A Context-Aware Internet of Things Framework for Remote Monitoring Services

Poster presented at the Cranfield Doctoral Network Annual Event 2018.With global competition and technological progress, there have been growing demands by industry for more efficiency in monitoring the health status of the manufacturing equipment in real time. Remote monitoring services in the era of Industry 4.0 are nonetheless faced some challenges. These are related to the whole data lifecycle, encompassing data acquisition, real-time data processing, transmission, storage, analysis, and higher added value service provision to users, with adequate data management and governance needed to be in place. While all these pose problems in conventional monitoring, they become even more challenges when integrating IoT and cloud computing to deliver advanced services to offer infrastructure availability and ubiquitous accessibility. The sheer complexity of such activities the need to ground such processing on sound domain knowledge emphasises the need for context information management.With the deeper penetration of IoT technologies in monitoring tasks, the need for context information management increasingly manifests itself as a requirement for industrial applications. Context gathering, modelling, reasoning, and dissemination are needed for the efficient handling of vast amounts of data, produced by numerous devices, and the integration with other systems or industrial processes. In fact, recent research has presented several solutions that are used in specific scenarios, therefore, difficult to apply in other situations. This research aims to fill the identified gaps in the literature review and develop a flexible, more effective architecture to facilitate an efficient remote monitoring system that can be applied to a wide range of IoT applications, rather just to a specific area.Consequently, the architecture will directly help companies by using simple and inexpensive IoT devices efficiently to provide new monitoring capabilities to identify equipment conditions and predict the probability of failure. Furthermore, this framework will help those companies to obtain the highest profits from a minimum investment in equipment. This could be achieved by improving equipment reliability through the effective prediction of equipment failures.