S. Sampath Kumar

An robust recursive ant colony optimization strategy in VANET for accident avoidance (RACO-VANET)

VANET (Vehicle Ad-hoc Network) [1] is an emerging technology that enables the vehicles to communicate with each other. Nowadays, we see there are lots of accidents occurring. An effective way to avoid accidents is to provide congestion free communication between vehicles. VANET allows wireless communication between vehicles and the Road Side Units. In VANET, the vehicles are connected with each other to form a network. The vehicles can pass information within the network. A vehicle can send a message to any vehicle which is present in the network or it can send a message to the Road Side Units. This message passing is done by using many algorithms. We use the Recursive Ant Colony Optimization (RACO) [2,26] Algorithm which is an extension of Ant Colony Optimization Algorithm to route the message within the network. Though VANET is a powerful technology, it also has some issues that has to be rectified. Some of the issues are signal fading, connectivity, security, small effective diameter and so on.

Gene Suppressor

Gene Suppressor, proposed as a new add-on phase in GA for attaining self-adaption and repairing.This regulates genes dosage and its functional expression with respect to its environment.Identifies suppressor genes to perform suppression activity for attaining specific phenotype.Allows adjustment by gene adaption and repairing to obtain best solution and improving it.Experiment focused on proving single problem but, the buildup model can be easily adopted to other problem that uses MKP as a base. Genetic algorithm (GA) is a branch of evolutionary algorithm, has proved its effectiveness in solving constrain based complex real world problems in variety of dimensions. The individual phases of GA are the mimic of the basic biological processes and hence the self-adaptability of GA varied in accordance to the adjustable natural processes. In some instances, self-adaptability in GA fails in identifying adaptable genes to form a solution set after recombination, which leads converge toward infeasible solution, sometimes, this, infeasible solution could not be converted into feasible form by means of any of the repairing techniques. In this perspective, Gene Suppressor (GS), a bio-inspired process is being proposed as a new phase after recombination in the classical GA life cycle. This phase works on new individuals generated after recombination to attain self-adaptability by adapting best genes in the environment to regulate chromosomes expression for achieving desired phenotype expression. Repairing in this phase converts infeasible solution into feasible solution by suppressing conflicting gene from the environment. Further, the solution vector expression is improved by inducing best genes expression in the environment within the set of intended constrains. Multiobjective Multiple Knapsack Problems (MMKP), one of the popular NP hard combinatorial problems is being considered as the test-bed for proving the competence of the proposed new phase of GA. The standard MMKP benchmark instances obtained from OR-library 22 are used for the experiments reported in this paper. The outcomes of the proposed method is compared with the existing repairing techniques, where the analyses proved the proficiency of the proposed GS model in terms of better error and convergence rates for all instances.

A Chaotic Krill Herd Optimization Approach In VANET For Congestion Free Effective Multi Hop Communication

VANET, which stands for Vehicular Ad-Hoc Network has many applications in Urban areas where congestion has become a drastic problem. VANET is a network where vehicles act as nodes. The Krill Herd algorithm recently designed by Gandomi and Alavi is one of the best optimization techniques. Even though Krill Herd algorithm satisfies many optimization problems, it lacks the three issues, namely local optima avoidance, high convergence speed and the absence of congestion. So in this paper, Chaotic theory is introduced into the krill herd algorithm to solve the three issues, thus forming the Chaotic Krill Herd algorithm (CKH). The Chaotic Krill Herd algorithm introduces three chaotic maps, namely Circle, Sine and Sinusoidal to provide chaotic behaviors and also enables the Krill herd algorithm to have a group of krills with chaotic induced movements. With the help of these three chaotic maps, Congestion can also be reduced to a greater extent.

A hybrid ACO-PSO based clustering protocol in VANET

VANET means vehicular ad-hoc network. The routing protocol ant colony optimization (ACO) is a swarm intelligence which is based on the behavior of the ant, the ACO algorithm includes the cooperation and adaption and it produces an optimal solution. ACO algorithm depends upon the amount of pheromone deposit. The particle swarm optimization is also swarm intelligence, the PSO algorithm is population based and depends upon the movement and the intelligence of the swarm. The pheromone value is used for the decision purpose. The benefit of particle swarm optimization technique is that the particle moves always in the same direction with the same velocity and it also selects the position that is better than the previous position thus the best path is followed by each particle. In PSO the set of potential solutions evolves to approach a convenient solution for a problem. This algorithm produces Global best (Gbest) and Personal best (Pbest) solution. The benefits of the ACO and PSO protocols are combine have a hybridACO-PSOalgorithm to have an optimal solution to a multicast network and thus it helps in clustering.

Intelligent Collision Avoidance Approach in VANET Using Artificial Bee Colony Algorithm

Clustering seems to be the most desired process in the network arena, especially in vehicular ad hoc network (VANET). Several algorithms were proposed for the optimization of the routing in VANET that enables efficient data transfer through better manipulated clustering. In spite of using those algorithms, the major impact lies in the clustering method, so it necessarily depends upon the effective manipulation of analysis of dynamic clustering (Kashan et al. in DisABC: a new artificial bee colony algorithm for binary optimization, 12(1):342–352, 2012). Bee colony optimization is another vibrant bio-inspired methodology that is being used in solving all the complex problems in the network sector. Since bee colony optimization is highly heuristic in nature we adhere to it to obtain good degree of clustering.

Research on QoS aware dynamic reconfiguration and performance measures in VANET

Vehicular Ad-Hoc Network (VANET) became known in wireless networks to influence the importance in growth of the transportation system. In VANET several QoS related routing protocols are available. At the moment, VANET be-comes a worlds biggest ad-hoc network. The parameters of Quality of Service (QoS) generally depends on layers such as MAC layer and physical layer in Vehicular Ad-Hoc Network. Managing the QoS in VANET is critical due to the movement of nodes. Many challenges are available in vehicular communication among them the most important issue is attaining the QoS. QoS advances the level of implementation. It specifies strong paths between the vehicles. QoS disputes are communication time span and packet delivery ratio. The safety and other major applications in VANET depends on QoS. In this paper, we survey some of the QoS parameters and QoS associated protocols and algorithms.

A Hidden Markov Model for Fault Tolerant Communication in VANETS

The Ad-Hoc Network provides various advantages in vehicular communication. For efficient communication of vehicles, Vehicular Ad-Hoc Network (VANET) has proven to provide better communication between vehicles. There are several problems associated with vehicular communication. Communication failures often occur between vehicle to vehicle and vehicle to infrastructure [13]. To overcome these problems Hidden Markov Model (HMM) is applied in VANET to improve intelligent vehicular communication. This model enables fault tolerant [15] communication by minimizing the communication failures and link failures while sharing resources among the vehicles. Hidden Markov Model consists of states and observations. In this model, the states are hidden, but the output of this model depends on the state. In this paper, we focus on Hidden Markov Model and how it is applied to VANET to improve the safer vehicular communication.

Quantitative analysis on various safety centric based approaches in VANET

VANET - Vehicular Ad-Hoc Networks performance is very significant in improving the transport effectiveness and increasing the safety. VANET comprises the capability in enlightening roadway safety, and in giving the passengers eases. VANET is the still other emergent transmission network which provides safety broadcasting. The function of VANETs was frequently recommended for usage in vehicular safety requests. Safety in vehicular communication between infra-structure to vehicle, vehicle to infrastructure and vehicle to the vehicle is more essential. Safety implementations hold as a fundamental specification for the capability to collect the message by way of a vehicles sensing elements. Safety applications in VANET are provided by standards IEEE 802.11, Dedicated Short Range Communication, Medium Access Control Layer, and Physical Layer. The accident recognition and prevention by broadcasting security communications are thought over at the same time as one of the majorities significant benefits of VANET. In this paper, we survey some of the recent approaches and protocol in VANET for safety-related applications in VANETs.