A new engineering blueprint is exploring the potential of energy harvesting to eliminate maintenance costs and battery waste in distributed sensor networks. Battery maintenance has become a significant barrier to large-scale Industrial IoT deployments, as the operational burden of maintaining thousands of battery-powered devices can quickly undermine project economics. This issue is particularly pressing in industrial environments where connected sensors are increasingly being used. As a result, there is a growing demand for solutions that can reduce maintenance costs and minimize environmental impact.
In response to this demand, a new blueprint has been published that examines the commercial and logistical challenges of powering large-scale, distributed sensor networks in remote, inaccessible, and hazardous environments. The blueprint covers key topics such as power budget calculations, supercapacitor selection, and the energy requirements of wireless protocols. It provides valuable insights for hardware developers, design engineers, and system architects looking to deploy maintenance-free Industrial IoT solutions.
Recent advances in ultra-low-power silicon, power management, and energy harvesting have made it possible for industrial devices to operate for years using ambient energy sources such as vibration, heat, and light. This technology has the potential to remove the need for routine battery replacement altogether, reducing waste and minimizing maintenance costs. Commercial perovskite photovoltaic cells, for example, are now achieving efficiencies of up to 38% under indoor lighting conditions, making self-powered industrial sensors a viable option.
A case study from a UK rail project demonstrates the practical viability of energy harvesting in industrial applications. The project involved the development of a zero-maintenance trackside backup system that combined solar, wind, and train-induced vibration to power LoRa transmissions. The system used an energy harvesting IC and a supercapacitor to manage the energy supply, eliminating the need for routine trackside maintenance. This example highlights the potential of energy harvesting to reduce maintenance costs and improve the overall efficiency of industrial systems.
However, harvesting energy is only half the battle, as extreme conservation is also essential for maintaining the viability of these systems. This is driving the integration of Edge AI, which enables real-time processing and analysis of data without the need for cloud processing. A recent demonstration of a live Edge AI vision system showcased the potential of this technology, using an embedded camera system and a rotating turntable to detect and identify objects in real time. The results were displayed instantly on screen, highlighting the potential of computer vision and on-device ML inference in low-power embedded systems.
The engineering blueprint includes a range of useful tools and resources, including a project feasibility calculator that enables engineering teams to assess the viability of an energy harvesting approach for their application. The blueprint also includes protocol comparisons and a full case study of the rail project, providing valuable insights and guidance for developers looking to deploy maintenance-free Industrial IoT solutions. By leveraging these resources, developers can make informed decisions about the design and deployment of their systems, reducing maintenance costs and minimizing environmental impact.
