An Efficient QoS Aware Hybrid Localization Model Using Pelican Optimization Algorithm for Cognitive Wireless Sensor Networks

The transmission of detected information to a central base station is critical in Wireless Sensor Networks (WSNs). These sensor devices work within the limits of restricted resources due to their compact size and reliance on battery power. To address the constraints of such resource-constrained surroundings, a ground-breaking framework for large-scale applications with an enormous number of sensors distributed across a broad expanse is provided. This novel protocol not only addresses communication challenges, but also ensures the development of a continually flawless communication system. Using cognitive sensors, the integration of learning and thought processes ensures dependable data delivery within the network. Localization is utilized in both sparsely and densely populated sensor networks by applying an intelligent cluster head election and route selection mechanism based on cognitive principles and the Pelican Optimization Algorithm (POA) algorithms. The proposed scheme is examined and compared with the existing methods such as CBSO, EW-DHO and HPSO-CSC. The proposed scheme outperforms Energy consumption by 0.5, Network Lifetime by 6200 and Throughput by 0.99% respectively.