Neuro-Fuzzy Optimization for Route Selection in FANETs through ANFIS Algorithm

Problem Definition

The existing literature on communication in FANETs highlights the limitations and problems faced by traditional models. While techniques such as fuzzy logic reinforcement learning and routing algorithms have been proposed to make communication more efficient, there are key pain points that need to be addressed. One major issue is the lack of consideration for mobility, which is identified as a crucial component in FANETs. Traditional models do not incorporate mobility in any stage of the protocol, leading to inefficiencies in network effectiveness. Additionally, delays in decision-making are observed due to the utilization of independent algorithms like learning and fuzzy logic.

These constraints highlight the need for a new approach that integrates mobility and addresses the shortcomings of existing techniques in order to optimize communication in FANETs.

Objective

The objective is to develop a new algorithm that integrates mobility into the decision-making process of FANETs to address the limitations of traditional models and optimize communication efficiency. This will involve utilizing the Adaptive Neuro-Fuzzy Inference System (ANFIS) technique for route selection, allowing for dynamic and adaptive decision-making that considers real-time changes in the network topology. By improving the effectiveness and reliability of communication channels, this approach aims to enhance communication capabilities in aerial networks and contribute to the advancement of research in FANETs.

Proposed Work

Therefore, our proposed work aims to address the research gap identified in the existing literature by developing a new algorithm that incorporates mobility into the decision-making process of FANETs. By introducing the concept of mobility, the network's effectiveness will be significantly improved, leading to more efficient communication channels. The use of the Adaptive Neuro-Fuzzy Inference System (ANFIS) technique for route selection will further enhance the decision-making matrix, ensuring the optimal path to the destination is chosen in a timely manner. This approach will not only overcome the limitations of traditional models but also provide a more reliable and efficient communication system for FANETs. The rationale behind choosing the ANFIS technique lies in its ability to combine the advantages of both fuzzy logic and neural networks, allowing for dynamic and adaptive decision-making in complex systems like FANETs.

By integrating mobility into the decision-making process, the proposed algorithm will be able to consider real-time changes in the network topology, thus improving the overall performance and reliability of the communication channels. This novel approach will contribute to the advancement of research in the field of FANETs by offering a more robust and efficient solution for route selection, ultimately leading to enhanced communication capabilities in aerial networks.

Application Area for Industry

This project's proposed solutions can be utilized in various industrial sectors such as aviation, military operations, disaster management, and environmental monitoring. In the aviation sector, the efficient communication network for FANETs can enhance the coordination of drones for surveillance and package delivery. In military operations, the optimal communication channel can improve the information exchange between unmanned aerial vehicles (UAVs) for reconnaissance and combat missions. For disaster management, the algorithm can assist in establishing reliable communication links between drones to collect real-time data in disaster-stricken areas. In environmental monitoring, the neuro-fuzzy system can optimize the network for drones to monitor pollution levels and wildlife habitats effectively.

The challenges faced by these industries include the need for real-time decision-making, reliable communication links, and efficient route planning for drones in FANETs. By implementing the proposed neuro-fuzzy system, these challenges can be addressed by providing a faster and more accurate decision-making module that incorporates mobility as a significant component. The benefits of implementing these solutions include improved communication efficiency, reduced delays in route determination, enhanced network effectiveness, and optimized decision-making processes for various industrial applications.

Application Area for Academics

The proposed project on using a neuro-fuzzy system for communication in FANETs can greatly enrich academic research, education, and training in the field of networking and communication systems. By introducing a novel algorithm that overcomes the limitations of traditional models, researchers and students can explore new avenues for improving communication efficiency in FANETs. This project's relevance lies in addressing the crucial component of mobility in FANETs, an aspect that was often overlooked in traditional models. By incorporating mobility into the decision-making process through the neuro-fuzzy system, the proposed technique has the potential to enhance the network's effectiveness and efficiency. Academically, this project opens up opportunities for innovative research methods, simulations, and data analysis within educational settings.

Researchers, MTech students, and PHD scholars in the field of networking and communication systems can leverage the code and literature of this project to further their research and explore new concepts in the domain. The use of the ANFIS algorithm in this project highlights its potential applications in network optimization and routing algorithms. By employing a neuro-fuzzy system, researchers can analyze complex data sets and make informed decisions to improve communication in FANETs. In terms of future scope, this project can serve as a foundation for exploring advanced techniques in communication systems for FANETs. Further research can focus on refining the neuro-fuzzy system, exploring other algorithms, and expanding the application of this technique to other areas of networking and communication.

Algorithms Used

ANFIS (Adaptive Neuro-Fuzzy Inference System) is the algorithm used in this project to optimize communication channels in FANETs. This novel technique combines the advantages of fuzzy logic and neural networks to create a hybrid system that can efficiently determine the optimal route in the network. By integrating both fuzzy logic and neural networks, ANFIS can improve the accuracy and efficiency of decision-making in FANETs, overcoming the limitations of traditional models. This algorithm contributes to achieving the project's objective of finding the optimal communication channel by providing a more advanced and effective solution for route determination.

Keywords

SEO-optimized keywords: FANET, Mobility, ANFIS, Decision-Making Matrix, Route Selection, Processing Delay, Network Performance, Efficient Communication, Ad Hoc Networks, Communication Optimization, Network Mobility, Autonomous Systems, UAVs, Communication Protocols, Wireless Networks, Network Routing, Network Efficiency, Network Management, Communication Technologies, Mobile Ad Hoc Networks, Decision Optimization, Network Performance Enhancement

SEO Tags

Flying Ad Hoc Networks, FANET, Mobility in Networks, ANFIS Algorithm, Route Selection Techniques, Processing Delay Reduction, Network Performance Enhancement, Efficient Communication Models, Ad Hoc Network Optimization, Network Mobility Solutions, Autonomous Systems Communication, UAV Communication Protocols, Wireless Network Routing, Efficient Network Management, Communication Technology Advancements, Mobile Ad Hoc Network Research, Decision Optimization Techniques, Network Performance Enhancement Strategies