Hybridization of Monarch Butterfly and Grey Wolf Optimization for Optimal Routing in VANET (original) (raw)
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Swarm Intelligence Based Algorithm for Efficient Routing in VANET
International journal of innovative technology and exploring engineering, 2020
Many recent researchers are working to optimize solutions in the field of Vehicular Adhoc Network. However, none of them has yet claimed that it will fulfill all the challenges of such a dynamic region. VANET in itself is a complete area of study, research and improvements. Most of the researchers and industry consortiums has given their hypothesis and solution that depends on their predefined scenarios but no complete solution has designed until yet. Through this research work, the authors concluded that bioinspired solutions can be used to integrate along with VANET for a much accurate and optimized solution. The performance of VANET depends on various scenarios and due to the unpredictable behavior of the vehicle movement, no concrete solution can be claimed as of now. We incorporated Swarm Intelligence in VANET through the Ant Colony Optimization algorithm and found that the performance of VANET has enhanced by avoiding the entire congested path as it senses the pheromone trail. We have implemented and tested the results using open source software like Instant Veins, Simulation of Urban MObility (SUMO) and MObility model generator for VEhicular networks (MOVE). SUMO has used for testing the traffic simulation and MOVE is used to design model. Python for the script. The OSM used to take a map of Dehradun city. When we performed the experimental setup and found that the result confirms in reducing the traveling time of the nodes, which makes nodes faster and managed even it helps in saving the hydrocarbon fuels. During our approach, we have devised our own algorithm that has improvised the present Ant Colony Optimization algorithm and has concluded that the average traveling time of the nodes minimized through our approach.
TIHOO: An Enhanced Hybrid Routing Protocol in Vehicular Ad-hoc Networks
EURASIP Journal on Wireless Communications and Networking
Recently, vehicular ad hoc network (VANET) is greatly considered by many service providers in urban environments. These networks can not only improve road safety and prevent accidents, but also provide a means of entertainment for passengers. However, according to recent studies, efficient routing has still remained as a big open issue in VANETs. In other words, broadcast storm can considerably degrade the routing performance. To address this problem, this research proposes TIHOO, an enhanced intelligent hybrid routing protocol based on improved fuzzy and cuckoo approaches to find the most stable path between a source and a destination node. In TIHOO, the route discovery phase is limited intelligently using the fuzzy logic system and, by limiting the route request messages, the imposed extra overhead can be efficiently controlled. Moreover, in figure of an intelligent hybrid approach, the improved cuckoo algorithm, which is one of the most effective meta-algorithms especially in the large search space, intelligently selects the most stable and optimal route among known routes by calculating an enhanced fitness function. The simulation results using NS2 tool demonstrate that TIHOO considerably improves network throughput, routing overhead, packet delivery ratio, packet loss ratio, and end-to-end delay compared to similar routing protocols.
Novel Approach for Routing in Vanet by Network Connectivity with Meta Heuristic
—. A popular example of opportunistic routing is the " delay tolerant " forwarding to vanet network when a direct path to destination does not exist. The evaluation of this work is twofold. We implemented two prototypes on off-the-shelf hardware to show the technical feasibility of our opportunistic network concepts. Also, the prototypes were used to carry out a number of runtime measurements. Then, we developed a novel two-step simulation method for opportunistic data dissemination. The simulation combines real world user traces with artificial user mobility models, in order to model user movements more realistically. We investigate our opportunistic data dissemination process under various settings, including different communication ranges and user behavior pattern in this use Conventional routing in this case would just " drop " the packet. With opportunistic routing, a node acts upon the available information, in this thesis optimize the routing by centrality information then refine by ant colony met heuristics. In this method validate our approach on different parameter like overhead, throughput. Keywords— ant colony, met heuristics, Vehicular Ad-hoc network (VANET), QOS.
IEEE Access, 2020
Recently, the Internet of Things (IoT) is widely considered in vehicular ad-hoc networks (VANETs) for use in intelligent transportation systems. In particular, the pervasive deployment of different sensors in modern vehicles has unlocked interesting possibilities for improving routing performance in VANETs. Nevertheless, the discovery of short single loop-free routes for effective and efficient information dissemination in VANETs remains a challenge. This challenge proves more difficult to solve since it reduces to the case of finding the shortest Hamiltonian path for effective routing in VANETs. Consequently, in this paper, we propose two discretized variants of the cuckoo search optimization (CSO) algorithm, namely, the Lévy flight-based discrete CSO (LF-DCSO) and the random walk-based discrete CSO (RW-DCSO) for effective route discovery in VANETs. In addition, we investigated the inverse mutation operator gleaned from genetic algorithm (GA) in order to improve the exploration properties of our DCSO variants. We describe a new objective function that effectively models the reliability of individual links between nodes that comprise a single route. A detailed report of the routing protocol that controls the routing process is presented. Our proposed methods were compared against the roulette wheel-based GA and the improved k-means-based GA termed IGAROT. Specifically, our findings reveal that there was no significant difference in the performance of the different methods in the low vehicle density scenario, however, in the medium vehicle density scenario, the RW-DCSO algorithm achieved 2.56%, 100%, and 128.57% percentage increment in its route reliability score over the LF-DCSO, RW-GA, and IGAROT algorithms, respectively. Whereas in the high vehicle density scenario, the LF-DCSO algorithm achieved a percentage increment of 42.85%, 525%, and 733.33% in the route reliability score obtained over the RW-DCSO, IGAROT, and RW-GA algorithms, respectively. Such results suggest that our methods are able to guarantee effective routing in VANETs. INDEX TERMS Discrete, cuckoo search optimization (CSO), route discovery, shortest path, VANET.
Novel Optimized Routing Scheme for VANETs
Procedia Computer Science, 2016
The Vehicular ad-hoc networks (VANETs) are a specific type of Mobile ad-hoc networks (MANETs). However, the main problem related to it is the potential high speed of moving vehicles. This special property causes frequent changing in network topology and instability of communication routes. Consequently, some of the challenges that researchers focus on are routing protocols for VANETs. They have proved that the existing MANET proactive routing protocols are the most used for vehicular communication. Yet, they are not as adequate as they are for VANETs. The main problem with these protocols in dynamic environment is their route instability. This paper combines multi-agent system approach and PSO algorithm to solve the above mentioned problems. We carried out a set of simulations tests to evaluate the performance of our scheme. The simulation part shows promising results regarding the adoption of the proposed scheme.
Optimal Route Selection for Vehicular Adhoc Networks using Lion Algorithm
The Journal of Engineering Research, 2019
Vehicular Ad-hoc NETworks (VANETs) is very important in the field of Intelligent Transportation system (ITS) for enhancing the safety of road. The communication between the vehicles will be covered under the VANETs. A lot of research works are there in the area of VANET development. The common problem that arises is achieving multi-constrained Quality of Service metrics. In order to solve this problem, this paper derives a cost model for vehicle routing problem by considering the network quality metrics such as travel cost, collision, congestion and the awareness about quality of service (QoS). The QoS awareness is fuzzified into cost model to include in the total routing cost. Since the routing cost model is a minimization function, a recently introduced bio-inspired optimization algorithm called as lion algorithm (LA) is used to solve it. The performance is investigated using three renowned analyses such as convergence analysis, cost analysis and complexity analysis. The simulated...
A Novel Routing Protocol for Realistic Traffic Network Scenarios in VANET
Wireless Communications and Mobile Computing
The vehicular ad hoc network (VANET) has traditional routing protocols that evolved from mobile ad hoc networks (MANET). The standard routing protocols of VANET are geocast, topology, broadcast, geographic, and cluster-based routing protocols. They have their limitations and are not suitable for all types of VANET traffic scenarios. Hence, metaheuristics algorithms like evolutionary, trajectory, nature-inspired, and ancient-inspired algorithms can be integrated with standard routing algorithms of VANET to achieve optimized routing performance results in desired VANET traffic scenarios. This paper proposes integrating genetic algorithm (GA) in ant colony optimization (ACO) technique (GAACO) for an optimized routing algorithm in three different realistic VANET network traffic scenarios. The paper compares the traditional VANET routing algorithm along with the metaheuristics approaches and also discusses the VANET simulation scenario for experimental purposes. The implementation of the...
Journal of Computer Science, 2018
Proposing an efficient strategy to reduce traffic congestion is an essential step towards improvement as we take into consideration the unpredictable and dynamic infrastructure of the road network. With the advances in computing technologies and communications protocols, we can retrieve any type of data and receive in real-time the state of traffic congestion at each road using Electronic Toll Collection System (ETCS), Vehicle Traffic Routing System (VTRS), Intelligent Transportation System (ITS) and Traffic Light Signals (TLS). This study introduces a new distributed strategy that aims to optimize traffic road congestion in realtime based on the Vehicular Ad-Hoc Network (VANET) communication system and the techniques of the Ant Colony Optimization (ACO). The VANET is used as a communication technology that will help us create a channel of communication between several vehicles and routes. The techniques of the ACO is used to compute the shortest path that can be followed by the driver to avoid congested routes. The proposed system is based on a multi-agent architecture, in which all agents work together to monitor the road traffic congestion and help drivers quickly arrive at their destinations by following the best routes with less congestion. Simulation results show that the proposed method can reduce the total distance traveled and time taken in order to reach a destination, as compared to the classic "shortest path method" (based only on the distance).
Multiobjective Optimized Routing Protocol for VANETs
is properly cited. Vehicular ad hoc network (VANET) routing protocols have been attracting a considerable attention of both research and industrial communities, due to their significant role in intelligent transportation system applications. The present paper adopts an optimized integrated multicast, multicriteria, adaptive route lifetime as a routing protocol for VANETs. Whereby only an optimal subset of neighbor vehicles is chosen to relay route request (RREQ) messages based on distance, direction, speed, and future direction information in a combined sender-receiver manner. Among those selected optimal paths for route discovery, the best route with lowest cost will be chosen for forwarding data packets for a specified duration assigned depending on the obtained cost and number of intermediate vehicles of that route. Fuzzy controllers were employed to assess routes' costs and their lifetimes. Furthermore, artificial bee colony (ABC) algorithm was used to concurrently optimize all used fuzzy systems and obtain the optimal highest rank of links' cost values within which the neighbors could be selected as relay nodes in route discovery process. Simulation results prove that the proposed routing scheme significantly improves the network performance in both urban and highway scenarios, under different situations of vehicle density.
Performance Analysis of Vanets Routing Protocols
Research Square (Research Square), 2021
Vehicular Ad Hoc Networks (VANETs) are a particular class of Mobile Ad Hoc Networks (MANETs). The VANETs provide wireless communication among vehicles and vehicle-to-roadside units. Even though the VANETs are a specific type of MANETs, a highly dynamic topology is a main feature that differentiates them from other kinds of ad hoc networks. As a result, designing an efficient routing protocol is considered a challenge. The performance of vehicle-to-vehicle communication depends on how better the routing protocol takes in consideration the particularities of the VANETs. Swarm Intelligence (SI) is considered as a promising solution to optimize vehicular communication costs. In this paper, we explore the SI approach to deal with the routing problems in the VANETs. We also evaluate and compare two swarming agent-based protocols using numerous QoS parameters, namely the average end-to-end delay and the ratio packet loss which influence the performance of network communication.