| Περίληψη: | Uncertainties in materials, machines, operators and other unexpected events demand real time decisions reducing significantly the production rate of manufacturing systems, increasing the demand for real time monitoring and control. Automated acknowledgment and investigation of such failures at the moment they occur interfaced with scheduling services who provide alternatives based on the current situation of resources, results in a flexible, less expensive and resource aware production. Easy obtainable, low cost, scalable cutting edge technologies including wireless sensor networks, cloud service and no-SQL databases have prepared the ground for an architecture which can achieve a monitoring module using mainly open source/hardware tools. Important information such as the current status of resources can be digitalized though the utilization of the measurements gathered from embedded sensors in the shop floor machine tools. A web platform which is also interconnected with the remote services to transmit necessary information using Web 3.0 standards stores, processes and visualizes data. This thesis is focused on the implementation of those technologies in a platform which is capable of sensing and monitoring the status of the machine tools and several performance indicators. The developed platform is a) constantly connected to a scheduling engine in order to provide a resource-aware, adaptive scheduling and condition based preventive maintenance, and b) remains scalable and flexible in order to provide a solid ground for further developments and integrations of services in the area of Industry 4.0
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