https://doi.or g/10.31449/inf.v47i10.5395 Informatica 47 (2023) 9–26 9
Designing A Blockchain Appr oach to Secur e Fir efighting Stations Based
Internet of Things
Samaher A. Y ousif f
1
, Raad A. Muhajjar
2
and Mishall Al-Zubaidie
3, ∗ 1
Ener gy Production for Southern Region, General Company of Electrical, Ministry of Electricity , Iraq
2
Department of Computer Science, Faculty of Computer Science and Information T echnology , University of Basrah,
Basrah 61004, Iraq
3
Department of Computer Sciences, Education College for Pure Sciences, University of Thi-Qar , Nasiriyah, 64001, Iraq
E-mail: samaheraltumma@gmail.com, raad.muhajjar@uobasrah.edu.iq, mishall_zubaidie@utq.edu.iq
∗ Corresponding author
Keywords: BC, consensus algorithms, firefighting equipment, IoT , proof of authority , PoS, PoW , smart network, SHA-
384
Received: October 31, 2023
Although the idea of communication between devices is not new , its development has been rapid and sig-
nificant since it helps people do their jobs mor e efficiently and keeps them fully informed of events at their
homes and workplaces thanks to technology like the Blockchain (BC) based Internet of Things (IoT). How-
ever , this new technology suffers fr om security issues and the existing r esear ch has not addr essed these
issues in depth. In this paper , a simulation of the smart network of the fir efighting station was made. BC
technology was used with one of the consensus algorithms which was pr oof of authority (PoA) to make this
network mor e secur e and private, in addition to the use of a hash function such as secur e hash algorithm
384 (SHA-384), which is a one-way encryption function and was used to verify the BC data integrity so
that it was difficult to hack the data and thus be the data transmission pr ocess is mor e secur e. Also, the
Espr essif 32 (ESP32) device was chosen for this pr oject because it offers several useful characteristics,
including W i-Fi and the capacity for rapid data transmission. It was observed fr om the r esults obtained
fr om the application of consensus algorithms that the fir efighting station network was made mor e secur e
and the PoA algorithm was better in several aspects such as execution time (maximum 0.4 S) and memory
used (maximum 610 KB). Finally , the pr oposed work that was applied to the fir efighting station was good
in terms of safety and privacy , as the work of this station became mor e efficient.
Povzetek: Pr edstavljen je razvoj varne IoT mr eže gasilske postaje z uporabo bločne tehnologije in algo-
ritma PoA za izboljšanje varnosti in zasebnosti.
1 Intr oduction
This section is subdivided into fire science technology and
firefighting stations and security issues.
1.1 Fir e science technology
Each year , fire incidents result in the death or serious
injury of numerous persons [1]. Since its discovery , fire
events emer ged, and they are directly correlated with the
advancement and growth of human civilization. According
to a world health or ganization (WHO) study , more than
300,000 individuals per year pass away from injuries
caused by fire. However , disconcerting data reveals that
low- and middle-income countries account for 95% of these
deaths [2]. According to U.S. Bureau of Labor Statistics
data from 2018, fires and explosions result in the deaths
of 66 building employees each year [2]. The national
fire protection association (NFP A) conducted five-year
research (2010–2014) that found that after excluding one-
and two-unit projects, home r epair or construction projects
caused $280 million in direct property destruction each
year [3]. The advancement of fire science and technology
has been extremely aided by the increased dif ficulties
for fire safety brought on by economic development.
Initially , the main goals of fire science and technology
were to protect huge companies, buildings, and people
from catastrophic fires [4]. In the area of monitoring and
early detection for fire safety , extensive research has been
done.
Numerous research studies have examined the detection
of heat and smoke using a range of tools and techniques,
including distributed temperature sensing (DTS), fiber
optics connected to very early smoke detection apparatus
(VESDA), linear infrared flammable gas detection, and
dual infrared (IR/IR) spectral band flam detection [5, 6].
Some studies have concentrated on fire safety tracking
and fire spotting, as well as preparing for evacuation from
finished as well as ongoing structures and tunnels [7].
Fire safety management should be a top priority for every
company , but it is especially critical in the construction

10 Informatica 47 (2023) 9–26 S. A. Y ousif f et al.
sector due to a variety of elements that frequently lead
to major fire risks. First, many construction sites expose
workers to combustible materials, and the presence of wind
near unfinished structures can quickly start a fire. Second,
the only preventive measures that building sites can use
are portable firefighting equipment (PFE) or , occasionally ,
water tanks because they lack permanent and adequate fire
protection systems. In this context, the occupational safety
and health administration (OSHA) mandates that every
construction project must have a site-specific safety plan
that includes a fire prevention plan [8, 9]. The former was
dealt with in a prior study utilizing a visual programming
approach [10]. In the field of fire protection today , it is
challenging to determine who is responsible based on the
condition of the firefighting equipment after an accident
because of the opaqueness of the maintenance of the equip-
ment [1 1]. The majority of firefighting equipment on the
market today manages identification information and us-
age status using obsolete handwritten recordkeeping. The
industry has also embraced the technique of implementing
a supervisory system to address the issue of the traceability
of fire fighting equipment [12]. Manufacturers, dealers,
and users of fire equipment can access the equipment
information management system using the appropriate key
[13] to sign in on behalf of their identities. W ith the use
of Bluetooth and RFID technologies, they may also record
and update the state of firefighting equipment. Employees
can use and maintain information by logging on to the
application using a 14-bit identity code to check the state
of fire fighting equipment [12], which can successfully
address challenges with data security , information sharing,
and fire safety systems.
1.2 Fir efighting stations and security issues
Some studies presented the technology of the IoT s with the
BC and were applied to firefighting stations in government
institutions such as electricity production, healthcare
and agriculture companies, for the purpose of obtaining
real data far from manipulation/tampering with it and
delivering it to the control units correctly . The IoT s is
a rapidly expanding field, and according to some recent
research, there will be 26 billion IoT devices worldwide
in 2020. The capabilities of IoT -restricted devices expand
with the deployment of cutting-edge network technologies
including cloud computing, fog computing and edge
transparent computing [14]. One of the important things
in the work of firefighting stations is sending the correct
information to the control units, and since this data is
transmitted through sensors distributed in important areas
in companies and institutions, the data transfer process may
be subject to change, whether by sabotage or a malfunction
in one of the sensors [15, 16], especially , the work of these
networks is centralized and thus leads to the exposure of
these institutions and their employees to danger . Therefore,
technology must be used that helps to transfer , maintain
and ensure the transfer of data, and one of them is BC
technology , which is a decentralized network where the
data is distributed over all network nodes, so any change
in the data, the transmitted or tampered data of one of the
nodes is easily detected by the rest of the nodes when it is
matched with its data.
The IoT is a central network that is controlled by a
central server that is responsible for all transactions,
and the rest of the network members are not entitled to
participate in its work. When this central server is attacked
[17], this causes the entire network to stop, so it lacks
security in its work. There are technologies in char ge to
address this issue, the most important of which is BC tech-
nology , which is a distributed ledger , i.e. a decentralized
network, where all participants in this network have the
right to contribute to its work [18]. There are types of this
technology , the most important of which is the public and
private BC. The public BC is a network open to all. There
is freedom of participation without restrictions, and this
type is not preferred by companies because they prefer to
form a network whose participating members are subject
to certain conditions set by the institution and are limited
to those working with it and refuse any participation by
members outside this institution. The technology is the
private BC, where the members of the network are selected
by the company according to the conditions set by this
institution.
An appropriate layer for an architecture that provides
safe services for IoT devices is required in order to accom-
modate a lar ge number of devices. The current architec-
tures use a centralized system in which internet-connected
IoT devices are linked to cloud servers. However , the rapid
proliferation of IoT devices may lead to network problems
such as bottlenecks, network congestion, bandwidth limi-
tations, security risks, single points of failure, and service
delays. A decentralized architecture is required to prevent
these problems. There are certain decentralized lar ge-scale
systems already in existence, including peer -to-peer net-
works [19]. IoT devices employ RFID, wireless sensor
networks (WSN), and other advancements in other tech-
nologies that detect, communicate, and act using already-
existing network infrastructure that has been improved by
IoT . As a result of the IoT , communicated machines are able
to share data, exchange information with one another , and
control goods remotely over the Internet, potentially with-
out human intervention [20]. WSNs are among the tech-
nologies with the fastest growth rates as a result of the de-
velopment of computer networks, wireless communication,
and microelectronic mechanical systems [21]. One of the
origins of the attack was the ”Internet of Things,” a term
used to represent a network of connected devices that in-
cludes printers and other internet-connected gadgets [22].
These devices were tar geted by the Mirai virus, which led
to distributed denial of service (DDoS) assaults as well as
brute force and collision threats. The frequency of attacks

Designing A Blockchain Approach to Secure… Informatica 47 (2023) 9–26 1 1
on IoT devices increased in 2018, with 32.7 million occur -
rences being recorded. The key flaw in this situation was
their dependence on a centralized cloud architecture as well
as the lack of security protocols [23]. Addressing IoT secu-
rity and privacy issues necessitates investigating and grasp-
ing the multiple components of the IoT architecture, iden-
tifying vulnerability areas in each section, and finding the
proper solutions to detect any vulnerabilities. In October
2016, Dyn Inc., a DNS service, was tar geted by a DDoS
attack that T ens of millions of Internet protocol (IP) ad-
dresses were af fected. The IoT is described as the fusion
of the physical world with the Internet to create a smart,
digitally managed environment. IoT technologies have im-
proved and changed how people connect with the environ-
ment, each other , and their surroundings [20]. A decen-
tralized alternative based on tamper -proof data exchange
on a digital ledger might solve many of the issues with the
centralized cloud approach. Every transaction on the chain
may be signed, secured, and verified in BC systems. Edit-
ing or removing data blocks kept on the ledger is quite dif-
ficult. Although there are many dif ferent BC topologies
available, they all follow the same core principles [23]:
– T ransactions between the parties are signed using
cryptography .
– T ransactions are recorded using a decentralized ap-
proach on a peer -to-peer network on a distributed
ledger .
– Agreeing with a decentralized strategy .
1.3 Main contributions
The following are the primary objectives of this study:
1. Ensuring the security of IoT -based firefighting stations
by relying on BC procedures.
2. Implementing private BC and SHA-256/SHA-384 to
meet the privacy and confidentiality of companies and
institutions.
3. Using ESP32 devices to ensure the high performance
of network devices, which is reflected in the overall
performance of the network.
4. Applying PoA in BC transactions to meet the security
requirements of firefighting stations.
1.4 Organization of r esear ch
Here is a characterization of the study route map: Section 1
includes a thorough overview . In Section 2, we investi-
gate the related work about BC and IoT security . Section 3
presents the necessary prerequisites for the proposed ap-
proach. The scientific gap and solution are of fered in Sec-
tion 4. Section 5 of this manuscript describes our proposed
approach. The outcomes of the suggested approach are ex-
amined in Section 6. Section 7 presents the study’ s conclu-
sion. Future tendencies are introduced in Section 8.
2 Existing r esear ch r elated to the
blockchain/IoT security
In this part of the paper , we will investigate existing
research related to Firefighting station security when using
BC and I oT , here we will explain the approaches used in
this field and their drawbacks, see T able 1.
In order to handle fire events, Ali et al. [24] submitted a
decentralized approach for access management, delegation
and permission, with requests on event- and query-based
delegation and permission for IoT applications. They also
used BC technology to decentralize, protect, rely on, and
verify delegation services. They used the PROMELA
(process meta language) basic PROMELA interpreter
model checker to investigate their suggested solution.
The PROMELA model is also used to verify the mutual
exclusion, verification and delegation characteristics stated
in linear temporal logic (L TL). Nonetheless, they do not
address the performance of networking devices when using
IoT -BC which makes their approach slow when applying
permission access and delegation.
T ukur et al. [25] suggested an edge-based, BC-enabled
irregularity disclosure technique to guard against internal
threats in firefighting station applications. The method
starts by utilizing edge computing to bring treating nearer
to the IoT devices, enhancing opportunity and reducing
individual points of insuf ficiency . This reduces latency
and bandwidth needs. Then, it integrates distributed edge
with BC, which provides smart contracts, to execute
the identification and rectification of irregularities in
forthcoming sensor raw data. This draws on some parts
of sequence-based irregularity disclosure. The evaluation
of their method using datasets from actual IoT systems
revealed that it accomplished the coveted outcome whilst
preserving the data’ s integrity and availability , which
is essential for the deployment of IoT platforms. They
referred to the use of a hash function in their BC. However ,
they did not specify the message digest length, which is
crucial for thwarting assaults.
Krishna et al. [26] discussed how wireless body area
networks are used in conjunction with BC technology
implementation methods employing sophisticated Solidity
scripts and embedded programming to address firefighter
issues. They asserted that the integration of implementa-
tion that was shown produced useful outcomes with BC
technology in terms of cryptographic security techniques.
Their approach relied on SHA-256 functions to prevent al-
tered BC transactions, however , SHA-256 may not be suf-
ficient to support high security sensitive applications such
as firefighting stations.
T o quickly handle the request for fire brigade response
and safeguard claims against fire for the business owner in
institutions, Kumar et al. [27] submitted a trusted service
approach of the fire brigade and insurance claims solutions
utilizing BC. In order to prevent significant fire damage, a

12 Informatica 47 (2023) 9–26 S. A. Y ousif f et al.
narrowband IoT -established network chopping is intended
to relay actual-time data to the observation firefighting
station. Additionally , a service queue length technique is
used to choose the best fire station. On the Hyperledger
Besu BC, a prototype of their strategy is created by the
incorporation of Solidity-programmed smart contracts.
However , their approach does not address the protection
of BC transactions with hash functions which means that
their approach could be vulnerable to the modification of
transaction data by attacks.
Khan et al. [28] developed an integrated drone-based BC
architecture in which drones, drone operators, firemen, and
administrators are network users or nodes. All nodes in a
distribution network can access data, ensuring continuous
data exchange and reducing the problems posed by spatial
distance. They came to a conclusion by talking about the
challenges and possibilities of combining BC with other
cutting-edge technology to control forest fires in distant
areas. However , they did not analyze the performance of
their approach, nor did they specify the type of BC used
that most af fects security .
A low-power IoT -based sensor network is created that
can detect forest fires automatically and relay the position
to a central observation station with push alerts in time of
actual life. As a result, the spread of the fire and the dam-
age it causes can be minimized [29]. This step enables the
prompt alerting of firefighters and assists in the early de-
tection of a fire. The suggested approach recognizes fires
when smoke is present and when there is a significant rise
in temperature. Additionally , the scheme records the hu-
midity , temperature, rainfall, carbon dioxide, wind speed
and light in various forest regions. Utilizing a long-range
wireless transmitter , the sensor nodes communicate infor -
mation to a hub, which then uses cellular Internet to transfer
the information to the central observation station. However ,
their approach does not provide technology to secure their
approach data in Firestation applications such as BC which
is important in the management and security of these appli-
cations which makes their approach weak to threats attacks.
3 Necessary pr eliminaries for
pr oposed appr oach
This section explains basic details related to the techniques
and technology adopted in this research.
3.1 Component of internet of things
It is crucial to understand what the IoT is and how it works
before learning about its components. The IoT , also re-
ferred to as the Internet of Everything, is a potentially
revolutionary technological pattern that explains a v ariety
of technologies, like short-range wireless communications,
RFID, and search domains, that may connect real-world
physical things to the environment of the Internet. The
technology’ s methodology must also be understood. Data
is sent from sensors or devices to the cloud server , where
it is processed and converted into a language that the ma-
chine can understand. After the data has been processed,
the results are converted into a language that the user can
understand and are sent as a signal, message, or other form
of communication [30]. As shown in Figure 1, an IoT sys-
tem’ s five main components are nodes/sensors, connectiv-
ity , cloud, processing of data, and the user interface (UI)
[31]:
Nodes/Sensors : These are fundamental IoT parts that
gather information from IP-addressed devices. These de-
vices could be as basic as monitors for the humidity and
temperature in a room or as sophisticated as autonomous
vehicles. These components frequently gather data related
to the settings they have been given, like temperature or
video. These sensors or gadgets work together as a unit
rather than independently . Devices that may transmit data
gathered from physical environments to the IoT ecosystem
make up IoT structures [28].
Connectivity : Using local area network (LAN), W i-Fi,
satellite, Bluetooth, cellular , and other infrastructure tech-
nologies, sensors and other devices are linked to the cloud.
As a result, IoT nodes connect to one another using com-
mon communication protocols as a consequence. For in-
stance, Bluetooth low ener gy (BLE) and W i-Fi are intended
expressly for the applications of IoT . It is anticipated that
the 5-generation (5G) cellular network will help the IoT by
boosting capacity and speed [32]. The development of new
techniques, e.g. edge computing, a modern paradigm for
dispersed nodes, has been forced by the collection of huge
amounts of data. In edge computing, processing and data
are distributed where they are most required. Any data that
the screened cloud can not treat, is transferred closer to the
customer , decreasing the time of lag and boosting band-
width. These devices or systems process the data, and only
the most pertinent information is sent back to the central
base for analysis [33].
Cloud : A vast network that accommodates IoT devices and
apps is known as an IoT cloud. This comprises the servers
and storage that are necessary for processing and real-time
operations. An IoT cloud also includes the standards and
services needed for connecting, managing, and securing di-
verse IoT devices and applications. Thanks to IoT clouds’
on-demand, inexpensive hyperscale, businesses can take
advantage of the huge potential of IoT without having to
build the required infrastructure and services from scratch.
T o gather and process information from IoT nodes, such as
sensors, and to remotely control the devices, t he IoT cloud
leverages cloud computing services. IoT cloud systems’
scalability makes it possible to analyze massive volumes
of data and to use analytics and artificial intelligence (AI)
tools.
Pr ocessing of data : Data is handled by the software once
it has been saved in the cloud. Before this data is of any

Designing A Blockchain Approach to Secure… Informatica 47 (2023) 9–26 13
T able 1: Comparison of methods, findings and limitations of existing research
Ref. Methodology Key findings Limitations
[24] BC with PROMELA V erification of delegation and mutual ex-
clusion
Applying for permission access and dele-
gation reduces notably performance
[25] Edge-based BC-IoT with irregularity dis-
closure
Data’ s integrity and availability are essen-
tial for the deployment of BC-IoT
Using hash message digest of unlimited
length is a vulnerability for attacks
[26] BC-SHA256 with Solidity scripts to com-
pute sensors’ data
BC-IoT to avoid attacking the transactions
with intentional threats
SHA-256 is vulnerable to firefighting ap-
plications
[27] Hyperledger Besu BC with narrowband
IoT and a service queue length technique
Performance in terms of latency and
throughput
No hash functions to prevent transaction
modification
[28] Drone-based BC architecture and physi-
cally unclonable functions
Conserving both power and the implemen-
tation area
Lack of analysis of BC performance and
type
[29] IoT -based sensor network to recognize fires Record the humidity , temperature, rainfall,
carbon dioxide, wind speed and light in var -
ious forest regions
No BC to manage and secure fire apps
Figure 1: Components of IoT architecture
use, it must be carefully processed, filtered, and examined.
Zettabytes of data are transmitted through each edge gate-
way prior to processing in order to prevent the system from
becoming overloaded. Big data analytics uses cloud data
analytics to evaluate production data at the highest level
feasible by integrating corporate applications. IoT data is
generated in real time and has a dif ferent structure. Mas-
sive volumes of IoT -created information need to be treated,
assessed, and classified before they can be used in decision-
making [34, 35].
User interface (UI) : Also, once it has been cleaned up and
coordinated, the gathered information has to be utilized to
notify the final clients. For instance, clients must be no-
tified whenever the temperature in cold storage exceeds a
certain point. This is made possible by using a user inter -
face, which gives end users the chance to preview the data
that has been collected. As a result, these end clients could
respond to network inputs based on the applications of IoT
[34, 36].
3.2 Blockchain technology
Blocks that have been cryptographically linked together
form data structures known as BCs. It could serve as a
secure historical ledger for the administration of data and
transactions [37]. BC, the technology that powers Bitcoin,
was first proposed by Satoshi Nakamoto. The security and
immutability of BC have been proven to be two of its most
important characteristics. Correspondingly , it could be a
practical remedy for a variety of issues that traditional secu-
rity schemes run into, such as privacy issues and centralized
networks with bottlenecks and single points of breakdown
[38]. Figure 2 illustrates the BC’ s structure. BC represents
a technology that uses a peer -to-peer network to maintain
an immutable distributed ledger . A consensus on the trans-
action statuses must be reached among network participants
for transactions uploaded to a BC network to be valid. It is
also critical to understand how this process works, which
entails placing transactions in blocks with a variety of data,
including nonce, timestamp, and prior hash, before using
that data to strengthen the hashing of sensitive data. Next,
under some circumstances, one of the consensus methods
is used to pick up the node that will add this block to the
BC [39]. The following are four fundamental properties of
BCs [34]:
– Ledger : In order to give a thorough value-based his-
tory , advertising is documented in this fashion. BCs

14 Informatica 47 (2023) 9–26 S. A. Y ousif f et al.
Figure 2: BC structure
do not have overruns, in contrast to conventional
databases.
– Secur e : No one can access the data recorded on a BC
without authorization since it is encrypted.
– Shar ed : Each record involves numerous parties,
which is why a hub member in a BC web.
– Distributed : T o maximize flexibility and lower the
likelihood of a successful assault, a BC can be dis-
persed and its node count can be altered. As the
name implies, BC represents a collection of blocks
with timestamps linked by cryptographic hash func-
tions [40].
3.2.1 Hash function
A BC can be dispersed and its nodes can be added or re-
moved to boost flexibility and lower the likelihood of a
successful assault. As the name suggests, BC is a network
of blocks with timestamps Interconnected by cryptographic
hashes [41]:
1. Consent preimages of any length.
2. The computed image is the same for identical preim-
ages.
3. Any preimage image has a set length that is easy to
calculate.
4. Exhaustion is the only method to discover the preim-
age through the image.
5. It is dif ficult to locate a dif ferent preimage that
matches the estimated image of a specific preimage.
6. The images before and after the modification are unre-
lated to one another; even little changes in the preim-
age result in significant changes in the image [42, 43].
3.2.2 Algorithm of consensus
The BC uses consensus techniques to make sure that every
new block that is appended to the network is the only accu-
rate representation of the data. A distributed/decentralized
computer network has unanimity among all nodes. Because
they guarantee the security and integrity of these distributed
computing platforms, consensus algorithms are essential
for BC networks [23]. There are many important consensus
algorithms in BC:
Pr oof of W ork (PoW) : It was the first technique used to ar -
rive at an international consensus [44]. The PoW has histor -
ically been the most common method of reaching consensus
inside BC designs. PoW makes it tough to create a legiti-
mate block and connect it to a BC by searching for hash
functions with dif ficulty proportionate to the network’ s pro-
cessing power . After altering a block, a user must revalidate
any further blocks. The more validations are required, the
older the block that is being updated is. Even a newly vali-
dated block update is expensive since only a small number
of miners or clusters of miners have the ability to manufac-
ture fresh blocks every 10 minutes [23].
Pr oof of Stake (PoS) : It is a substitute strategy that was de-
veloped in response to criticism of PoW . W ith PoS, comput-
ing activity is replaced by a random selection process, and
the likelihood of successful mining is inversely correlated
with the number of validators. The likelihood of generating
a block is influenced by the stake nodes’ financial commit-
ment to the network, or coin ownership.
Pr oof of Authority(PoA) : It was initially suggested as an
extension to the Ethereum BC for monitoring Internet use

Designing A Blockchain Approach to Secure… Informatica 47 (2023) 9–26 15
in response to the anxiety issues associated with PoW . The
foundational principle of PoA is that only chosen, trusted
nodes are permitted to produce new blocks. It is great for
small networks and test nets despite centralizing the BC’ s
total membership [45].
3.2.3 Blockchain technology types
Public, private, consortium and hybrid BCs are the four cat-
egories [46].
1. Public BC: It allows for the observation of transactions
by all users, but it conceals the identities of the initi-
ating nodes [47]. A single entity does not govern this
decentralized peer -to-peer network [47]. Figure 3 (a)
describes the public BC [48].
2. Private BC: A BC with permissions establishes a set
of privileges that users must possess in order to func-
tion on the network [46, 49]. The entity that owns this
kind is the only one in control of block construction. A
private BC is generally utilized by businesses to keep
track of transactions or send information to a select
group of consumers [20]. Figure 3 (b) describes the
private BC [48].
3. Consortium BC: The BC network is controlled by a
number of dif ferent entities in this semi-decentralized
type. The private BC, which is administered by a sin-
gle entity , is distinct from this. In this type of BC,
multiple entities act as the central authority for infor -
mation exchange and mining. BC technology is used
by a number of sectors, including banking and gov-
ernment or ganizations [50]. Figure 3 (c) describes the
consortium BC [48].
4. Hybrid BC: It refers to the integration of the two BC
types, namely the private and the public BCs. By
mer ging the best features of public and private BCs,
this kind may achieve higher security and faster BC
solutions. Due to the limitations of both private and
public BC systems, hybrid BC allows the or ganization
to have more influence over user preferences without
having to make changes to their original desirations.
Figure 3 (d) describes the hybrid BC [48].
3.3 Blockchain in IoT
The scientific community frequently uses WSNs, which are
viewed as an essential part of ubiquitous computing be-
cause they have the ability to create and manage a wider
range of data with higher resolution, the IoT has become
a supporter of many areas. BC’ s features include decen-
tralization, anonymity , persistency , and auditability [51].
Keep in mind that the special characteristics of BC may pro-
vide a remedy for IoT security problems. IoT systems have
enhanced decentralization, resilience, security , and identity
management. As a result, IoT networks can have a safe base
thanks to the BC. Before being authorized and published to
the distributed public ledger , transactions must first be val-
idated by the majority of BC participants. This guarantees
visibility and public visibility . Moreover , no central author -
ity exists to sanction transactions or establish precise rules
for participant communication or service access. Because
most IoT application participants have to agree to authenti-
cate transactions, there is a greater and more general level
of trust [38, 52, 53]. Figure 4 illustrates IoT transaction data
and how it could be protected by employing BC. Since the
IoT composes a core network that transfers sensitive and
essential data, security is one of the most crucial problems
that must be resolved in this network. BC technology is
one of the most crucial methods used to address security .
When using the application programming interface (API)
and the technology’ s built-in consensus methods, data is
moved from the IoT to the BC network. After the data has
been broken up into smaller pieces using the hash function,
it is then saved in the form of blocks that will later be added
to the BC after their validity has been confirmed by network
nodes. Among the most important advantages of using the
BC with the IoT are:
1. Making the IoT more secure, because the BC is a de-
centralized network, so the process of penetrating it is
dif ficult.
2. It is more private, so it is preferred to be used in many
companies and institutions.
On the other hand, there are some disadvantages in inte-
grating the BC with the IoT , the most important of which
are:
1. It needs high computing power .
2. It needs continuous ener gy because its work depends
on the Internet, and in the event of any disconnection
of the Internet, it leads to some problems.
4 Resear ch gap and solution
This section explains the limitations, modelling and secu-
rity requirements of Firefighting station systems.
4.1 Restrictions of existing construction fir e
safety administration practices
People have traditionally relied on themselves or neighbors
to help with rescue and relief ef forts in fire events in the dis-
tant past [34]. Several fires have broken out in construction
sites over the past few years, many of which have resulted in
significant property loss and fatalities [54]. T o address the
crucial issue of fire safety , several nations around the world
have created various measures, such as updating fire safety
codes and enforcing fire safety inspection procedures [55].
Falsifying and for ging PFE paperwork or tags is a serious
problem connected to the existing procedure, and multiple
instances of this have been documented [56]. A building in-
formation modelling (BIM) relied on a strategy eligible of

16 Informatica 47 (2023) 9–26 S. A. Y ousif f et al.
Figure 3: T ypes of BC networks for IoT
saving the essential data associated with these devices, such
as manufacturer and device names, equipment type, main-
tenance personnel, exterior features, prior inspection/repair
time, and other specifications, as well as the coordinates of
the firefighting equipment’ s location, has been suggested to
address this issue [9].
4.2 Constructing visual pr ogramming
languages and information modeling
languages
Construction is one of many worldwide businesses that a
51-percent assault (also known as a majority attack) is the
most dangerous type of threat, in which a single miner can
take control of the entire BC and make any transaction they
choose. Although data are available in this situation, the
attacker who controls the BC may be able to prevent trans-
actions from taking place. This type of assault also compro-
mises data security [40]. Several techniques to ensure se-
curity for IoT -BC applications have been proposed. Some
have used machine learning techniques, while others have
used more traditional methods [57]. BC technology has the
potential to disrupt [58, 59] by providing distributed, en-
crypted, and ensure logging of electronic transactions data.
Seven categories can be used to categorize previous initia-
tives to examine BC technology for various domains: smart
homes, smart centres, smart cities, smart factories, smart
transportation, smart governments, smart ener gy , or ganiza-
tional frameworks and business models, and BIM and con-
struction administration [59].
The scope of the literature reviewed in this manuscript is
restricted to research pertaining to buildings and construc-
tion, despite the fact that research on BC technology in the
field of construction management is still in its early stages.
This technology has the possibility to treat a few issues that
block the construction industry from utilizing recent tech-
nologies such as BIM [60] by of fering properties like disin-
termediation, confidentiality , non-repudiation, provenance
tracking, change tracing, inter -or ganizational recordkeep-
ing, and information ownership. For ener gy management,
a hybrid strategy combining BC and BIM technology has
been used to measure indoor temperature using IoT -based
smart house devices [61]. T o address one of the complex
problems relating to the payment of wages to construction
workers, a BC-based smart contract was introduced by Ro-
faida et al. (2023) [62]. However , no studies have been
done yet that examine the application of BC technology
in the process of checking and repairing construction gear ,
to the best of the authors’ information. Optical character
recognition (OCR) methods, on the other hand, involve pre-
processing, segmentation, image acquisition, classification,
feature extraction, and pattern recognition [61]. Numerous
investigations have concentrated on character recognition
through optical vision when using a BC and OCR. OCR
technology attempts to translate any handwritten or typed
text included inside an image into the text [61]; however ,
handwritten text recognition is more dif ficult than that of
typewritten or printed text. In general, handwriting styles
dif fer from person to person; hence, controlling these vari-

Designing A Blockchain Approach to Secure… Informatica 47 (2023) 9–26 17
Figure 4: BC’ s work
ances is essential for OCR [63].
4.3 Security
An information system must traditionally meet three con-
ditions to be secure:
– Confidentiality is one of the most important aspects of
any business. Unauthorized access to the most sensi-
tive data should be avoided.
– Integrity and reliability ensure that unauthorized par -
ties cannot change or delete data. It is also common
to include the requirement that if an authorized person
messes with the information, the adjustments must be
undo-able.
– A vailability is when necessary , data can be accessed.
Regarding confidentiality , the previously addressed topic
of their privacy is related to the section on transaction data.
Existing IoT applications tend to consolidate connections
in a server , a group of servers, or the cloud in terms of the
architecture that supports the stored data. A strategy like
this is workable as long as the centralized infrastructure’ s
managers are reliable and the network is protected from
both internal and external attacks [40]. A 51-percent as-
sault (also famous as a majority threat) is the most danger -
ous kind of attack, in which a single miner could take con-
trol of the entire BC and make any transaction they choose.
Although data are available in this situation, the attacker
who controls the BC may be able to prevent transactions
from taking place. This type of assault also compromises
data security [40]. Several techniques to ensure security for
IoT -BC applications have been proposed. Some have used
machine learning techniques, while others have used more
traditional methods [57].
5 Pr oposed appr oach methodology
This part of the research will explain the work details of the
proposed approach.
5.1 General pr oposed framework
The proposed framework is presented in this section’ s
general structure, which is divided into many phases, each
of which serves a particular purpose. Since the outcome of
one phase determines whether or not a user can move on to
the next, these phases are interconnected. Components of
our approach are temperature sensor , ESP32 device, server ,
PoA and network nodes. This ef fort involved simulating
a firestation’ s smart grid as part of a power plant project.
The ESP32 device was employed in this network due to
its many benefits (such as W i-fi capabilities, low ener gy ,
supporting Bluetooth, Rich PIO interface, dual-core, Mi-
croPython compatibility , extremely cheap, MicroPython
compatibility and supporting Arduino), and specific BC
technology used to increase network security because it
is appropriate for businesses and institutions. The PoA
algorithm is one of the consensus algorithms used in BC.
Also, the hashing function (SHA384) was utilized in this
work. First, to raise the network’ s security , the simulation
must be built on the IoT before the BC is connected. The
phases of this proposed approach are depicted in Figure 5.
The implementation of this work’ s mechanism is shown
in the diagram. This IoT technique reduces the amount
of time and ef fort needed to transfer information between
nearby and distant locations, but it has a severe flaw that
could af fect the data being communicated over the Internet
and render the station’ s personnel utterly unusable. Also,
it results in a lack of electrical power supply , necessitating
an increase in security for this system. It was determined
to combine BC technology with IoT technology to more
ef fectively secure the transmitted data after researching and
analyzing the best ways to do so. As we already discussed,
a simulation of the smart network of the firefighting
station in the power -producing firms was created for this
study since it contains temperature sensors placed in key
locations.
According to the suggested project depicted in Figure 5.
W i-Fi is used for communication between the TX transmit-
ter and the RX receiver , and the TX attached to the temper -
ature sensors chooses the data at random. The data is sub-
sequently sent to the server , which retains the information
obtained from the TX before sending it to the RX. Process-
ing carried out in the receiver RX to ascertain the values
received from TX, such as information received: V alue 20
such as the temperature. The parties that receive the infor -
mation from the transmitter and pass it on to the receiver

18 Informatica 47 (2023) 9–26 S. A. Y ousif f et al.
also decide the server address. The data is kept in random
memory throughout program execution. After that, it up-
loads the information it has just received to the BC net-
work, which verifies it and adds it to the IoT network. By
contrasting the current and prior temperatures, the verifi-
cation process is carried out. If the discrepancy is lar ge,
the proper action will be performed. The functioning of the
phases of proposed approach, linking temperature sensors,
using ESP32 device, PoA algorithm and the secure hash
algorithm (SHA384) function are demonstrated in the fol-
lowing subsections.
Figure 5: Methodology structure of proposed work
5.2 Phases of pr oposed appr oach
The phases of our work will be as follows:
1. T emperature data is collected by temperature sensors
to measure temperatures at Firefighting stations. This
data accuracy is an essential element in avoiding fire
problems in production companies.
2. The use of EPS32 devices is characterized by benefits
as mentioned in Section 5.1. These devices are char -
acterized by high performance which will be useful in
dealing with temperature sensor data collected.
3. Building IoT applications that transmit sensor data via
EPS32 to servers. These applications include TX and
RX processes to perform sending and receiving oper -
ations.
4. Server receives temperature sensors data based on IoT
applications.
5. Collected information is directed to one of the BC al-
gorithms (PoA) for review and validity testing after
being transferred through the Internet to the receiving
device (ESP32), depending on the method, before be-
ing shared with the network nodes.
6. Network nodes are the network end-edge devices that
receive reports from firefighting station servers.
5.3 Linking temperatur e sensors
These sensors are located in the firefighting stations and the
main control units of the electricity production company ,
where the two units are linked together . These sensors are
with variable resistors or glass filled with a substance, so
this glass breaks and this substance is released when the
temperature rises above the normal rate, and thus an alarm
is sent to the main control units, which send a signal to
the firefighting stations to take the necessary action. Fig-
ure 6 shows the types of temperature sensors used. T o link
a temperature sensor with an ESP32, we will typically need
a temperature sensor module that communicates with the
ESP32. Listed below are basic points to link the tempera-
ture sensor with the ESP32 device:
– Select a temperature sensor: Choose a temperature
sensor module that works with the ESP32 device. The
DS18B20-digital, DHT1 1/DHT22-digital, and LM35-
analog sensors are common choices. Make that the op-
erating voltage and connection of the sensor are com-
patible with the ESP32 device.
– Connect the temperature sensor to the ESP32: Make
the appropriate connections between the sensor mod-
ule and the ESP32 depending on the sensor and com-
munication. For I2C, connect the sensor ’ s data and
clock pins to the corresponding pins on the ESP32 de-
vice.
– Install the required libraries: Install the required li-
braries for the selected connection and the temperature
sensor .
– W rite the code: Open the development environment
(e.g., Arduino IDE) and start a new project. W rite the
code to read data from the temperature sensor after im-
porting the required libraries.
5.4 Using ESP32 device
The ESP32 is a microcontroller and W i-Fi module combo
that has many features and functionalities to of fer . Due
to its adaptability and low power consumption, it may be
used in IoT applications. The following are some impor -
tant points to keep in mind when researchers begin using
our ESP32 device:

Designing A Blockchain Approach to Secure… Informatica 47 (2023) 9–26 19
Figure 6: T emperature sensors of proposed methodology
– Set up the development environment: Install the nec-
essary software tools, such as the Arduino IDE or Plat-
formIO, which provide an easy-to-use interface for
programming the ESP32 device.
– Connect ESP32 to network device: Use a USB cable to
communicate the ESP32 device to the network device
such as a computer . Ensure that the device is properly
recognized by the operating system.
– Select the appropriate board and port: In the develop-
ment environment, select the ESP32 board we are uti-
lizing from the list of available options. Also, select
the correct serial port to establish a connection with
the device.
– W rite and upload code: Start by writing code uti-
lizing the programming language supported by the
researcher ’ s chosen development environment. Re-
searchers could find many example codes and libraries
online to help them get started. Once the code is ready ,
upload it to the ESP32 using the upload button in the
development environment.
– Observe the output: Depending on the code, the re-
searcher can monitor the output of the ESP32 through
the serial monitor in the development environment.
This allows us to debug and verify that the code is run-
ning as expected.
– T est device: Connect ESP32 to the required temper -
ature sensors, actuators, or other peripherals. V erify
that the device is interacting correctly with these com-
ponents.
5.5 Pr oof of authority algorithm
This subsection explains how the PoA algorithm works, as
seen in Figure 7. According to the PoA approach, reliability
or the number of previously contributed blocks is taken into
consideration when selecting the header node that uploads
Figure 7: The PoA ’ s work
the block. The network of choice has a list of nodes ranked
by authority and dependability . When the other nodes have
confirmed the data, this trustworthy node will seek for the
correct hash, add the block, and then share the block with
them. A dif ferent node is chosen for each epoch until every
node on the list has finished adding blocks to the BC. The
technique is then carried out once more. Upon confirmation
by the other nodes, this trustworthy node will seek for the
correct hash, add the block, and share it. Until each node
on the list has finished adding blocks to the BC, a dif ferent
node is selected for each epoch. At that point, the process
is repeated. The block will be added after the node that ac-
quired the hash, and data will be shared by each node in turn
after that until all nodes have finished adding blocks. For
example, when the program is first executed, node num-
ber (1) is chosen in order to obtain the correct hash prior to
adding the block. This process will only be repeated once
all nodes have finished adding blocks, so after the node ob-
tained the hash, each node in the listing will append the
block and engage the information in turn. For instance,
when the program is run for the first time, node number
(1) is selected to obtain the proper hash before adding the
block. The selected node is known as the miner in this pro-
cess, which is called mining. The block will be canceled if
it is discovered that the data contributed to the block by the
chosen node is inaccurate, and if these mistakes continue,
the node will be removed from the list of nodes. After -
ward, node number (2) will obtain the hash and include the
block, and node number (3) will mine information to obtain
the proper hash and include the fresh block to the BC. This
method greatly accelerates program execution, which cuts
down on the amount of time needed to share data among
network nodes and conserves resources.

20 Informatica 47 (2023) 9–26 S. A. Y ousif f et al.
5.6 Secur e hash algorithm
It is a one-way hash function, used with many technologies
such as BC technology , where this function performs trans-
action hashing with a 384-bit digest. Thus, the length of the
abstract is 96 after converting to the hexadecimal system.
Although the process of hashing and obtaining the required
digest takes more time and space than other functions such
as SHA-384, it is more secure than SHA-224/256, so it is
preferred in many applications. SHA-384 and SHA-256 are
both cryptographic hash functions that belong to the SHA-
2 family . While they share similarities, there are important
dif ferences between the two:
– Output size: SHA-384 produces a hash value with a
size of 384 bits (48 bytes), while SHA-256 produces
a hash value with a size of 256 bits (32 bytes). The
lar ger output size of SHA-384 presents a higher level
of security against collision attacks compared to SHA-
256.
– Security strength: Due to its longer output size,
SHA-384 of fers a higher security strength than SHA-
256. Security strength refers to the level of resis-
tance against brute-force attacks. SHA-384 provides
a higher resistance, making it more suitable for appli-
cations that require stronger security .
– Computational ef ficiency: SHA-256 is generally
faster to compute compared to SHA-384. SHA-384’ s
greater output size necessitates more processing power
and may cause slower hashing performance.
– Application use cases: SHA-384 is typically used in
applications that demand a higher level of security ,
such as production enterprises such as electricity in
particular firefighting issues. SHA-256 is widely used
in various applications and communication protocols.
– Compatibility: Both SHA-384 and SHA-256 are
widely supported and implemented in modern cryp-
tographic libraries and frameworks.
When choosing between SHA-384 and SHA-256, it is cru-
cial to take into account the application’ s particular perfor -
mance and security restrictions. If the application needs
a higher level of security with an emphasis on resistance
against collision attacks, SHA-384 is a suitable choice.
Therefore, we choose to use an SHA-384 algorithm.
6 Our pr oposed r esults
In this section, we will explain our results in terms of per -
formance and security .
6.1 Results of our pr oposed performance
In this part, the performance of our approach will be ex-
plained. The algorithms get more ef ficient as the hardware
utilized at work becomes more ef ficient. A Fujitsu PC with
a Core TM i5 M520 CPU running at 2.4 GHz and 6 GB
of random memory was utilized to run the simulation. In
addition to that, a microcontroller (ESP32) was used for its
advantages, as it has W i-Fi and is characterized by fast data
transfer . As for the software components, the proposed sys-
tem was programmed using the MA TLAB 2021a language,
due to its ease of programming and containing many li-
braries necessary for this work.
The results of PoA algorithms with IoT technology will
be presented as the next step in order to enhance its ef fi-
ciency and security . The number of blocks used for sim-
ulation in the proposed approach is 18 blocks. Figures 8,
9 and 10 show that the results were satisfactory in terms
of execution time and memory use. Using this method in
the network of firefighting stations included in the power
plants project has increased security and ef ficiency . BC
algorithms enhance IoT technology’ s security and perfor -
mance while also boosting its capacity to share accurate and
unmistakable information. Misinformation given by sen-
sors may cause various issues when dealing with IoT itself.
In this work, we simulated the smart network of the fire-
fighting station in the power production company , as men-
tioned previously , and the following results were obtained
in terms of PoA ’ s nonce attempts, time of adding the block
and PoA ’ s used memory for both SHA-256 and SHA-384
as well as performance comparison.
6.1.1 Nonce attempts
In this work, the nonce represents a number of iterations
and attempts until the required solution is obtained by the
network participants according to the dif ficulty conditions
imposed in this work, as shown in Figure 8. As can be seen
from the figure, our approach with SHA-384 can be better
than using SHA-256 in terms of nonce iterations.
Figure 8: PoA ’ s nonce iterations in both SHA256 and
SHA384
6.1.2 T imes to add blocks
This subsection describes the creation time of each block
after the data is transferred to the network nodes, and the

Designing A Blockchain Approach to Secure… Informatica 47 (2023) 9–26 21
SHA256/SHA384 algorithm is applied to the block data and
the required digest is obtained in the hexadecimal system,
as shown in Figure 9 that shows times to add the blocks.
Although sometimes our proposed approach with SHA256
provides an advantage over SHA384, our approach with
SHA384 still provides an advantage over SHA256 in some
block numbers (see Figure 9). This means that the perfor -
mance of our approach with SHA384 can be suitable for
firefighting station applications.
Figure 9: T imes to add the blocks
6.1.3 Requir ed memory size
This part of our research characterizes the size of the used
memory in this work, although our proposed approach with
SHA384 takes up more space than similar functions, how-
ever , it is more secure. The memory size with both SHA256
and SHA384 can be seen in Figure 10. W e can sacrifice a
little bit of memory , but we can not lose important infor -
mation through hacks that can destroy the entire system or
application.
Figure 10: PoA ’ s memory used
6.1.4 Performance comparison
Our approach theoretically provides better performance
compared to [24], [25], [26], [27], [28] and [29] because
our proposed approach uses EPS32 devices to support net-
work performance of temperature sensors (as presented in
Section 5.1) and thus support the performance of the net-
work as a whole. Furthermore, [24] needs additional per -
formance overhead to accomplish the licensing operations
in their IoT -BC approach. Also, both [28, 29] do not rely
on BC to secure IoT applications. Compared with our ap-
proach, it uses BC because it performs processing of fire-
fighting station requests in fast processing time, thus its use
in modern applications such as firefighting applications be-
comes inevitable and desirable.
6.2 Security analysis
There are types of attacks that many electronic systems
and smart networks may be exposed to, and one of these
attacks is a DDoS threat is a malicious trial to disrupt
the normal flow to a tar get server , service, or network by
flooding the tar get or its embracing infrastructure with an
excessive amount of Internet requests. A DDoS will spread
to other computers in the same network once it begins
on one, leading to a catastrophic collapse. All network
resources, including the technology that underpins a firm
website, are subject to certain capacity limits that are used
in this type of assault. The ultimate objective of an attacker
is typically to entirely obstruct an online resource’ s normal
operation. Users will not be able to access a website or
application, if one exists. Additionally , the assailant might
demand payment to halt the assault. A DDoS assault could
occasionally be an ef fort to harm or harm the reputation
of a rival business. Due to this, safety measures must be
performed. Every day , more than 2000 DDoS attacks are
recorded worldwide. DDoS attacks are to blame for a third
of all outages. It can take a variety of shapes, including
Smurfs, t eardrops, and Pings of Death. DDoS assaults are
seen as ”weapons of mass destruction” online.
DDoS attacks are more challenging to protect against
since no business can take enough security measures
to be completely secure. But BC technology can face
these attacks by making sure that all nodes have enough
processing power , storage, and network bandwidth is the
main defense against BC DDoS. According to the general
rule, a BC network will be more resistant to a DDOS attack
the more decentralized it is. Because it is decentralized,
the BC network is protected so that transactions can go on
even if certain nodes are of fline for a period. Any node can
fall of fline due to a DDoS assault or another occurrence
without taking over the entire network. There is also
another type of attack such as brute force and collision that
can tar get hash functions to expose or destroy information
in firefighting stations. These attacks are not possible on
our approach because it provides a long and unhackable
message digest as we explained in Section 5.6.
Additionally , our approach provides better security
when using SHA384 as explained in Section 5.6. When
compared with all included searches in Section 2, our

22 Informatica 47 (2023) 9–26 S. A. Y ousif f et al.
approach is superior to all included searches in terms
of security because it uses SHA384 which is capable of
blocking DDoS, brute force, and collision attacks. For
example, Krishna et al. [26] are based on BC-SHA256,
Kumar et al. [27] are based on BC-Ethash with 256 bits.
T ukur et al. [25], Ali et al. [24], Khan et al. [28], Khan
[29] are mentioned because they used a hash function but
did not specify the message digest length. This illustrates
that our approach provides better security than existing
research.
7 Conclusion
The use of an approach to reduce fire problems in electric
power production companies is a priority for this sector .
Also, any breakthroughs can greatly increase these prob-
lems. Our suggested design makes use of exclusive BC
technology since it of fers anonymity and security , works
well with the IoT to boost ef ficiency , and is thus appro-
priate for electric power production businesses and or ga-
nizations. This technology is dif ferent from the other en-
cryption methods in that it employs a one-way encryption
mechanism, we adopted SHA384, making it more secure
and resistant to attacks than the others. It has been used
at the firefighting station to make the procedure of sending
data from sensors dispersed across the power plant’ s vari-
ous locations to the control unit safer . Its implementation
employs the PoA method, one of the BC’ s consensus algo-
rithms, to test how quickly and how much memory it needs
in a firefighting station. Because of its benefits in terms of
performance, the ESP32 device was employed in this work.
Also, a local server network was incorporated into this de-
sign for increased secrecy , and this tactic worked well for
this work. Due to the requirement to work for various li-
braries that are useful in this task, picking the programming
language was one of the most significant problems we en-
countered. Matlab 2021a was used to program this project
since it is appropriate for our needs and provides the re-
sources we need to create a network simulation. Based on
the results of the performance analysis (use of ESP32 hard-
ware) and security (blocking of DDoS, brute force and col-
lision), our approach is superior to the research approaches
included in Section 2 and thus our approach can be very
suitable for firefighting station applications.
8 Futur e dir ection
Our proposal is directed to firefighting applications, as the
user devices are limited, and therefore our proposal may not
be suitable for other applications, such as e-education and
e-health applications, which depend on a lar ge number of
user devices and sensors. In the future, we should examine
the compatibility of our approach with dif ferent electronic
applications. T o develop our proposed approach, we will
experiment with combining our suggested network with a
dif ferent kind of BC, the consortium BC, to increase the
security of the IoT . Since it is administered by a number
of institutions rather than just one, this kind of BC may be
suitable for many electric power companies or government
or ganizations. T o exchange data or conduct mining opera-
tions, each institution chooses a group of reliable nodes that
are joined into a single network. Then, we intend to expand
our security analysis on detailed types of DDoS attacks such
as Slowloris, Zero-day , V olumetric, etc. Finally , we plan to
support our approach by using GraphChain with BC to im-
prove performance by supporting multi-nodes and multi-
servers in parallel processing, this will improve network
performance, especially servers that experiencing huge mo-
mentum from IoT -BC network requests.
Conflict of inter est
The authors declare that they have no conflict of interest.
Data availability
Data sharing not applicable to this article as no datasets
were generated or analyzed during the current study .
Refer ences
[1] M. Shokouhi, K. Nasiriani, Z. Cheraghi, A. Ardalan,
H. Khankeh, H. Fallahzadeh, and D. Khorasani-
Zavareh, “Preventive measures for fire-related in-
juries and their risk factors in residential buildings: A
systematic review ,” Journal of injury and violence r e-
sear ch , vol. 1 1, no. 1, p. 1, 2019. https://doi.org/
10.5249\%2Fjivr.v11i1.1057
[2] B. o. L. Statistics, “Census of fatal occupational in-
juries (CFOI)-current and revised data,” 2019.
[3] R. B. Campbell, Fir es in structur es under construc-
tion, under going major r enovation, or being demol-
ished . National Fire Protection Association Quincy ,
MA, 2017.
[4] J. Gehandler , “The theoretical framework of fire
safety design: Reflections and alternatives,” Fir e
safety journal , vol. 91, pp. 973–981, 2017. https:
//doi.org/10.1016/j.firesaf.2017.03.034
[5] D. Cram, C. E. Hatch, S. T yler , and C. Ochoa, “Use of
distributed temperature sensing technology to charac-
terize fire behavior ,” Sensors , vol. 16, no. 10, p. 1712,
2016. https://doi.org/10.3390/s16101712
[6] P . Johnson, C. Beyler , P . Croce, C. Dubay , and
M. McNamee, “V ery early smoke detection apparatus
(VESDA), david packham, john petersen, martin cole:
2017 dinenno prize,” Fir e Science Reviews , vol. 6,
no. 1, pp. 1–12, 2017. https://doi.org/10.1186/
s40038- 017- 0019- 4
[7] M.-Y . Cheng, K.-C. Chiu, Y .-M. Hsieh, I.-T . Y ang,
and J.-S. Chou, “Development of bim-based real-time
evacuation and rescue system for complex buildings,”

Designing A Blockchain Approach to Secure… Informatica 47 (2023) 9–26 23
in ISARC. Pr oceedings of the International Sympo-
sium on Automation and Robotics in Construction ,
vol. 33. IAARC Publications, 2016, p. 1.
[8] N. Khana, D. Leea, A. K. Alia, and C. Parka, “Ar -
tificial intelligence and blockchain-based inspection
data recording system for portable firefighting equip-
ment,” in Pr oceedings of the International Sympo-
sium on Automation and Robotics in Construction, Ki-
takyushu, Japan , 2020, pp. 27–28.
[9] S.-H. W ang, W .-C. W ang, K.-C. W ang, and S.-Y . Shih,
“Applying building information modeling to support
fire safety management,” Automation in construction ,
vol. 59, pp. 158–167, 2015. https://doi.org/10.
1016/j.autcon.2015.02.001
[10] N. Khan, A. K. Ali, S. V an-T ien T ran, D. Lee,
and C. Park, “V isual language-aided construction
fire safety planning approach in building information
modeling,” Applied Sciences , vol. 10, no. 5, p. 1704,
2020. https://doi.org/10.3390/app10051704
[1 1] V . Kodur and M. Naser , “Fire hazard in transporta-
tion infrastructure: Review , assessment, and mitiga-
tion strategies,” Fr ontiers of Structural and Civil En-
gineering , vol. 15, pp. 46–60, 2021. https://doi.
org/10.1007/s11709- 020- 0676- 6
[12] X. Bao, “Fire equipment information traceability sys-
tem based on blockchain,” in E3S W eb of Confer ences ,
vol. 251. EDP Sciences, 2021, p. 03099. https:
//doi.org/10.1051/e3sconf/202125103099
[13] M. Al-Zubaidie, “Implication of lightweight and ro-
bust hash function to support key exchange in health
sensor networks,” Symmetry , vol. 15, no. 1, p. 152,
2023. https://doi.org/10.3390/sym15010152
[14] M. Rehman, N. Javaid, M. A wais, M. Imran,
and N. Naseer , “Cloud based secure service pro-
viding for IoT s using blockchain,” in 2019 IEEE
Global Communications Confer ence (GLOBECOM) .
IEEE, 2019, pp. 1–7. https://doi.org/10.1109/
GLOBECOM38437.2019.9013413
[15] M. Al-Zubaidie, Z. Zhang, and J. Zhang, “REISCH:
Incorporating lightweight and reliable algorithms into
healthcare applications of WSNs,” Applied Sciences ,
vol. 10, no. 6, p. 2007, 2020. https://doi.org/10.
3390/app10062007
[16] D. Mahmudnia, M. Arashpour , Y . Bai, and H. Feng,
“Drones and blockchain integration to manage for -
est fires in remote regions,” Dr ones , vol. 6,
no. 1 1, p. 331, 2022. https://doi.org/10.3390/
drones6110331
[17] M. Al-Zubaidie, Z. Zhang, and J. Zhang, “RAMHU:
A new robust lightweight scheme for mutual users au-
thentication in healthcare applications,” Security and
Communication Networks , vol. 2019, 2019. https:
//doi.org/10.1155/2019/3263902
[18] A. Allouch, O. Cheikhrouhou, A. Koubâa, K. T oumi,
M. Khalgui, and T . Nguyen Gia, “UTM-chain:
Blockchain-based secure unmanned traf fic manage-
ment for Internet of drones,” Sensors , vol. 21,
no. 9, p. 3049, 2021. https://doi.org/10.3390/
s21093049
[19] E. A. Soto, L. B. Bosman, E. W ollega, and W . D.
Leon-Salas, “Peer -to-peer ener gy trading: A re-
view of the literature,” Applied Ener gy , vol. 283,
p. 1 16268, 2021. https://doi.org/10.1016/j.
apenergy.2020.116268
[20] A. Al Sadawi, M. S. Hassan, and M. Ndiaye, “A sur -
vey on the integration of blockchain with IoT to en-
hance performance and eliminate challenges,” IEEE
Access , vol. 9, pp. 54 478–54 497, 2021. https://
doi.org/10.1109/ACCESS.2021.3070555
[21] R. A. Muhajjar , N. A. Flayh, and M. Al-
Zubaidie, “A perfect security key manage-
ment method for hierarchical wireless sensor
networks in medical environments,” Electr on-
ics , vol. 12, no. 4, p. 101 1, 2023. https:
//doi.org/10.3390/electronics12041011
[22] M. Al-Zubaidie and G. S. Shyaa, “Applying detec-
tion leakage on hybrid cryptography to secure transac-
tion information in e-commerce apps,” Futur e Inter -
net , vol. 15, no. 8, p. 262, 2023. https://doi.org/
10.3390/fi15080262
[23] A. Pieroni, N . Scarpato, and L. Felli, “Blockchain
and IoT conver gence–A systematic survey on tech-
nologies, protocols and security ,” Applied Sciences ,
vol. 10, no. 19, p. 6749, 2020. https://doi.org/
10.3390/app10196749
[24] G. Ali, N. Ahmad, Y . Cao, M. Asif, H. Cruick-
shank, and Q. E. Ali, “Blockchain based permission
delegation and access control in Internet of things
(BACI),” Computers & Security , vol. 86, pp. 318–
334, 2019. https://doi.org/10.1016/j.cose.
2019.06.010
[25] Y . M. T ukur , D. Thakker , and I.-U. A wan, “Edge-
based blockchain enabled anomaly detection for in-
sider attack prevention in Internet of things,” T rans-
actions on Emer ging T elecommunications T echnolo-
gies , vol. 32, no. 6, p. e4158, 2021. https://doi.
org/10.1002/ett.4158
[26] B. Krishna, P . Rajkumar , and V . V elde, “Integra-
tion of blockchain technology for security and privacy
in Internet of things,” Materials T oday: Pr oceed-
ings , pp. 1–5, 2021. https://doi.org/10.1016/
j.matpr.2021.01.606
[27] S. Kumar , U. Dohare, O. Kaiwartya et al. , “FLAME:
T rusted fire brigade service and insurance claim sys-
tem using blockchain for enterprises,” IEEE T rans-
actions on Industrial Informatics , pp. 1–8, 2022.
https://doi.org/10.1109/TII.2022.3212172
[28] S. Khan, A. P . Shah, S. S. Chouhan, S. Rani, N. Gupta,
J. G. Pandey , and S. K. V ishvakarma, “Utilizing
manufacturing variations to design a tri-state flip-
flop PUF for IoT security applications,” Analog In-
tegrated Cir cuits and Signal Pr ocessing , vol. 103,
no. 3, pp. 477–492, 2020. https://doi.org/10.
1007/s10470- 020- 01642- 9

24 Informatica 47 (2023) 9–26 S. A. Y ousif f et al.
[29] T . Khan, “Ultra-low-power architecture for the de-
tection and notification of wildfires using the Inter -
net of things,” IoT , vol. 4, no. 1, pp. 1–26, 2023.
https://doi.org/10.3390/iot4010001
[30] K. M. Harahsheh and C.-H. Chen, “A survey of us-
ing machine learning in IoT security and the chal-
lenges faced by researchers,” Informatica , vol. 47,
no. 6, 2023. https://doi.org/10.31449/inf.
v47i6.4635
[31] T eachV idvan, “Architecture of IoT ,” 2022,
https://techvidvan.com/tutorials/
architecture- of- iot/ .
[32] B. D. Deebak, F . Al-T urjman, M. Aloqaily , and
O. Alfandi, “IoT -BSFCAN: A smart context-aware
system in IoT -cloud using mobile-fogging,” Fu-
tur e Generation Computer Systems , vol. 109, pp.
368–381, 2020. https://doi.org/10.1016/j.
future.2020.03.050
[33] I. F . Akyildiz and A. Kak, “The Internet of space
things/cubesats: A ubiquitous cyber -physical sys-
tem for the connected world,” Computer Networks ,
vol. 150, pp. 134–149, 2019. https://doi.org/
10.1016/j.comnet.2018.12.017
[34] I. Al_Barazanchi, A. Murthy , A. A. Al Rababah,
G. Khader , H . R. Abdulshaheed, H. T . Rauf,
E. Daghighi, and Y . Niu, “Blockchain technology-
based solutions for IoT security ,” Iraqi Journal for
Computer Science and Mathematics , vol. 3, no. 1,
pp. 53–63, 2022. https://doi.org/10.52866/
ijcsm.2022.01.01.006
[35] B. Shang, S. Liu, S. Lu, Y . Y i, W . Shi, and L. Liu,
“A cross-layer optimization framework for distributed
computing in IoT networks,” in 2020 IEEE/ACM
Symposium on Edge Computing (SEC) . IEEE,
2020, pp. 440–444. https://doi.org/10.1109/
SEC50012.2020.00067
[36] F . Al-T urjman and J. P . Lemayian, “Intelligence,
security , and vehicular sensor networks in In-
ternet of things (IoT)-enabled smart-cities: An
overview ,” Computers & Electrical Engineering ,
vol. 87, p. 106776, 2020. https://doi.org/10.
1016/j.compeleceng.2020.106776
[37] M. J. Baucas, S. A. Gadsden, and P . Spachos,
“IoT -based smart home device monitor using
private blockchain technology and localization,”
IEEE Networking Letters , vol. 3, no. 2, pp. 52–55,
2021. https://doi.org/10.1109/LNET.2021.
3070270
[38] Z. Shah, I. Ullah, H. Li, A. Levula, and K. Khur -
shid, “Blockchain based solutions to mitigate dis-
tributed denial of service (DDoS) attacks in the In-
ternet of things (IoT): A survey ,” Sensors , vol. 22,
no. 3, p. 1094, 2022. https://doi.org/10.3390/
s22031094
[39] S. K. Lo, Y . Liu, S. Y . Chia, X. Xu, Q. Lu, L. Zhu, and
H. Ning, “Analysis of blockchain solutions for IoT : A
systematic literature review ,” IEEE Access , vol. 7, pp.
58 822–58 835, 2019. https://doi.org/10.1109/
ACCESS.2019.2914675
[40] T . M. Fernández-Caramés and P . Fraga-Lamas,
“A review on the use of blockchain for the
Internet of things,” IEEE Access , vol. 6, pp.
32 979–33 001, 2018. https://doi.org/10.1109/
ACCESS.2018.2842685
[41] H. Zhang, W . Lang, C. Liu, and B. Zhang, “A
blockchain-based security approach architecture for
the Internet of things,” in 2020 IEEE 4th Informa-
tion T echnology , Networking, Electr onic and Automa-
tion Contr ol Confer ence (ITNEC) , vol. 1. IEEE,
2020, pp. 310–313. https://doi.org/10.1109/
ITNEC48623.2020.9084997
[42] M. Al-Zubaidie, Z. Zhang, and J. Zhang, “P AX: Us-
ing pseudonymization and anonymization to protect
patients’ identities and data in the healthcare system,”
International Journal of Envir onmental Resear ch and
Public Health , vol. 16, no. 9, p. 1490, 2019. https:
//doi.org/10.3390/ijerph16091490
[43] M. Al-Zubaidie, Z. Zhang, and J. Zhang, “Ef fi-
cient and secure ECDSA algorithm and its appli-
cations: A survey ,” International Journal of Com-
munication Networks and Information Security (IJC-
NIS) , vol. 1 1, pp. 7–35, 2019. https://doi.org/
10.17762/ijcnis.v11i1.3827
[44] A. A. Brincat, A. Lombardo, G. Morabito, and
S. Quattropani, “On the use of blockchain technolo-
gies in wifi networks,” Computer Networks , vol. 162,
p. 106855, 2019. https://doi.org/10.1016/j.
comnet.2019.07.011
[45] N. Al Asad, M. T . Elahi, A. Al Hasan, and M. A.
Y ousuf, “Permission-based blockchain with proof of
authority for secured healthcare data sharing,” in
2020 2nd International Confer ence on Advanced In-
formation and Communication T echnology (ICAICT) .
IEEE, 2020, pp. 35–40. https://doi.org/10.
1109/ICAICT51780.2020.9333488
[46] Y . W ang, M. Singgih, J. W ang, and M. Rit, “Mak-
ing sense of blockchain technology: How will
it transform supply chains?” International Jour -
nal of Pr oduction Economics , vol. 21 1, pp. 221–
236, 2019. https://doi.org/10.1016/j.ijpe.
2019.02.002
[47] W . Mougayar , The business blockchain: Pr omise,
practice, and application of the next Internet technol-
ogy . John W iley & Sons, 2016.
[48] Komodo, “4 types of blockchain technology ex-
plained,” 2021, https://komodoplatform.com/
en/academy/blockchain- technology- types/ .
[49] F . Casino, T . K. Dasaklis, and C. Patsakis, “A sys-
tematic literature review of blockchain-based appli-
cations: Current status, classification and open is-
sues,” T elematics and informatics , vol. 36, pp. 55–
81, 2019. https://doi.org/10.1016/j.tele.
2018.11.006

Designing A Blockchain Approach to Secure… Informatica 47 (2023) 9–26 25
[50] M. W azid, A. K. Das, S. Shetty , and M. Jo, “A tu-
torial and future research for building a blockchain-
based secure communication scheme for Internet
of intelligent things,” IEEE Access , vol. 8, pp.
88 700–88 716, 2020. https://doi.org/10.1109/
ACCESS.2020.2992467
[51] I. Makhdoom, M. Abolhasan, H. Abbas, and W . Ni,
“Blockchain’ s adoption in IoT : The challenges, and
a way forward,” Journal of Network and Computer
Applications , vol. 125, pp. 251–279, 2019. https:
//doi.org/10.1016/j.jnca.2018.10.019
[52] A. S. Musleh, G. Y ao, and S. Muyeen, “Blockchain
applications in smart grid–review and frameworks,”
IEEE Access , vol. 7, pp. 86 746–86 757, 2019.
https://doi.org/10.1109/ACCESS.2019.
2920682
[53] G. S. Shyaa and M. Al-Zubaidie, “Utilizing trusted
lightweight ciphers to support electronic-commerce
transaction cryptography ,” Applied Sciences , vol. 13,
no. 12, p. 7085, 2023. https://doi.org/10.3390/
app13127085
[54] V . Kodur , P . Kumar , and M. M. Rafi, “Fire hazard
in buildings: review , assessment and strategies for
improving fire safety ,” PSU r esear ch r eview , vol. 4,
no. 1, pp. 1–23, 2020. http://dx.doi.org/10.
1108/PRR- 12- 2018- 0033
[55] H. Zhang, D. Y ang, V . W . T am, Y . T ao, G. Zhang,
S. Setunge, and L. Shi, “A critical review of combined
natural ventilation techniques in sustainable build-
ings,” Renewable and Sustainable Ener gy Reviews ,
vol. 141, p. 1 10795, 2021. https://doi.org/10.
1016/j.rser.2021.110795
[56] N. Khan, D. Lee, C. Baek, and C.-S. Park,
“Conver ging technologies for safety planning and
inspection information system of portable fire-
fighting equipment,” IEEE Access , vol. 8, pp.
21 1 173–21 1 188, 2020. http://dx.doi.org/10.
1109/ACCESS.2020.3039512
[57] C. Nartey , E. T . T chao, J. D. Gadze, E. Keelson,
G. S. Klogo, B. Kommey , and K. Diawuo, “On
blockchain and IoT integration platforms: Current
implementation challenges and future perspectives,”
W ir eless Communications and Mobile Computing ,
vol. 2021, pp. 1–25, 2021. https://doi.org/10.
1155/2021/6672482
[58] T . M. Fernandez-Carames and P . Fraga-Lamas,
“T owards post-quantum blockchain: A re-
view on blockchain cryptography resistant
to quantum computing attacks,” IEEE ac-
cess , vol. 8, pp. 21 091–21 1 16, 2020. https:
//doi.org/10.1109/ACCESS.2020.2968985
[59] K. T soulias, G. Palaiokrassas, G. Fragkos,
A. Litke, and T . A. V arvarigou, “A graph
model based blockchain implementation for
increasing performance and security in de-
centralized ledger systems,” IEEE Access ,
vol. 8, pp. 130 952–130 965, 2020. http:
//dx.doi.org/10.1109/ACCESS.2020.3006383
[60] Z. T urk and R. Klinc, “Potentials of blockchain tech-
nology for construction management,” Pr ocedia engi-
neering , vol. 196, pp. 638–645, 2017. https://doi.
org/10.1016/j.proeng.2017.08.052
[61] Q. Lu, L. Chen, S. Li, and M. P itt, “Semi-automatic
geometric digital twinning for existing buildings
based on images and CAD drawings,” Automation
in Construction , vol. 1 15, p. 103183, 2020. https:
//doi.org/10.1016/j.autcon.2020.103183
[62] K. Rofaida, M. Derdour , M. A. Ferrag, and M. M.
Bouhamed, “Prschain: A blockchain based privacy
preserving approach for data service composition,”
Informatica , vol. 47, no. 9, 2023. https://doi.
org/10.31449/inf.v47i9.5081
[63] N. S. Rani, T . V asudev , M. Chandrajith, and
N. Manohar , “2d morphable feature space for hand-
written character recognition,” Pr ocedia Computer
Science , vol. 167, pp. 2276–2285, 2020. https://
doi.org/10.1016/j.procs.2020.03.280

26 Informatica 47 (2023) 9–26 S. A. Y ousif f et al.