Radio propagation digital twin to evaluate pedestrian localization in smart streetscapes

By Alyssa Falcon
Slide 1: Title slide presenting radio propagation digital twin project by Alyssa Falcon

Slide-1

Radio propagation digital twin to evaluate pedestrian localization in smart streetscapes

REU Scholar: Alyssa Falcon

Advisor: Dr. Georgios Sklivanitis

Lehman College, City University of New York

Slide 2: About section describing I-SENSE sensors and project overview with wireless detection capabilities

Slide-2

About

• I-SENSE has sensors deployed that detect RSSI values from Wireless emissions and can be found on Mobile Intel (wifi, phones, cars etc.)

• For the project we use 14 emitter locations and focusing on 4 sensors

• Digital Twin would help eliminate collecting data in person and help generate additional RSSI values

• Pedestrian finding on Clematis Street

• Mobility pattern of pedestrians or cars

Slide 3:

Slide-3

Left side: A rectangular box with two antennas attached. A label with the number "11" is on the front. Red arrows point to the antennas with text labels "Allow wireless access" and "Detects RSSI."

Right side: The inside of the box is shown. It contains components including cylindrical batteries, circuit boards, wiring, a metal grid-like component, and a device labeled "VELA." A red light and a blue light are visible on the components.

Slide 4: Device with antennas for wireless access and internal components including batteries, circuit board, and wiring

Slide-4

Tools Used

Sionna Ray Tracing

Simulates how radio signals travel through an environment as they bounce off objects

Blender

Tool that supports 3d modeling and animation

Open Street Maps

Map of the world and you can get coordinates of an area you want to focus on

Slide 5: Research Goals outlining three main objectives for the digital twin project

Slide-5

Research Goals

Create a Digital Twin of Clematis Street using Blender

Gather RSSI values using Sionna Ray Tracing

Apply localization methods to the Digital Twin

Slide 6: How RSSI collection is done - workflow showing steps from coordinates to coverage map

Slide-6

How RSSI collection is done?

Get coordinates from OSM and import the 3d map into Blender

Add in materials for objects and additional items in scene

Export the .xml file needed to import into Sionna

place receivers and transmitters in Sionna at specific coordinates from Blender

Set up cars to move in the scene and updated coverage map

Slide 7: Trilateration method explanation with mathematical approach using three known points

Slide-7

Trilateration

Localization method which uses three known points to find an unknown point

RSSI values from each known point were applied to the Friis Equation to find the Distance to the actual location

Tested with different values to see if that would help our accuracy

Slide 8: RSSI Collection Variance showing comparison between real data and digital twin data with graphs

Slide-8

RSSI Collection Variance

This slide shows comparative charts displaying RSSI values. The left section is labeled "Real Data" and the right section is labeled "Digital Twin Data". The charts appear to show signal strength measurements over different positions or time periods, allowing for comparison between actual collected data and simulated digital twin data. The visual comparison demonstrates the variance patterns between real-world measurements and digital simulation results.

Slide 9: Graphs and equations comparing real data and digital twin data for sensor-based trilateration

Slide-9

Left section titled "Real Data": Contains a mathematical formula and parameter values

Below, a graph is shown with X and Y axes labeled in meters. It has colored markers and a legend with entries: Sensor 22, Sensor 22 Position, Sensor 57, Sensor 57 Position, Sensor 06, Sensor 06 Position, Sensor 20, Sensor 20 Position, Estimated Position, and Transmitter Location.

The graph title is "Trilateration Location 1 - Distance Error: 13.30 meters."

Right section titled "Digital Twin Data": Contains a mathematical formula and parameter values

Below, another graph is shown with X and Y axes labeled in meters. It has similar markers and a legend with the same sensor labels plus "Estimated Transmitter Location" and "Actual Transmitter Location."

The graph title is "Sionna Trilateration Location 1 - Distance Error: 9.40meters."

Slide 10: Graphs and equations comparing real data and digital twin data for sensor-based trilateration

Slide-10

Left section titled "Real Data": Contains a mathematical formula and parameter values

Below, a graph is shown with X and Y axes labeled in meters. It has colored markers and a legend with entries: Sensor 20, Sensor 20 Position,Sensor 22, Sensor 22 Position, Sensor 57, Sensor 57 Position, Sensor 06, Sensor 06 Position, Estimated Position, and Transmitter Location.

The graph title is "Trilateration Location 2 - Distance Error: 12.85 meters."

Right section titled "Digital Twin Data": Contains a mathematical formula and parameter values

Below, another graph is shown with X and Y axes labeled in meters. It has similar markers and a legend with the sensor labels Sensor 22, Sensor 22 Position, Sensor 57, Sensor 57 Position, Sensor 06, Sensor 06 Position, Estimated Transmitter Location and Actual Transmitter Location.

The graph title is "Sionna Trilateration Location 2 - Distance Error: 7.93meters."

Slide 11: Estimation Comparison showing distance error analysis between estimated and actual emitter locations

Slide-11

Estimation Comparison

• Distance error between the estimated location and actual location of emitter

• Similarities in distance errors

The slide includes visual graph showing the comparison of estimation accuracy between different methods or datasets, demonstrating the performance of the localization system.

Slide 12: Conclusion summarizing digital twin benefits and future work recommendations

Slide-12

Conclusion

• Digital Twin helps with the what if situations since it allows you to control the environment

• Tool that provides efficiency, ability to make informed decisions, and an easy to implement simulation

• Future work to be added on to this Digital Twin can be implementing a fingerprinting localization method

Slide 13: Acknowledgment and References listing contributors and academic sources

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Acknowledgment

Mobile Intel

Dr. Georgios Sklivanitis

Jose Baca

Slide 14: Acknowledgment and References listing contributors and academic sources

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References

NVIDIA Labs. (n.d.). Sionna ray tracing API. Retrieved June 2024, from https://nvlabs.github.io/sionna/api/rt.html#

R. Bembenik and K. Falcman, "BLE indoor positioning system using RSSI-based trilateration," Warsaw University of Technology, Institute of Computer Science, Warsaw, Poland, Apr. 2020. [Online]. Available: https://isyou.info/jowua/papers/jowua-v11n3-3.pdf

R. R. Hastari, M. Yuliana, and P. Kristalina, "Students trajectory pattern finding scheme based on RSSI geolocation as a part of smart campus," in 2021 International Electronics Symposium (IES), Surabaya, Indonesia, 2021, pp. 337-342, doi: 10.1109/IES53407.2021.9594045

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For a downloadable version of this presentation, email: I-SENSE@FAU.

Additional Information
The Institute for Sensing and Embedded Network Systems Engineering (I-SENSE) was established in early 2015 to coordinate university-wide activities in the Sensing and Smart Systems pillar of FAU’s Strategic Plan for the Race to Excellence.
Address
Florida Atlantic University
777 Glades Road
Boca Raton, FL 33431
i-sense@fau.edu