Fuzzy AMGRP: A Fuzzy Logic-Based Approach for Efficient Geographical Routing in VANETs

Problem Definition

The previous protocol discussed in the reference problem definition relies on a single weighing function to determine the next hop node within a specified range for improved forwarding. However, a significant challenge highlighted in this work is the difficulty in defining the optimal weight value. While the protocol is yielding satisfactory results in the specified scenario, the process of determining the best weight value remains a complex problem. This limitation underscores the need for revisiting and updating the concept of weight value in order to enhance the efficiency and effectiveness of the forwarding process. Addressing this issue is crucial for optimizing network performance and ensuring successful data transmission within the defined range.

Objective

The objective of this study is to improve the efficiency and effectiveness of the forwarding process in VANETs by automatically evaluating the weight function using a fuzzy inference system in the Fuzzy AMGRP Routing Protocol. This will address the existing challenge of defining the optimal weight value and optimize the node selection process based on factors such as node mobility, link lifetime, node status, node density, and PDR. By enhancing the performance of the routing protocol through automated weight function evaluation, the study aims to improve network performance and successful data transmission within the defined range.

Proposed Work

The proposed work aims to address the research gap in the existing protocol by automatically evaluating the weight function using a fuzzy inference system in the Fuzzy AMGRP Routing Protocol for VANETs. By considering factors such as node mobility, link lifetime, node status, node density, and PDR, the node selection process can be optimized to improve the efficiency of the routing protocol. This study builds upon the previous work and is designed to analyze the performance of the proposed approach compared to the traditional AMGRP routing protocol. The methodology of the project involves defining initial network parameters, deploying the network, electing a source node, applying the fuzzy AMGRP approach to elect the Cluster Head (CH), selecting the next hop for data transmission, and evaluating the performance of the proposed protocol. By implementing the fuzzy inference system for electing CH nodes based on key factors, the proposed work aims to enhance the forwarding process in VANETs.

The rationale behind choosing this approach is to automate the weight function evaluation process and improve the overall efficiency of the routing protocol.

Application Area for Industry

This project can be utilized in various industrial sectors such as transportation, logistics, and supply chain management. In the transportation sector, the proposed fuzzy AMGRP approach can help in improving the efficiency of communication and data transfer within Vehicular Ad-Hoc Networks (VANETs). By automatically evaluating the weight function using a fuzzy inference system based on factors like node mobility and link lifetime, the project addresses the challenge of defining the best weight value for enhanced routing. Implementing this solution can lead to more reliable and optimized routing decisions, ultimately improving the overall performance of the network in the transportation industry. In the logistics and supply chain management sector, the benefits of the proposed Fuzzy-AMGRP routing protocol can be significant.

By considering factors such as node density and Packet Delivery Ratio (PDR) in node selection and route creation, the project offers a more intelligent and adaptive approach to data transmission in VANETs. This can help in creating more efficient communication networks for monitoring and managing logistics operations, leading to better coordination, real-time tracking, and optimized decision-making processes. Overall, the project's solutions can be applied within different industrial domains to address specific challenges related to communication, data transfer, and network efficiency, ultimately improving operational performance and enhancing overall productivity.

Application Area for Academics

The proposed project can enrich academic research, education, and training by offering a novel approach to routing in VANETs. By incorporating fuzzy inference systems to automatically evaluate the weight function for node selection, this project presents a more efficient and effective way to determine next hop nodes based on factors such as node mobility, link lifetime, node status, node density, and PDR. This project can be highly relevant in the field of computer science and engineering, specifically in the domain of wireless communication and networking. Researchers, MTech students, and PhD scholars can benefit from the code and literature of this project to explore innovative research methods, simulations, and data analysis within educational settings. By utilizing fuzzy logic algorithms, this project opens up opportunities for exploring advanced routing techniques in VANETs, which can lead to improved network performance and communication reliability.

Furthermore, by focusing on the evaluation and selection of CH nodes through fuzzy inference systems, this project can cater to the growing need for more intelligent and adaptive routing protocols. In future research, the scope of this project can be expanded to incorporate machine learning techniques for further enhancing routing efficiency in VANETs. Additionally, exploring the application of this fuzzy-AMGRP approach in real-world deployment scenarios can provide valuable insights into its practical implications and performance.

Algorithms Used

Fuzzy logic is used in the proposed work to automatically evaluate weight functions for the fuzzy inference system. This helps in selecting nodes based on factors such as node mobility, link lifetime, node status, node density, and PDR. By implementing the Fuzzy-AMGRP routing protocol, the efficiency of the proposed work over traditional AMGRP routing is analyzed. The methodology involves defining initial network parameters, deploying the network, electing a source node, using the fuzzy AMGRP approach to elect cluster heads (CH), selecting next hops for routing, and evaluating performance through data transmission. The fuzzy inference system plays a key role in electing CH nodes based on major factors like Mobility, Link Lifetime, Node Status, Node Density, and PDR.

Keywords

SEO-optimized keywords: Vehicular Ad hoc Networks, VANETs, security, Packet Delivery Ratio, PDR, next-hop selection, urban environment, fuzzy logic, decision model, route selection, communication efficiency, robust routing, reliable routing, weight function, fuzzy inference system, node mobility, link lifetime, node status, node density, AMGRP routing protocol, CH election, data transmission, performance evaluation, initial network parameters, data packet length, carrier frequency, propagation model, traffic type, physical layer, network deployment, source node, communication process, Fuzzy-AMGRP routing protocol, CH nodes.

SEO Tags

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