The overall vision of LoLiPoP IoT is to develop Energy Harvesting/micro-power management solutions for Wireless IoT edge devices that enable long battery life sensors to be retrofitted on, in or near equipment and infrastructure. Wireless sensors are the key technology platform to enable us to collect data that will be used for anomaly detection, efficiency and performance monitoring. The trillion-sensor economy of 2025 gives us unprecedented opportunities to exploit such data bringing billions of € in savings and disruptive benefits for industry and society (reduced carbons emission, increased renewable integration, making the world a safer and better-connected place).
In this context, LoLiPoP IoT targets challenges in three types of FUNCTIONALITIES for multiple application domains:
A) ASSET TRACKING: (from raw materials to finished good and equipment): This can be used to optimise flow, management and throughput of assets. In a factory environment this can identify bottlenecks resulting in reductions of >10% in production, cycle time and inventory costs. In a smart mobility environment this can helpmonitor assets to avoid loss, theft, minimize downtime as well as make savings in transportation cycle times and energy/carbon footprint.
B) CONDITION MONITORING (predictive maintenance): this is the process of monitoring a parameter of condition in machinery (vibration, temperature etc.), in order to identify a significant change which is indicative of a developing fault. For industry 4.0 maintenance overheads can potentially be reduced from 40% to <15% with additional improvements in cycle time and downtime. At present maintenance costs range between 15% and 40% of total production costs and unplanned downtime costs industrial manufacturers ~ $50 billion annually. Predictive maintenance saves ~ 8% to 12% over preventative maintenance, and up to 40% over reactive maintenance.
C) ENERGY EFFICIENCY & COMFORT OPTIMISATION: Key sensory data can be used to predict, understand and adjust the energy load (e.g. equipment, buildings) and energy needs. This can be done by optimizing the work environment for the human needs and avoid unnecessary consumption of energy. In this way, energy and fuel consumption can be reduced by up to 20% and significant reductions per year in battery replacement costs can be achieved. This will deliver carbon footprint savings due to less energy usage (as well as less batteries going into landfill if energy harvesting is used to prolong WSN device battery life). Meanwhile, workplace satisfaction and wellbeing can be increased significantly, resulting in a more productive workforce and lowering absence and care-related costs. This potentially also makes it easier to seamlessly integrate renewable energies.
