Photovoltaic cells for energy harvesting and indoor positioning

• Main Authors: RIZK, Hamada, MA, Dong, HASSAN, Mahbub, YOUSSEF, Moustafa
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Photovoltaic-based localization; Deep learning; Indoor localization; Device-free localization; Energy-free localization; Artificial Intelligence and Robotics; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/7584; https://ink.library.smu.edu.sg/context/sis_research/article/8587/viewcontent/3557915.3560952.pdf
• Institution: Singapore Management University

We propose SoLoc, a lightweight probabilistic fingerprinting-based technique for energy-free device-free indoor localization. The system harnesses photovoltaic currents harvested by the photovoltaic cells in smart environments for simultaneously powering digital devices and user positioning. The basic principle is that the location of the human interferes with the lighting received by the photovoltaic cells, thus producing a location fingerprint on the generated photocurrents. To ensure resilience to noisy measurements, SoLoc constructs probability distributions as a photovoltaic fingerprint at each location. Then, we employ a probabilistic graphical model for estimating the user location in the continuous space. Results show that SoLoc can localize the user at sub-meter accuracy in a real indoor environment.