Performance Evaluation of EADQR Across Various Path Loss Models Through Propagation Analysis

—Wireless Sensor Networks (WSNs) play a vital role in Internet of Things (IoT) technology by facilitating data collection and transmission through small wireless sensors. Path loss, influenced by environmental factors, significantly impacts WSN performance, affecting communication range and sensor reliability. This emphasizes the importance of considering path loss in WSN design and optimization. The proposed work aims to evaluate a sink-led decentralized routing system designed to enhance network longevity and minimize energy consumption under various propagation loss models. The methodology employs an energy-aware model to select initiator nodes, creating multiple paths and reducing redundancy. For improved quality of service, the system picks a forward relay node based on factors like remaining energy, the quality of the radio link between adjacent nodes, and proximity to the sink node. A fuzzy logic-based decision-making process is used to identify the most optimal path among the multitude of possible pathways. The research seeks to demonstrate the impact of path loss on crucial network metrics, such as end-to-end delay, hop count, energy usage, and the number of active nodes in a WSN topology. Simulations provide a comprehensive understanding of the impact of path loss on key network metrics. Computational outcomes, derived from Received Signal Strength Indicator (RSSI) values for near-surface wave propagation, showcase that the Energy Aware Data Centric Query Driven Receiver initiated (EADQR) protocol excels in scenarios characterized by substantial environmental clutter, as represented by the clutter factor and HATA suburban models. The energy-aware strategy mitigates path loss and energy depletion, thereby prolonging the operational lifespan of the network.