Abstract

Wireless Sensor Networks (WSNs) have become a cornerstone of modern intelligent monitoring systems enabling real-time sensing data collection and environmental analytics in a wide variety of applications including healthcare smart cities industrial automation disaster management and agricultural monitoring. One of the most persistent challenges in WSN operations is limited energy availability due to the constrained battery capacity of sensor nodes. Conventional clustering algorithms such as LEACH TEEN and PEGASIS address energy conservation by grouping nodes and selecting cluster heads but these techniques often suffer from uneven energy distribution premature node death scalability issues and reduced network lifetime in dynamic or heterogeneous environments. This research proposes an unconventional clustering-based energy-efficient algorithm specifically designed to optimize energy consumption for networks containing various types of nodes with differing capabilities densities and mob