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T semantic technologies address the problem of various heterogeneous devices, communication protocols, and data formats of the generated data in the Internet of Things. Annotation of IoT sensor data is the substance of IoT semantics [1]. The future generation of IoT not only deals with the physical sensor devices but also the meanings they carry with virtual representation of smart data. On an average, every day around 3.2 quintillion bytes of data are generated on the Internet. The CISCO predictions state that more than 60 billion devices will be connected to the internet by 2025, as a result zetta bytes of sensor data will be generated continuously and exponentially. The IoT sensors generated raw data is stored in the data repositories and it supports to heterogeneous smart city applications. Therefore, applying the raw data into applications may result in structural data with pre-notified format, date, source, affiliation, unit, and encryption. The next level of data is perception data that contains the multi abstraction from low-level to high-level applications to perform actionable and predictive data for the final evaluation. For understanding the perception data more concisely, the structural information is needed. Without structural information, the data may mislead to false results and may fail to integrate the realtime application data [2]. The perception data is extracted from the structured data that is more compressive and occupies less space than the raw data. Machine Learning (ML) clustering techniques are used for performing analysis on the perception data and automatic generation of semantic annotations. Moreover, in IoT, the real-time streaming data plays a major role to perform cluster analysis. The streaming data is flowing continuously as data stream from the IoT device to the peer network. The stream processing has been effectively analyzes the cluster data, improve the cluster efficiency, and able to make quicker decisions on clustered data [3]-[5]. The hierarchical clustering techniques are used for representing logical, temporal, and spatial relations on the IoT streaming data. The most important aspect of clustering IoT streaming data is its dynamic and heterogeneous nature. Therefore, a novel clustering mechanism is needed to represent the hierarchical relationships-based annotations for the IoT streaming data [6]. In this paper, incremental hierarchical clustering is deployed for unifying the streaming data in a hierarchical manner. SPARQL queries are used for extracting semantic annotations between the hierarchical clustered data. The agents will receive the raw data streams as input data from the IoT sensor devices and then perform the classification between the data streams for generating the RDF data patterns for the hierarchical clustering. The RDF data patterns are combined with the pre-notified metadata of the IoT sensors for the incremental hierarchical clustering process. At last, the hierarchical streaming data is annotated with the automatic semantic annotations using SPARQL queries. Incremental Hierarchical Clustering driven Automatic Annotations for Unifying IoT Streaming Data