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CALIFORNIA STATE UNIVERSITY, NORTHRIDGE
Temperature Classification using Smart Phone WiFi Signal Monitoring
A thesis submitted in partial fulfillment of the requirements
For the degree of Master of Science in Software Engineering
By
Vincent Ha
May 2021
California State University Northridge
ii
The thesis of Vincent Ha is approved:
________________________________ ________________
Dr. Katya Mkrtchyan Date
________________________________ ________________
Dr. Richard G. Covington Date
________________________________ ________________
Dr. Ani Nahapetian, Chair Date
iii
Table of Contents
Signature Page ii
List of Tables v
List of Figures vi
Abstract vii
Chapter 1 - Introduction 1
Chapter 2 - Related Work 2
2.1. WiFi for Localization 2
2.1.1 WiFi Sampling 2
2.1.2 WiFi RSSI Noise Filtering 2
2.2 RF Monitoring of Human Vital Signs 3
2.2.1 RF Interference 3
2.3 CSI monitoring of Human Activity 3
2.4 Infrared Temperature Tracking Accuracy 3
Chapter 3 - Approach 4
3.1 RSSI 4
3.2 System Software 5
3.3 System Hardware 5
3.4 Data Approach 5
Chapter 4 - Experimentation 6
4.1 Experiment #1 - Stove Top 6
4.1.1 Setup 6
4.1.2 Result 7
4.1.3 Analysis 8
4.2 Experiment #2 - Water Bowl Test – Instance 9
4.2.1 Setup 9
4.2.2 Result 10
4.2.3 Analysis 13
4.3 Experiment #3 - Water Bowl Test – Gradual 14
4.3.1 Setup 14
4.3.2 Result - Round 1 15
iv
4.3.3 Analysis - Round 1 17
4.3.4 Result - Round 2 18
4.3.5 Analysis - Round 2 20
4.4 Experiment #4 - Water Bowl Test - Submerged 20
4.4.1 Setup 21
4.4.2 Result 21
4.4.3 Analysis 22
4.5 Experiment #5 - Hand Test 22
4.5.1 Setup 23
4.5.2 Result 24
4.5.3 Analysis 27
4.6 Classifications 28
Conclusion 30
Reference 31
v
List of Tables
Table 4.1.1 – Average dBm while cooling down is stronger during heating up 8
Table 4.2.1 – A nonlinear relationship between dBm and temperature 10
Table 4.2.2 – Side by side comparison among the different temperature 13
Table 4.3.1 – Data summary - Round 1 16
Table 4.3.2 – Data Summary of Phone after the reflecting signal had been isolated to its own
group * shows the stdev after the 2 outliers (-71, -70 dBm) have been removed from the first
group of signals 16
Table 4.3.3 – Data summary - Round 2 19
Table 4.4.1 – Summary of Experiment #4 result 22
Table 4.5.1 – Summary of the average peak dBm and their standard deviation 26
Table 4.6.1 - Confusion Matrix 29
vi
List of Figures
Figure 3.1 – A high level view of the thesis’s model 4
Figure 4.1 – The floor plan used in the experiment. 6
Figure 4.1.1 – Signal and Temperature vs Time, stove top temperature and signal change 7
Figure 4.1.2 – Signal and Temperature vs Time, dBm increases when stove is heating up 7
Figure 4.1.3 – Signal and Temperature vs Time, dBm increases when cooling down 8
Figure 4.2.1 – Initial setup of experiment 9
Figure 4.2.2 – A revised setup where all potential disruptors (phone case, metal ruler) have been
removed 10
Figure 4.2.3 – Signal and Temperature vs Time (revised setup), slope shows a positive slope in
relation to a decrease in temperature 11
Figure 4.2.4 – Signal and Temperature vs Time, signal is relatively stable within the stable room
temperature water 12
Figure 4.2.5 – Signal and Temperature vs Time, dBm stabilizes in cooler temperature 12
Figure 4.2.6 – Variability and average dBm across three groups of temperature 13
Figure 3 – Initial setup of Water Bowl Test - Gradual 14
Figure 4.3.1 – Signal and Temperature vs Time, First Round - Phone 15
Figure 4.3.2 – Signal and Temperature vs Time, First Round - Router 15
Figure 4.3.3 – Signal and Temperature vs Time, top half of the reading dBm < -30 17
Figure 4.3.4 – Signal and Temperature vs Time, bottom half of the phone reading dBm > -30. 17
Figure 4.3.5 – Signal and Temperature vs Time, Second Round (increased distance) - Phone 18
Figure 4.3.6 – Signal and Temperature vs Time, Second Round - Router 19
Figure 4.4.1 – Setup with the device submerged entirely into the water 21
Figure 4.4.2 – Signal and Temperature vs Time, dBm of the access point increases and stabilizes
as temperature decreases. 21
Figure 4.5.1 – Signal vs Time. The dot indicates each moment of detection 23
Figure 4.5.2 – Setup for the Hand experiment 23
Figure 4.5.3 – Signal vs Time under different temperature range, Trial #1 24
Figure 4.5.4 – Signal vs Time under different temperature range, Trial #2 24
Figure 4.5.5 – Signal vs Time under different temperature range, Trial #3 25
Figure 4.5.6 – Signal vs Time under different temperature range, Trial #4 25
Figure 4.5.7 – Visualization of statistical analysis 26
vii
Abstract
Temperature Classification using Smart Phone WiFi Signal Monitoring
By
Vincent Ha
Master of Science in Software Engineering
With current technology, temperature monitoring requires specialized tools and thermometers,
such as non-contact infrared thermometers (NCIT), to achieve a reading. The thesis explores an
alternative way of classifying temperature using a more ubiquitous wave form, WiFi. The
temperature is inferred by using a WiFi transmitter emitting a signal and a WiFi receiver examining
the change in the received signal strength indicator (RSSI) measured in dBm. Using a smart phone,
as the transmitter and a router as the receiver, experimental results show that temperature is
correlated with change in RSSI. The findings also indicate that the WiFi signal performs with more
stability when the temperature is cooler. Given the sensitivity of WiFi to disturbance, the current
classification method requires a specified parameter for each temperature group. The classification
can correctly identify temperature group of RSSI reading 56.86% of the time. It can correctly
identify a cool reading 61.11% of the time, a normal reading 58.82% of the time, and a warm
reading 50% of the time.
1
Chapter 1 - Introduction
Ambient temperature has always been an important piece of information that humans use to
analyze their environment. Body temperature is a crucial piece of information within the medical
field. A cold or a flu can often be distinguished just by the measurement of temperature.
Traditional body temperature measurement requires physical contact to detect the change in
temperature. This is seen in mercury thermometers and some digital thermometers. However,
while this method has high accuracy, it requires prolonged contact to a person’s skin to record the
reading.
Recently, commercial contactless thermometers, also known as non-contact infrared thermometer
(NCIT), use technology that measures the reflected infrared radiation. This type of thermometer
can give a fast temperature reading. While infrared sensors are cheap and affordable, there are
alternative waveforms that are much more ubiquitous, wireless signals.
Due to the ubiquity of smart phones and other mobile devices, people have ready access to a WiFi
transducer. In this thesis, the use of WiFi signals received signal strength indicator (RSSI) and its
relationship to temperature is explored, thus opening up the potential for measuring temperature
using only the WiFi hardware of a smart phone and an installed app.
In this thesis, a series of experiments were carried out to measure the RSSI of a WiFi access point
as temperature was varied, thus quantifying the relationship between temperature and WiFi
received signal strength. The experiments show that the accessibility of temperature monitoring
can be improved by leveraging the nature of wireless signals and the infrastructure that comes with
living in the digital age.
2
Chapter 2 - Related Work
The main functionality of WiFi is to provide wireless connectivity in a local area network.
However, WiFi has also been used for localization purposes, mostly due to its ubiquity. In this
thesis, WiFi is used for temperature monitoring.
2.1. WiFi for Localization
When it comes to monitoring using wireless signals, there have been many studies that use wireless
signals for indoor localization and positioning monitoring. The approaches in these papers deal
with Access Points (AP) and RSSI to calculate the position [3]. One study finds that the RSSI of
WiFi is highly susceptible to human’s positioning and orientation due to its wavelength 2.4GHz
and 5GHz [1]. Another study suggests that RSSI may not necessarily be a good method to measure
positions [4].
The idea behind these studies is that there are many flaws and factors when it comes to monitoring
using RSSI. As such, experiments and analysis must consider these flaws and adjust accordingly.
2.1.1 WiFi Sampling
The following study discusses the potential error in WiFi RSSI collection. While it is somewhat
negligible to this thesis, it does offer some insight for certain irregularities that can occur during
the process.
In 2017, a study notes the importance of filtering WiFi RSSI due to the inaccuracy delays of data
during the process of startScan() to getScanResult() [6]. In the same study, it notes that Android
API 17 and later allows users to use timestamp to acquire more accurate data [6]. WiFi signals are
also prone for interference due to multiple access points emitting signals on the same channel.
Certain weaker signals will sometimes be dropped during the scanning process [6].
2.1.2 WiFi RSSI Noise Filtering
WiFi RSSI is inevitably heavily influenced by the environment. As such, data sometimes require
filtering. In 2016, Kalman Filter, a linear filtering method, is shown to have increased accuracy
over others [8]. The Gaussian filter, which was improved in 2000 [9], offers a nonlinear filtering
to the data. However, in 2014, a new study suggests that using Gaussian distribution is inaccurate
without careful examination of standard deviation [10]. It further shows that different hardware
and phones can also record different RSSI signals under the same positioning and environment
[10]. In 2020, a recent study proposes a newer filtering method that shows a 20.5% increase in
accuracy [11].
3
2.2 RF Monitoring of Human Vital Signs
Other alternatives work that monitor human vital signs requires deployment of hardware and
sensors. A study from this approach uses mmWave (60GHz) and RSSI to track human’s heartbeat
and breathing [2]. A more recent study performs a similar approach to analyzing human’s sleeping
posture using RF-reflection [5]. Both studies use reflection and orientation of the subject to
determine their vital sign.
In 2016, a study suggests the use of radio waves and wear-able devices to track a person’s activity
[12]. Their approach uses specially made wearable devices called BodyScan that are designed to
be contactless and are less susceptible to environmental interference.
A more recent study in 2018 shows how Bluetooth signals can be used to track a person’s action
[8]. The process utilizes unpaired devices that keep track of the RSSI and analyze the distinction
of RSSI between different actions. This approach does not use reflection and is analyzed purely
on the RSSI reading.
2.2.1 RF Interference
A study in 2016 demonstrates that the RF release by microwave oven has the greatest effect on
most WiFi and Bluetooth signals at 2.4GHz to 2.5GHz [13].
2.3 CSI monitoring of Human Activity
While CSI is a technology that is not yet available for mobile phones, certain research uses CSI to
count individuals in a crowd [14]. It leverages the response of movement by the Channel
Frequency Response and using CSI to extract useful information. The study’s trained-once model
has an accuracy of 74% to 52% [14].
2.4 Infrared Temperature Tracking Accuracy
A study in 2011 compares the accuracy of NCIT to a temporal artery thermometer (TAT) and a
rectal glass mercury thermometer (RGMT) in adolescence and children’s body temperature
reading. Its finding concludes that NCIT performs with 97% sensitivity and specificity with a
negative predictive value of 99% [16].
A more recent study in 2020 examines the accuracy of NCIT regarding temperature reading and
finds that NCIT has a low sensitivity for adult’s temperature reading above 37.5°C or 99.5°F. It
performs similarly to a TAT when temperature is less than 37.5°C. The study concludes that NCIT
may not necessarily be the best method for mass fever screening [15].
4
Chapter 3 - Approach
In order to achieve the goal of leveraging the ubiquitous nature of WiFi Signal to examine the
human’s body temperature, it must first be proven that temperature does, indeed, influence WiFi
signal.
In a real-world environment, access points are plentiful. WiFi signals are constantly being
transmitted around people’s daily life and their strength varies. Consider Figure 3.1, as people
come in between these signals, the reading received shows numerous levels of fluctuation. These
interference patterns are recorded for analysis.
Ideally, the transmitting device should be placed directly behind the person, but in a real-world
situation, this may not always be possible.
Figure 3.1 – A high level view of the thesis’s model
3.1 RSSI
The strength of WiFi Signal is known as the received signal strength indicator (RSSI), which is
measured in decibels in relation to milliwatts (dBm). The range of the reference is usually between
-30 dBm to -100 dBm, but in some cases, it can have a dBm closer to 0. A reading closer to 0
indicates stronger signal, while a reading closer to -100 indicates a weak reading.
RSSI can be affected by many factors, but the main factor is obstruction. Certain material such as
metal or reflective surfaces are also known to interfere with RSSI.
Transmitting
Device
Receiving
Device
5
3.2 System Software
A bespoke software app was developed that recorded the RSSI reading over time with a sample
rate of approximately one scan per every 3 to 4 seconds. It carries out a WiFi scan for access points.
Upon receipt of a result, the app records the strength and the time of the receipt for each access
point that it has found. The timestamps identify events during the experimentation process. This
raw data is saved in a txt file for data analysis.
3.3 System Hardware
The thesis’s setup utilizes two separate mobile devices, with one designated as the WiFi wireless
signal transmitter and the other as the WiFi wireless signal receiver. A router is also used.
There are some hardware limitations and problems that can interfere with the reading. A paired
device will greatly interfere with the process. Ensuring that neither devices are paired to a network
or another wireless device eliminates this concern.
As downloading or uploading data during the data collection process can cause interference in the
reading of RSSI. Both devices are disconnected from any mobile network that they were connected
to. The phones do not have access over the internet during the experiments.
WiFi signals are sensitive to various factors. The relative location of the WiFi adapter is found to
have an impact on the transmission and readings of RSSI. Most WiFi adapters in phones are
located at the front or the back of the phone. Having a clear unobstructed path between the adapter
to adapter is found to improve the signal’s quality.
Phone cases and other physical obstructions can also interfere with the signal. Metallic surfaces
are found to interfere with the readings, causing a clear distinction of oscillation in the signal.
When the user is moving relative to the phone, they can also impact the reading with their body
serving as an obstruction.
3.4 Data Approach
The model collects multiple sections of readings for analysis. The collected RSSI in dBm is plotted
onto a graph and the slope of the regression line is used to determine the overall trend of the dBm.
Any spikes or oscillations within the reading is recorded, except for outliers that lie far beyond the
clustered set of data. The readings and new findings of each section help find tune the next iteration
of the experiment’s setup.
6
Chapter 4 - Experimentation
To make use of WiFi signal as a mean of temperature’s tracking, the thesis’s first experiment tests
the following hypothesis: Temperature does influence WiFi wireless signal.
For the purpose of these experiments, a Samsung Galaxy S10 is used as a receiving device. A
Samsung Galaxy S10e is used as a mobile transmitting device. Additionally, a SageCom Router
is used as an access point for those experiments that called for it.
Proper configuration of the phone was made prior to all experiments. The receiving phone is not
connected to the internet. It is not paired with any wireless devices or networks. WiFi scan
throttling is disabled to ensure continuous scan.
4.1 Experiment #1 - Stove Top
The experiment seeks to confirm the effect of temperature on WiFi. It does so by observing the
changes of dBm under the gradual increase and decrease of temperature.
4.1.1 Setup
Figure 4.1 – The floor plan used in the experiment.
The receiving phone was placed around 9 inches away from the stove top. The distance from the
router was approximately 10.17 ft away from the phone. The phone was placed with the screen
facing upward on the counter. The phone was also encased.
The Stove was gradually heated up and then turned off to let it cool down naturally. Temperature
was measured periodically at the spot between the phone and the stove. A timestamp method was
used to keep track of the signal recorded.
7
4.1.2 Result
Figure 4.1.1 – Signal and Temperature vs Time, stove top temperature and signal change
Figure 4.1.1 shows an increase in temperature as the stove was heating up as well as when it was
cooling down.
A total of 219 samples were collected. The data was further broken down into two sections:
Heating up and Cooling down.
Figure 4.1.2 – Signal and Temperature vs Time, dBm increases when stove is heating up
8
Figure 4.1.3 – Signal and Temperature vs Time, dBm increases when cooling down
HEATING UP: COOLING DOWN:
AVG: -41.63865546 AVG: -39.63265306
STDEV 1.880908059 STDEV: 1.334612613
DATA SIZE: 119 DATA SIZE: 98
Table 4.1.1 – Average dBm while cooling down is stronger during heating up
4.1.3 Analysis
The graph (Figure 4.1.1) shows an increase in dBm, which goes against the hypothesis that dBm
is affected by temperature. However, after breaking down the two graphs (Figure 4.1.2, Figure
4.1.3), the average dBm when it was heating up is worse than the average dBm when it was cooling
down.
There is a possible explanation for this observation. As the stove is heating up, the fluctuation of
energy generated by the open flame could possibly be a factor in disrupting the receiving signal.
When the stove has been turned off, the variability of the reading stabilizes. This is evident in the
standard deviation (Table 4.1.1) of each section. A preliminary conclusion can be made from this
experiment. An increase in temperature results in a worsening of WiFi signal. As temperature
cools, the WiFi signal improves.
9
However, there is much variability in this experiment setup that can be improved with a newer
model - the Water Bowl Test model.
4.2 Experiment #2 - Water Bowl Test – Instance
The Water Bowl Test - Instance seeks to improve on the stove top experiment. This setup is
designed to eliminate interference caused by the fluctuation of temperature during the heating up
phase. The experiment will observe the temperature as three instances: Hot, Cold and Room
Temperature.
4.2.1 Setup
A bowl of water was placed in between two mobile devices. The devices were placed at equidistant
from the foot of the bowl.
Figure 4.2.1 illustrates the prototype setup of the experiment. Temperature was measured
externally by an infrared thermometer. A metal ruler was placed to measure the distance between
the phones for distance referencing. As later experiments revealed, the metal ruler interfered with
the data, which resulted in a set of data that could be improved on. Temperature was checked
before each trial.
Figure 4.2.1 – Initial setup of experiment
Figure 4.2.2 illustrates the proper setup of this experiment using new findings from Experiment
#3 and Experiment #4. Within the water bowl, a temperature sensor was placed. This eliminates
10
the need to move periodically to take temperature, which will minimize disruption caused by this
obstruction and movement.
Figure 4.2.2 – A revised setup where all potential disruptors (phone case, metal ruler) have been
removed
The experiment was carried out three times using three different water temperature group: hot,
cold, and room temperature.
Ice cubes were used to cool the water temperature. To prevent interference, the experiment ensured
that all the ice cubes had completely melted before proceeding. Data were collected at a distance
that would have the least potential to interfere with the signal.
4.2.2 Result
The following is the summary and result of the experiment using Figure 4.2.1 (initial) setup.
Average dBm STDEV
Hot -37.14925373 0.9886282407
Rm Temp -34.14 1.277912839
Cold -36.28571429 0.9576276371
Table 4.2.1 – A nonlinear relationship between dBm and temperature
11
The result is not linear (Table 4.2.1). This result contradicts the preliminary finding from the Stove
Top Experiment. This suggests that this experiment setup could be improved.
Future experiments setup provides sufficient proof that this current setup did not consider some of
the possible interfering factors discussed in Section 3-Approach. Refer to Experiment #3 (Figure
4.3.1), which uses a similar setup as Figure 4.2.1, for more details. Upon repeating the result with
proper setup, the following findings coincide with the preliminary conclusion.
The following graph observes dBm under the effect of cooling hot water. A total of 65 samples
were collected over the course of 4 minutes. The initial temperature of the water is 149.5 °F, which
fell to 131 °F by the end of the observation period. The average dBm for this round was -23.1538.
Figure 4.2.3 – Signal and Temperature vs Time (revised setup), slope shows a positive slope in
relation to a decrease in temperature
In Figure 4.2.3, the overall trendline for the signal shows a positive slope. Despite the oscillation
of signal, as temperature decreases, there is a slight increase in signal strength. In fact, it can also
be concluded that the variability of dBm stabilizes as temperature decreases, which results in a
stronger overall signal.
Refers to Table 4.2.2 for a summary of the finding.
12
Figure 4.2.4 – Signal and Temperature vs Time, signal is relatively stable within the stable room
temperature water
In Figure 4.2.4, despite a few spikes in the graph, the overall tendency of signal remains stable.
Water temperature is also kept stable throughout the observation period, remaining at a constant
73.5 °F.
A total of 75 samples were collected over the course of 6 minutes. The average dBm for this round
was -16.0181.
Figure 4.2.5 – Signal and Temperature vs Time, dBm stabilizes in cooler temperature
In Figure 4.2.5, the observed dBm is relatively constant. Although temperature increases by a
small amount, there is not much variability in the signal strength. The trendline shows a little
increase in dBm, but it is much less when compared to the results found in Figure 4.2.3 and Figure
4.2.4.
13
A total of 75 samples were collected over the course of 4 minutes. The initial water temperature
was 44.2 °F, which gradually increased to 46.9 °F. The average dBm for this round was -14.5811.
Figure 4.2.6 – Variability and average dBm across three groups of temperature
AVG STDEV
SAMPLE
SIZE
START
TEMP END TEMP
TIME
SPAN
COLD -14.58108108 2.330757909 75 44.2 46.9 4 min
RM TEMP -16.08108108 4.869467292 75 73.5 73.5 6 min
HOT -23.15384615 5.67403838 65 149.5 131 4 min
Table 4.2.2 – Side by side comparison among the different temperature
Table 4.2.2 and Figure 4.2.6 shows a negative tendency of dBm as water temperature increases.
Comparing the results from three different instances, taking the standard deviation and the average
into account, the data demonstrates an inverse relationship between temperature and WiFi Signals.
Figure 4.2.6 demonstrates a linear relationship between temperature and average signal strength.
As temperature increases, not only does the signal worsen, but the variability also increases.
4.2.3 Analysis
Using the proper setup, the summary shows a linear relationship between temperature and WiFi
signal’s performance. As temperature increases, the signal strength decreases. The data shows
strong support for this hypothesis.
14
The result (Figure 4.2.3, 4.2.4, 4.2.5) shows that oscillation occurs much more often at higher
temperature’s range. A secondary hypothesis can be made: Temperature also affects the stability
of signal’s strength.
4.3 Experiment #3 - Water Bowl Test – Gradual
4.3.1 Setup
Figure 3 – Initial setup of Water Bowl Test - Gradual
The devices were placed at equidistant from the foot of the bowl.
This setup replaces the process of manual temperature checking with a sensor to minimize the
interferences cause by movement and obstruction. A clock was placed at the end to keep track of
time that was used to analyze the data. Later experiment shows that this setup can be further
improved on.
The experiment was carried out as two separate instances on different dates. The receiving phone
simultaneously kept track of the signal dBm from both the transmitting phone and the router.
Water was boiled and cooled naturally to a determined range of temperature during the observation
period. In the first round, the water was boiled to 135.1 °F and cooled to a temperature of 94.9 °F
15
by the end of the experiment. The distance between the phone and the foot of the bowl was 2
inches.
In the second round, slight modification was made to the setup. The water was boiled to a
temperature of 105.8 °F and cooled to 94.7 °F by the end of the experiment. The reason for this
modification is to simulate the measurable range of temperature of the human’s body. The distance
between the phone and the foot of the bowl was increased to 4 inches.
4.3.2 Result - Round 1
The following is the result of the first round of experiment.
Figure 4.3.1 – Signal and Temperature vs Time, First Round - Phone
Figure 4.3.2 – Signal and Temperature vs Time, First Round - Router
16
Phone Router
stdev 10.53091436 1.157841357
avg -20.74324324 -55.59452412
size 740 767
Table 4.3.1 – Data summary - Round 1
A total sample size of 740 was collected for this round for the phone and 767 for the router. Initial
temperature was 135.1°F and cooled to 94.9°F over the course of 39 minutes.
Figure 4.3.1 depicts a clear signal from two sources, despite coming from the same transmitter.
There is a clear distinction of separation found in the signal strength. The receiving phone is
picking up a secondary reflection signal from the transmitting device.
Figure 4.3.2 depicts a slight oscillation that is somewhat systematic, suggesting that the lower half
of the graph is a reflection signal. For clarity, an excerpt of the graph had been magnified. The gap
in between the two readings is far too wide and repetitive to be considered as normal variability.
There is no noticeable reflection signal in comparison to the Figure 4.3.1. The overall trendline is
a positive slope.
The STDEV for the Phone is 10.5309 and the Router is 1.1578 (Table 4.3.1).
dBm > -30 dBm > -30* dBm < -30
stdev 3.169937093 0.6883753939 1.602256402
average -31.540625 -31.29559748 -10.87760417
sample size 320 318 384
Table 4.3.2 – Data Summary of Phone after the reflecting signal had been isolated to its own
group
* shows the stdev after the 2 outliers (-71, -70 dBm) have been removed from the first group of
signals
Upon noticing the split found in Figure 4.3.1, the data set is split into two groups, dBm > -30 and
dBm < -30. There is no dBm reading of -30 and the gap among the two sets is large enough that
the resulting ranges after the split are valid. In Figure 4.3.1, the variability is expected. It shows a
positive slope as temperature falls.
17
Two outliers that far exceed the normal range are removed (Table 4.3.2). Figure 4.3.2 shows a
positive slope as temperature decreases.
Figure 4.3.3 – Signal and Temperature vs Time, top half of the reading dBm < -30
Figure 4.3.4 – Signal and Temperature vs Time, bottom half of the phone reading dBm > -30.
4.3.3 Analysis - Round 1
Figure 4.3.1 indicates that the receiving phone was picking up a secondary signal that was coming
from the transmitting phone. A possible explanation for this behavior is due to the distance
18
placement of the bowl. In Round 1, the phones were placed 2 inches away from the foot of the
bowl and above them was a steel ruler.
The secondary signal could be a reflection signal that was either caused by the bowl’s curvature
or reflected by the reflective surface on the metal ruler. The standard deviation of 11 supports this
observation.
In comparison, the router, which was placed in another room did not have this behavior. This
finding led to the modification of Experiment #2 and all future experiments.
The phone’s result does not support the thesis, but the clear gap in signal suggests that the data can
be split using -30 dBm as the benchmark (Figure 4.3.3, 4.3.4). Table 4.3.2 shows that after this
split, the STDEV, after considering the outlier, is small. Both lines of regression (Figure 4.3.3,
4.3.4) show a positive slope. When examined this way, this result supports the hypothesis from
the previous experiments.
The regression line in Figure 4.3.2 shows a small increase. While the slope is small, the data lends
credibility to the hypothesis.
4.3.4 Result - Round 2
Figure 4.3.5 – Signal and Temperature vs Time, Second Round (increased distance) - Phone
19
Figure 4.3.6 – Signal and Temperature vs Time, Second Round - Router
Phone Router
STDEV 7.772065146 1.59944961
Average -30.68994413 -53.88022284
Count 358 359
Table 4.3.3 – Data summary - Round 2
A total of 358 and 359 samples were collected for the phone and the router, respectively. The
phone’s placement is further from the foot of the bowl. This modification is made based on round
one’s result. Water temperature started at 105.8 °F and cooled to 94.7 °F.
Figure 4.3.5 shows signs of capturing the reflective signal from the phone, but it is much less in
comparison to the first round where the metal ruler was present. This suggests that the distance
between the phone can also be a cause for the oscillation found in Round 1.
Figure 4.3.6 shows the result of the router, which shows a negative slope of dBm as temperature
decreases. Table 4.3.3 summarizes the data with the STDEV being 7.77 and 1.599 for the Phone
and Router, respectively.
20
4.3.5 Analysis - Round 2
The change in distance between the phones and the bowl shows a noticeable change in comparison
to Round 1. Figure 4.3.5 suggests that the curvature of the bowl could have interfered with the
signal’s transmission route, especially given the proximity of the distance. The STDEV is lesser
than Round 1, which suggests that this round of data is much more accurate than the first. The
regression line is slightly negative, but this can be explained by the reflected signal near the end
of the trial, which skewed the line more toward the bottom.
There are no noticeable changes in the router’s data using this new setup. It contradicts the first
result in the first round, however.
The results of this round do not support the initial hypothesis, but it does offer up new explanations
for possible signal interference. The inconsistency in this experiment suggests that the experiment
can be improved.
There are three factors that could also affect the reading of this setup.
1. The steel ruler that was used to keep track of placement could have had a much stronger
impact. Due to the reflective nature of metal, eliminating this factor can improve the signal
reading.
2. The placement of the phone can be adjusted while taken into consideration the location of
the WiFi adapter.
3. The fluctuation of ambient temperature of the surrounding air and possibly the wooden
table can affect variability of the signal.
4.4 Experiment #4 - Water Bowl Test - Submerged
This experiment seeks to eliminate the interference component and attempts to isolate the key
hypothesis: Signal strength increases when ambient temperature is cooled.
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4.4.1 Setup
Figure 4.4.1 – Setup with the device submerged entirely into the water
The phone was placed within 2 resealable plastic bags to prevent water from damaging the phone.
It was then placed submerged into the bowl. There was ample space between the bottom of the
bowl and the phone (Figure 4.4.1). A sensor was placed inside the bowl like all other previous
setups.
4.4.2 Result
Figure 4.4.2 – Signal and Temperature vs Time, dBm of the access point increases and stabilizes
as temperature decreases.
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STDEV 2.988982112
AVERAGE -75.4007286
Sample Size 549
Table 4.4.1 – Summary of Experiment #4 result
A total sample size of 549 was collected. The starting temperature of the water was 108.1 °F that
ended in 92.9°F.
4.4.3 Analysis
The STDEV of this experiment is 2.988, which suggests some variance within this experiment.
When temperature reaches 96.6°F, there is a noticeable stabilization effect on the reading. The
overall regression line follows a positive slope as temperature falls.
This result matches with the finding found in experiment #2. It also answers the concerns raised
in experiment #3.
This experiment confirms the hypothesis that temperature does affect dBm of WiFi signal. It also
shows that signal strength’s variability improves in cooler temperatures. As temperature decreases,
WiFi signal increases.
Up to this point, the conducted experiments have been through the use of inorganic objects. The
next step is to determine if this finding is replicable using a person’s body’s temperature. In theory,
since the human’s body is made up of mostly water, the finding is expected to yield similar results.
4.5 Experiment #5 - Hand Test
There are many challenges to this experiment. Unlike the water bowl test, where the device is
placed submerged into the water with minimal movement, using part of the human’s body in the
test will inevitably introduce obstructions.
As body temperature is very resilient to changes, it is also difficult to control the temperature of
the body. The initial setup uses many methods to cool and warm up the forehead. The person is
then instructed to walk across the two devices. However, this setup has many steps that are error
prone.
23
For instance, body temperature reaches equilibrium quickly despite repeated attempts to get an
accurate temperature reading. The back of the head had a different temperature reading to the front
and the motion of moving such a large object across the phone creates obstruction.
A preliminary finding shows (Figure 4.5.1) that the devices can detect when a person stands in
front of it. This indicates the need to isolate the impacted dBm signal caused by obstruction from
the impacted dBm signal caused by temperature changes.
Figure 4.5.1 – Signal vs Time. The dot indicates each moment of detection
As a result, the experiment chooses to use the hand for the experiment. Given its size and mobility,
a person can cool and warm up their hand quickly by dipping into warm and cold water. This
minimizes the time needed to cool and warm up a person’s forehead.
4.5.1 Setup
Figure 4.5.2 – Setup for the Hand experiment
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The setup (Figure 4.5.2) requires both phones to have their backs facing one another. This
orientation was found to have the clearest signal due to the direct exposure of the WiFi adapter on
both devices. A person places his or her hand in between the two phones with a wait time of 5
seconds before removing the hand.
If the movement is too quick, the devices will not be able to capture the change in dBm. As each
scan occurs every 4 second, 5 second wait time ensures the best possibility of capturing the
disrupted signal. 5 second is also an ideal time to prevent the hand’s temperature from reaching
equilibrium.
After measuring the baseline, the person dips his or her hand into a bowl of either warm or cold
water. His or her hand is dried before being extended into the space between the devices. A total
of four trials were ran.
To ensure the base WiFi signal is stable, all three temperature ranges were performed in one single
run per trial. A stable baseline reading allows for comparison of readings among the temperature
groups.
4.5.2 Result
Figure 4.5.3 – Signal vs Time under different temperature range, Trial #1
Figure 4.5.4 – Signal vs Time under different temperature range, Trial #2
25
Figure 4.5.5 – Signal vs Time under different temperature range, Trial #3
Figure 4.5.6 – Signal vs Time under different temperature range, Trial #4
In each of the trials (Figure 4.5.3, 4.5.4, 4.5.5, Figure 4.5.6), the color-coded entry indicates the
peak of the detection of the hand. Green means normal temperature, which is 98° F. Red indicates
a temperature reading of 103° F to 104° F. Cyan indicates a reading of 82° F to 84° F.
Due to heat loss or heat gain, it is difficult to pinpoint the exact temperature reading at the time of
detection. The reading of the hand is taken just before and after the detection to ensure the closest
temperature estimation.
A total of 4 instances are made for each temperature range for the first Trial. All 4 instances are
detected across each temperature range. Trial 2 and 3 each have a total of 5 instances per
temperature range, but only ⅘ are captured most of the time.
Each of the color-coded entries are extracted and put into another table (Table 4.5.1) for further
analysis.
26
Trial #1 Trial #2 Trial #3 Trial #4
Average Cool Temp -36.875 -40.25 -36 -35.9
STDEV Cool Temp 0.6291528696 0.9574271078 2 1.197219
Average Normal Temp -35.5 -35.25 -36.375 -36.2
STDEV Normal Temp 2.516611478 1.658312395 0.4787135539 1.619327707
Average Warm Temp -37.25 -40.33333333 -36.83333333 -37.55555556
STDEV Warm Temp 1.5 3.511884584 0.8819171037 1.666666667
Table 4.5.1 – Summary of the average peak dBm and their standard deviation
Figure 4.5.7 – Visualization of statistical analysis
In Trial 1 (Figure 4.5.7), the average peak dBm of cool temperature is found to be worse than
normal temperature but is slightly better than warm temperature. Cool temperature has the lowest
standard deviation while normal has the highest standard deviation.
27
In Trial 2 (Figure 4.5.7), the average peak dBm of cool temperature is worse than normal
temperature but is slightly better than warm temperature. Cool temperature has the lowest standard
deviation while warm temperature has the highest standard deviation.
In Trial 3 (Figure 4.5.7), the average peak dBm of cool temperature is better than normal
temperature, which is better than warm temperature. The standard deviation of cool temperature
is the highest, followed by warm temperature and normal temperature.
In Trial 4 (Figure 4.5.7), the average peak dBm of cool temperature is better than normal
temperature, which is better than warm temperature. The standard of cool temperature is the
lowest, followed by normal temperature and warm temperature.
4.5.3 Analysis
The results indicate that WiFi signals perform better under cool temperature. This is reflected in
all three trials, where cooler temperature results in a reading that is better than when the
temperature is warmer.
The smaller deviation of cooler temperature suggests that the signal is much more stable. This is
observed in Figure 4.5.5. The end of the graph, which has the temperature range of 82° F to 84°
F, performs much more stable than the earlier part of the graph. There are two spikes (-37 and -
39) that result in a larger standard deviation. However, later part of the graph suggests that the
dBm tends to dip to -35 only. A possible explanation for this behavior is due to the sudden change
in temperature, which causes fluctuation in the reading.
Another trial is conducted to verify this hypothesis. Figure 4.5.6 demonstrates the same finding
as Trial #3. It shows that there is a tendency for dBm to perform better under cooler temperature
than warm temperature. This observation agrees with the results found in previous experiments.
In Trial #4, the standard deviation of cool temperature is also the lowest. This agrees with the
secondary hypothesis that temperature can affect the stability of signal strength.
This experiment is successful at isolating the obstruction element from the temperature element.
In comparison to the base measurement (room temperature), cooler temperature yields slightly
more stable results in 3 trials out of 4 trials. 2 out of 4 trials shows that cooler temperature performs
better than normal temperature. In all trials, cooler temperature performs better than warm
temperature.
28
4.6 Classifications
With the given information, this thesis will attempt to classify a reading into three group: Cool,
Normal, and Warm. Using the data gathered in experiment #5, cool group contains readings with
temperature of 82°F to 84°F. Normal group contains readings with temperature of 97°F to 98°F.
Warm temperature group contains reading with temperature of 101°F to 105°F. The classification
proposes can be generalized for other temperature ranges as well.
This is a classification with parameter. The prerequisite is that an average reading of dBm is taken
for each known group of temperature. This process can be done during calibration in a real-world
application before each use.
From the three averages, a MIN and a MAX is chosen. If the reading is done correctly and based
on the data showed thus far in this thesis, warmer temperature range will always be the MIN in the
formula. Due to the sensitivity of WiFi signal to outside disturbance, it is possible to have error
during the calibration process. If the MIN is not of a warm reading, the calibration should be redo.
A new calibration is needed for each new trial. Since the base dBm reading can change, calibration
is needed per trial to ensure that the captured signal is within the same scale. Otherwise, the number
will become meaningless if the base dBm varies across all three temperatures range.
For example, there are four trials in experimentation #5. Each of these trials will have its own MIN
and MAX which is compared against the detection that occurs within those individual trials.
In this thesis, experimentation #5’s data shows that cool reading can sometimes be worse than
normal reading but is always better than warm reading. Therefore, the thesis proposes the
following such that, if the average of cool reading is better than normal reading, then use this
formula: Warm Reading < MIN ≤ Normal Reading < MAX ≤ Cool Reading.
Otherwise, if the average of normal reading is better than cool reading, then use this formula:
Warm Reading < MIN ≤ Cool Reading < MAX ≤ Normal Reading. Table 4.6.1 shows the
confusion matrix after using these two formulas. A total 51 samples are recorded across all 4 trials.
29
Raw Data Actual
Predicted Cool Normal Warm Predicted Total
Cool 11 5 7 23
Normal 1 10 1 12
Warm 6 2 8 16
Actual Total 18 17 16 51
Sensitivity Summary Actual
Predicted Cool Normal Warm
Cool 61.11% 29.41% 43.75%
Normal 5.56% 58.82% 6.25%
Warm 33.33% 11.76% 50.00%
Overall Accuracy 56.86%
Table 4.6.1 - Confusion Matrix
If a reading is classified as cool, there is a 33.33% chance of it being a warm reading and 5.56%
of it being a normal reading. There is a 61.11% chance of accurately identifying a cool reading.
If a reading is classified as normal, there is a 29.41% chance of it being a cool reading and 11.76%
of it being a warm reading. There is a 58.82% chance of accurately identifying a normal reading.
If a reading is classified as warm, there is a 43.75% chance of it being a cool reading and 6.25%
chance of it being a normal reading. There is a 50% chance of accurately identifying a warm
reading.
The chance of getting a false positive during a normal temperature reading is high. This is due to
the limitation of technology such that RSSI is measured in whole number. Due to the sensitivity
of WiFi and the limitation of whole number, the device is unable to completely capture the finer
changes of the signal. The chance of getting a false negative of the opposite temperature group is
high in both warm and cool. However, this misclassification can be easily recognized due to the
gap between warm and cool temperature.
Overall, there a 56.86% chance of a reading being correctly identify. This is an improvement from
the 33% chance of randomly guessing from among three temperature group.
30
Conclusion
The result of the experiments confirms the validity of the hypothesis, that temperature does
influence WiFi signals. The results further validate that temperature can influence WiFi signal
stability. Specifically, cooler temperature results in much more stable dBm as well as better
performance than in warmer temperatures. These findings help answer the problem this thesis is
trying to solve, that, it is possible to utilize WiFi signal as a mean to classify temperature.
The current classification requires that a known reading of cool, normal, and warm temperature is
known. In an application, this data can be obtained during the calibration phase. While the current
study can classify temperature with a 56.86% accuracy, it is possible that this number will increase
if there are more training data or if multiple readings are taken for each temperature recording. The
current model is limited by the technology of WiFi adapter, which only records RSSI as a whole
number. Given the sensitivity of dBm to temperature, having a more accurate way of measuring
RSSI will improve the accuracy of the classification.
31
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