This work encourages the cooperation of different IoT technologies in industrial environments for avoiding social disruption in novel Industry 4.0 paradigms. For this, Green I3A Smart Factory (GreenISF) scenario is presented, where the deployment of a collaborative mesh network based on Bluetooth Low Energy (BLE) and Long Range Wide-Area Network (LoRaWAN) technologies promotes Human-Machine collaboration towards Socially Sustainable Factories. In order to encourage human-centric architectures in such environments, our smart wristband prototype OperaBLE is introduced in this work as key enabler for Human-in-the-Loop systems. Thus, the network deployed in GreenISF is composed of Body Area Networks (BANs) which monitor labour activity and reinforce security at work by embedding sensors in regulatory industrial clothes. These networks cooperate with our BLE mesh infrastructure aimed to fulfil the major challenges Industry 4.0: zero fails, total coverage and sustainability. Furthermore, a Context Information Network based on LoRaWAN complements our mesh network to provide the system with valuable environmental data and prevent operators from harmful workplaces. The system is connected to a fog server responsible for preprocessing raw data that will be stored in a global cloud server. The experiments carried out highlight the contribution of OperaBLE towards safer working conditions and a sustainable digitalisation of Industry 4.0.

Smart solutions increasingly involve the use of sensor data to represent the physical world in the digital world and apply intelligence to such representation. The main approach is a vertical end-to-end solution from sensors to intelligence and back to actuators. Recently, a new holistic approach, the so-called Digital Twin, has emerged. This goes beyond traditional smart solutions by replicating, with high-fidelity cross-domain aspects, a physical object into the digital world. The key differentiation is the inclusion of semantics information in the digital replica in a form of knowledge graph. Further, analytics continuously enrich Digital Twin’s information with predictions and insights. In this article, we adopt the Digital Twin approach for the creation of a digital replica of Espinardo’s campus at the University of Murcia (Spain). The starting point is the existing sensor network deployment and the infrastructure under development of Fog–Edge–Cloud computing based on a 5G private network. The smart campus Digital Twin is formed by the Digital Twins of 23 buildings for which different sets of data features have been thoroughly selected. We implement the concept of Digital Twin by merging sensor data with external open data sources, analytics models implemented, and information processed by these analytics. We report our experience showing the issues encountered handling the data and producing various analytics models for predicting energy consumption, building occupancy, room usage, solar energy, and anomaly detection. From our experience, we highlight some lessons learned and directions toward the full operational smart campus Digital Twin.

With product life-cycles getting shorter and limited availability of natural resources, the paradigm shift towards the circular economy is being impulsed. In this domain, the successful adoption of remanufacturing is key. However, its associated process efficiency is to date limited given high flexibility requirements for product disassembly. With the emergence of Industry 4.0, natural human-robot interaction is expected to provide numerous benefits in terms of (re)manufacturing efficiency and cost. In this regard, vision-based and wearable-based approaches are the most extended when it comes to establishing a gesture-based interaction interface. In this work, an experimental comparison of two different movement-estimation systems—(i) position data collected from Microsoft Kinect RGB-D cameras and (ii) acceleration data collected from inertial measurement units (IMUs)—is addressed. The results point to our IMU-based proposal, OperaBLE, having recognition accuracy rates up to 8.5 times higher than these of Microsoft Kinect, which proved to be dependent on the movement’s execution plane, subject’s posture, and focal distance.