In data integration, entity resolution is an important technique to improve data quality. Existing researches typically assume that the target dataset only contain string-type data and use single similarity metric. For larger high-dimensional dataset, redundant information needs to be verified using traditional blocking or windowing techniques. In this work, we propose a novel ER-resolving method using a hybrid approach, including type-based multiblocks, varying window size, and more flexible similarity metrics. In our new ER workflow, we reduce the searching space for entity pairs by the constraint of redundant attributes and matching likelihood. We develop a reference implementation of our proposed approach and validate its performance using real-life dataset from one Internet of Things project. We evaluate the data processing system using five standard metrics including effectiveness, efficiency, accuracy, recall, and precision. Experimental results indicate that the proposed approach could be a promising alternative for entity resolution and could be feasibly applied in real-world data cleaning for large datasets.