Rapid energy loss from continuous sensing and data transmission often shortens network lifespan. This work suggests bernoulli holomorphic swarm optimized hermite polynomial compressive sensing (BHSO-HPCS), an energy-efficient data collection method, to overcome this difficulty. The suggested approach minimizes energy usage without sacrificing network performance by combining compressive sensing and load-balanced clustering algorithms. Sensor nodes (SNs) initially support large-scale wireless sensor network (WSN) deployments. Cluster heads (CHs) are chosen from nodes with relatively more remaining energy. The bernoulli holomorphic (BH) balanced iterative reducing and clustering using hierarchies (BIRCH)-tchebychev particle swarm optimization model is then used to create load-balanced clusters. A BH radius-of-convergence method improves the standard cluster threshold, and the tchebychev function minimizes reaction time during data aggregation by optimizing average communication distance. In order to reduce communication overhead, sensor data is sent to CHs before being sent to the mobile sink HPCS is also used for effective redundancy reduction and data aggregation. This extends the network's lifespan and boosts throughput. Simulation results using network simulator 2 (NS2) show that BHSO-HPCS significantly outperforms circular clustering using the hybrid crow search algorithm (HCSA) and weighted data aggregation trees with optimum mobile sink(s) (WDAT-OMS) in terms of energy consumption, reaction time, throughput, and overall network efficiency.
