Abstract: Effective data collection is crucial for improving the performance and energy use of Wireless Sensor Networks (WSNs) in areas like industrial automation and environmental monitoring. WSNs struggle with balancing important Quality of Service (QoS) factors, such as data transfer speed, energy use, data delivery, and transmission delays. To tackle these problems, this research compares three data aggregation methods, namely 'CH Reputation-Based', 'CH Hybrid (Residual Energy + Distance)', and 'Hybrid (Time + Packet)'. The 'CH Reputation-Based' method uses reputation-based metrics to distribute the workload among cluster heads evenly, ensuring the best network performance. Simulation results show that this method is better, with a 12.28% increase in throughput, a 14.83% improvement in packet delivery, and a 12.54% decrease in energy consumption compared to the 'CH Hybrid (RE + Distance)' method. This analysis shows the effectiveness of integrating a reputation-based DA scheme in WSN. Additionally, comparative analysis against existing approaches shows that although other methods have certain benefits, the 'CH Reputation-Based' approach provides the best mix of performance and resource use. This makes it the most trustworthy and energy-saving choice for data transfer in the current WSN. It meets the growing needs of applications that require high-quality service and network reliability.

Key words: wireless sensor network, cluster head based aggregation, reputation-based aggregation, data aggregation, time and length based aggregation