• augmented reality (AR)
• automation pyramid
• cloud computing
• data digitization
• digital twin
• embedded systems
• energy harvesting
• IIoT (Industrial Internet of Things)
• industry 4.0
• IoT edge
• networked manufacturing
• real-time monitoring
• RFID
• virtual reality (VR)

Summary of the Ph.D. thesis. This research focuses on enhancing the flow of information within the manufacturing network, aiming to strengthen connections in industry integration. Using the automation pyramid as a guide, we identified and addressed key areas across three main layers:

Field Layer: We began by exploring energy-efficient methods to power sensors, emphasizing the need for cost-effective and wireless solutions. We then introduced the use of passive technology to digitize basic devices, like pressure indicators, which were previously overlooked due to cost and complexity. A significant achievement was improving the range of these devices, making them more effective in capturing data.

Control Layer: Moving to this layer, we developed an affordable solution to update older devices to modern industry standards. One notable project involved modernizing a hydraulic machine's sensor system, allowing for better tracking and maintenance predictions.

Supervisory Layer: At this level, we recognized the need for a more comprehensive monitoring system. Traditional methods provided a limited view of operations. To address this, we integrated Virtual Reality technology, offering a more immersive and complete view of the factory's activities.

In essence, this thesis offers a holistic approach to improving data transfer and monitoring in industrial settings, making operations more efficient and aligned with modern standards.