A Hybrid approach to cluster head selection in space-air-ground integrated networks: leveraging SMC and OOA for optimal performance
摘要
Energy efficiency and prolonging network lifetime of Internet of Things (IoT) sensor node management in the context of Space-Air-Ground Integrated Networks (SAGINs) has high importance. This work presents an Energy-Efficient Cluster Head Selection using the Osprey Optimization Algorithm (EECHOOA), utilizing clustering as the first layer in SAGINs that clusters IoT sensor nodes into clusters to optimize data aggregation and communication. Existing methods, such as ZFO-SHO, PUAG, NCOGA and MMABC, often struggle with limited adaptability to dynamic network conditions and suboptimal energy efficiency. The proposed EECHOOA address these shortcomings by introducing dynamic cluster head (CH) selection and scalable clustering techniques optimized for dense IoT environment. At the individual node level, each sensor consumes energy. A CH controls the process and relays information to the upper layers, minimizing total energy consumption. We further enhance the clustering process with the osprey optimization algorithm (OOA) for intelligent CH selection. The OOA employs the unique behaviors of ospreys to identify optimal CHs dynamically as a function of node energy levels and proximity. Simulation results indicate that our proposed clustering approach integrated with OOA for CH selection achieves 56.25