BLE Beacon with Data Accumulation Functionality

BLE Beacon with Data Accumulation
Functionality⋆
KOBAYASHI Kakeru and FUJITA Satoshi
Department of Information Science, Graduate School of
Advanced Science and Engineering, Hiroshima University,
Kagamiyama 1-4-1, Higashi-Hiroshima, 739-8527, Japan
Abstract. This research looks into the implementation and utilization of BLE beacons with data
accumulation functionality. BLE applications are typically divided into two categories: one that
broadcasts a fixed value for each device, and the other that identifies contact with a particular user
by scanning the data broadcasted in its surroundings. The proposed framework, however, follows
a different approach where each beacon scans for data broadcasted in the area, then changes state
based on the scan result, and finally, broadcasts data based on the new state. This can be regarded
as an extension of the BLE mesh standard, which enables data flooding through repeated scanning
and broadcasting. The paper also explores two practical use cases of the technology: detecting and
announcing congestion around devices, and a stamp rally application.
Keywords: Bluetooth Low Energy, advertisement, data accumulation, congestion detection.
1 Introduction
Recently Bluetooth Low Energy (BLE) is widely used for periodically broadcasting
binary data from a beacon installed at a location to its surroundings. The data
transmitted by a beacon can be easily picked up by nearby devices such as smart-
phones equipped with BLE capabilities and the advertisement can contain a fixed
ID (in the case of iBeacon [12]) or a shortened URL of a website related to the in-
stallation location (in the case of Physical Web [17]), depending on the application.
The main advantage of advertisement used in BLE protocol is that there is no need
to pre-establish a connection between the sender and receiver, while in addition to
the advertisement-based communication, BLE supports connection-based protocol
to provide secure communication encrypted with 128-bit AES in CCM mode.
BLE communication has a wide range of applications, such as providing direc-
tions in a museum or promoting products in a retail store. In these scenarios, it
is important to accurately identify the location of the beacon from the received
data, which is typically done by sending a fixed value depending on the location.
Another emerging use case of BLE communication is the contact trace of smart-
phone users to suppress the spread of COVID-19. In such an application, a unique
⋆
A part of this paper was presented at CANDAR 2021 as “An Enhancement of Physical Web
with Stateful BLE Beacons” by the same authors.
DOI: 10.5121/ijcnc.2023.15501 1
International Journal of Computer Networks & Communications (IJCNC) Vol.15, No.5, September 2023

random token is assigned to each user and broadcasted to the surrounding area,
allowing proximity between users to be detected by scanning the advertisement.
To maintain user privacy, the relationship between the token and the user must be
securely managed on the server and to identify the date and time of contacts in
detail, the transmitted data is encoded with both the user’s identifier and the time
of transmission. The BLE mesh standard released in 2017 shows another direction
for the future research in which the notion of data routing is supported. In this
protocol, data routing occurs in a way that a device can both receive data from
a nearby device and advertise it to its surroundings, where such a dynamic role
switching in a single node is a new addition to BLE 5.0.
This paper proposes a new framework that utilizes BLE devices as an accumula-
tor of peripheral information. In this framework, state transitions are triggered by
input in BLE beacons, and a cycle of information collection, state transition, and
dissemination is repeated by the beacon. Information collection can be achieved
through ordinary scanning, and dissemination can be achieved through ordinary
advertisement. Thus the challenge in this framework lies in efficiently implement-
ing state transitions triggered by input, and it is also important to draw concrete
use cases for the framework. It is worth noting that the simple cycle of collection
and dissemination without state transitions is already in practice, such as in the
previously mentioned BLE mesh standard. In this sense, our proposal could be
regarded as an extension of the BLE mesh standard.
In this paper, we summarize findings related to these issues. At first, we study
three different ways of implementing state transitions, and show that the storing of
the state inside the beacon and updating it with test-and-set operation is promising
(this part is an extension of the paper we presented at [14]). Next, we consider
two applications of the proposed framework: congestion detection/announcement
around beacons and stamp rally application. The former uses a mechanism similar
to the contact tracing to determine the number of smartphone users around a
beacon, and advertises the estimated congestion to the surrounding users. There
are existing applications that upload the contact information detected by BLE
devices to a server and announce the predicted congestion status calculated on
the server through the Web, but a big difference from these methods is that the
beacon itself advertises the results it has collected and calculated. In fact, there is
great potential for a method that quickly notifies (only) those who are currently
at the location of the beacon of the processing results. For example, a smartwatch
that detects arrhythmia, such as ventricular fibrillation, can notify an alert to the
surrounding beacons, and a beacon that detects “a user in need of rescue is nearby”
can announce the location of the nearest AED to the users at that location. In the
stamp rally application, on the other hand, each beacon can give individual stamp
to the users who pass near the beacon using connection-based communication. Each
stamp can contain hints for solving the quests presented in the stamp rally, and
2

the beacons can have states that allow for more flexible and precise hints based
on the distribution of the number of people who have passed through the beacon,
the time of day, and other factors. These two applications were implemented on a
firmware-less IoT system called Obniz.
The remainder of this paper is organized as follows. After reviewing related
work in Section 2, Section 3 describes an overview of the BLE protocol. Section 4
discusses the way of implementing stateful BLE beacons. Sections 5 and 6 describe
the details of the implementation of the congestion detection system and stamp
rally application. Section 7 presents the results of experiments conducted on the
implemented system. Finally, Section 8 concludes the paper with future issues.
2 Related Work
BLE (Bluetooth Low Energy) is widely used for its ability to broadcast an identi-
fier through a BLE beacon [7]. This makes it possible for nearby devices, such as
smartphones and tablets, to take specific actions when they are in proximity to the
beacon. BLE is also used for proximity detection solutions like indoor positioning
[15, 6], action recognition [5, 26], and vehicle wake-up systems [20], as well as for
mobile crowd sensing and contact tracing [11] (A review of context-aware mobile
crowdsensing systems can be found in [22]). Additionally, BLE can be used to con-
nect wireless sensors and receivers in healthcare and smart home applications [25].
However, its use in new, non-beacon applications such as Vehicular Ad Hoc Network
(VANET) [2, 24], smart infrastructure [4, 13, 18, 23], multimedia streaming devices
[8, 16, 19], and mobile payment systems [1, 21] is limited by its lack of support for
high-capacity data transmission, mesh networking, and other factors.
In Building Management Systems (BMS), it is crucial to keep track of both in-
dividual activities, like walking and moving, and group activities, such as meetings.
Smartphones offer promising possibilities for such Human Activity Recognition
(HAR) research. Chena et al. [3] focused specifically on Group Activity Recogni-
tion (GAR) and proposed a GADAR framework using smartphones and beacons in
a smart building environment to recognize a wide range of human activities. The
paper delves into the following topics: (1) what human behavior features should be
considered in GAR, (2) how smartphones sensors and Bluetooth beacons can be
integrated into group activity recognition, and (3) how effective the framework is
in addressing GAR compared to other studies.
In [9], a framework named SocializeME was introduced to detect social interac-
tions among users using commercially available devices. This framework collects and
analyzes data from BLE signals emitted from wearable devices. The paper describes
an experiment with high school students to gather wireless data and correctly label
data related to various interaction phases (non-interaction, proximity, and interac-
tion). The experiment was designed to mimic real-life human interactions, and the
analysis focuses particularly on the differences in signals emitted, such as frequency
3

and RSS, from different mobile device configurations and uses (e.g. standing with
a smartphone in hand, standing with a smartphone in a pocket, sitting, etc.). The
results of the study offer important insights into the practical usage of commer-
cial mobile devices for social interaction monitoring and the characteristics of BLE
signals in real-world scenarios. The dataset used in the experiment is available in
[10].
3 Overview of BLE Protocol
The communication protocol that utilizes BLE technology is governed by two
rules: GAP (Generic Access Profile) which outlines communication procedures, and
GATT (Generic Attribute Profile) which determines how data is accessed.
3.1 GAP
In BLE communication, each device (or node) is assigned one of four roles: broad-
caster, observer, central, or peripheral. These roles determine the node’s function
and responsibilities within the communication. It is worth noting that one node
can play multiple roles in the same application, depending on the requirements of
the communication. A broadcaster, for example, periodically sends out advertis-
ing packets to nearby nodes, while an observer listens for these packets that are
transmitted by a broadcaster.
The broadcaster is responsible for advertising its presence and the data it
wants to share, while the observer listens and receives the data. This allows the
observer to discover and connect to the broadcaster, enabling communication be-
tween the two devices1. Each advertising packet can contain data of up to 31 bytes
in length. iBeacon is a typical implementation of BLE technology that uses a trans-
mitter and receiver to play the roles of broadcaster and observer respectively. The
iBeacon transmits advertising packets at a regular interval, and nearby devices that
have the iBeacon app installed can receive and interpret the data contained in the
packets.
In BLE, the central and peripheral roles are used to create a star-shaped network
with a central node at its center. In this network, the central can communicate
directly with every peripheral device in both directions after explicitly establishing
a connection. To do this, the central detects advertising packets transmitted by
surrounding peripherals, decides whether to add them to the network, and then
initiates the pairing process with the selected peripherals. Figure 1 shows such
a procedure. This relationship between the central and peripherals is asymmetric,
1
As per the BLE standard, the transmission interval, which is the time between sending out
advertising packets, should be set within a range of 20 milliseconds to 10.24 seconds. A shorter
transmission interval results in higher responsiveness, but also leads to an increase in power
consumption.
4

(3) approval
(2) request for
connection
beacon
(1) advertising
packet
beacon
Central Peripheral
Broadcaster
Observer
Fig. 1. The BLE protocol, as specified in GAP, defines four roles for nodes to take on: broadcaster,
observer, central, and peripheral. Beacon devices transmit radio waves in an omni-directional
manner, and their transmission range is approximately 10 meters for Class 2 beacons.
with the central able to connect to any number of peripherals, while each peripheral
can connect to only one central. There are several proposals to extend this procedure
to create a mesh-structured network, such as the BLE mesh standard supported in
BLE 5.0. In this case, a message issued by a node is propagated over the network by
using both the broadcaster and observer roles. A node, after receiving an advertising
packet from a neighbor, it forwards the received packet to its own neighbors by
switching its role from observer to broadcaster. This allows for a more efficient and
robust communication between all the devices in the network.
3.2 GATT
Data access in the BLE protocol is based on two entities called service and char-
acteristic, and three types of access patterns, known as Read, Write, and Notify.
A service is a bundle of one or more characteristics and a characteristic is a tu-
ple of value, property, and descriptor. For example, a profile for a multifunctional
scale service could have several characteristics such as weight, body fat percentage,
and BMI. For each characteristic, the value corresponds to binary data carried by
BLE packets, properties are attributes that define the access rights to the values,
and descriptors provide additional information. Services and characteristics have a
unique identifier of 16-byte length called UUID. In addition, we can use an abbre-
viated form of UUID (2-byte length) for several services defined in the Bluetooth
SIG standard.
5

Preamble
(1 Oct)
Access Address
(4 Oct)
Protocol Data Unit
(up to 39 Oct)
CRC
(3 Oct)
Header
(2 Oct)
Advertiser’s Address
(6 Oct)
Advertiser’s Data
(up to 31 Oct)
BLE packet
Advertising channel PDU
Header
(2 Oct)
MIC
(4 Oct)
Payload
(24 Oct for ver. 4.1 or less; 251 Oct for ver. 4.2 or more)
Data channel PDU
Fig. 2. This figure displays the format of BLE packets, including advertising packets transmitted
uni-directionally by a broadcaster and data packets exchanged bidirectionally between central and
peripheral devices. It is significant to note that the format of payload in iBeacon differs from that
in Eddystone.
In the BLE protocol, specific UUIDs must be designated to access the desired
value of a characteristic. If the central device connected to a peripheral device
is unaware of the services and characteristics available on that peripheral, it can
discover the necessary UUIDs by sending requests for service and characteristic
discovery to the peripheral. Among the three access patterns (Read, Write, and
Notify), Write is used by the central to write a value to the peripheral, while
Read and Notify are used to read a value from the peripheral. The main difference
between Read and Notify is that Read retrieves the value from the peripheral only
once, while Notify retrieves the value repeatedly at intervals determined by the
peripheral (a common use case for Notify is continuous reading of a value from a
temperature sensor). The value size is limited to 20 bytes for Write and 22 bytes
for Read, as determined by the maximum payload of a BLE packet. See Figure 2
for illustration.
4 How to Make BLE Beacons Stateful
Building a location-aware service with BLE beacons that support iBeacon/Eddystone
protocols is cost-effective. However, these services are limited because they only
6

Table 1. Specification of peripheral devices.
Pixel 3 XL Raspberry Pi 4
CPU Snapdragon 845,
Kryo 385 Gold x4
& Kryo 385 Silver
x4 8-core, 2.8 GHz
BCM2711, 4-core
Cortex-A72 (ARM
v8) 64-bit SoC
Memory 4GB 4GB
Bluetooth 5.0 5.0
transmit advertising packets with pre-stored values. A more convenient solution
would be to transmit values that change depending on the environment, such as
the number of visitors and congestion levels. To do this, we need to make the
protocols stateful.
4.1 Three Typical Implementations
This section considers the following three methods for creating stateful functional-
ity. The first method involves preparing a variable for the state in the beacon and
updating it using Read and Write commands from an external device connected to
the beacon. This method follows the standard BLE protocol and allows for inex-
pensive devices to be used as beacons, but it has several drawbacks such as long
latency, instability as the number of user devices increases, and increased costs for
guaranteed state updates.
The second method involves each beacon updating its internal state based on
specific external events. Examples of such events include connections from user
devices and signals from nearby sensors. This method requires a single board com-
puter or microcontroller as the beacon device and a program to handle state updates
such as Test-and-Set. Although this method is highly flexible, it loses compatibility
with the BLE standard and may require lightweight protocols such as MQTT to
communicate with sensors and servers.
The third method involves using an external server to store the environment’s
state. In the case of Physical Web [17], a relay server is placed between user devices
and web servers to relay messages from user devices to the corresponding web
servers. The status of the environment is recorded and updated on the relay server.
4.2 Comparison of the Performance
This subsection compares the response time of three implementations of stateful
BLE beacon. More specifically, we measure the time it took to connect to the pe-
ripheral beacon (Experiment 1), access the state of the beacon from the connected
central device (Experiment 2), and communicate with the external server (Exper-
iment 3). The total response time experienced by the users of the stateful BLE
7

Table 2. Result of Experiment 1 (over 100 trials).
Android Raspberry Pi
avg. 979.05 ms 631.84 ms
state change max 1513 ms 1133 ms
min 737 ms 391 ms
avg. 1747.63 ms 1350.65 ms
discover service max 2283 ms 1839 ms
min 1445 ms 1073 ms
Table 3. Result of Experiment 2 (over 100 trials).
Android Raspberry Pi
avg. 114.31 ms 109.2 ms
max 284 ms 184 ms
min 51 ms 50 ms
service is the sum of these values. In the experiments, we used an Android smart-
phone (Pixel 3 XL) as the central device, a Raspberry Pi 4 and another Pixel 3 XL
as peripheral beacons, and Heroku as the external server. Table 1 summarizes the
specifications of each device, which are all compatible with BLE 5.0.
Experiment 1: Connection time At first, we evaluate the connection time to a
peripheral beacon that is placed 1 meter away from the central device. The adver-
tising interval of the peripheral is fixed at 130 ms, and we measure the connection
time by calling the currentTimeMillis() method 100 times on the central device.
We define two different end times for the connection process: the “state change,”
which is the time when the connection state becomes connected and the “discover
service,” which is the time when the central device identifies a service in the beacon
with a designated UUID. The peripheral beacon alternates between advertising and
scanning for connection requests from surrounding central devices. As the adver-
tising interval is fixed at 130 ms, the average time taken to establish a connection
is 65 ms. The results, shown in Table 2 and Figure 3, indicate that the Raspberry
Pi is a more suitable device for a peripheral beacon, as it have a quicker and more
stable connection time compared to the Android smartphone2.
Experiment 2: Time for accessing the state We then evaluate the time it
takes for the central to access the state of a connected peripheral beacon in the
same environment as Experiment 1. The results, shown in Table 3, indicate that
2
It is important to note that the connection time can be affected by both the scan window
size of the central device and the advertising interval of the peripheral. To further reduce the
connection time, we need to consider the trade-off between power consumption and scan window
size, and aim to increase the scan window size.
8

0 10 20 30 40 50
Trials
0
500
1000
1500
2000
2500
3000
Reading
time
[ms]
Distribution of reading time (state connect)
Raspberry Pi
Android
(a) state change.
0 10 20 30 40 50
Trials
0
500
1000
1500
2000
2500
3000
Connection
time
[ms]
Distribution of connection time (discover service)
Raspberry Pi
Android
(b) discover service.
Fig. 3. The distribution of connection times over 50 trials.
9

Table 4. Communication time with the external server (over 50 trials).
Method 2 Method 3
avg. 875.28 ms 886.33 ms
max 1110 ms 1326 ms
min 772 ms 774 ms
while the minimum access time is not significantly affected by the type of peripheral
device, there is a difference of 1.5 times in the maximum access time, the range
between the first and third quartiles, and the average access time. This difference
can be attributed to the performance of the devices, as both support BLE 5.0.
However, the actual data transmission rate can vary depending on the data size
being transmitted. This means that we could potentially reduce the access time by
optimizing the data format and packet length.
In conclusion, on average, the central can connect to a peripheral within 1.5
seconds and once connected, access the state in 100 ms. The use of single-board
computers like Raspberry Pi is a better choice for peripherals over Android smart-
phones in terms of response time. Note that the first method requires accessing the
state at least twice, as it issues a Write instruction after reading the state.
Experiment 3 Finally, we evaluate the communication time between the central
and an external server. To do this, we measure the time it takes for the central
to send a JSON file to the external server and receive the returned JSON file.
We use the currentTimeMillis() method on the central to get these times. We
also separately measure the database access time on the external server using the
microtime() function. The results are summarized in Table 4. As expected, the
third method takes longer to communicate with the external server compared to the
second method, with an average time of 900 ms and an additional 20 ms for database
access. This is still a bottleneck for the third method, with a communication time
of 770 ms even in the best case scenario. In contrast, the first and second methods
do not require communication with an external server as the packet returned from
the beacon can include a concrete state.
5 First Use Case: Sharing Store Congestion
5.1 Assumed Scenario
Suppose that in a shopping mall, beacons are installed in each store to monitor store
congestion, and beacons at the entrance provide visitors with the information. The
beacons are split into two functions, one to gather information and the other to
transmit it, and these functions are run in a single program file. The Obniz Board
is used in this implementation, so the data collected by the beacons is shared in
10

the cloud. However, an edge server, which covers a smaller area, could also be used
in the future to reduce costs.
The flow of information in this system is as follows: User devices send out
packets as a peripheral, and the sensor beacons (A) scan the number of nearby
user devices. Beacon A then shares the results with the broadcaster beacon (B),
which broadcasts the information to its surroundings. Users in the vicinity receive
the information from Beacon B. The details of how beacons function as sensors and
broadcasters are described in the following subsections.
5.2 Sensing Functionality
To ensure accurate detection, only devices related to the service have a uniform de-
vice name, such as “iPhone XXX”. Beacons can distinguish between registered and
unregistered devices by checking the names in the advertisement packets they scan.
The sensor beacon retrieves the names of the sending devices from the scanned
packets and temporarily stores them. After the scan is completed, it removes dupli-
cates and calculates the estimated number of people. This information is stored in
an array and shared with the broadcaster beacon (B). The scan is then repeated.
In addition to the current number of people near the beacon, the array also
keeps track of the number of scans and the average level of congestion, which can
be used in the process of congestion prediction. This information is continually
updated, with the name of the sensing beacon serving as the key.
5.3 Broadcasting Functionality
Beacon B receives the updated information from Beacon A and broadcasts it to the
surrounding area. In this implementation, in addition to the congestion information
for each beacon, the average number of people per location is also included in
the characteristic, so that users connected to the beacon can access this detailed
information individually.
In each advertisement packet, only the values in the shared array are written,
and the congestion level for each beacon location is represented by a numerical
value. To properly match the values of the fields with the beacons, the correspon-
dence must be shared between the user device and the beacon beforehand. Although
it is possible to add fields to the advertisement packet to represent the corresponding
beacon, it would increase the payload, so the trade-off must be carefully considered
based on the application.
Note that while Beacon B broadcasts at regular intervals, to reflect the updated
values in the shared array from Beacon A in the advertisements, it is necessary to
stop sending packets, check the contents of the shared array, and reconfigure the
characteristics of the advertisement packet to be updated one.
11

6 Second Use Case: Stamp Rally App
6.1 Assumed Scenario
Imagine a scenario where smartphone users visit various locations equipped with
BLE beacons in a specific order to collect stamps. In this system, the smartphone
user writes their location and attributes to the BLE beacon and receives a hint
about their next destination, which is calculated based on their input information.
The process flow between the beacon and the user device is as follows:
1. The user device connects to the beacon and writes the necessary information.
2. The beacon performs a calculation based on the written information and sets
the result in the characteristic.
3. The user reads the calculation result from the characteristic of the beacon.
The next subsection explains how to implement beacons with these functions.
6.2 Implementation
The payload of a packet in this implementation is a sequence of bytes that can either
be variable-length data or fixed-length data. When a beacon receives a packet from
a user, it checks the label at the beginning of the payload to determine the type of
data. If the label indicates variable-length data, the beacon reads the next byte to
find out the length of the data and then reads the value accordingly. For instance,
in the stamp rally app, the packet from the user will contain labels for the user’s
previous destinations, and the beacon can use this information along with the list
of all installed beacons to determine the user’s next destination by finding a set of
unvisited beacons. If the label shows that the data is fixed-length data, the beacon
reads the next sequence of bytes and stores the value. In this implementation, the
beacon maintains information such as the total number of visits and a breakdown
by gender, so these values are updated based on the received data. However, since
a single packet has a limited capacity, it might be necessary to divide the data into
multiple writes if it exceeds that capacity.
7 Evaluation
In this section, we evaluate two use cases described in the previous section. Firstly,
we assess the time it takes for the BLE beacon to locate the target device in a
scan, by considering the distance to the target, and examine the scalability of the
proposed system. Secondly, we determine the optimal advertising period for a user
device to send packets by analyzing the miss rate of the central during the scan.
The Obniz Board, a microcomputer board produced by Obniz, Inc., was used
as the beacon and the user device in the experiment. It can connect to Wi-Fi
12

Table 5. The time taken by Central to find the target device.
avg. max min
1m 2156.15 ms 7457 ms 592 ms
5m 2161.89 ms 8847 ms 961 ms
10m 7700.29 ms 25617 ms 1105 ms
1 min 5 min 10 min
Distance to the target device
0
5000
10000
15000
20000
25000
Time
taken
to
identify
the
target
device
[ms]
Fig. 4. The time taken by the central to find the target device.
and control devices and connected sensors through the cloud. The program for
the experiments was implemented using HTML, JavaScript and the Obniz SDK
(obniz.js). The Obniz Board supports Bluetooth v4.2 and the advertisement packet
transmission interval was fixed at 1.28 seconds. The experiments were carried out
in an indoor environment that resembles a typical apartment complex, where BLE
devices and Wi-Fi signals are prevalent. All the Obniz boards were connected to
the same Wi-Fi access point.
7.1 Effect of Distance between Devices on Scan Time
The aim of the first experiment is to determine the time it takes for the central
(using an Obniz board with experimental programs) to scan for and locate a pe-
ripheral, and to find out the size of the area in which the central can reliably detect
the peripheral. The experiment will indicate: in the first use case, how far away the
installed beacon can locate a user device with certainty; and in the second use case,
how close the user needs to be to the beacon to access the service.
13

Table 6. The number of successful scans out of 25 trials (central and peripheral were kept 1 meter
apart).
advertisement period Number of successes
3 sec 20
5 sec 24
10 sec 25
In the experiment, the peripheral constantly emits packets and the central scans
the surrounding area, knowing the name of the target peripheral. We measured the
time from the start of the scan to when the central found the advertisement packet
of the target device. The time was measured using the performance.now() method
of the Performance API. The distance between the central and the target device
was varied as 1, 5 and 10 meters.
The results are shown in Figure 6 and the statistics derived from them are listed
in Table 5. In the case of 1 meter and 5 meter distances, there are differences in the
minimum value and range of the first and second quartiles for the 1 meter scenario.
However, there was no significant difference in the mean value. At a communication
distance of 10 meters, the minimum value is not significantly different from the other
scenarios, but the range and average value of each quartile increases and the time
to discovery becomes unstable due to the variation in time. This indicates that
the communication between two devices becomes more challenging as the distance
between them increases.
In summary, the results indicate that in the first use case, peripherals can be
found stably within a 5 meter radius of the central. Additionally, the more pe-
ripherals found in a short scan time, the more devices are present in the central’s
vicinity. However, the time to find all objects increases as the number of periph-
erals grows, so longer scan times are necessary for applications that require high
detection accuracy. The second use case also shows stable communication when the
user is within 5 meters of the beacon’s location.
7.2 Impact of Advertising Period to the Success Rate of Scans
In the second experiment, we examine how the length of the advertisement packet
transmission period affects the central’s miss rate from a power-saving perspective.
In the experiment, the central and peripheral were kept 1 meter apart and we
recorded the number of successful scans out of 25 trials, where BLE scans search for
peripherals in the vicinity and a successful scan occurs when an advertisement from
a specific peripheral is detected. We tested three different advertisement periods
of 3, 5, and 10 seconds and the results, displayed in Table 6, indicate that at a
distance of 1 meter, having the peripheral transmit for more than 5 seconds greatly
reduces the chance of being missed by the central.
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8 Concluding Remarks
In this paper, we introduce the concept of states for BLE beacons, which were
previously used for transmitting a fixed value to nearby devices through advertising
packets. To demonstrate this functionality, we implement three different methods
using commercially available devices and assess their performance. Next, we explore
the design of stateful BLE beacons and implement two use cases using the Obniz
board. Our evaluation shows that the surrounding BLE devices were accurately
detected under normal usage conditions with a distance of about 3 meters, and
that phone identifiers such as names were correctly obtained if the phone continued
to advertise for more than 3 seconds at 1.28 second intervals.
Future concerns encompass the need for a social implementation of the proposed
framework and the conduction of a large-scale simulation to enhance the reflection
of real-world usage. It is also imperative to experimentally evaluate the extent of
the increase in power consumption resulting from the introduction of the concept
of state into the beacon. Moreover, it is essential to establish a mechanism for
safeguarding the accumulated data and preventing attacks by third parties.
Conflicts of Interest
The authors declare no conflict of interest.
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