Integrated Clustering and Optimization for IoT Sensor Networks in Space–Air–Ground Frameworks: A Focus on Energy Savings

Abstract

The integration of space–air–ground networks necessitates effective strategies for managing the energy consumption and operational longevity of IoT sensor nodes. Clustering, implemented as the foundational tier in SAGINs, is instrumental in organizing sensor nodes into efficient communication groups, thereby optimizing data aggregation and minimizing redundant transmissions. This study introduces an energy-efficient cluster head (CH) selection using osprey optimization algorithm (EECHOOA), to minimize the total energy expenditure by each sensor at the individual node level. These sensors are controlled by a CH that transmits data to the top levels. With the osprey optimization algorithm (OOA), we further improve the clustering procedure for accurate CH election. By emulating the unique strategies of ospreys, the OOA adaptively determines the best CHs, taking into account both the distance between nodes and their energy reserve. In comparison to cutting-edge techniques like MMABC, NCOGA, PUAG, and ZFO-SHO, the simulation findings show that our suggested clustering strategy combined with OOA to CH election made the network lifetime is extended by 50%–100% and the network consumption of energy is reduced by 56.25%–76.35% than existing protocols. In order to provide more sustainable and streamlined IoT-based networks capable of responding to a range of fields of application, this study shows how clustering techniques may be used in combination with expert optimization algorithms in SAGINs.