Hello My name is Daeyong Kim.
I m a phD student at the Yonsei university under the supervision of professor hojung cha.
I am going to introduce you the ray tracing based light energy predictions for indoor batteryless sensors.
Batteries are the threat to a sustainable sensor application.
They are expensive, short-lived, and hazardous.
To deal the problem of battery,
energy harvesting devices are proposed as a solution.
Particularly, light energy harvesting is used in many applications because it provides a relatively stable energy.
However, devices that operate through light energy harvesting have one major problem.
Since the Light energy harvesting is dependent on the position of the device,
Users do not know whether the device will work when they install the device in a desired location.
Of course, the user may have an intuition that a place near the window will have more energy.
However, not all devices can be installed near the window,
In this situation, users have no choice but to use a trial and error approach.
In this work, our goal is to predict harvesting capability at a random position.
Then, user can find optimal deployment of sensor device without trial and error approach.
First, we tried to understand light energy harvesting in indoor space.
We used the solar-powered BLE sensor that can be used to measure the harvesting capability.
And we installed the BLE sensors in different position and orientation.
Let me briefly tell you the results.
As you can see, The performance of each node varies widely.
Some sensors work really well but some are not.
We propose Solacle to predict harvesting capability.
Harvesting capability is determined by the amount of light reaching the harvester.
We thought that it would be possible to predict harvesting through ray tracing since it computes physical behavior of light .
However, to use ray tracing at a random space,
We need a 3D model of indoor space, and we need to be able to figure out the physical properties of the space.
We propose the following approach to solve the problem.
Users perform room scan and harvesting pattern collection,
and Solacle predict harvesting energy at any position of a space.
This allows users to understand the harvesting capability of the entire space.
For room scan, we suggest using Apple's mobile device with LiDAR sensor.
Currently, iPhone Pro and iPad Pro with LiDAR sensors offer 3d scan of space in a short time.
And for estimating the physical properties of the space, user has to collect the harvesting energy during a day.
I will briefly explain about Luminous efficacy,
even with the same intensity of light, energy can be different as shown in the graph below.
To obtain these energy-related physical properties, we use collected harvesting patterns.
By performing the two procedures, users can now estimate harvesting energy at any location in space.
This is the interface we implemented.
In a game-like interface, the user selects a location and checks the harvesting energy.
We have evaluated our proposed approach in a controlled environment and a real environment.
In a controlled environment, experiments were performed in different position and orientation,
and high accuracy was confirmed.
Then we chose two places for real environment experiment.
And below are the two selected real environment and lidar scan results.
In the lobby of the high-ceilinged building, lidar scans did not work well, so we manually created a 3D model.
Solacle was able to perform energy prediction in real environments.
Finally, we propose one use case using Solacle.
It is an application that suggests which location is best for the user by exhaustively searching the space.
It can intuitively show harvesting capability to a user.
And this is our conclusion.
To the best of our knowledge, Solacle is the first attempt to predict the energy harvesting,
by considering the spatiotemporal characteristics of the surrounding environment.
Using Solacle, we can find the optimal location where a batteryless sensor can harvest enough energy for operation.
For the future work we are planning to expand Solacle to find the optimal deployment of batteryless sensors,
further considering communication and sensing coverage.
Thank you for listening.