In recent years, the exponential growth of data has brought significant challenges to cloud computing. Addressing the issues associated with edge computing has emerged as a promising solution to enable efficient and timely data processing. Wireless Sensor Networks (WSNs) are widely used in various applications such as environment monitoring, healthcare, and military surveillance. In WSNs, data aggregation is a critical process to reduce network traffic, prolong the network lifetime, and improve energy efficiency. This research paper evaluates the performance of data aggregation algorithms in WSNs using NS2, NSG, and NAM. The proposed work investigates the impact of different data aggregation algorithms on network throughput, delay, and energy consumption. Simulation results show that the data aggregation algorithms significantly improve network performance by reducing the amount of data transmitted and decreasing network congestion.