Analysis of dimensionality reduction techniques on Internet of Things data using machine learning

Internet of Things (IoT), being a global infrastructure, aims to improve the quality of our lives. The potential applicability of IoT to almost every field of life, gives rise to an increased adoption of IoT solutions across the world. This increase in the number of IoT solutions leads to generation of large amount of data. Collecting, communicating and extracting useful information from this huge amount of data is a challenging task. Some effective methods are, therefore, required to handle and analyze IoT data, in order to extract some meaningful information. Dimension reduction is a method which retains only the meaningful features of the data and helps to have reduced data with only the necessary information. This study aims to identify the influence of dimension reduction on IoT data in terms of storage, and communication cost. it also analyses how dimensionality reduction of IoT data affects the performance of different classification algorithms applied to it. It has been found that dimension reduction helps to reduce the storage and communication costs of IoT data, while reducing the performance of classification algorithms applied to dimensionally reduced data. However, this reduction in performance is negligible as compared to storage and communication cost optimization.