DEVELOPMENT OF ENERGY EFFICIENT HIERARCHICAL DATA AGGREGATION ROUTING FRAMEWORKS FOR ENHANCING RELIABILITY IN SUB-NATIONAL WIRELESS SENSOR NETWORKS

Abstract

Wireless Sensor Networks (WSNs) are being used more and more in smart cities to support vital applications like infrastructure management traffic monitoring and air quality assessment. Traditional routing protocols are frequently unreliable energy-inefficient and prone to redundant data transmission in expansive diverse urban environments. Nonetheless these networks offer real-time data that is necessary for urban planning and citizen services. The bulk of prior research has concentrated on static or flat routing schemes neglecting hierarchical and adaptive aggregation techniques that can increase network longevity and energy efficiency in municipal deployments. This paper presents the Energy-Efficient Hierarchical Data Aggregation Routing Framework (EH-DRF) designed for citywide WSNs. Multi-tier clustering context-aware data aggregation and energy-adaptive routing are used by the framework to balance network load minimize redundant transmissions and guarantee reliable data delivery. Using real-world datasets like the SmartSantander IoT testbed data and the Intel Berkeley Research Lab Sensor Dataset it simulates various urban topologies under various traffic and environmental conditions. Through adaptive link-quality evaluation and dynamic cluster head selection EH-DRF improves fault tolerance and permits continuous monitoring in dense sensor deployments. Energy consumption packet delivery ratio network lifetime latency throughput routing overhead and fault tolerance are just a few of the metrics used to assess performance. The findings demonstrate that EH-DRF performs better than traditional routing protocols in terms of reliability scalability and efficiency. By addressing the main shortcomings of earlier research this study offers a dependable scalable and energy-efficient routing framework for municipal WSNs enabling sustainable smart city operations and continuous real-time urban service delivery.