Temperature Classification using Smart Phone WiFi Signal ...

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 4.5.7 – Visualization of statistical analysis 26

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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.

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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.

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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].

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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].

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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

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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.

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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.

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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

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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.

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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

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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

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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.

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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.

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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.

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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

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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

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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.

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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

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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

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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.

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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.

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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


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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.

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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.

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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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