A Comprehensive Handover Decision Model for Unmanned Vehicles in Wireless Networks Using Fuzzy Logic

Problem Definition

Although some existing studies offer valuable insight into the handover probability in drone networks, the logical characterization of this aspect remains a significant challenge. Current research on handover in drones is limited, with only a few studies based on fuzzy logics. Fuzzy logics stand out due to their ability to process concepts similar to human thoughts and allow designers to model input and output relationships without considering their physical impact. While existing methods focus on quality of service (QoS) factors such as Received Signal Strength (RSS), data rate, and cost, a system proposed in the literature introduces the concepts of coverage and speed limit for improvement. However, factors like security and connection time for handover decision making in drones have not received much attention.

This gap in the research highlights the need for a more comprehensive approach to address the various complexities and challenges associated with handover in drone networks.

Objective

The objective is to develop a comprehensive system for handover decision-making in drone networks by incorporating fuzzy logic to model input-output relationships without physical constraints. This system aims to address the limitations of existing research by considering factors such as network coverage, speed limits, cost, connection time, and security in addition to traditional quality of service factors like signal strength and data rates. With three main modules for decision evaluation, information gathering, and fuzzification/defuzzification processes, the goal is to provide a more thorough evaluation of handover decisions in drone networks.

Proposed Work

The problem at hand involves the logical characterization of handover probability in drone networks, which remains a significant challenge despite existing research in the field. Previous studies focusing on handover in drones have lacked a comprehensive application of fuzzy logic, which is recommended for its ability to mimic human thought processes and model input-output relationships without physical constraints. While current methods consider factors like signal strength and data rates, this proposed system aims to address the gaps by incorporating additional parameters such as network coverage, speed limits, cost, connection time, and security for making handover decisions in drones. The proposed system consists of three main modules: a fuzzy decision system for evaluating input factors and generating handover decisions, an information gathering layer for collecting relevant parameters, and a process of fuzzification and defuzzification to ultimately determine the handover status for the drone based on the gathered information. By considering a broader set of criteria, this system aims to provide a more comprehensive evaluation of handover decisions in drone networks.

Application Area for Industry

This project can be applied in various industrial sectors such as telecommunications, agriculture, construction, and surveillance. One of the main challenges that industries face is ensuring seamless connectivity and handover processes in drone networks. By incorporating fuzzy logic-based decision-making systems that consider factors like network coverage, speed, cost, connection time, and security, this project offers a comprehensive solution to address these challenges. Implementing the proposed handover decision model can lead to more efficient and reliable drone operations, resulting in increased productivity, improved data security, and enhanced overall performance within different industrial domains.

Application Area for Academics

The proposed project can enrich academic research, education, and training by addressing the open problem of logical characterization of handover probability in drone networks using fuzzy logics. The inclusion of factors such as network coverage, speed limits, cost, connection time, and security in the handover decision model provides a comprehensive approach to improving drone network performance. Researchers in the field of drone communication and network optimization can benefit from the code and literature of this project to explore innovative research methods and simulations. MTech students and PhD scholars can use the proposed system to enhance their understanding of fuzzy logic systems and apply them in real-world scenarios. The relevance of this project lies in its potential applications in optimizing drone handover decisions, ensuring secure and efficient data transfer, and enhancing the overall performance of drone networks.

The use of fuzzy logic in decision-making processes adds a layer of complexity and intelligence to drone systems, making them more adaptive and responsive to changing network conditions. In future, the scope of this project could be extended to incorporate machine learning algorithms for decision-making, integrating more complex factors into the handover model, and conducting real-world experiments to validate the effectiveness of the proposed system.

Algorithms Used

The proposed system utilizes fuzzy logic algorithm to enhance decision making for drone handover. The system considers factors such as network coverage, speed limit, cost, connecting time, and security to provide a comprehensive handover decision model. The algorithm processes input parameters collected from communication protocol and converts them into membership functions for fuzzification. Fuzzy rules are applied to evaluate the input parameters and generate a handover decision for the drone. This process is conducted once to estimate the handover level effectively.

Keywords

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SEO Tags

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