J. Amudhavel

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.

A krill herd optimization based fault tolerance strategy in MANETs for dynamic mobility

Mobile ad hoc network (MANET) is the multi-hop network, infrastructure less network with autonomously self-organizing in the distributed network. In case of node failure or link failure, MANET has the capacity to self-heal by changing the topology dynamically. There are many issues due to its dynamic nature, with the change in topology which leads to more routing. In multi-hop networking it uses the routing protocols such as AODV and DSR are due to its dynamic infrastructure and high mobility. The krill herd bio - inspired optimization algorithm in MANET is proposed for the dynamic network for the efficient power saver and reducing the routing process by the local and global optimization. The local search can be calculated by the sensing distance in KH algorithm to find the nearest neighbors with in the infrastructure. The local optimization uses AODV protocol and in global optimization, the DSR protocol is used for the power consumption. The node failure or node link failure can be detected by the KH algorithm as random diffusion and motion influencing other node.

A bio-inspired artificial bee colony approach for dynamic independent connectivity patterns in VANET

Vehicular Ad-Hoc Network (VANET) [1] which is a sub-type of the mobile ad-hoc networks provides wireless communication between the vehicles and to the Road Side Units (RSU) [2]. In VANET, the communication between the vehicles is used for various purposes like safety, comfort and entertainment. We use the vehicles as the portable nodes to create the mobile network. The VANET uses all the vehicles in the nearby distance as a wireless router or node to form a network. The vehicles can dynamically come in or drop out of the network as they move. The working of the VANET depends on how the routing of data is done in the network. In the recent years, the number of accidents has been increased to a greater count. In order to eradicate this, the implementation of VANET technology is being used in various modes of transportation. Even though VANET has been proposed to be a successful network model, it has some issues to overcome. The most major issues are the connectivity and the signal fading issue. We can overcome some of the issues in VANET by using the Artificial Bee Colony Algorithm (ABC) [3]. The proposed use of the algorithm in VANET may resolve some of the issues effectively.

A Novel Bio-Inspired Krill Herd Optimization in Wireless Ad-Hoc Network (WANET) for Effective Routing

In this paper, a bio-inspired algorithm called krill herd optimization algorithm is applied in the wireless ad-hoc network to solve the challenges present in the wireless network. The main challenges faced in the wireless ad-hoc network are standardization and routing. Although several algorithms have been developed for routing still there exists, delay [3] in delivering the packets. The traditional algorithms are not effective in both cost and time constraints. The best solutions are given by optimization technique. Hence krill herd optimization is adopted to solve the issues and to enhance the efficiency of wireless ad-hoc network. Routing delay can be side-stepped by developing an algorithm using krill optimization by updating the forwarding table placed in the nodes, whenever a message is passed through the node. Power consumption is also a major issue which affects the efficiency. As nodes in ad hoc are battery operated, transmission will be more when less power is consumed. So energy-efficient [8] algorithm must be developed in such a way that routing is not affected by the name of power consumption.

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.

Multi-Objective Clustering Methodologies and its Applications in VANET

Vehicular ad-hoc network is the novel and powerful network which provides wireless communication between the vehicles within the network. VANET are networks with high dynamic topology and their communication is vulnerable to attacks where the attackers can send spurious information to deceive the other vehicles. Nodes in VANET should be confident with the information that is shared between them. This communication between the networks can be made more efficient by the formation of clusters in the networks. The cluster formation helps in free flow the data between the nodes. In this paper, we have discussed about the different techniques used for cluster formation. Clustering serves as a solution to scalability helps in load balancing and resource consumption in huge networks. Randomized algorithms used in clustering helps simultaneous data transmission and synchronization between the clusters. Clustering using content-centric approach helps in collective sensing, processing and distribution of information in the network traffic by adapting the volume effect. Clustering techniques also help in maintaining the integrity, non-repudiation and confidentiality of data between the nodes in the network.

Demystifying Challenges, Opportunities and Issues of Big Data Frameworks

The Big data has a huge volume of data sets, which is used to process the conventional data processing applications. In this paper, the Flex Analytics framework is used to enhance the scalability and flexibility of analyzing and processing the data and provide visualization of data. The MapReduce framework of the fuzzy logic model is used in big data for the degree of uncertainty and to reduce the imbalance of data. The MapReduce framework for large-scale extreme learning machine for massive and dispersed data and machine learning approach is used to find the bottleneck in the peer-peer network. The emerging distributed cloud data centers with the security framework using Hadoop technology is used to process larger datasets. The financial data standard is utilized to eliminate the data redundancy and noise.

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.