COMPONENT BASED ROUTING PROTOCOL DESIGNING METHODOLOGY FOR MANET

Mobile Ad Hoc Network is designed and deployed to achieve self-configuring and self-healing. MANET utilizes distributed wireless stations for relaying data packets. Every single station in the MANET can decide routing path for an incoming data packet. MANET has the most unfavorable conditions for routing path discovery due to node mobility and constant topology changes. Large variation of performance due to various environment inputs is a major impediment of implementing existing routing protocols for MANET in the battlefield. Therefore, it is a major challenge to design a routing protocol that can adapt its behavior to environment alteration. In consideration of adaptability to the environment and flexibility in protocol construction, a novel component based routing protocol methodology is proposed in this paper. Distinguished from conventional investigation of routing protocols as individual entities, this paper will firstly generalize four fundamental components for MANET routing protocols. Then, a significant component diagnosis process is proposed to detect significant component and enhance the overall performance. Finally, preliminary simulation results demonstrate the power of the component based methodology for improving overall performance and reducing performance variation. In conclusion, the evaluation and improvement at the component level is more insightful and effective than that at the protocol level. The primary contribution of the work is proposing the Component Dependence Network the first time and innovative quantitative methods are proposed to learn the structure and significant component to analyze the impact of component on performance metrics. Based on conditional independence test, hierarchical structure of Component Dependence Network can be discovered. An Inclusion and Exclusion algorithm is introduced to guarantee the minimal cut set returned for a pair of source and destination nodes. To determine the significant component, a significance indicator will be calculated based on comparing each component's impact by using a backward deriving method. Once the significant component being determined, the parameter of the significant component can be tuned to achieve the best performance. At the end, two real implementations are presented to show the achievement in performance improvement of the component dependence network, structure learning method and significant component indicator

Bio-inspired route estimation in cognitive radio networks

Cognitive radio is a technique that was originally created for the proper use of the radio electric spectrum due its underuse. A few methods were used to predict the network traffic to determine the occupancy of the spectrum and then use the ‘holes’ between the transmissions of primary users. The goal is to guarantee a complete transmission for the second user while not interrupting the trans-mission of primary users. This study seeks the multifractal generation of traffic for a specific radio electric spectrum as well as a bio-inspired route estimation for secondary users. It uses the MFHW algorithm to generate multifractal traces and two bio-inspired algo-rithms: Ant Colony Optimization and Max Feeding to calculate the secondary user’s path. Multifractal characteristics offer a predic-tion, which is 10% lower in comparison with the original traffic values and a complete transmission for secondary users. In fact, a hybrid strategy combining both bio-inspired algorithms promise a reduction in handoff. The purpose of this research consists on deriving future investigation in the generation of multifractal traffic and a mobility spectrum using bio-inspired algorithms

Application of Ant Colony optimization for MANETS

All networks tend to become more and more complicated. They can be wired, with lots of routers, or wireless, with lots of mobile nodes… The problem remains the same: in order to get the best from the network, there is a need to find the shortest path. The more complicated the network is, the more difficult it is to manage the routes and indicate which one is the best. The Nature gives us a solution to find the shortest path. The ants, in their necessity to find food and brings it back to the nest, manage not only to explore a vast area, but also to indicate to their peers the location of the food while bringing it back to the nest. Thus, they know where their nest is, and also their destination, without having a global view of the ground. Most of the time, they will find the shortest path and adapt to ground changes, hence proving their great efficiency toward this difficult task. The purpose of this project is to provide a clear understanding of the Ants-based algorithm, by giving a formal and comprehensive systematization of the subject. The simulation developed in Java will be a support of a deeper analysis of the factors of the algorithm, its potentialities and its limitations