2017 IEEE International Congress on Internet of Things (ICIOT) (2017)
Honolulu, Hawaii, USA
June 25, 2017 to June 30, 2017
The Internet of Things (IoT) enables connected objects to capture, communicate, and collect information over the network through a multitude of sensors, setting the foundation for applications such as smart grids, smart cars, and smart cities. In this context, large scale analytics is needed to extract knowledge and value from the data produced by these sensors. The ability to perform analytics on these data, however, is highly limited by the difficulties of collecting labels. Indeed, the machine learning techniques used to perform analytics rely upon data labels to learn and to validate results. Historically, crowdsourcing platforms have been used to gather labels, yet they cannot be directly used in the IoT because of poor human readability of sensor data. To overcome these limitations, this paper proposes a framework for sensor data analytics which leverages the power of crowdsourcing through gamification to acquire sensor data labels. The framework uses gamification as a socially engaging vehicle and as a way to motivate users to participate in various labelling tasks. To demonstrate the framework proposed, a case study is also presented. Evaluation results show the framework can successfully translate gamification events into sensor data labels.
Labeling, Games, Crowdsourcing, Data analysis, Event detection, Data mining, Machine learning algorithms
A. L'Heureux, K. Grolinger, W. A. Higashino and M. A. Capretz, "A Gamification Framework for Sensor Data Analytics," 2017 IEEE International Congress on Internet of Things (ICIOT), Honolulu, Hawaii, USA, 2017, pp. 74-81.