Fog computing monitoring system for a flexible assembly line

Abstract

Network-embedded systems equipped with monitoring, computing and communication features, enable the development of custom applications suitable for today requirements. These are the components of the Internet of Things (IoT) paradigm, characterized by functional and geographic decentralization. Traditional architectures through which data is collected include IoT devices that can transmit raw measurements values from a heterogeneous distributed network to a Cloud platform for high-level storage, analysis and processing. By improving the overall data distribution across the network, one can manage shortcomings generated by high latency and storage cost of the traditional Cloud Computing architecture. A particular use case for such implementation can be applied in the industrial environment. The new concept of Industry 4.0, an interplay of IoT and cyber-physical systems (CPS), is based, among others, on decentralized networks for data manipulation. Fog Computing can be synergistically coupled with the local control layer in a nonintrusive manner in order to provide performance information upload to the Cloud level. The article presents a framework architecture for data monitoring in an industrial environment. The data is used for predictive maintenance and performance KPIs (key performance indicators) concerning a flexible assembly line.