# Leveraging Large Language Models for Sensor Data Retrieval
## 2 Related Work
### Sensor Data Retrieval  
Information retrieval systems for sensor data encompasses advancements in techniques and technologies aimed at enhancing the efficiency, accuracy, and scalability of data collection, processing, and analysis from diverse sensor networks. These systems are increasingly critical in various domains, including environmental monitoring, healthcare, smart cities, and industrial automation. With the exponential growth of Internet of Things (IoT) devices, efficient information retrieval from large-scale sensor networks has become paramount.  
In the context of sensor data storage in information retrieval systems, various schemas and data models are utilised to efficiently manage and retrieve sensor data. Location-Free schemas do not rely on geographic information for data storage and retrieval. Instead, they may use distributed hash tables on combinatorial tile adjacency graphs and double-ruling schemes within each tile for efficient routing and load balancing [39].  
A uniform representation model for sensor information recordings was proposed in Ref. [40] to handle heterogeneous, high-dimensional, and voluminous data in IoT-based sensor networks. This includes designing query models that represent different information query styles and developing indexing mechanisms to support historical and real-time search for spatiotemporal observation data.  
The use of NoSQL databases like Hypertable ([[https://hypertable.org/](https://hypertable.org/)]([https://hypertable.org/](https://hypertable.org/)), accessed on 15 January 2024) is considered suitable for storing massive amounts of sensor data due to their ability to handle heterogeneous, redundant, real-time streaming data [41]. These databases require manual query programming and can be optimised for sensor data storage and retrieval tasks.  
Data-centric storage schemes, such as Geographic Hash Tables (GHT), distribute sensor data across the network based on the data content itself, allowing queries to be directed towards the location where relevant data are stored, thus reducing overall network traffic and improving efficiency [42].  
Another way of storing sensor data is by applying data warehousing techniques, developing multidimensional schemata to capture contextual metadata alongside sensor datasets, supporting the analysis of datasets long after collection and providing a structured way to manage and query large volumes of sensor data [43].  
The application of word embeddings and LLMs for sensor data retrieval is a largely unexplored field. One relevant work in this area is presented in Ref. [44]. This paper proposes a method to generate word embeddings for IoT devices and sensors in a smart home based on their activity, extending the theory of word embeddings to the area of IoT devices. The model, akin to Word2 Vec and App2 Vec [45], considers the time between device firings in its calculations. These embeddings can be used to find similar devices in an IoT device store or as a signature for each type of IoT device, among other use cases. The feasibility study on the CASAS dataset and a private real-world dataset of IoT device activity logs demonstrated the potential of this approach for identifying patterns in embeddings of various types of IoT devices in a household.  
Although the method of representing sensor data information relies on word embeddings, the purpose of the previous article differs from the one proposed here in the task objective, as the present work aims at sensor data retrieval with the intention of augmenting datasets with similar data. Furthermore, the embedding models used in Ref. [44] are static, with the limitations mentioned earlier, whereas in the present work contextual embeddings are employed.