Battery-Free RFID Sensor Network with Spatiotemporal ...
1
On-Site Server Sensor Hub 1 Wireless Router Sensor Hub 2 Sensor Hub 3 Sensor Hub 4 Sensor Hub 5 VM 1 VM 5 VM 4 VM 3 VM 2 HPDmobile Data Acquisition System & Database Captured Data Sensor Selection We captured data from six residences to learn about factors affecting occupancy predictions. We created a publicly available data set that other researchers could utilize for occupancy prediction tasks. ID Occupants Pets Type Home Size (m 2 ) % Area Monitored Hubs Deployed Days % Occ H1 4 adults 1 cat House 149 32 5 30 0.82 H2 1 adult None Apartment 72 53 4 17 0.61 H3 4 adults 1 dog House 285 28 5 35 0.78 H4 3 adults None Apartment 93 36 5 17 0.82 H5 2 adults 1 cat Apartment 54 48 5 15 0.76 H6 1 adult None Apartment 62 58 4 38 0.47 Measurement Units or Description Sampling Frequency Precision Temperature Degree Celsius (°C) 0.1 Hz ± 0.1 °C Relative Humidity Percentage (% rH) 0.1 Hz ± 0.1 % rH Carbon Dioxide (eCO 2 ) Parts per million (ppm) 0.1 Hz ± 1-31 ppm Total Volatile Organic Compounds (TVOC) Parts per billion (ppb) 0.1 Hz ± 1-32 ppb Ambient Illuminance Lux (lx) 0.1 Hz unspecified Ground truth occupancy Binary: occupied/vacant 5 minute N/A Audio (as collected) 10-second WAV file 8 kHz 18 bit Images (as collected) 112k pixel b\w PNG file 1 Hz 36 dB S/N ratio Audio (as available) Audio signal as CSV file 80 Hz N/A Images (as available) 1,024 pixel b&w PNG file 1 Hz N/A Table 1: Home and occupant attributes. Days refers to number of days of data that were released from home, while % Occ refers to percentage of time home was occupied. Table 2: Summary of all modalities as collected by data acquisition system and as available for download. Precision is as specified by sensor product sheets. Figure 1 (above right): Home diagram with sensor locations marked. Figure 2 (below right): Hardware components and network connections. Figure 3 (above left): Sensor hub with sensors with battery. BEDROOM KITCHEN BATHROOM LIVING ROOM 8.1 m 6.4 m RS1 RS2 RS3 RS5 RS4 Figures 4: Data collected in a 24 hour period. Clockwise from top left: illuminance, TVOC, occupancy, CO 2 , relative humidity, and temperature. 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 Home H1 H2 H3 H5 H6 Image to Audio False Positive Rate True Positive Rate To identify the most important sensing modalities and locations, we performed a full factorial analysis comparing predictive results of our sensor fusion algorithm for all combinations of hubs and modalities, with the environmental modalities considered jointly. -0.08 -0.06 -0.04 -0.02 0 0.02 0.04 0.06 0.08 H1 H2 H3 H5 H6 None to Image Audio to Image None to Audio Figure 6: Audio waveforms before (left) and after (right) multistep processing procedure for obfuscation. Figure 5: Images as collected (left) and after downsizing to 32x32 pixels (right). Figure 8: Changes in classification performance when the kitchen hub is switched form nothing to audio, nothing to image, and audio to image. Figure 7 (right): Results of factorial analysis comparing audio and image collection. Each point shows one permutation. Above shows one result. Testing and System Integration Contact: Gregor Henze, University of Colorado Boulder, [email protected] Battery-Free RFID Sensor Network with Spatiotemporal Pattern Network Based Data Fusion System for Human Presence Sensing Investigators: Gregor Henze (PI, UCB), Anthony Florita (NREL), Soumik Sarkar (ISU), Joshua Smith (UW) ❦ Research Assistants: Maggie Jacoby (UCB), Jasmine Garland (UCB), Sin Yong Tan (ISU), Ali Saffari (UW), Mohamad Katanbaf (UW) Date: May 2021 Project Status: Month 36 of 36
Transcript of Battery-Free RFID Sensor Network with Spatiotemporal ...
On-Site Server
Sensor Hub 1
Wireless Router
Sensor Hub 2
Sensor Hub 3
Sensor Hub 4
Sensor Hub 5
VM 1 VM 5VM 4VM 3VM 2
(a)
(b)
(c)
Figure 1. (a) System architecture, hardware components, and network connections of the HPDmobile data acquisition system.(b) Custom designed printed circuit board with sensors attached. Sensors, clockwise from top right, are: camera, microphone,light, temperature/humidity, gas (CO2 and TVOC), and distance. (c) Final sensor hub (attached to an external battery), asinstalled in the homes.
9/16
HPDmobile Data Acquisition System & Database
Captured Data
Sensor Selection
We captured data from six residences to learn about factors affecting occupancy predictions.
We created a publicly available data set that other researchers could utilize for occupancy prediction tasks.
ID Occupants Pets Type Home Size (m2)
% Area Monitored
Hubs Deployed Days % Occ
H1 4 adults 1 cat House 149 32 5 30 0.82H2 1 adult None Apartment 72 53 4 17 0.61H3 4 adults 1 dog House 285 28 5 35 0.78H4 3 adults None Apartment 93 36 5 17 0.82H5 2 adults 1 cat Apartment 54 48 5 15 0.76H6 1 adult None Apartment 62 58 4 38 0.47
Measurement Units or Description Sampling Frequency Precision
Temperature Degree Celsius (°C) 0.1 Hz ± 0.1 °CRelative Humidity Percentage (% rH) 0.1 Hz ± 0.1 % rHCarbon Dioxide (eCO2) Parts per million (ppm) 0.1 Hz ± 1-31 ppmTotal Volatile Organic Compounds (TVOC) Parts per billion (ppb) 0.1 Hz ± 1-32 ppbAmbient Illuminance Lux (lx) 0.1 Hz unspecifiedGround truth occupancy Binary: occupied/vacant 5 minute N/AAudio (as collected) 10-second WAV file 8 kHz 18 bitImages (as collected) 112k pixel b\w PNG file 1 Hz 36 dB S/N ratioAudio (as available) Audio signal as CSV file 80 Hz N/AImages (as available) 1,024 pixel b&w PNG file 1 Hz N/A
Table 1: Home and occupant attributes. Days refers to number of days of data that were released from home, while % Occ refers to percentage of time home was occupied.
Table 2: Summary of all modalities as collected by data acquisition system and as available for download. Precision is as specified by sensor product sheets.
Figure 1 (above right): Home diagram with sensor locations marked.Figure 2 (below right): Hardware components and network connections.Figure 3 (above left): Sensor hub with sensors with battery.
BEDROOM
KITCHEN BATHROOM
LIVING ROOM
PATIO
8.1
m
6.4 m
N
RS1RS2
RS3
RS5
RS4H6-red
Figures 4: Data collected in a 24 hour period. Clockwise from top left: illuminance, TVOC, occupancy, CO2, relative humidity, and temperature.
0 0.2 0.4 0.6 0.8 10
0.2
0.4
0.6
0.8
1Home
H1H2H3H5H6
Image to Audio
False Positive Rate
True
Pos
itive
Rat
e
To identify the most important sensing modalities and locations, we performed a full factorial analysis comparing predictive results of our sensor fusion algorithm for all combinations of hubs and modalities, with the environmental modalities considered jointly.
-0.08
-0.06
-0.04
-0.02
0
0.02
0.04
0.06
0.08
H1 H2 H3 H5 H6
None to Image Audio to ImageNone to Audio
Figure 6: Audio waveforms before (left) and after (right) multistep processing procedure for obfuscation.
Figure 5: Images as collected (left) and after downsizing to 32x32 pixels (right).
Figure 8: Changes in classification performance when the kitchen hub is switched form nothing to audio, nothing to image, and audio to image.
Figure 7 (right): Results of factorial analysis comparing audio and image collection. Each point shows one permutation. Above shows one result.
Testing and System Integration
Contact: Gregor Henze, University of Colorado Boulder, [email protected]
Battery-Free RFID Sensor Network with Spatiotemporal Pattern Network Based Data Fusion System for Human Presence Sensing
Investigators: Gregor Henze (PI, UCB), Anthony Florita (NREL), Soumik Sarkar (ISU), Joshua Smith (UW) ❦ Research Assistants: Maggie Jacoby (UCB), Jasmine Garland (UCB), Sin Yong Tan (ISU), Ali Saffari (UW), Mohamad Katanbaf (UW)
Date: May 2021 Project Status: Month 36 of 36
