The expanding Internet of Things landscape, combined with a significant reduction in the cost of connected devices, has enabled the widespread deployment of sensors. These sensors, often situated in close proximity to each other, frequently produce overlapping data. This paper aims to identify such overlaps in sensor data to effectively cluster similar sensors. The benefits of clustering include enhanced detection of sensor failures and a reduction in data transmissions. We examine a typical scenario where sensors are deployed asynchronously, operate for a limited time within the environment, and emit data that is noisy, irregular, and unsynchronized. To overcome these challenges, we have developed a similarity metric that employs interpolation techniques to manage noisy, irregular, and unsynchronized data. This metric supports a hierarchical clustering algorithm featuring a novel linkage method tailored to the dynamic nature of sensor deployments. The goal is to cluster sensors that monitor the same phenomenon, regardless of their active periods not coinciding. Through simulations, we demonstrate the superiority of our method compared to the state-of-the-art Dynamic Time Warping distance and a hierarchical clustering with complete linkage inspired by related works. Our results establish a mean improvement of $23\%$ from our approach in terms of V-Measure. We provide comprehensive experiments assessing the robustness of our solution under various sensor measurement noise levels and employing different stopping criterion strategies.