Multi-Level Fuzzy Inference System for Enhanced Handover Decision Making in Unmanned Vehicles

Problem Definition

From the analysis of the literature survey, it is evident that the current methods for making handover decisions are limited in scope and may not be able to effectively handle the increasing complexities of modern systems. Despite the advancements in technology and a growing number of users, most researchers have only considered a limited number of parameters when developing handover decision systems. This narrow focus may not be sufficient to address the various dependency factors that come into play during the handover process. Moreover, while fuzzy systems have been recommended for their ability to handle system complexities and allow users to define rules as needed, it is important to recognize that as the number of parameters increases, the rule complexity and time consumption of the fuzzy system also increase. This can lead to a decrease in system performance and an overall increase in complexity.

Therefore, there is a pressing need to develop a novel method that can take into account a wider range of parameters for making handover decisions while simultaneously reducing complexity and time consumption. By addressing these limitations, the proposed method aims to improve the efficiency and effectiveness of handover decision systems in the face of evolving technology and user demands.

Objective

The objective of the proposed work is to develop a new handover decision system based on soft computing methods that address the complexity and low accuracy issues present in current handover decision techniques. This will involve the implementation of a multi-level fuzzy system that considers various parameters at different levels to reduce system complexity and increase accuracy for effective handover decisions. The goal is to enhance the efficiency and effectiveness of handover decision systems by taking into account a wider range of parameters, such as coverage, speed limit, cost, connection time, security, and power consumption, and evaluating them at different fuzzy levels. The proposed system aims to improve the overall performance of handover decision processes, reduce complexity, minimize time consumption, and adapt to evolving technology and user demands.

Proposed Work

After analyzing the literature review in the prior section, we have observed that current HO decision technique has complexity and low accuracy issues that degrade their performance. Keeping this in mind, a new HO decision system is proposed in this manuscript that is based on soft computing methods. In the proposed work, a multi-level fuzzy system is proposed in which various parameters are considered as inputs at different level so that complexity of the overall system is reduced. The main objective of the proposed model is to reduce the complexity of HO system while also increasing its accuracy for effective HO. To combat this task, a multi-level fuzzy system HO model is designed wherein different parameters of drones are analyzed at different levels for making the HO decision easy and accurate.

As mentioned earlier, that traditional HO system analyzes only few parameters for making the HO decision, however, after analyzing literature survey we analyzed that number of parameters must be considered for making the HO efficient. Therefore, in proposed work we considered parameters like coverage, speed limit, cost at first fuzzy level and at second fuzzy level factors like connection time, security and power consumption were evaluated. The output generated by two fuzzy system in the form of probability, serves as input to the third fuzzy system that evaluates these two inputs and generates output “estimation level” that determines whether HO should take place or not. The novelty of this work is that we have considered various important HO parameters at different levels for increasing the accuracy of HO. Moreover, we also analyzed that complexity of fuzzy systems arises by increasing the evaluating parameters, therefore, to reduce this complexity we evaluated HO factors of drones at three different fuzzy levels.

A fuzzy inference technique in which multiple attributes are examined to decide the handover is the heart of the smart handover decision systems. The specific range of every attribute specifies the criteria for determining the estimation level which allows the handover appropriately. The proposed handover system takes three inputs in first fuzzy system which upon processing generates the first output as F1out. Similarly, another different set of parameters are taken into consideration for the second fuzzy system to generate the second output as F2out. The outputs of the first and second fuzzy system then serves as the input to the third FIS which again is processed by the defined set of rules to get the estimation level as the final output.

This output specifies whether handover should take place or not. The main motive of using the multi-level fuzzy system in the proposed scheme is to reduce rule complexity at each level which in turn reduces the overall system complexity and delay and improves the throughput. The suggested scheme works by utilizing the same computing approaches that were used in traditional systems but in an advanced way just to make the handover decision more effective. By doing so, the proposed system will have the ability to minimize the time and complexity with effective decision strength.

Application Area for Industry

The proposed handover decision system based on multi-level fuzzy logic can be applied in various industrial sectors such as telecommunications, logistics, manufacturing, and transportation. In the telecommunications sector, the system can be used to optimize the handover process between different communication networks for seamless connectivity. In logistics, the system can help in the efficient tracking and handover of goods between different warehouses. In manufacturing, the system can be utilized for the smooth transition of production processes between different machines or operations. In the transportation sector, the system can enhance the handover of passengers or cargo between different modes of transport.

The main challenge that industries face in handover processes is the complexity and time consumption involved in making the decision. The proposed multi-level fuzzy system addresses this challenge by considering multiple important parameters at different levels, thus reducing the overall complexity of the system. By analyzing various factors such as coverage, speed limit, cost, connection time, security, and power consumption, the system can make more accurate handover decisions. The use of fuzzy inference techniques and advanced computing approaches in the proposed system increases the decision strength while minimizing delays and improving throughput. Implementing this solution can lead to increased efficiency, reduced downtime, and enhanced overall performance in various industrial domains.

Application Area for Academics

The proposed project on developing a multi-level fuzzy system for handover decision-making in drones can greatly enrich academic research, education, and training in the field of soft computing and decision-making systems. This project will provide insights into the application of fuzzy logic in improving the accuracy and efficiency of handover decisions in drone systems, which can be valuable for researchers, MTech students, and PhD scholars working in the domain of wireless communication and autonomous systems. The relevance of this project lies in addressing the complexity and low accuracy issues of current handover decision techniques in drones by proposing a novel approach that considers multiple parameters at different levels. By utilizing fuzzy logic algorithms, the proposed system aims to reduce the overall system complexity, decrease decision-making time, and enhance the accuracy of handover decisions. This innovative research method can inspire researchers to explore the potential of multi-level fuzzy systems in other applications as well.

Furthermore, the simulations and data analysis conducted in this project can serve as valuable learning resources for educational purposes. MTech students and PhD scholars can benefit from studying the code and literature of this project to understand the practical implementation of fuzzy logic in real-world scenarios, particularly in the context of wireless communication networks and drone systems. In the future, the scope of this project could extend to exploring additional parameters and optimizing the fuzzy system for even more efficient handover decision-making in drones. Further research could also focus on integrating machine learning techniques or artificial intelligence algorithms to enhance the performance of the proposed system. Overall, this project has the potential to advance the field of soft computing and decision-making systems, offering valuable insights and practical applications for academic research, education, and training.

Algorithms Used

The proposed work introduces a multi-level fuzzy system for handover decision-making in drones. Traditional handover systems often have complexity and accuracy issues, which this new system aims to address. By considering various parameters at different levels in the fuzzy system, the complexity of the overall system is reduced while increasing accuracy. Parameters such as coverage, speed limit, cost, connection time, security, and power consumption are evaluated at different levels to determine the estimation level for handover. The outputs of each fuzzy system serve as inputs to the next level, ultimately generating the estimation level that decides whether handover should occur.

This multi-level fuzzy system reduces rule complexity, system complexity, and delays, while improving throughput and efficiency in handover decision-making. The system utilizes traditional computing approaches in a new and advanced way to enhance the effectiveness of handover decisions.

Keywords

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SEO Tags

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