UAVs (Unmanned Aerial Vehicles) Are Promising Tools For Efficient Data Collections Of Sensors In IoT Networks. Existing Studies Exploited Both Spatial And Temporal Data Correlations To Reduce The Amount Of Collected Redundant Data, In Which Sensors Are First Partitioned Into Different Clusters, A Master Sensor In Each Cluster Then Collects Raw Data From Other Sensors And Compresses The Received Data. An Energy-Constrained UAV Finally Collects The Maximum Amount Of Compressed Data From Different Master Sensors. We However Notice That The Compressed Data From Only A Portion Of Clusters Are Collected By The UAV In The Existing Studies, While The Data From Other Clusters Are Not Collected At All. In This Paper, We Study A Problem Of Finding A Data Collection Trajectory For An Energy-Constrained UAV, So That The Accumulative Utility Of Collected Data Is Maximized, Where The Accumulative Utility Measures The Quality Of Spatiotemporally Correlated Data Collected From Different Clusters. We Propose A Novel 16+-Approximation Algorithm For The Problem, Where Is A Given Constant With >0. Experimental Results With Real Datasets Show That The Accumulative Utility By The Proposed Algorithm Is At Least 23% Larger Than Those By The Existing Studies, And The Number Of Clusters Collected By The Proposed Algorithm Is From 45% To 105% Larger Than Those By The Existing Studies.