Maximizing Communication Efficiency in VANETs Using Fuzzy Interface System (FIS)

Problem Definition

The existing approach of utilizing a center-based clustering algorithm for effective communication between vehicles has shown promising results. However, there are significant limitations that need to be addressed. One major concern is the performance impact of increased traffic on the highway, resulting in difficulty in handling beacons and complex clustering. Another issue lies in the manual selection of weighted coefficients for parameters like velocity, acceleration, and current location, which can significantly impact system performance if not chosen carefully. These limitations highlight the need for a novel approach that eliminates the use of weighted coefficients and reduces overall complexity to improve the efficiency and effectiveness of vehicle communication systems.

Objective

The objective of the proposed work is to improve the efficiency and effectiveness of vehicle communication systems by introducing a Fuzzy Interface System (FIS) based mechanism for decision-making. This mechanism aims to eliminate the manual selection of weighted coefficients and simplify the clustering process by selecting cluster heads based on various parameters of the vehicles. By dividing the network into small clusters and using fuzzy rules and membership functions, the system seeks to reduce complexity and enhance communication between vehicles on the highway.

Proposed Work

In the reviewed literature, it is evident that the existing approach of using a center-based clustering algorithm for communication between vehicles has certain shortcomings that need to be addressed. The method of forming clusters using beacons becomes challenging with increased traffic on the highway as handling the beacons becomes complex. Additionally, the manual selection of weighted coefficients for velocity, acceleration, and current location parameters can impact system performance if any coefficient devalues. To tackle these issues, a novel approach is proposed to eliminate weighted coefficients and simplify the clustering process to enhance system efficiency. The main objective of the proposed work is to introduce a Fuzzy Interface System (FIS) based mechanism for facilitating decision-making in an efficient manner.

The focus is on selecting a cluster head in the network based on various parameters of the vehicles to initiate data transmission effectively. By dividing the network into small cells that represent clusters and using an intelligent system for cluster head selection, the complexity is reduced. The FIS-based mechanism utilizes fuzzy rules and membership functions to make decisions based on vehicle parameters like velocity, acceleration, and current position. This automated and intelligent system aims to resolve existing concerns and create a more efficient communication network between vehicles.

Application Area for Industry

This project's proposed solutions can be applied across various industrial sectors where effective communication and coordination between entities are crucial. For example, in the transportation sector, the proposed approach can be utilized to improve communication between vehicles on highways, leading to better traffic management and safety. In the manufacturing industry, the use of small cells and intelligent systems for decision-making can enhance the efficiency of supply chain management and production processes. Additionally, in the healthcare sector, the implementation of cluster-based systems can optimize patient care coordination and resource allocation in hospitals. Overall, the benefits of implementing these solutions include increased operational efficiency, reduced complexity, improved decision-making processes, and enhanced overall performance in the respective industrial domains.

Application Area for Academics

The proposed project can greatly enrich academic research, education, and training in the field of intelligent transportation systems. By introducing a novel approach using fuzzy logic for cluster head selection in vehicular communication networks, researchers can explore new avenues for improving communication efficiency and reducing complexity in highly dynamic environments such as highway traffic. This project's relevance lies in its potential to enhance data analysis and simulation methods for studying vehicle-to-vehicle communication protocols. By eliminating the need for manually selected weighted coefficients, the proposed approach offers a more automated and intelligent system for cluster formation, leading to more accurate and optimized communication between vehicles. Researchers, MTech students, and PhD scholars in the field of intelligent transportation systems can benefit from this project by using the code and literature to further explore fuzzy logic applications in vehicular communication networks.

They can utilize the proposed methodology to develop new algorithms, simulations, and data analysis techniques for improving communication performance in dynamic traffic scenarios. The future scope of this project includes expanding the use of fuzzy logic in other aspects of vehicular communication systems and exploring the integration of artificial intelligence techniques for even more efficient cluster formation and data transmission. With the continuous evolution of technology in the transportation sector, this project opens up new possibilities for innovative research methods and advanced data analysis techniques in academic settings.

Algorithms Used

Fuzzy Logics are used in the proposed work to divide the network into small cells, with each cell representing clusters and nodes representing vehicles on the highway. This division reduces node overlap and complexity in the network. The selection of cluster heads (CH) is done using an intelligent fuzzy interface system (FIS). FIS utilizes fuzzy rules and membership functions to make decisions based on parameters such as velocity, acceleration, and current positions of vehicles. Once the CH is selected, data transmission occurs between vehicles.

The use of FIS makes the system automatic and intelligent, addressing concerns of the existing system and improving overall efficiency.

Keywords

SEO-optimized Keywords: VANETs, cluster head selection, intelligent routing, fuzzy inference system, cluster-based routing, intelligent algorithms, routing protocols, vehicular communication, intelligent transportation systems, network optimization, traffic management, data dissemination, congestion control, network performance, intelligent decision-making, center based clustering algorithm, vehicle communication, highway traffic, cluster formation, performance optimization, small cell division, CH election, fuzzy interface system, FIS mechanism, fuzzy rules, membership functions, automatic system, traffic congestion, network efficiency.

SEO Tags

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