An energy-aware data cleaning workflow for real-time stream processing in the internet of things
The Internet of things (IoT) has recently transformed the internet, enabling the communication between every kind of objects (things). The growing number of sensors and smart devices enhanced data creation and collection capabilities and led to an explosion of generated data in the form of Data Streams. Processing these data streams is complex and presents challenges and opportunities in the stream processing field. Due to the inherent lacking of accuracy and completeness of sensor generated data, the quality of raw data is often poor. Data cleaning tasks are required to help increasing the quality of the data being processed in an IoT application. This work proposes a data stream processing workflow for IoT to be deployed at the edge of the network. It performs a fast data cleaning with low power consumption from edge and sensor nodes. The edge computing paradigm is used to bring the data cleaning task closer to the data sources and allow actions to be triggered immediately. In addition, an energy-aware data collection component is designed to reduce the network traffic and, as a consequence, decrease the power consumption of the network devices. The proposed workflow enables the deployment of long running real-time processing systems on remote outdoor environments.
Akyildiz, I., Su, W., Sankarasubramaniam, Y., and Cayirci, E. (2002). Wireless sensor networks: a survey. Computer Networks, 38(4):393 — 422.
Atzori, L., Iera, A., and Morabito, G. (2010). The Internet of Things: A survey. Computer Networks, 54(15):2787-2805.
Dautov, R., Distefano, S., Bruneo, D., Longo, F., Merlino, G., and Puliafito, A. (2018).Pushing intelligence to the edge with a stream processing architecture. In Proceedings- 2017 IEEE International Conference on Internet of Things, IEEE Green Computing and Communications, IEEE Cyber, Physical and Social Computing, IEEE Smart Data, iThings-GreenCom-CPSCom-SmartData 2017.
Dias de Assunção, M., da Silva Veith, A., and Buyya, R. (2018). Distributed data stream processing and edge computing: A survey on resource elasticity and future directions. Journal of Network and Computer Applications.
Janjua, Z. H., Vecchio, M., Antonini, M., and Antonelli, F. (2019). IRESE: An intelligent rare-event detection system using unsupervised learning on the IoT edge. Engineering Applications of Artificial Intelligence, 84:41-50.
Karkouch, A., Mousannif, H., Al Moatassime, H., and Noel, T. (2016). Data quality in internet of things: A state-of-the-art survey.
Klein, A. and Lehner, W. (2010). Quality and Performance Optimization of Sensor Data Stream Processing. International Journal on Advances in Networks and Services.
Li, W., Santos, I., Delicato, F. C., Pires, P. F., Pirmez, L., Wei, W., Song, H., Zomaya,A., and Khan, S. (2017). System modelling and performance evaluation of a three-tier Cloud of Things. Future Generation Computer Systems.
Sarkar, C., Rao, V. S., Venkatesha Prasad, R., Das, S. N., Misra, S., and Vasilakos, A.(2016). VSF: An Energy-Efficient Sensing Framework Using Virtual Sensors. IEEE Sensors Journal, 16(12):5046-5059.
Shi, W. and Dustdar, S. (2016). The Promise of Edge Computing. Computer.
Tsai, C.-W., Lai, C.-F., Chiang, M.-C., and Yang, L. T. (2014). Data Mining for Internet of Things: A Survey. IEEE Communications Surveys & Tutorials, 16(1):77-97.
Wang, T., Ke, H., Zheng, X., Wang, K., Sangaiah, A. K., and Liu, A. (2019). Big Data Cleaning Based on Mobile Edge Computing in Industrial Sensor-Cloud. IEEE Transactions on Industrial Informatics, pages 1-1.