Data Fusion Method for Multi-Sensor Internet of Things Systems Including Data Imputation

In the age of the Internet of Things (IoT), IoT devices scattered across various locations gather and store data in a decentralized manner to improve computational efficiency. Nevertheless, within IoT networks, factors such as fragile devices, challenging deployment conditions, and unreliable data transmission are raising the likelihood of data gaps, potentially having a substantial impact on the subsequent data processing resulting in failure of the system. Conventional imputation approach relies on using historical trend or sensor fusion techniques to combine information from different sensors to fill in the gaps in where information is missing. Historical trend struggles to capture new or emerging patterns, whereas using sensor fusion, even though it shows promising results, relies on information from multiple sensors from same target environment, making it vulnerable to single-point failures. This article presents an alternative strategy: using sensor-based fusion, but in this case, multiple sensors gather data from different targets independently. The architecture intelligently looks and gathers the sensor information from other location/target (multiple locations), sensing the same environmental information, learns the distribution and correlation and employ algorithm to generate synthetic data for imputing missing information. The study conducted experiments by fusing weather station data from various US locations and comparing the effectiveness of this approach to conventional methods. Further, the proposed synthetic data generation approach outperformed other algorithms when applied to the fused weather station dataset. This innovative approach mitigates the risk of single-point failures and offers a more robust solution for dealing with missing data in IoT networks.