Abstract
Optimization algorithms
such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) are employed to
determine the optimal aggregation and transmission schedules, taking into account factors
such as network topology, node energy levels, and data urgency. The proposed approach is
validated through extensive simulations, demonstrating significant improvements in energy
consumption, packet delivery ratio, and overall network performance. The results suggest that
the optimized aggregated packet transmission method can effectively extend the lifespan of
duty-cycled WSNs while ensuring reliable data communication. Future work will explore the
integration of machine learning techniques for adaptive scheduling and the application of the
proposed method to heterogeneous WSNs.