Enhancing Secure Routing in Wireless Sensor Networks with Hybrid Optimization for Route Selection

Problem Definition

Wireless sensor networks provide a crucial infrastructure for various applications such as monitoring and data collection. However, ensuring secure and efficient communication within these networks remains a significant challenge. One of the key limitations identified in the existing literature is the need for optimized route selection to minimize energy consumption while maintaining high levels of security. This is particularly important due to the limited power capabilities of sensor nodes. Additionally, the presence of attacker nodes within the network further complicates the situation, as they can disrupt communication and compromise data integrity.

Therefore, devising efficient routing strategies that can effectively tackle these challenges is essential for ensuring the reliability and security of wireless sensor networks. The research project aims to address these issues by developing a hybrid optimization approach that can enhance secure routing in wireless sensor networks. By considering both energy efficiency and security concerns, the proposed strategies seek to mitigate the risks posed by malicious nodes and improve overall network performance.

Objective

The objective of the research project is to develop a hybrid optimization approach for secure routing in wireless sensor networks. This approach will focus on minimizing energy consumption and maximizing security by using trust calculations to identify potential attacker nodes. By combining Grey Wolf Optimization and Genetic Algorithm with machine learning techniques, the system aims to efficiently select routes and detect network anomalies. Performance evaluation will be done based on parameters like delay, energy consumption, and packet delivery ratio in various scenarios to optimize efficiency. The use of MATLAB software will facilitate the implementation and testing of the proposed algorithm to achieve the project goals successfully.

Proposed Work

The research project aims to address the challenge of enhancing secure routing in wireless sensor networks through a hybrid optimization approach for route selection. By utilizing trust calculations for each node to identify potential attacker nodes, the proposed solution focuses on minimizing energy consumption while ensuring high security levels. The hybrid optimization algorithm, combining Grey Wolf Optimization and Genetic Algorithm, along with machine learning techniques, allows for efficient root selection and the detection of any network anomalies. By analyzing various output parameters such as delay, energy consumption, and packet delivery ratio, the system's performance is evaluated in different scenarios to optimize its efficiency. The use of MATLAB software enables the implementation and testing of the proposed algorithm to achieve the project objectives successfully.

Application Area for Industry

This project's proposed solutions can be applied across various industrial sectors such as smart manufacturing, agriculture, healthcare, and environmental monitoring. In smart manufacturing, the efficient routing strategies can optimize communication between sensors in the production line, ensuring seamless data transmission and minimizing energy consumption. In agriculture, the secure routing in wireless sensor networks can be utilized to monitor soil moisture levels and crop health, enabling timely interventions and maximizing yield. In healthcare, the hybrid optimization for route selection can enhance the security of patient monitoring systems, ensuring sensitive data remains protected. Lastly, in environmental monitoring, the trust calculation for each node can help in tracking pollution levels and wildlife movements, contributing to better conservation efforts.

By implementing these solutions, industries can improve operational efficiency, enhance data security, and make informed decisions based on accurate and timely information.

Application Area for Academics

The proposed project has the potential to enrich academic research, education, and training in the field of wireless sensor networks and network security. By focusing on enhancing secure routing through hybrid optimization for route selection, this project tackles critical research challenges such as energy consumption minimization, maintaining high security, and detecting malicious nodes within the network. Researchers, MTech students, and PHD scholars can benefit from the code and literature of this project by exploring innovative research methods, conducting simulations, and analyzing data within educational settings. The use of algorithms such as Grey Wolf Optimization (GWO) and Genetic Algorithm (GA) in combination with machine learning techniques provides a valuable learning experience in developing efficient routing strategies for wireless sensor networks. The relevance and potential applications of this project extend to various technology domains, including network security, optimization, and machine learning.

Researchers can apply the proposed solution to conduct experiments, evaluate system performance, and test different scenarios to optimize routing strategies in wireless sensor networks. This project offers a practical approach for exploring novel research methods and developing innovative solutions to address complex challenges in network security. The future scope of this project includes expanding the research to incorporate additional optimization algorithms, exploring different machine learning techniques, and analyzing the impact of various network parameters on routing efficiency. By continuing to advance research in secure routing for wireless sensor networks, this project has the potential to contribute valuable insights to the academic community and pave the way for further developments in network security and optimization.

Algorithms Used

This project utilizes the Grey Wolf Optimization (GWO) algorithm and the Genetic Algorithm (GA) for the root selection process. These algorithms are enhanced with machine learning for improved efficiency. The GWO and GA algorithms iteratively optimize the root selection by analyzing the system's fitness function. The proposed solution is to design a network which allows the calculation of trust for each node, indicating the number of connection requests. Energy checks are conducted using three parameters.

The root selection process uses a hybrid optimization algorithm combining GWO and GA, with machine learning to detect intuition in the system. Outputs such as delay, energy consumption, packet delivery ratio, and packet loss are analyzed and compared in various scenarios to enhance system efficiency.

Keywords

secure routing, wireless sensor network, hybrid optimization, route selection, MATLAB, Grey Wolf Optimization, Genetic Algorithm, machine learning, trust calculation, energy check, malicious nodes, network design, simulation, comparison results, intuition detection, shortest path, energy consumption, attacker nodes, efficient routing strategies, packet delivery ratio, packet loss.

SEO Tags

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