Enabling Self-Powered Internet of Things with Integrated Cyber-Physical Modeling and Deployment Tools

As the internet of things (IoT) technology evolves and matures, billions of new devices are expected to be deployed and provide value for applications in many different areas such as building automation, healthcare, industry, city management and farming. While some IoT devices can rely on mains power, other devices might be deployed in locations without access to any cabled infrastructure and require either battery power or energy harvesting to operate. As the number of IoT devices increases or if the device’s location is hard to reach, the burden of periodical battery replacement becomes important. Therefore, self-powered IoT (SPIoT) devices capable of generating the energy they need to operate is a promising solution to enable sustainable and scalable IoT infrastructure. However, enabling SPIoT applications is challenging due to the coupled nature between energy generation, device's hardware operation, and application requirements, and due to the technological complexity of integrating and deploying end-to-end IoT applications. For instance, if a SPIoT adopter wants to replace a battery-powered device for a self-powered one, estimating if this device would work as well as the old one depends on: (1) how much energy this device can capture in the space; (2) how often does the application requires the device to become active; (3) how efficiently does the device uses energy; (4) how much energy the device can store; and (5) how easy is to integrate the device to the old device's infrastructure. Answering these questions requires an integrated cyber physical perspective on SPIoT applications, combining models of environments, devices and application requirements to provide a framework that supports the design, evaluation, and deployment of SPIoT applications.

To address these challenges, we introduce an integrated cyber physical SPIoT modelling framework and design tools that: (1) Enables evaluation of SPIoT applications by modeling energy generation, storage, and consumption; (2) Informs energy harvesting data collection that supports SPIoT hardware designers; (3) Allows SPIoT designers to select harvesters and storage components that meet their application requirements; and (4) Support SPIoT deployments by introducing the energy harvesting score, a 100-0 value that reflects how much useful energy is available in an environment, and, if used as a rating, indicating minimal energy requirements to support SPIoT operation. We also introduce a cloud-based data storage and visualization tool, easing end-to-end IoT application development. We use two practical examples to motivate the design and evaluation of SPIoT, one being a water quality station powered by solar and water flow kinectic energy, and the other example being a water leak sensor powered by a thermal energy source. With this modeling framework and deployment tools, we will enable ubiquitous end-to-end SPIoT applications by supporting the design and deployment of SPIoT devices comparable with battery-powered devices without battery’s limited lifetime constraint.