Abstract: The sensor node coverage plays a significant role in the design of Wireless Sensor Networks (WSN). In addition to coverage, shape and area is also important in wireless sensor network to limit the power consumption which is taken as the current research work for effective sensor network structure. Neighbor Position Verification (NPV) strategy with the help of fully distributed cooperative scheme enabled each node to acquire the neighbor locations but did not acquire data aggregation accuracy during node deployment. Decentralized estimation process using Decentralized Power Iteration (DPI) algorithm permitted every representative to track the algebraic sensor network connectivity but was not effective in deploying the sensor nodes with higher throughput ratio. In order to overcome such limitations, Two Dimensional Gaussian distribution based Dynamic Node Deployment (2D-GDDND) model is developed in this paper to deploy the sensor node in an efficient manner. The 2D-GDDND model initially identifies the directional position of sensor node based on the angle measurement (i.e.,) length and width of the sensor node position using the proposed 2-D Statistical Triangulation algorithm. The 2-D statistical triangulation algorithm focuses on entire sensor network area coverage to reduce the power consumption for the whole node deployment structure. Then, 2D-GDDND model is used Gaussian distribution model to efficiently deploy the dynamic sensor node in sensor network with the objective of improving the data aggregation accuracy and throughput level. In 2D-GDDND model, Gaussian distribution estimates angular difference between the sensor nodes and mobile robot. Then, 2D-GDDND model phase shift the sensor nodes according to their computed angular difference. Therefore, sensor nodes can easily gather and aggregates the data with another node in sensor network. For that reason the data aggregation accuracy and throughput level using 2D-GDDND model is improved in a significant manner. Experimental evaluation of 2D-GDDND model is done with the performance metrics such as power consumption, data aggregation accuracy, throughput level, dynamic node deployment time. Experimental analysis shows that the 2D-GDDND model is able to improve the data aggregation accuracy and also improves the throughput level of sensor nodes as compared to the state-of-the-art works.