Ray Tracing-based Light Energy Prediction for Indoor Batteryless Sensors

Daeyong Kim, Junick Ahn, Jun Shin, Hojung Cha

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


Light energy harvesting is a valuable technique for batteryless sensors located indoors. A key challenge is finding the right locations to deploy sensors to provide sufficient harvesting capability. A trial-and-error approach or energy prediction method is used as the solution, but existing schemes are either time-consuming or employing a naïve prediction mechanism primarily developed for outdoor environments. In this paper, we propose a light energy prediction technique, called Solacle, which accounts for various factors in indoor light harvesting to provide accuracy at any given location. Exploiting the ray tracing technique, Solacle estimates the illuminance and the luminous efficacy of light sources to predict the harvesting capability, by considering the spatiotemporal characteristics of the surrounding environment. To this end, we defined the optical properties of a space, and devised an optimization approach, specifically a gradient-free-based scheme, to acquire adequate values for the combination of optical properties. We implemented the system and evaluated its efficacy in controlled and real environments. The experiment results show that the proposed approach delivers a significant improvement over previous work in light energy prediction of indoor space.

Original languageEnglish
Article number3448086
JournalProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Issue number1
Publication statusPublished - 2021 Mar 29

Bibliographical note

Publisher Copyright:
© 2021 ACM.

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Hardware and Architecture
  • Human-Computer Interaction


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