
Exploitation
Genetic algorithms
Heuristic methods
Infrastructure
Intelligent transportation systems
Internet of Things
Learning
Mobile ad hoc networks
Monkeys
Optimization algorithms
Particle swarm optimization
Route optimization
Search algorithms
Search process
Social behavior
Vehicles
ISSN:2227-9717, 2227-9717
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Zusammenfassung: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).
Bibliographie:ObjectType-Article-1
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ISSN:2227-9717
2227-9717
DOI:10.3390/pr9122171