HarvAR: Mobile Augmented-Reality-Assisted Photovoltaic Energy-Harvesting Sensor Management

The capability of energy harvesting application powered by indoor photovoltaic energy is severely affected by dynamic light environments. Accordingly, accurate understanding of the target environment and deploying energy harvesting sensors is practically very hard. In this article, we propose HarvAR, which manages photovoltaic energy harvesting sensors with mobile augmented reality (AR)-empowered techniques. HarvAR utilizes the error-prone RGBD data of mobile device to construct a digital twin (DT), performing depth error compensation and estimating the optical properties of the target space. Using the DT, the proposed system predicts the harvesting capability with low overhead, and recommends adequate locations for installing or relocating harvesting sensors. We implemented the HarvAR system and evaluated its accuracy and efficiency in three indoor environments. Our experiments show that DT configuration and harvesting prediction can be performed in minutes, compared to over 10 h using existing techniques, and harvesting prediction is provided with less than 20% error.