Abstract

The growing trend of devices participation in Internet of Things (IoTs) platforms have created billions of IoT devices in both consumer and industrial environments. IoT devices form the network of devices connected to each other by communication technologies in different environments to monitor, collect, exchange, and to take actions. Due to the growth of IoT devices, it is cheaper and easily available so users started using these devices to achieve their personal goals, such as to reduce electricity cost at home. Existing research has proposed new interconnection implementation mechanisms for IoT devices to monitor environments by low cost systems. However, existing work does not investigate the historical data of IoT device usage to assist users in achieving their goals. In our research, we propose an engine that identifies the behavioural patterns of IoT device users. Our engine works in three steps: First, the engine uses a database to store the IoT devices usage data. Second, our engine prepares the data in a suitable model for data analysis. Finally, our engine analyses the represented data to extract user behavioural patterns. We perform an empirical study to evaluate our engine. Our results shows that users, on average, use less than 50% of their IoT devices at specific times and have a relatively small impact across other devices in the environment.

Bibtex

@inproceedings{venkatesh2017framework,
  title={A framework to extract personalized behavioural patterns of user's IoT devices data},
  author={Venkatesh, Pradeep K and da Costa, Daniel Alencar and Zou, Ying and Ng, Joanna W},
  booktitle={Proceedings of the 27th Annual International Conference on Computer Science and Software Engineering},
  pages={19--27},
  year={2017},
  organization={IBM Corp.}
}