An IoT Platform with Monitoring Robot Applying CNN-Based Context-Aware Learning

• Published in: Sensors (Basel, Switzerland) Switzerland), 2019-06, Vol.19 (11), p.2525
• Main Authors: Shin, Moonsun, Paik, Woojin, Kim, Byungcheol, Hwang, Seonmin
• Format: Article
• Language: English
• Subjects: active CCTV; Ambient intelligence; Artificial intelligence; Closed circuit television; Collaboration; Communications systems; convolutional neural network; Design; Domains; Health surveillance; Image acquisition; Image detection; Image enhancement; Intelligence services; intelligent monitoring robot; International conferences; Internet of Things; IoT platform; learning model; Logistics; Machine learning; Model accuracy; Monitoring; Neural networks; Ontology; Pattern recognition; Remote control; Robot control; Robots; Sensors; Smartphones; Warehouses
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s19112525

Internet of Things (IoT) technology has been attracted lots of interests over the recent years, due to its applicability across the various domains. In particular, an IoT-based robot with artificial intelligence may be utilized in various fields of surveillance. In this paper, we propose an IoT platform with an intelligent surveillance robot using machine learning in order to overcome the limitations of the existing closed-circuit television (CCTV) which is installed fixed type. The IoT platform with a surveillance robot provides the smart monitoring as a role of active CCTV. The intelligent surveillance robot, which has been built with its own IoT server, and can carry out line tracing and acquire contextual information through the sensors to detect abnormal status in an environment. In addition, photos taken by its camera can be compared with stored images of normal state. If an abnormal status is detected, the manager receives an alarm via a smart phone. For user convenience, the client is provided with an app to control the robot remotely. In the case of image context processing it is useful to apply convolutional neural network (CNN)-based machine learning (ML), which is introduced for the precise detection and recognition of images or patterns, and from which can be expected a high performance of recognition. We designed the CNN model to support contextually-aware services of the IoT platform and to perform experiments for learning accuracy of the designed CNN model using dataset of images acquired from the robot. Experimental results showed that the accuracy of learning is over 0.98, which means that we achieved enhanced learning in image context recognition. The contribution of this paper is not only to implement an IoT platform with active CCTV robot but also to construct a CNN model for image-and-context-aware learning and intelligence enhancement of the proposed IoT platform. The proposed IoT platform, with an intelligent surveillance robot using machine learning, can be used to detect abnormal status in various industrial fields such as factory, smart farms, logistics warehouses, and public places.