Design and Implementation of an Intelligent IoT Platform Based on Information Technology

With the development of the Internet of Things (IoT) technology, various intelligent devices generate massive heterogeneous data, posing higher requirements for real-time processing and efficient analysis of the data. Achieving accurate clustering and anomaly detection of dynamic data has become a critical issue in enhancing the intelligence level of IoT platforms. To address this challenge, this study proposes a solution based on an optimized K-means algorithm and deploys it in a smart community environment to improve the system's real-time response and data processing capabilities. Experimental results demonstrate that the algorithm performs excellently in data clustering and anomaly detection. The average clustering accuracy ranges from 93.8% to 94.1%, with a noise point identification rate of 89.2% and a false positive rate controlled at around 4.1%. In tests conducted at different time periods, the running time of the optimized algorithm remained around 120 milliseconds, and the anomaly detection response time was 83 to 86 milliseconds, significantly outperforming DBSCAN, GMM, and hierarchical clustering algorithms. The optimized K-means algorithm exhibits excellent performance in practical applications of IoT platforms, achieving a balance between data processing efficiency and detection accuracy.