Location Based Energy Efficient Security Mechanism in Wireless Sensor Networks using Qlearning-PSO-AES Algorithm

Wireless Sensor Networks (WSNs) are gaining popularity in various applications, including environmental monitoring, healthcare, and security surveillance. However, ensuring the security of WSNs is challenging due to resource limitations and dynamic nature of WSNs. Location-based security management has emerged as an effective approach to enhance WSN security by leveraging node location information to mitigate security threats. A hybrid Qlearning-PSO-AES technique for location-based security management in WSNs has been proposed in this paper. The proposed technique incorporates location information, dynamic trust evaluation, and advanced encryption algorithms to enhance WSN security. In this proposed work, the Qlearning algorithm is applied to learn the node's current state, including position and velocity, and to estimate trust values of nodes based on their past interactions. The PSO algorithm is used to determine the optimal placement of sensor nodes within the network, optimizing network lifetime, coverage, connectivity and energy consumption. Furthermore, the optimal solution obtained from PSO, represented by the best fitness value, is utilized for selecting encryption keys in the AES algorithm. The encryption keys are dynamically updated based on node movement to enhance WSN security. Simulation results clearly demonstrate that the proposed technique not only enhances the security of WSNs but also optimizes network performance, making it well-suited for real-world applications in various domains.