Abstract: Recent years witnessed an interest in the use of Wireless Sensor Networks (WSNs) in a widespread range of applications in many field related to military, surveillance, monitoring health, observing habitat and so on. WSNs contain individual nodes that interact with the environment by sensing and processing physical parameters. Sensor nodes usually generate a big amount of sequential tuple-oriented and small data that is called data streams. Data streams usually are huge data that arrive in an online mode, flowing rapidly in a very high speed, unlimited and there is no control on the arrival processing order. Due to WSN limitations, some challenges are faced and need to be solved. Such challenges include extending network lifetime and reducing energy consumption. Data mining could deal with WSN challenges. Clustering is a data mining technique that plays an important part in organizing WSNs. It has proven its efficiency on network performance by extending network life time and saving energy of sensor nodes. This study develops a grid-density clustering algorithm that enhances clustering in WSNs by combining grid and density techniques. The algorithm helps to face limitations found in WSNs that carry data streams. By using MATLAB, the grid-density clustering algorithm is compared with another clustering algorithm in WSNs that manipulate with data streams called k-means algorithm. The simulation results prove that the grid-density algorithm outperforms FCM by 17% in network lifetime and 11% in energy consumption performance metrics.