Foundations of Data Quality Assurance for IoT-based Smart Applications

Most current scientific and industrial efforts in IoT are geared towards building integrated platforms to finally realize its potential in commercial scale applications. The IoT and Big Data contemporary context brings a number of challenges, such as providing quality assurance (defined by availability and veracity) for sensor data. Traditional signal processing approaches are no longer sufficient, requiring combined approaches in both architectural and analytical layers. This paper proposes a discussion on the adequate foundations of a new general approach aimed at increasing robustness and antifragility of IoT-based smart applications. In addition, it shows results of preliminary experiments with real data in the context of precision irrigation using multivariate methods to identify relevant situations, such as sensor failures and the mismatch of contextual sensor information due to different spatial granularities capture. Our results provide initial indications of the adequacy of the proposed framework.

[1]  Juha-Pekka Soininen,et al.  Smart Water Management Platform: IoT-Based Precision Irrigation for Agriculture † , 2019, Sensors.

[2]  Lei Cao,et al.  Distributed Local Outlier Detection in Big Data , 2017, KDD.

[3]  Philippe Preux,et al.  Towards Antifragile Software: Knowledge-driven Perturbation of Software Systems with Active Learning , 2016 .

[4]  Vishnu Pendyala.  Veracity of Big Data , 2018, Apress.

[5]  L. S. Pereira,et al.  Crop evapotranspiration : guidelines for computing crop water requirements , 1998 .

[6]  VARUN CHANDOLA,et al.  Anomaly detection: A survey , 2009, CSUR.

[7]  Runliang Dou,et al.  Optimizing Sensor Network Coverage and Regional Connectivity in Industrial IoT Systems , 2017, IEEE Systems Journal.

[8]  Jeffrey H. Reed,et al.  Antifragile Communications , 2018, IEEE Systems Journal.

[9]  N. Taleb.  Antifragile: Things That Gain from Disorder , 2012 .

[10]  Puning Zhang,et al.  Improving Quality of Data: IoT Data Aggregation Using Device to Device Communications , 2018, IEEE Access.

[11]  Peter Bak,et al.  Multivariate anomaly detection for ensuring data quality of dendrometer sensor networks , 2019, Comput. Electron. Agric..

[12]  Hajar Mousannif,et al.  Data quality in internet of things: A state-of-the-art survey , 2016, J. Netw. Comput. Appl..

[13]  Amit P. Sheth,et al.  IoT Quality Control for Data and Application Needs , 2017, IEEE Intelligent Systems.

[14]  Noman Islam,et al.  A review of wireless sensors and networks' applications in agriculture , 2014, Comput. Stand. Interfaces.

[15]  Marek Kulbacki,et al.  Survey of Drones for Agriculture Automation from Planting to Harvest , 2018, 2018 IEEE 22nd International Conference on Intelligent Engineering Systems (INES).

[16]  Jukka Riekki,et al.  Enhancing Veracity of IoT Generated Big Data in Decision Making , 2018, 2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops).