Practical Evaluation of Low-Frequency Vibration Energy Harvesting for Creating Green RFID and IoT Devices

One of the main limitations for the development and deployment of many Green Radio Frequency Identification (RFID) and Internet of Things (IoT) systems is the access to energy sources. In this aspect batteries are the main option to be used in energy constrained scenarios, but their use is limited to certain cases, either because of the constraints imposed by a reduced-form factor, their limited lifespan, or the characteristics of the environment itself (e.g. operating temperature, risk of burning, need for fast response, sudden voltage variations). In this regard, supercapacitors present an interesting alternative for the previously mentioned type of environment, although, due to their short-term capacity, they must be combined with an alternative energy supply mechanism. Energy harvesting mechanisms, in conjunction with ultra-low-power electronics, supercapacitors and various methods to improve the efficiency of communications, have enabled the emergence of battery-less passive electronic devices such as sensors, actuators or transmitters. This paper presents a novel analysis of the performance of an energy harvesting system based on vibrations for Green RFID and IoT applications in the field of maritime transport. The results show that the proposed system allows for charging half of a 1.2 F supercapacitor in about 72 minutes, providing a stable current of around 210 uA and a power output of 0.38 mW.

A Cost-Effective Thermal Imaging Safety Sensor for Industry 5.0 and Collaborative Robotics

The Industry 5.0 paradigm focuses on industrial operator well-being and sustainable manufacturing practices, where humans play a central role, not only during the repetitive and collaborative tasks of the manufacturing process, but also in the management of the factory floor assets. Human factors, such as ergonomics, safety, and well-being, push the human-centric smart factory to efficiently adopt novel technologies while minimizing environmental and social impact. As operations at the factory floor increasingly rely on collaborative robots (CoBots) and flexible manufacturing systems, there is a growing demand for redundant safety mechanisms (i.e., automatic human detection in the proximity of machinery that is under operation). Fostering enhanced process safety for human proximity detection allows for the protection against possible incidents or accidents with the deployed industrial devices and machinery. This paper introduces the design and implementation of a cost-effective thermal imaging Safety Sensor that can be used in the scope of Industry 5.0 to trigger distinct safe mode states in manufacturing processes that rely on collaborative robotics. The proposed Safety Sensor uses a hybrid detection approach and has been evaluated under controlled environmental conditions. The obtained results show a 97% accuracy at low computational cost when using the developed hybrid method to detect the presence of humans in thermal images.