# A Tagging Solution to Discover IoT Devices in Apartments

Berkay Kaplan<EMAIL_ADDRESS>0000-0002-4365-7606 University of
Illinois Urbana-ChampaignP.O. Box 1212ChampaignIllinoisUSA61820 ,  Jingyu Qian
<EMAIL_ADDRESS>0000-0002-3953-5382 University of Illinois Urbana-
ChampaignP.O. Box 1212ChampaignIllinoisUSA61820 ,  Israel J Lopez-Toledo
<EMAIL_ADDRESS>University of Illinois Urbana-ChampaignP.O. Box
1212ChampaignIllinoisUSA61820 and  Carl Gunter<EMAIL_ADDRESS>University of Illinois Urbana-ChampaignP.O. Box 1212ChampaignIllinoisUSA61820

(2023)

###### Abstract.

The number of Internet of Things (IoT) devices in smart homes is increasing.
This broad adoption facilitates users’ lives, but it also brings problems. One
such issue is that some IoT devices may invade users’ privacy through obscure
data collection practices or hidden devices. Specific IoT devices can exist
out of sight and still collect user data to send to third parties via the
Internet. Owners can easily forget the location or even the existence of these
devices, especially if the owner is a landlord managing several properties.
The landlord-owner scenario creates multi-user problems as designers typically
build IoT devices for single users. We developed tag models that use wireless
protocols, buzzers, and LED lighting to guide users toward the hidden device
in shared spaces and accommodate multi-user scenarios. They are attached to
IoT devices inside a residential unit during their installation to be later
discovered by a tenant. These tags are similar to Tile models or Airtag but
have different features based on our privacy use case. For instance, our tags
do not require pairing; multiple users can interact with them through our
Android application. Our tags can also embed the IoT device’s information
while protecting against unwanted access to that information through a
proximity requirement. Researchers have developed several other tools, such as
thermal cameras or virtual reality (VR), for discovering devices, but we
focused on wireless technologies. We measured specific performance metrics of
our tags to analyze their feasibility for this problem. We also conducted a
user study to measure the participants’ comfort levels while finding objects
with our tags attached. Our results indicate that wireless tags can be viable
for device tracking in residential properties.

Wireless, IoT, Privacy, Smart Homes

††copyright: acmcopyright††journalyear: 2022††doi:
10.1145/xxxxxxx.xxxxxxx††conference: ACSAC ’23: Annual Computer Security
Applications Conference; December 4–8, 2023; Austin, TX, USA††booktitle: ACSAC
’23: Annual Computer Security Applications Conference, December 4–8, 2023,
Austin, TX, USA††price: 15.00††isbn: 978-1-4503-XXXX-X/21/06††ccs: Computer
systems organization Embedded software††ccs: Networks Mobile ad hoc
networks††ccs: Security and privacy Privacy-preserving protocols††ccs:
Hardware Emerging technologies

##  1\. Introduction

Smart homes use IoT devices to improve the occupants ’ lives. As of 2021,
approximately 43% of households in the U.S. own a smart device, increasing
from 33% in 2019 (Vailshery, 2021). Commonly available IoT devices, such as
learning thermostats, video doorbells, smart baby monitors, and voice-
controlled devices, are also relatively affordable for any consumer (Zheng et
al., 2018). As IoT devices’ popularity increases and they upload more data to
the cloud, privacy questions, such as data collection practices, arise (Zheng
et al., 2018; Castelli et al., 2017). For instance, although voice assistants
only activate when they hear specific keywords, their implementation requires
them to listen to their environments constantly (Chen et al., 2020). They can
start recording conversations maliciously or by misconfiguration (Chen et al.,
2020). In addition, IoT devices contain several design flaws and
vulnerabilities that may have devastating consequences on users’ privacy (Shen
and Vervier, 2019; Colnago et al., 2020). These design flaws may cause
sensitive information, such as conversation recordings, to leak onto the
Internet.

These privacy questions created a new branch that researchers attempt to
understand: intelligent home IoT devices in multi-user scenarios (Geeng and
Roesner, 2019; Marky et al., 2020). IoT devices in shared environments become
shared devices that affect multiple people (Geeng and Roesner, 2019; Zeng and
Roesner, 2019). Nevertheless, it is crucial to make home data more accountable
in shared settings (Castelli et al., 2017). Furthermore, some popular IoT
platforms might not comprehensively address multi-user scenarios (Zeng and
Roesner, 2019). For instance, a question thread created in the SmartThings
community forum in 2017 indicated that end-users could create multiple
accounts. However, they cannot give the accounts different access levels to
information (sma, 2017). The lack of shared-space settings in IoT devices can
further exacerbate privacy concerns.

Such environments affected by this inadequacy are rental apartments, such as
Airbnb, and hotels, which all concerned researchers (Dey et al., 2020).
Especially, hidden devices in these properties can make users uncomfortable
(Emami-Naeini et al., 2019). For instance, a computer science professor in an
Airbnb rental found a camera that views a field close to the bathroom,
according to the Washington Post (Song et al., 2020; Shaban, 2019). Tenants
unknowingly living with hidden IoT devices may be victims of privacy
violations (Yao et al., 2019). The landlord also may not remember the location
and information of each installed device as they may have several apartments.
Tenants need an automatic solution to alert them of each IoT device’s
existence.

We offer to tag each device inside the apartment. The primary purpose of these
tags is to allow tenants to discover, locate, and inventory each device inside
a room via wireless protocols, buzzers, and LED lights. The tags will alert
each user nearby that a device exists. Thus, users will learn the location and
the device information using wireless capabilities. With this transparent
mechanism, user privacy is respected, giving users a choice to leave the
apartment or shared space. They can also opt to contact the owners for the
device’s removal.

Our solution contains two tag models that consist of small circuit boards that
utilize various wireless tools to interact with an Android application named
DIAL. It is an interface for users to discover every tag nearby and identify
and inventory the device. This identifying information can be a web link
pointing to the device details, such as an Amazon sales page. The user can
Google further information regarding its data collection practices. Another
alternative is to point to the product’s manufacturer privacy page to prevent
users from spending effort. We gave the freedom for tag administrators to
decide what to store in them.

We used relatively cheap and publicly available circuit boards from vendors,
including Qorvo and Adafruit. These boards use Bluetooth Low Energy (BLE),
Ultra-wideband (UWB), and LED lights. We also used coin-sized tags for Near-
field communication (NFC) as the medium to transmit device information and a
buzzer to enable tags to make noise to reveal their location. The LED lights
present in both models also help users locate the device if the tag is
visible. We built different tag models to provide users with the cheapest and
longest-ranged tags. Our tag models do not require initial pairing and can
attach to devices with all communication protocols. To our knowledge, the
wireless device discovery and identification solution has not been proposed.

Besides discussing our model implementation, we also conducted a user study to
understand potential users’ perceptions of our tag models. We conducted trials
where we asked participants to find our tags attached to things using DIAL in
an apartment. Afterward, the participants took the System Usability Scale
(SUS) survey, a quick method to evaluate the usability of human-machine
systems (Peres et al., 2013). Finally, we discussed the feasibility of our
solution concerning user perception and technical facts. Our primary and novel
contributions to the literature are:

  * •

a tagging implementation for discovering and identifying hidden IoT devices,

  * •

a user study on the tags that focuses on participants’ comfort levels and
price acceptability,

  * •

an analysis of our wireless tagging solution’s feasibility.

We organize our paper as follows. In the background section, we will first
explain some of the wireless protocols we are using to give readers an
understanding of the implementation of the tags. The model overview section
will overview the total model and the solution’s workflow. The implementation
section will describe the electronic components and algorithms we use in our
system. In the evaluation section, we will present our user study and evaluate
the tags’ performance. The discussion section will contain the tagging
feasibility analysis and possible future tracks to extend this project. Then,
we will present related work from the literature focusing on device discovery.
Finally, we summarize the key points of this paper and describe future
predictions using wireless for device discovery.

##  2\. Background

We used several wireless protocols, some of which may not be familiar to
readers. Thus, it would be helpful to give a background on these protocols.

We used NFC to identify the device our tag is attached to, as it will hold a
link that points to a web page. NFC is a wireless protocol that allows users
to transfer information between a tag to a reader using NFC Data Exchange
Format (NDEF) (Want, 2011). It operates at 13.56 MHz and has a data rate of
424 kbit per second (Want, 2011). The range is only 10 centimeters and
supports data transfer between two readers. NFC is a great candidate for our
project to limit physical access to sensitive information.

We also needed a protocol with a range suitable for a residential unit’s room
while preserving power. One such tool we found is the BLE, which consumed less
than one milliampere during our trials. BLE is a complementary technology to
Bluetooth Classic and borrows several techniques from its parent tool while
having completely different goals and market segments. BLE optimized its power
consumption for ultra-low power rather than focusing on increasing its data
rate (Heydon and Hunn, 2012). Although designers intended BLE to work with
coin-cell batteries, we want our BLE tag to have a battery life of more than a
year (Heydon and Hunn, 2012). Due to its ultra-low power consumption and
configurable ranges through transmitter power, it is a good option for our
design. A buzzer would allow the user to find its location in out-of-sight
cases with a sound.

A more advanced method than a buzzer making noise for location tracking is
UWB. It aims to provide a low-complexity, low-cost, low-power consumption, and
high data-rate wireless alternative in personal ranges (Aiello and Rogerson,
2003). The Federal Communications Commission (FCC) allocated 7,500 MHz of
spectrum for the unlicensed use of UWB in the 3.1 to 10.6 GHz frequency band.
UWB’s range of 12 feet was suitable for residential environments, although the
power consumption of our boards was high for our batteries (Aiello and
Rogerson, 2003). Nevertheless, we still decided on this tool as a discovery
and location tracking method. Some residential properties may have noise in
certain rooms. Offering another solution for a noisy environment would enhance
the tags’ use cases.

##  3\. Model Overview

Figure 1. Two-tag model for location and inventory modes.

This section discusses our model designs to discover and locate hidden IoT
devices and extract their information (Fig. 1). We divide the two steps into
location mode and inventory mode because of the different purposes of each
step and privacy requirements. In the location mode, the user tries to
discover and locate the hidden device. The information the user gains in
location mode will only indicate the existence and location of an IoT device,
as it will not contain valuable data. In contrast, the user intends to learn
detailed information about the discovered device in the inventory mode, such
as its vendor and software versions. The smartphone app works as the user
interface to guide the user to the IoT device via sounds or UWB. Once the user
is within reach of the tag, he can scan the NFC tag to display detailed
information. Finally, he can inventory all the IoT devices he found in the
apartment.

###  3.1. Threat Model

Our design assumes that the landlord or the device manufacturer is
collaborative. Collaborative landlords are willing to attach the tags to the
IoT devices. This assumption is reasonable because tags are cheap, and
landlords want to keep tenants comfortable to avoid bad reviews and potential
privacy infringements. Collaborative device manufacturers may also want to
improve public relations by implementing privacy-friendly product solutions.

The tags do not require configuration except for the NFC coin holding
information about the attached thing. We assume the landlord or manufacturer
loads this URL into the NFC coin. With this setup, the renter can identify the
things with DIAL.

If the renter is tech-savvy, he can still configure the NFC coin with desired
extra information through his mobile phone using a third-party NFC reader-
writer application. However, we do not consider malicious renters. In everyday
scenarios, renters can plant pinhole cameras, change the information inside
the NFC coin, or perform malicious operations. For the scope of our design, we
assume renters are not evil.

We assume that our tag models are only intended for those smart home devices
inside the apartment. Our targeted users are the residents of the apartment.
We assume they are more concerned about what those devices are and how they
collect personal data in their environment. Temporary guests interacting with
the tags is the tenant’s responsibility and is an out-of-scope scenario for
our project.

###  3.2. Device Discovery and Locating

Tags are attached to IoT devices and broadcast signals to the smartphone via
BLE or UWB. Then, DIAL guides the user to the IoT device. When DIAL is in the
location mode screen, it will display all the tags in approximately 15 - 20
meters. The user can buzz each tag individually or find the distance to the
tag via UWB. These BLE beacons and UWB signals contain no information
regarding the attached thing to prevent privacy leakage. Thus, an attacker can
only gain the number of tags in an environment if he has DIAL or knows our
beacon formatting. In addition, since the tags do not contain an
authentication method for activating the buzzer and UWB, the attacker can also
buzz his neighbor’s tags or find the tag’s location via UWB. Future work can
include developing a more advanced formatting methodology to avoid this issue.
Another theoretical solution would be that the NFC tag can hold a password
that activates the buzzer. Since the NFC has a proximity requirement, only the
resident can access this password. Without the password, the buzzer feature
would be dormant.

###  3.3. Device Information Extraction

Once the user finds the hidden IoT device, he needs to learn about the device
to be more familiar with the smart home environment. He must also educate
himself regarding the device vendor or any third parties using his private
data. Due to the privacy level of this information, the user should be the
sole person to extract this information. Therefore, we rely on NFC to transmit
device information. We integrated an NFC tag into our tag models so the
smartphone equipped with NFC capability can easily extract device information.
This design also guarantees the proximity requirement if anyone wants to read
from the tag.

Users can place several types of data in the NFC tag. Basic IoT device
information, such as the device name, functionalities, and vendors, should be
put in the NFC tag first. Moreover, data collection activities are critical to
helping users make better security and privacy decisions. Finally, some extra
information, such as firmware versions and software vulnerability histories,
can inform users whether updates are required to guard against the latest
attacks. All such critical device information can exist on a single NFC tag.

###  3.4. Tag Models

Listening to and broadcasting BLE beacons is an inexpensive protocol in terms
of battery life. Our BLE boards range approximately 15 meters, which we found
optimal in an apartment setting. Therefore, we decided to use it to detect the
device’s existence and a buzzer to locate it. On the other hand, our UWB
boards provide more accurate device locations and have a range of 5 meters.
Since UWB and BLE have similar functionalities in the location mode, we did
not combine them in one tag model. We decided to offer them separately to give
users a choice, as UWB can provide exact locations even in noisy environments.
However, BLE also has the advantage of low power consumption and a higher
range. We wanted to give our users a choice based on their needs.

We design two tag models for users. Our first model uses BLE with a buzzer
attached. The model uses BLE to achieve a coarse tag location through received
signal strength indication (RSSI), while the buzzer can provide a more
accurate location service. We named this model BLE-AC. Our second model uses
UWB. This model allows us to achieve a precise device location within the
apartment due to UWB’s strong indoor positioning capability. We named this
model UWB-RAW. We use a coin-size NFC tag for the inventory mode in both tag
models to ensure that only the nearby reader can extract the device
information. Both tag models have LEDs that the user can activate to locate
the tags once they become visible to the user.

###  3.5. Tag Reader

Table 1. Comparison of our tags to other popular device discovery and inventory solutions. Technology | No pairing | Network protocol unrestricted | Support locating device | Inventory device information
---|---|---|---|---
Privacy Labels | ✓ | ✓ |  | ✓
Airtags and Tiles |  | ✓ | ✓ | ✓
SmartThings |  |  |  | ✓
Lumos | ✓ |  | ✓ |
Our Tags | ✓ | ✓ | ✓ | ✓

One of the primary considerations when selecting a tag model is its reader’s
price. Our current design utilizes BLE, NFC, and UWB tags. For BLE and NFC, we
can rely on smartphones as tag readers. Almost all smartphones today support
Bluetooth and BLE. In addition, 73% of smartphones in 2018 support NFC (UWB,
2021). On the other hand, UWB used to be an expensive technology, but now it
is cheaper. Few smartphones now have UWB, including iPhones after the iPhone
11 and Samsung Galaxy S21 series. We have an adapter with UWB capabilities for
older smartphones that can connect to the phone through the USB serial port,
working as the UWB tag reader. This UWB reader can communicate with the tag.
We then built DIAL to read the distance from the tag reader through the
Android USB serial port, which provides the distance in meters to guide the
user to the IoT device. Although our current design requires an external
adapter for older smartphones, we expect smartphone companies will incorporate
UWB in their following models as manufacturers phase out older phones.

###  3.6. Comparison of Our Tags to Existing Solutions

In this section, we show the novelty of our tag models by comparing them with
existing solutions for IoT device discovery and inventory, including privacy
labels, commercial tags (e.g., Airtag (app, [n. d.]) and Tiles (til, [n.
d.])), the smart home manager (e.g., SmartThings (sma, 2022)), and research
work (e.g., Lumos (Sharma et al., 2022)). Our comparison is from two angles:
utility and privacy.

####  3.6.1. Utility Analysis

We compare our tag models with the techniques mentioned above based on the
following features (Table 1):

  * •

Does the technology require initial pairing?

  * •

Is the technology designed for devices with some specific network protocol
(e.g., WiFi devices)?

  * •

Can the technology locate the device?

  * •

Can the technology reveal device information to the user?

The privacy labeling scheme conveys high-level security and privacy facts
regarding the IoT device to users to raise their privacy awareness (Shen and
Vervier, 2019). Our tags have similar purposes of privacy labels, but Shen et
al. (Shen and Vervier, 2019) did not provide a medium to transmit this label
to the users. However, we can integrate the contents of the privacy labels
from this work with our tags. Our tags can hold a certain amount of
information, such as web links, leading to a privacy label page. Privacy
labels do not require initial pairing and are not restricted by the device’s
communication protocol. It can be in paperback or transmitted via any wireless
tool. Although they do not support locating the device, they still enable the
user to learn device information.

Airtag from Apple and Tile models are commercial solutions for problems
including lost item tracking, but they can still be attached to home IoT
devices after being paired with the tenant’s phone. We design our tags to fit
multi-user scenarios more and provide a solution for device discovery in a
cooperative environment where the manufacturer, vendor, or landlord helps
implement our tags. Our tag models cover the multi-user scenario by removing
pairing and allowing any tenant to download DIAL to discover nearby devices.
Nevertheless, Airtags and Tiles still provide accurate locating and can hold
information regarding the device it is attached to. It is also independent of
the attached device’s communication protocols, as each tag can be attached to
any device, even if it does not connect to the Internet.

SmartThings is a home automation platform developed by Samsung for device
discovery that would enable users to control nearby IoT devices. Although it
uses a broad range of wireless protocols, including ZigBee and Z-Wave, it does
not cover a comprehensive set of communication protocols. Users must add
compatible devices to SmartThings or pair them with the hub. Although some
SmartThings compatible devices have location-tracking features, SmartThings,
by default, does not help locate the device but can inventory and manage
device information. However, not all devices are compatible with SmartThings.
Our tags can be attached to any device, even if they do not connect to the
Internet, are in sleep mode, or have not been turned on.

Lumos (Sharma et al., 2022) utilizes wireless traffic monitoring to identify
and locate hidden WiFi-connected IoT devices in public locations, such as
hotels and Airbnb. It is designed only for WiFi-connected devices and requires
them to be on so that it can sniff the ongoing traffic. Therefore, Lumos
(Sharma et al., 2022) does not need initial pairing to locate the devices.
However, Lumos (Sharma et al., 2022) does not transmit or inventory device
information.

Compared to existing solutions, our tag models fulfill all the features in
Table 1. Our tag models resemble popular tracking tags, such as Airtags and
Tile. However, these solutions mainly track lost items and do not provide
enough information about the attached object, except for metadata, such as a
name or picture. Therefore, they could only partially solve the device
discovery problem. In addition, users must pair the tracking tag and their
mobile phones each time. The requirement of initial configuration for
permission to interact with the tag is not well-suited for multi-user
scenarios. Every shared space or apartment will have temporary tenants, and it
would be inconvenient for each tenant to find every hidden device, possibly
hidden inside the walls, and pair it with his phone for his stay or lease.

####  3.6.2. Privacy Analysis

Here, we illustrate the advantage of our tag models in terms of privacy
compared to Airtag and Tiles.

Airtag and Tiles can be exploited for malicious purposes, such as spying on
someone. Although they provide a certain level of protection, a journalist’s
experiment showed that the victim could not find the Airtag, although he
received a notification of its presence (Hill and Heisler, 2022).
Nevertheless, Airtags can still play sounds if a tracking victim cannot find
it (app, [n. d.]).

Since Airtags communicate with billions of phones worldwide to track their
locations, they are much better informed than our tags or the Tiles. However,
if an iPhone detects an Airtag continuously moving with it, the iPhone alerts
its owner with a notification (Hill and Heisler, 2022). The notification
includes the entry point where the tracking started. Tiles do not have this
feature, while Airtags are too well-informed. The intensive iPhone network for
location tracking from Airtag and the lack of spying protection from Tiles do
not make them suitable for privacy-sensitive use cases.

Our tag models are designed to protect IoT user privacy in a multi-user
setting. We identify different utility and privacy requirements in various
stages of IoT device discovery. We need a wireless protocol with a reasonable
range covering the typical apartment space to locate the device. Therefore, we
attach the BLE or UWB tag to the device. Unlike Airtag, we do not use
crowdsourcing techniques to help find the device, which we believe to be
overkill for a multi-user apartment setting and can open more attack vectors.
To prevent privacy leakage due to BLE or UWB’s range, we carefully keep only
the required information in the beacon to guide the user to the device. Any
detailed information related to the device is never sent out. On the other
hand, we assume that proximity can be used as an authorization method to
access the IoT device. Therefore, we use an NFC tag with a minimal range to
transmit detailed device information to the user. This design allows only the
user near the device to gather such private information.

##  4\. Implementation

This section will discuss the specific boards and techniques used for the tag
models. We will also present an overview of DIAL to interact with the tags.

###  4.1. Tag Implementations

Our two tag models include electronic boards, a buzzer, and a coin-sized NFC
tag. The first tag model, BLE-AC, is built with an Adafruit ItsyBitsy nRF52840
Express (Industries, [n. d.]). This board also serves as the microcontroller
to broadcast BLE beacons periodically and activate the buzzer when the user
signals. The LED light will also be active while the buzzer makes a noise. The
board will broadcast a beacon A with a specific formatting every half a second
and simultaneously listen to beacons in the area for the buzzer and LED
activation. The beacon the board accepts will be the same as beacon A, so only
one tag’s buzzer will be activated.

Our Android application will first receive the board’s beacon. It will
recognize this beacon as its data has a specific formatting type. Afterward,
the app will broadcast it back to activate the board’s buzzer when the user
prompts DIAL. The particular formatting is a simple method, as the sum of
every hex value in the beacon will yield a specific value. A more advanced way
can avoid security issues, such as buzzing strangers’ tags. The price of this
board is only $19.95 (Industries, [n. d.]).

We found the Adafruit ItsyBitsy nRF52840 Express board easier to program. The
board uses Arduino IDE to upload code, and we found rich documentation online.
We also attached the buzzer with two pins to the Adafruit board. The first pin
is connected to one analog output pin, and the second to the ground pin. This
analog pin outputs pulse-width modulation signals with variable frequency.
This frequency-varied signal allows the buzzer to make sounds at different
frequencies. The bottom of the board houses the NFC tag via an attachment
tool, such as duct tape.

The second tag model, UWB-RAW, is built with the $19.50 DWM1001-DEV board from
Qorvo (sym, [n. d.]). This board has the UWB feature and BLE for configuration
purposes (mou, 2020). We found this board to be more challenging to program
with SEGGER. Nevertheless, we completed our action items for this board as we
found that Decawave offered a handful of example programs on GitHub (Decawave,
[n. d.]). The only issue is that the DWM1001 design is an anchor point where
designers assume it can access unlimited power (Nudel and Mciholas, 2019).
Thus, its developers did not focus on power consumption optimization (Nudel
and Mciholas, 2019). Therefore, the battery life will be only a few days, even
with three AA batteries.

Figure 2. The size comparison of an Adafruit ItsyBitsy nRF52840 Express and
the buzzer to an American quarter. Figure 3. The size comparison of a
DWM1001-DEV to an American quarter.

Once a phone with UWB enabled comes closer, it will start receiving the
distance in meters through DIAL. This distance is between the tag and the
phone with our app. The user can determine a direction to move towards that
continuously decreases the length. The LED light will always be on, helping
the user locate the tag once he gains visual contact. The DWM1001 development
board also has the NFC tag attached at the bottom, similar to the first tag
model. Both these tag models are in Fig. 2 and 3.

(a) DIAL’s home screen

(b) DIAL’s location mode screen

(c) DIAL’s inventory mode screen

Figure 4. DIAL’s screens

Besides the boards that serve as the microcontroller of each tag model, there
are two other cheaper electronic components inside the tags. One such
component is the $0.95 buzzer that activates when the Adafruit boards receive
a specifically formatted beacon (mou, [n. d.]). The other component is the
coin-sized NFC card sold as 50 pieces from Walmart (wal, [n. d.]). Each card
costs around $0.76 and has a diameter of 25 millimeters (wal, [n. d.]).

###  4.2. DIAL Android Application

The DIAL we developed greets users with its homepage that has two buttons. A
home page snapshot is in Fig. 4(a).

The first button, labeled, “Location Mode,” redirects the user to another page
containing all detected devices through their transmitted BLE beacons. Each
discovered device will occupy one row of this list. Each row will have a
button on its right side labeled “Activate Buzzer” or “Activate Radar.” The
buzzer activation button will command the buzzer at the tag to play three
different frequencies, three seconds long each. The radar activation button
will take the user to an empty page with only the distance in meters printed
on the screen. This distance will indicate how far the phone is from the tag.
The location mode screen displaying tags is in Fig. 4(b).

The second button, labeled, “Inventory Mode,” takes the user to the NFC page.
This page activates the NFC reader. Once an NFC tag is in proximity,
specifically within the centimeters range, the reader will print the
information from the tag to the screen. This information may be a URL pointing
to a web page for the attached device’s data. An example of DIAL reading a
shortened URL from the coin-sized NFC tag is in Fig. 4(c).

##  5\. Evaluation

We list all research questions as follows:

  * •

RQ1: What is the battery life of each tag?

  * •

RQ2: What is the cost of each tag?

  * •

RQ3: How quickly do users find the tags via DIAL in an apartment setting?

  * •

RQ4: What is the user opinion on the usability of DIAL?

This section will answer our research questions. We chose these metrics as
they directly influence user convenience and the tags’ adoption rate. We only
evaluated the tags’ discovery mode, as the inventory modes’ underlying
technology (i.e., NFC) did not have reliability issues in our experiments.
Also, the information in the NFC is flexible depending on different design
choices and privacy concerns. NFC also does not use a battery, and each NFC
tag’s cost is negligible.

We will then present our user study with 23 participants and deliver our SUS
results. We did not stick the tags to sample IoT devices as it would not
influence the results. Even if IoT devices use BLE and NFC, those protocols
can reliably operate in environments with multiple transmitters. In addition,
although our quarter-sized tags have three AA batteries attached to them, we
expect them to fit with most IoT devices.

###  5.1. RQ1: Battery Life of Each Tag

Table 2. Performance results from each tag model. Model Name | Price | Battery Life Minimum (6000 mAh) | Battery Life Maximum (9000 mAh)
---|---|---|---
BLE-AC | $21.66 | 250 days | 375 days
UWB-RAW | $20.26 | 3.3 days | 5 days

We chose specific metrics to measure the quality of a tag. The first and most
important metric for us is battery life. To provide users with the best
experience, we wanted them to spend the least time with the physical tag. One
possible scenario in which a user can interact with the physical tag is for
maintenance reasons. The most common maintenance type we expect from users is
battery replacement.

The first design decision we made to reduce the maintenance time was to
increase the battery capacity without drastically increasing its size. Most
tags in the industry use coin batteries to keep the tags’ size minimal. We
assumed that increasing the battery life is more crucial than the tag
aesthetics when attaching our tags to IoT devices. Thus, we chose three
typical alkaline batteries in the standard AA size for our primary power
source. They have a battery capacity of 2000 mAh to 3000 mAh with a cell
voltage of 1.2 V to 1.5V (Jan, 2010). To calculate battery life, we divided
the battery capacity by the current consumption of the tag. The result gave us
the battery life in hours (bat, [n. d.]). We divided the battery life metric
into two sections: the minimum battery life, which assumes each battery has a
capacity of 2000 mAh, and the maximum battery life, with a total of 3000 mAh
(Jan, 2010). In our testing assumptions, we did not consider the board
lifetime. We assume the boards function forever.

We also measured the current draw of the board via a standard USB power meter
by plugging it into our computer. We connected the USB side of the micro-USB
to the power meter and the other side to the board. Unfortunately, our power
meter could only detect at least one milliampere. This limitation was
problematic as we observed that the Adafruit ItsyBitsy nRF52840 Express board
drew less than one milliampere of current when it both emitted and listened to
BLE beacons. There are more expensive alternatives near $5570 in the market
that can measure currents at Femto levels (tes, [n. d.]). However, our power
meter is much cheaper as it only costs approximately $17 (eba, [n. d.]).

We assume the Adafruit board has a current draw of 1 milliampere to circumvent
this limitation and give an actual battery life. This assumption should
provide us with an upper bound. Nevertheless, we found online posts discussing
that the Adafruit board’s current draw is less than one nano amperes when they
transmit beacons with an interval of 100 milliseconds (hathach, 2019).
Measuring the current draw from the DWM1001-DEV board was more straightforward
as the current draw was above one milliampere. The current draw for this board
was 75mA when its UWB function was continuously running. Since the board in
the UWB-RAW model was not power-optimized, our battery life for this solution
is minimal (Nudel and Mciholas, 2019).

###  5.2. RQ2: Tag Prices

Another metric we used is the price of the solution. Price significantly
influences consumers’ purchasing behavior and sales (Han et al., 2001). Thus,
we aimed to provide the cheapest solution. We mostly found this solution with
devices aimed at hobbyists. Our research showed that vendors like Adafruit and
Sparkfun give the most inexpensive boards. These vendors also use the Arduino
IDE as their programmers. This practice is advantageous as Arduino IDE has an
easy interface (Louis, 2016; Pan and Zhu, 2018). We also found the Arduino IDE
to have the most documentation online compared to other board IDEs, such as
SEGGER and Mbed.

However, we have not found any UWB boards designed for hobbyists from these
vendors. The cheapest option was the Qorvo DWM1001-DEV board that used SEGGER
as its programming interface. Although SEGGER caused our process to slow down
due to the scarcity of documentation, the DWM1001-DEV board still provided
accurate results for us. After adding the buzzer and NFC card costs, each tag
model price is in Table 2, combined with battery results from RQ1.

###  5.3. RQ3: Functionality of the Tags

Figure 5. The layout of the apartment and locations of the tags. The yellow
dots indicate BLE-AC models, while the blue dot inside the refrigerator is the
UWB-RAW tag. The yellow dot closer to the right bottom corner is BLE-AC 1,
while the other one on the top-right corner is BLE-AC 2.

We hid two BLE-AC tags and one UWB-RAW tag inside an apartment. The
apartment’s layout and the tags’ locations are in Fig. 5. While we put the
UWB-RAW model inside the refrigerator to make it non-line-of-sight, we placed
one BLE-AC tag, named BLE-AC 1, on top of the desk, while the other one, named
BLE-AC 2, is on the floor next to the bed. Both tags were visible to users
during the trials. In these trials, we named the BLE-AC tags 1 and 2 to
distinguish each one.

We conducted these trials with 23 users whom 16 were undergraduate students,
and 7 were graduates in various majors. six undergraduate students were in a
STEM field, while four graduate students were in it too. We chose the users by
asking random pedestrians near the University of Illinois Urbana Champaign
campus to participate in our experiment and take our SUS survey in exchange
for a doughnut. We explained that we hid three devices in an apartment they
had not seen before and asked them to find them using ApplicationName’s
Location Mode. We gave them our Android phone with DIAL and timed them while
they completed this task.

Each participant started at the unit’s entrance, at the bottom middle of Fig.
5. We first discussed the order in which they should find the tags since DIAL
already had all tags on its screen as it was in their range. We asked them to
locate the UWB-RAW tag, then the BLE-AC 1 and 2, respectively. Once the user
found a tag, we reset the timer to record the next tag hunt. The average times
of each trial and their standard deviations are in Table 3. It is also worth
noting that we received IRB approval for this user study.

After the experiments, we examined our results in Table 3. Each trial has an
average completion time of less than one minute, meaning that users spent less
than a minute finding each tag. BLE-AC 1 and 2 had less than half the
completion time of UWB-RAW. However, UWB-RAW did not provide a direction
toward itself. Thus, users had to experiment with moving around to find the
direction where the distance decreased. This process prolonged the average
trial completion time and negatively affected user convenience. However, since
it is less than a minute, UWB-RAW is still a viable solution. Future
iterations for this project can use the angle of arrival metric to give users
a direction toward the tag.

Table 3. The tag hunt trial results: average time and standard deviation in seconds. | UWB-RAW (s) | BLE-AC 1 (s) | BLE-AC 2 (s)
---|---|---|---
Avg. | 52.56 | 22.13 | 25.78
Std. | 13.42 | 9.40 | 19.01

###  5.4. RQ4: Usability of the Tags

Table 4. SUS survey score results. Question | Average Score
---|---
I think that I want to use this system frequently. | 4.43
I found the system unnecessarily complex. | 1.30
I thought the system was easy to use. | 4.78
I think that I would need the support of a technical person to be able to use this system. | 1.61
I found that the various functions in this system were well integrated. | 4.74
I thought there was too much inconsistency in this system. | 1.35
I would imagine that most people would learn to use this system quickly. | 4.52
I found the system very cumbersome to use. | 1.48
I felt very confident using the system. | 4.74
I needed to learn a lot of things before I could get going with this system. | 1.56

After our tag hunt trials, we asked the 23 users to rate their experience with
DIAL and the tags as a whole system. We used the SUS template to design our
survey. The SUS survey has ten questions about user experience; each
question’s answer is rated from one to five.

Our SUS survey results in Table 4 were satisfactory. SUS questions’ order
determined the answer’s scoring in SUS (Brooke et al., 1996). Each question’s
answer is on a linear scale between one to five, with one strongly disagreed
and five strongly agreed. Even-ordered questions’ highest score was one, while
their lowest score was five. Even-ordered questions are the second, fourth,
sixth, eighth, and tenth questions, while the odd-ordered questions are the
other ones. Even-ordered questions inquire about a negative aspect of the
system. This inquiry method means a five would indicate a negative experience
while a one would be positive. Odd-ordered questions had the highest score of
five, while their lowest score was one. Odd-ordered questions would inquire
about a positive aspect of the system. Thus, a five would indicate a positive
experience.

We took the average of all the participants’ answers to produce a final result
for each question. Our overall SUS score is 89.77, while the highest SUS score
is 100. SUS scores above 68 indicate above-average performance, while anything
below 68 would be below average.

Our lowest-scored question is whether the user can interact with this system
without a technical person. Although there are lower-scored questions than
1.61 for this question, the highest score for even-ordered questions is one.
So, the closer it is to five, the worse experience a user has. So, 1.61 would
be equivalent to 4.39 for odd-numbered questions’ reference. We suspect we
have not included tutorials or self-explanatory tips in the mobile
application. Future iterations can involve tip boxes and directions to solve
this gap. Our second lowest-rated question is the first, with a 4.43, asking
if the user would use this system frequently. We do not envision users
interacting with the tags often, as they will perform device discovery only
once when they move into a unit.

##  6\. Discussion

This section will discuss the feasibility of the solutions and our future work
for this project.

###  6.1. Feasibility of our Tag Models: Price and Battery

Here, we illustrate the feasibility of our tag models by comparing them with
commercial options, Tiles, and AirTags because their technical specifications
are easy to access.

From the price perspective, the BLE-AC model is a couple of USD cheaper than
the $25 Tile Mate (til, [n. d.]). Other Tile models, such as the $35 Pro and
the $30 Sticker, are more expensive. In addition, alternative BLE breakout
boards are cheaper than the Adafruit breakout board. The Nordic Semiconductor
nRF52840-Dongle is only $10 (don, [n. d.]). We used the DWM1001-DEV board in
the UWB-RAW model because it was the cheapest and most user-friendly option
that we could find. The Airtag price is $29, while UWB-RAW costs $20.26 (app,
[n. d.]).

From the battery consumption perspective, the BLE-AC model has nearly a year
of battery life. However, we found online posts stating that the battery
consumption was under one nano ampere during beacon transmission with a
100-millisecond interval. We believe the life of the BLE-AC model with
replaceable AA batteries is much longer than one year. Tile models have a
battery life of either three years with a non-replaceable battery or one year
with a replaceable battery (til, [n. d.]). The limitation for BLE-AC is the
lifetime of the ItsyBitsy microcontroller and its battery, as certain
environmental conditions can cause electronics to degrade faster.

The battery life of UWB ranges from 3.3 to 5 days as the DWM1001-DEV board is
not optimized for power consumption (Nudel and Mciholas, 2019). Engineers
designed it as an anchor point with an unlimited power supply (Nudel and
Mciholas, 2019). Finding an alternative UWB board can be a challenging task.
However, Airtags use UWB with their U1 chip (app, [n. d.]). Their solution can
last longer than one year with a replaceable CR2032 coin cell battery (app,
[n. d.]). Thus, a power-optimized UWB breakout board can last longer with
three AA batteries. The U1 chip is not available for purchase, to our
knowledge. Additional work is feasible for using U1 in our UWB-RAW model to
decrease power consumption. We expect that the UWB-RAW model will be more
practical given technical advancement in UWB that optimizes for battery in the
future.

In this paper, we did not conduct a user study for our tags’ price
acceptability, so we’re unaware of our tags’ attractiveness to landlords and
manufacturers. An interview with landlords and smart home device manufacturers
can improve our work by providing more concrete proof of price acceptability
and an insight into how they can accept our tag models. We view this problem
as one of the future works.

###  6.2. Future Work

We only evaluated our tags in an apartment and shared space setting.
Nevertheless, an evaluation in a generalized setting can be more informative
to the community. A generalized environment would include buildings, such as
large stadiums or skyscrapers. We can design a solution where a user could
discover the tags without being in a room, like being on a farm. Several
wireless protocols, such as Long Range (LoRa) or Zigbee, can prove helpful in
a generalized environment. For instance, LoRa would be an excellent solution
in grande buildings for tag discovery. A longer-range locating strategy with
LoRa would be more convenient for users.

Two tag models to evaluate may not give readers a comprehensible understanding
of solving device discovery and identification with wireless tags. There are
other low-power wireless solutions designed for IoT systems. With the
mentioned wireless protocols, we can build several other tag models useful in
different scenarios, such as models for adverse environments or large
buildings.

Our current models cannot accommodate some of these scenarios. For instance,
we only use NFC for identification with centimeter ranges. If the IoT device
the tag is attached to is in an adverse environment, such as inside the walls,
it would be inconvenient for the user to identify it. Therefore, another
protocol can be helpful. BLE can be a candidate technology, but the range is
too extensive that neighbors could gain information regarding the hidden
devices inside a unit. The Adafruit board and API allow programmers to
configure the transmitter power to combat this. Observing the range of BLE in
the lowest power configuration would be a worthwhile effort to monitor the
feasibility of BLE in inventory mode.

The energy consumption of DWM1001-DEV is also not optimized, causing a battery
life of only days (Nudel and Mciholas, 2019). We found that UWB-RAW’s UWB
feature continuously runs in the example files we used (Decawave, [n. d.]). A
more energy-saving method can be used, such as using the low-power BLE option
to activate the UWB. The example files we use also have the nRF5 SDK. This
powerful SDK tool allows developers to configure the UWB feature for various
actions.

One last possibility for extending our work is to write a tutorial on what
specific boards are needed and upload our example code to GitHub. The tutorial
can guide users step-by-step on implementing the tag models if they need a
customized solution.

##  7\. Related Work

Researchers tackled the problem of device existence and location discovery in
the literature using several tools. One such tool is PriView, which allows
users to visualize nearby privacy-invasive devices via thermal cameras and VR
(Prange et al., 2021). Although some home appliances heat up when performing
heavy loads, small devices like hidden sensors may only slightly heat. These
sensors’ heat may not be distinguishable if they are close to a larger
appliance. Their VR solution only uses mockup solutions and does not have
device detection implemented (Prange et al., 2021). Some devices are out-of-
sight, such as inside walls, and our solution covers these use cases.
Fernandez et al. (Fernandez et al., 2021) utilized augmented reality to
contextualize data disclosure and allow users to customize privacy filters on
collected data. However, their AR interface is limited to two types of IoT
devices and is restricted to line-of-sight, in which the user knows the
device’s existence in advance. Sharma et al. (Sharma et al., 2022) proposed
Lumos, which enables users to identify and locate WiFi-connected hidden IoT
devices and visualize their presence using an augmented reality interface.
Lumos (Sharma et al., 2022) uses machine learning to tackle the challenge of
identifying diverse devices with only limited features.

Another solution comes from Song et al., as they used LED, WiFi, and mini
speakers to have their participants locate their devices in their prototype
(Song et al., 2020). Although this combination can help find and identify
these IoT devices, WiFi is a power-intensive protocol, and they need to design
their model with an unlimited power supply assumption. We focus on lowering
the power consumption of our tags so users can attach them to any device that
does not operate using the power grid.

Several previous works focus on energy-efficient IoT device discovery. Chen et
al. (Chen et al., 2017) proposed a smart device discovery mechanism that
adapts the scan window and the scan interval based on the number of redundant
scanned devices within a scan window. Their mechanism reduces power
consumption significantly compared to previous solutions, though it is limited
to BLE devices. On the other hand, Sharma et al. (Sharma et al., 2017)
presented an energy-efficient architecture for device discovery in 5G-based
IoT and Body Sensor Networks, i.e., BSNs, using uncrewed aerial vehicles,
i.e., UAVs. However, the authors also pointed out several challenges
surrounding the usage of UAVs (Sharma et al., 2017).

##  8\. Conclusion

We will explain the future trends of the device and identification problems
and examine the future of some wireless protocols. Afterward, we will conclude
this paper with final remarks.

###  8.1. Future Trends

As the number of IoT devices is increasing exponentially, more types of these
devices will emerge in residential buildings (OConnor et al., 2019). With this
increase, new problems will occur, and the severity of existing issues will
increase. One existing problem is device discovery and identification, as some
IoT apparatuses can be malicious. A user should be aware of all the devices in
his surroundings to protect his privacy. We expect researchers to leverage
additional tools as solutions besides wireless. However, solving this problem
via wireless protocols will provide a long-term solution.

It is also worth noting that researchers may develop additional wireless
protocols that may be useful to the problem. WiFi HaLow is a tool that can be
helpful in larger shared spaces, such as a stadium. It can operate on a cell
battery for a year and has a one-kilometer range. 5G is also another wireless
solution that can accommodate IoT devices. Our future work can use these tools
to provide more capable tag models.

###  8.2. Final Remarks

The primary purpose of the tags we developed is to allow more than a single
user to interact with them to obtain the list of the devices in the shared
space and learn about them. We created two tag models that use different
wireless protocols: BLE and UWB. These protocols are for device discovery and
identification. We use NFC in both tag models for device information
extraction by users only in proximity. An Android application, DIAL, also
allows the user to interact with the tags, specifically seeing every tag in
the room as a list and options to find tags via its buzzer or UWB. This
solution has not been done before for device discovery.

Relevant solutions we found for this problem are tools, such as thermal
cameras, LEDs, or VR (Song et al., 2020; Prange et al., 2021). However, each
solution has issues, such as LEDs needing to be in sight, and our tagging
solution is robust to these errors. Therefore, we believe using BLE and UWB to
solve this problem is worthwhile.

###### Acknowledgements.

We would like to thank Hyun Bin Lee for participating in our discussion and
finding related papers to our project. His insights were very valuable and
helped us understand the field.

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