A Wireless Low-power System for Digital Identiﬁcation of Examinees

(Including Covid-19 Checks)

Danilo Weber Nunes

and Klaus Volbert

Faculty of Computer Science and Mathematics, Ostbayerische Technische Hochschule Regensburg, Regensburg, Germany

Keywords:

Indoor Navigation, Indoor Localisation, Low-power Devices, Internet of Things, RSSI, BLE Beacons.

Abstract:

Indoor localization has been, for the past decade, a subject under intense development. There is, however,

no currently available solution that covers all possible scenarios. Received Signal Strength Indicator (RSSI)

based methods, although the most widely researched, still suffer from problems due to environment noise. In

this paper, we present a system using Bluetooth Low Energy (BLE) beacons attached to the desks to localize

students in exam rooms and, at the same time, automatically register them for the given exam. By using

Kalman Filters (KFs) and discretizing the location task, the presented solution is capable of achieving 100%

accuracy within a distance of 45cm from the center of the desk. As the pandemic gets more controlled, with

our lives slowly transitioning back to normal, there are still sanitary measures being applied. An example

being the necessity to show a certiﬁcation of vaccination or previous disease. Those certiﬁcations need to be

manually checked for everyone entering the university’s building, which requires time and staff. With that in

mind, the automatic check for Covid certiﬁcates feature is also built into our system.

1 INTRODUCTION

Industry projections estimate that nearly 75 billion

Internet-of-Things (IoT) devices will be online by

2025 (Ikpehai et al., 2019). A common IoT appli-

cation is to provide location services in indoor en-

vironments. The indoor localization and navigation

involving the use of Received Signal Strength Indi-

cator (RSSI) information of radio signals is an area

in active development (Spachos et al., 2018). As

Global Navigation Satellite Systems (GNSS) systems

are not suitable for the indoor application (Laoudias

et al., 2018), most attempts used RSSI information

from WiFi or Bluetooth signals, emitted by routers

and Bluetooth Low Energy (BLE) beacons, respec-

tively (Zafari et al., 2019).

In order to perform localization, signal ﬁnger-

printing is commonly applied and performed in a two

phase process (He and Chan, 2016). First, the RSSI

ﬁngerprint of each emitter in the room/building has to

be collected, this phase is denominated the ”ofﬂine”

one. During the ”online” phase, the collected signals

used to build a signal map of the scanned environment

enables the location by means of trilateration, when 3

beacons are detected, or by multilateration, in case

more beacons are detected (Spachos et al., 2018).

https://orcid.org/0000-0002-0401-0842

In 2020, the whole world was hit by surprise by

the outbreak caused by the Severe Acute Respiratory

Syndrome Coronavirus 2 (SARS-CoV-2). It forced

governments for actions inhibiting the spread of the

deadly disease. Lockdowns and curfews were im-

posed, masks were made mandatory and places where

social gatherings used to take place, like restaurants,

had to be closed and ofﬁces and universities had to

move to an online format (Ciotti et al., 2020).

At the OTH Regensburg, all lectures were con-

verted to the online format. However, the exams were

still held in presence. This required some adaptations.

The room should allow the placement of the desks up

to 2 meters apart, and all students obligatory should

wear a mask during the period of the exam. Each stu-

dent was assigned to a table, before leaving the room

he/she should inform this to the examiner.

In parallel, contact tracing measurements for pos-

itive tested persons were adopted. At ﬁrst, manually

by health vigilance professionals, but, as far as the

number of infected individuals increased exponen-

tially, it quickly turned out unfeasible. Thus, govern-

mental and private companies developed mobile ap-

plications for the tracking of infected individuals and

their contacts, detecting the Bluetooth signals of the

nearby devices (Li and Guo, 2020). However, this ap-

proach had some pitfalls. As the determination of the

Nunes, D. and Volbert, K.

A Wireless Low-power System for Digital Identiﬁcation of Examinees (Including Covid-19 Checks).

DOI: 10.5220/0010912800003118

In Proceedings of the 11th International Conference on Sensor Networks (SENSORNETS 2022), pages 51-59

ISBN: 978-989-758-551-7; ISSN: 2184-4380

distance between 2 phones is based on the RSSI, this

information can be affected by multiple factors as the

multipath signal propagation, interference, or simply

different power levels of the Bluetooth signal of the

different smartphones, resulting in imprecise and in-

consistent measurements (Ahmed et al., 2020).

In this study, we present a different approach to in-

door location that solves the problems involving RSSI

based distance estimation for both contact tracing as

well as for indoor localization. For this, BLE beacons

were attached to each desk and used as anchors, there-

fore, with no need to rely on trilateration / multilater-

ation methods using the RSSI information from mul-

tiple beacons to locate. By ﬁltering the environmental

noise with Kalman Filters (KFs) and discretizing the

location task, it allows to achieve better metrics, as

well as more efﬁcient contact tracing, all comprised

in a single phase with no need for calibration, saving

a considerable amount of time.

The developed solution also allows automatic

checkup of Covid vaccination or previous infection

certiﬁcates, user identiﬁcation and exam registration

with no need of the user’s interaction.

Section 2 shows the related work; Section 3

presents the motivations and the goals of this study;

Section 4 reports the description of the hardware; Sec-

tion 5 presents the architecture of the system and the

software stack. Section 6 shows the accuracy of the

system, section 7 the conclusion and further studies.

2 RELATED WORK

In recent years, wireless systems especially so-called

low-power or even ultra-low-power wireless systems

become more and more popular. In the theoretical

area (Schindelhauer et al., 2007; Lukovszki et al.,

2006; Meyer auf der Heide et al., 2004) as well as in

practice, with applications like smart metering, smart

submetering, and/or smart grid (Kenner et al., 2017).

Concerning indoor localization, recent studies

have shown that it is possible to achieve sub-meter

precision in indoor scenarios using various techniques

and technologies.

Klipp et al. reported that it is possible to achieve

sub-meter precision localization by using magnetic

signatures in combination with Inertial Measurement

Unit (IMU) data. The results, however, depend on

an unambiguous magnetic disturbance pattern and a

known initial position (Klipp et al., 2018).

Neges et al. have presented a solution combin-

ing IMU information with natural visual markers,

not requiring investments in additional infrastructure

(Neges et al., 2017). Gong et al., presented a sim-

ilar method, using Convolutional Neural Networks

(CNNs) to perform image recognition and enable lo-

calization, achieving an error rate of 2.3 meters (Gong

et al., 2021). These methods still require a calibration

phase to collect the natural markers and a high degree

of user interaction to work.

Despite early claims of the unreliability of RSSI-

based methods (Dong and Dargie, 2012), many de-

veloped solutions are based on these methods due to

its ease of use and implementation and the wide hard-

ware availability (El-Sheimy and Li, 2021).

The RSSI value, measured in dBm, is given by the

following equation (Dong and Dargie, 2012):

RSSI = −10 ·n · log

(d) − A (1)

In Equation 1, n is the signal propagation constant

in the environment, d is the distance between the stu-

dent’s smartphone and the BLE beacon attached to the

desk and A is a reference received signal strength in

dBm. It represents the value measured when the dis-

tance between the smartphone and the BLE beacon

is one meter. RSSI Values closer to zero indicate a

stronger signal.

RSSI-based methods are highly subjected to insta-

bilities (Xiao et al., 2013) which may alter the signal

map collected before an exam. This is due to errors

such as multipath signal propagation, Non-Line-of-

Sight (NLoS) conditions, and signal interference.

To mitigate the signal instabilities, KFs can be ap-

plied to reduce the impact of noise in the environment

(Bulten et al., 2016). Mackey et al. has shown an im-

provement in the localization accuracy of up to 78.9%

in a four beacon setup (Mackey et al., 2018).

Many works apply a ﬁngerprinting approach to

perform indoor localization using the RSSI and chan-

nel state information (CSI) based information (Al-

homayani and Mahoor, 2020). Luo and Gao have

shown improved localization accuracy when employ-

ing Deep Belief Networks for ﬁngerprinting on Ul-

tra Wide Band (UWB) signals (Luo and Gao, 2016)

and Ayyalasomayajula et al. introduced a two-step

process applying CNNs on WiFi CSI data (Ayyalaso-

mayajula et al., 2020). These methods, however, lack

of support in consumer devices and require a higher

energy consumption (on the client-side) when com-

pared to BLE, respectively. Another problem involv-

ing ﬁngerprinting approaches emerges by the fact that

it is subjected to inconsistencies between the data col-

lected during the ”ofﬂine” phase and the data being

presented during the ”online” phase. Fingerprinting

in an empty room would generate a different signal

map than when done in a room full of students.

This paper describes a simple approach to indoor

localization that uses BLE beacons as anchors, there-

fore, avoiding weaknesses and extra complexity in-

SENSORNETS 2022 - 11th International Conference on Sensor Networks

volving signal trilateration or ﬁngerprinting. The task

consists of ﬁnding the closest beacon instead of cal-

culating the distance itself.

3 MOTIVATION AND GOALS

While the pandemic is getting more controlled, our

lives are slowly transitioning back to normal social

activities, but there will still be a need for sanitary

vigilance in all spheres of the society, as safety dis-

tance, the wear of face masks, vaccination/previous

infection certiﬁcation, and the further integration of

contact tracing apps.

Actually in Germany, for any subject to enter the

university building it is necessary to present a certiﬁ-

cation of vaccination or previous disease, or a neg-

ative Covid test (not older than 24 hours). In sce-

narios with more intense social gathering as restau-

rants or bars, in addition to the previously mentioned

measurements, a manual registration step, which one

has to share his/her contact information through a QR

code or through the Luca App

, is required. All these

steps need to done repetitively by each customer.

As all certiﬁcations and registrations on the app

have to be checked by the staff before entering the

venue. Although all these checks and measurements

are necessary, they will slow down a previously ﬂuid

process, creating unnecessary waiting queues, beside

increasing the human error.

Another problem which emerges from this pro-

cess is the necessity of the precise registration of the

check-out time, to avoid false warning of contact with

a positive infected person, due to a false timeline over-

lap of the presence. The Luca App offers an ”auto-

check out” function, however, it requires, however,

access to GPS, rising privacy concerns.

At the OTH Regensburg, all these difﬁculties

would be seen during the exams in presence, with ad-

ditional steps as the proof of identity and the exami-

nee’s signature of the list conﬁrming that he/she had

taken the exam. In order to reduce the manual ac-

tions required for the registration and consequently,

the waiting time for the students enrolled in the exam,

and the work load of the examiner, normally the pro-

fessor, we developed an app integrated to the system

of the faculty, which enables the automatic registra-

tion for the exam, the check in/out time, as well as

the precise localization of each user in the room. The

system also reduced the need of repetitive checks of

Covid certiﬁcate by staff. The user needs minimal in-

teraction with the app, just being required a one time

See https://www.luca-app.de/system-2/

user account setup step, similar to the Luca App. The

integration of our app with the system of the faculty

facilitates the students’ identiﬁcation, and automati-

cally inserts his/her presence into the faculty system.

With these problems in mind, we developed a sys-

tem that enables the automatic check in/out step and

the registration into an exam as well as the precise

location of the user in the room. It also completely

eliminates the need from repetitive Covid certiﬁcate

checks by the staff, all that, at the same time, requir-

ing no user interaction with the app. The only interac-

tion required with the App is a one time user account

setup step, similar to what is done in the Luca App,

but with integration with the faculty’s system.

This system also facilitates and improves contact

tracing. The automatic registration for check- in and

check- out also facilitates and improves the correct

contact tracing, minimizing false positives. The more

precise desk localization of each examinee based on

discrete and not on distance inferred from RSSI data,

also allows the identiﬁcation of at risk neighbours

(within a distance of 4 desks) to an infected person,

limiting the necessary notiﬁcation.

As we discuss in section 7, this system could be

adapted and applied to other contexts like restaurants

and other events.

4 HARDWARE

For this work, ten iBKS105 BLE beacons from the

Spanish manufacturer Accent Systems were used.

The use of ultra-low power solutions was consid-

ered, as they would require less power and, there-

fore, have easier maintenance regarding the batteries.

The use of BLE beacons was chosen, however, due

to the ease of development, compatibility with tech-

nologies currently present in smartphones, thus with

no requirement for special gateways (Kenner and Vol-

bert, 2016; Altmann et al., 2017).

The beacons have a diameter of 52.6 mm and a

thickness of 11.3 mm (closed case). Figure 1 show

a view of it inserted in its acrylonitrile butadiene

styrene (ABS) case.

Each beacon is powered by a nRF51822 Bluetooth

Low Energy System-on-a-Chip (SoC) from Nordic

Semiconductor and a CR2477 coin cell 3V battery.

It has a programmable output power from +4 dBm to

-30 dBm, 4 available Eddystone slots, with 4 possible

frames (UID, URL, TLM and EID) and 2 available

iBeacon slots.

The selection of the beacons was based on their

high quality, the ease of their conﬁguration, and their

affordable price, with each unit costing 13e. The con-

A Wireless Low-power System for Digital Identiﬁcation of Examinees (Including Covid-19 Checks)

(a) View from the side.

(b) View from the top.

Figure 1: Example of beacon used. It has 52.6 mm of diam-

eter and 11.3 mm of thickness.

ﬁguration of the beacons was performed by the app

provided by the manufacturer, iBKS Conﬁgure.

The app also allows for Over-The-Air (OTA) up-

dates, so there is also no need to develop update pro-

tocols (Schwindl et al., 2019).

The beacons were conﬁgured to transmit only one

Eddystone frame containing the beacon’s UID. This is

performed 3 times a second with a transmission power

set to -30 dBm. This low transmission power was set

to assure that nearby beacons would not interfere with

each other, as well as to extend the life of the battery,

estimating the consumption to about 97.78 µA, with

an expected battery life of 14 months. All other slots

were disabled with the same intention.

5 SOFTWARE

In the following Section we present the software used

to build the presented system. An overview of the ar-

chitecture of the system can be seen in Figure 2. The

data ﬂow diagrams from the students’ and examiner’s

perspective is presented in ﬁgures 3 and 4, respec-

tively. The localization algorithm is shown in Figure

Web Server

Students's Phone Students's Phone

Students's PhoneStudents's Phone

Exams

BLE

Beacon

IDs

Desks

Examiner's

Phone

BLE

Beacon

BLE

Beacon

BLE

Beacon

Beacons

Room

Maps

Certiﬁcates

Database

Figure 2: System’s Architecture.

5. The communication between the mobile Apps and

the server is done through a REST API (Masse, 2011).

5.1 Mobile Apps

The mobile Apps are the central part of this system,

having one app to be used by the students, and a sec-

ond one by the examiner.

Both apps were written using the React Native

framework (Eisenman, 2015), as it enables rapid de-

velopment and testing, with support for hot-reloading.

It also eases multi-platform development, allowing

the sharing of most of the code base between both

iOS and Android apps.

The student’s app was designed to require the less

amount of user interaction as possible to work.

For the ﬁrst use, the student will register with

his/her university credentials, thus, allowing access to

their examination schedules. The app will ask for the

upload of the certiﬁcation of vaccination or recovery.

SENSORNETS 2022 - 11th International Conference on Sensor Networks

Verify

Covid Certiﬁcate

Selected Exam

Student

Send Conﬁrmation

Add Covid

certiﬁcate

Permission to register

Assigned Desk

Gets a desk

Conﬁrm Identity

User Database

Figure 3: System’s Data ﬂow Diagram from the user’s per-

spective.

It will then scan the uploaded pdf or picture and ex-

tract the QR code that it holds. This QR code will then

be decoded, extracting the Base45 encoded CBOR

Web Token (CWT). The Base45 encoded CWT will

then be sent to a self-hosted instance of the Open

Covid Certiﬁcate Validator API

. The API response

will contain, between other pieces of information, a

Boolean value indicating if the uploaded certiﬁcate is

valid or not. Except for this information, no other

information is stored neither in the student’s smart-

phone nor in the servers.

The student will receive the conﬁrmation of the

registration with an assigned desk and a map of the

room, which are then stored by the app, facilitating

the localization of his/her desk.

With his/her user setup complete, there is no more

https://github.com/merlinschumacher/

Open-Covid-Certiﬁcate-Validator

Verify

Assigned Examination

List of Students

Examiner

Students Registered

User Database

Conﬁrm Identity

Examiner Database

Examination

Details

Figure 4: System’s Data ﬂow Diagram from the examiner’s

perspective.

required interaction of the user with the app. The app

will run as a background process, shortly scanning for

nearby beacons every ﬁfteen minutes.

When the app detects a known beacon (one that

match the set UID), it will start a foreground service

(Android) for the app to increase the rate of signals

being collected. Due to the already mentioned unsta-

ble nature of BLE signals, KFs were used to stabilise

the signals of the beacons.

KFs are recursive stochastic ﬁlters and will be

used to process and smooth the noisy RSSI infor-

mation received by the smartphone. For that, one-

dimensional KFs were used.

A KF is divided in two stages, prediction and up-

date. In the prediction stage, the current state of sys-

tem and the next state estimate uncertainty are pre-

dicted through the Equations 2 and 3, respectively.

In the update stage, a new Kalman gain is calcu-

lated using Equation 4 and the state estimate uncer-

tainty is updated following Equation 5.

The ﬁltered signal is obtained by the following

equation:

ˆx

n,n

= ˆx

n,n−1

+ K

− ˆx

n,n−1

) (2)

Where ˆx

n,n−1

is the previous system state estimate,

the Kalman gain, obtained by Equation 4, and z

the measured system state.

A Wireless Low-power System for Digital Identiﬁcation of Examinees (Including Covid-19 Checks)

Start

Collect RSSI

information from the

environment

Yes

Instantiate a new KF

for that beacon

Filter Signal

Set the Student's

desk as the one with

strongest ﬁltered

signal

Send current desk ID

to the Server

Yes

Does the UID

match?

Was the

beacon

previously

detected?

Have 2

seconds

passed?

Discard

Measurement

Figure 5: Flowchart of the localization algorithm.

The prediction of the next state estimate uncer-

tainty is performed as follows:

n+1,n

= p

n,n

+ q

(3)

Where p

n,n

is the current state estimate uncer-

tainty and q

the process noise variance, which is the

variance of the uncertainty of our dynamic model.

The Kalman gain is calculated by the equation:

n,n−1

+ r

(4)

Where r

is the measurement error, calculated by

Equation 5, and p

n,n−1

represents the estimate uncer-

tainty calculated during the previous ﬁlter estimation.

The Kalman gain the intensity of which the estimate

will change given a measurement.

The update of the current state estimate uncer-

tainty is done by:

n,n

= (1 − K

n,n−1

(5)

The app will localize the student and the desk at

every two seconds, avoiding false positioning due to

loss of signal. An overview of the localization algo-

rithm is shown in Figure 5.

If a student is not sitting on his/her correct desk,

the app will vibrate, indicating the error. Simultane-

ously, the examiner will receive the information of the

incorrect match between the student and the desk, to

adjust the positioning promptly before the beginning

of the exam. The student being recognized on the cor-

rect desk, automatically will be registered as ”taking

the exam”, sending this information to the backend

server, making it available for the examiner’s app.

The examiner’s app is designed as a more tradi-

tional app. The examiner will login with his/her uni-

versity credentials, allowing to access the list of ex-

ams, the layout of the examination rooms, the list of

student’s who registered for the exam and the desks

they were assigned to. The examiner will receive a

map of the occupied and free desks, at the end of the

exam generating a list of the students who attended.

If necessary, the examiner can replace a student to an-

other desk on the app.

5.2 Backend Server

The backend server was implemented using the

FastAPI Python framework (Voron, 2021). It was

chosen due to its simplicity and development speed.

In the current state of the system, the backend is a

simple component. It has to perform only two tasks:

to query and store the data of both apps, and to assign

each student a desk.

A PostgreSQL database (Obe and Hsu, 2017) was

used to store the student’s and the examiner’s data.

For the students, their credentials, registered exams

and each assigned desk, and the conﬁrmation of a

Covid certiﬁcate are stored, and for the examiners,

their credentials and assigned exams. The map of the

examination room and the localization of each beacon

are also stored in the database.

6 EVALUATION

As the localization through the beacons is the most

critical part of the system, we performed a series of

tests to analyse the reliability of the signal localization

of each beacon and the limits of the system.

SENSORNETS 2022 - 11th International Conference on Sensor Networks

The tests were performed in the laboratory of In-

telligent and Connected Systems in the computer sci-

ence building at the OTH Regensburg. Ten beacons,

numbered from 1 to 10, were placed in the room un-

der the desks, right in the middle, within a distance of

1.5 meters apart from each other. A ﬂoor plan of the

room is exposed in Figure 6.

To perform the measurement as realistically as

possible, the phones were placed in a pocket of a

trouser, and signals were collected while sitting at the

desk for two minutes. Two examples of collected sig-

nals from two different desks are shown in Figure 7.

Figure 7a a shows the graph with the highest in-

terference scenario from a nearby beacon. Even the

signal of beacon n

being the strongest and the most

stable, thus detecting the correct desk, it was clearly

subjected to destructive interference from beacon n

Figure 7b shows the more common scenario, the sig-

nal of the desk being signiﬁcantly stronger than the

ones coming from nearby beacons.

Considering the challenging results at desk n

, we

tested the accuracy of the signal, changing the stu-

dent’s position from the center of the desk. Position

was moved towards the desk n

in 15 cm steps, within

a range from 15 to 75 cm. The samples were collected

in these 5 different distances (15, 30, 45, 60 and 75

cm) during two minutes each. The results are exposed

in Figure 8.

As we can observe, the strength of the signal of

the beacon placed at desk n

is still the highest within

the range of 45 cm away from the center of the desk.

The signal strength drops signiﬁcantly when increas-

ing the distance to 60 and 75 cm, with a prominent

increase of the signal of the neighbour desk n

, as

well as a smaller, but noticeable increase of the sig-

nal strength of desk n

7 CONCLUSIONS AND FUTURE

WORK

In this section we conclude our work and discuss

some ideas for future work.

7.1 Conclusion

In this work, the authors present a feasible solution for

the monitoring of examinee’s localization in a room,

as well as an automatic exam registration and Covid

certiﬁcation check app in an university environment.

Our results reinforce the reliable use of BLE bea-

cons, exposing also the limitations of such a system.

Considering the placement of the beacons on the

desks and the fact that a person sitting at the as-

3,92

3,45

3,92

3,45

3,63

3,74

Dacheinlauf

Tiefpunkt Gefälledämmung

d = 16 cm

Dacheinlauf

Tiefpunkt Gefälledämmung

d = 16 cm

Dacheinlauf

Tiefpunkt Gefälledämmung

d = 16 cm

Dacheinlauf

Tiefpunkt Gefälledämmung

d = 16 cm

Gefälledämmung

d = 27 cm

Gefälledämmung

d = 30 cm

Hochpunkt Gefälledämmung

d = 30 cm

Gefälledämmung

d = 30 cm

Gefälledämmung

d = 24 cm

Gefälledämmung

d = 30 cm

Gefälledämmung

d = 30 cm

Gefälledämmung

d = 30 cm

Dacheinlauf

Tiefpunkt Gefälledämmung

d = 16 cm

Dacheinlauf

Tiefpunkt Gefälledämmung

d = 16 cm

Dacheinlauf

Tiefpunkt Gefälledämmung

d = 16 cm

Dacheinlauf

Tiefpunkt Gefälledämmung

d = 16 cm

Gefälledämmung

d = 27 cm

Gefälledämmung

d = 30 cm

Hochpunkt Gefälledämmung

d = 30 cm

Gefälledämmung

d = 30 cm

Gefälledämmung

d = 24 cm

Gefälledämmung

d = 30 cm

Gefälledämmung

d = 30 cm

Gefälledämmung

d = 30 cm

Dacheinlauf

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Dacheinlauf

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maximale Überstauhöhe Notentwässerung

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maximale Überstauhöhe Notentwässerung

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Brandschutzanforderungen der Wände Raum K134 und K135 angepasst

mm N29.08.16

Türbezeichnungen angepasst; Türdrehung Zugang K120 geändert; Höhe Doppelboden Serverraum

korrigiert; Bodenbelag Putzmittelraum K166 angepasst

29.08.16

n10

1.60 m

Figure 6: Experimental environment indicating the posi-

tions of the beacons from n

to n

signed desk would absorb most of the signal coming

from his/her desk’s beacon, we believe that our results

would even be better in a real life testing set (with

a full room) as the interference between the beacons

would be considerably reduced.

Taking into account the limitations of movement

in an examination scenario and the imposed sanitary

restrictions, in which the examinees are required to

stay apart within a distance of 1.5 m, the situation of

an examinee being 60 or 75 cm away from his/her

desk’s beacon (exposed in Figure 8) would hardly

happen in a real life scenario.

Beside the accuracy of supervising the contact of

possibly infected individuals, the system will also re-

duce the time to ingress in an examination room, fa-

cilitating the localization of the assigned desk. The

integration to the university system also helps the ex-

aminer to generate the report of the exam.

7.2 Further Work

As for future works, the reliability of the system

might be improved, avoiding misplacement of an ex-

aminee as in the edge test. In addition to the Blue-

tooth based localization strategy, information of IMU

sensor data can be combined, detecting the movement

of an examinee, thus, avoiding situation in which the

system would not be able to determine if the student

changed the place or not.

As this app would be designed for the facilitation

of registration and realization of exams, the function-

ality of the app can be enlarged, muting automatically

the student’s phones and restoring their original state

A Wireless Low-power System for Digital Identiﬁcation of Examinees (Including Covid-19 Checks)

(a) Signals collected at desk n

. This represents the worst

scenario found in our test environment.

(b) Signals collected at desk n

. This represents the most

common scenario found in our test environment.

Figure 7: Graphs of signal strength from the beacons around

each desk during 120 seconds.

Figure 8: Average of the signal strengths during a 2 minute

measuring period for each detected beacon when varying

the phone’s distance from the desk’s beacon. Values equal

to -125 dBm can be considerate as the nonexistence of sig-

nal.

after ﬁnishing the exam.

Taking into consideration the good results of our

study, the beneﬁts of this solution for an examination

scenario could also be transported to a more social

environment, like restaurants, bars, theaters, etc, with

each table/seat having a beacon. This scenario will

offer even more noticeable beneﬁts for contact tracing

and ease of registration.

ACKNOWLEDGEMENTS

This work was supported by the Regensburg Center of

Energy and Resources (RCER) and the Technology-

and Science Network Oberpfalz (TWO). Further in-

formation under www.rcer.de.

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A Wireless Low-power System for Digital Identiﬁcation of Examinees (Including Covid-19 Checks)