A data-driven situation-aware framework for predictive analysis in smart environments

In the era of Internet of Things (IoT), it is vital for smart environments to be able to efficiently provide effective predictions of user's situations and take actions in a proactive manner to achieve the highest performance. However, there are two main challenges. First, the sensor environment is equipped with a heterogeneous set of data sources including hardware and software sensors, and oftentimes humans as complex sensors, too. These sensors generate a huge amount of raw data. In order to extract knowledge and do predictive analysis, it is necessary that the raw sensor data be cleaned, understood, analyzed, and interpreted. Second challenge refers to predictive modeling. Traditional predictive models predict situations that are likely to happen in the near future by keeping and analyzing the history of past user's situations. Traditional predictive analysis approaches have become less effective because of the massive amount of data continuously streamed in that both affects data processing efficiency and complicates the data semantics. To address the above challenges, we propose a data-driven, situation-aware framework for predictive analysis in smart environments. First, to effectively analyze the sensor data, we employ the Situ-Morphism method to transfer sensor-enabled situation information to vector information. Then we introduce new similarity metrics and implement similarity prediction based on Locality Sensitive Hashing to improve data processing efficiency and effectively handle the data semantics. Experiment results show that the predictive analysis method proposed in this paper can be effective.