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1
Enhancing cybersecurity using blockchain
technology based on IoT data fusion
Sung-Jung Hsiao and Wen-Tsai Sung
Abstract—In this study, blockchain technology is used to
strengthen the security of wireless sensing data in the network
architecture of the Internet of Things. For many organic farms
that contain crops with high unit prices, the environmental
parameters of the crops grown on the farm are often regarded as
confidential data. First, a wireless sensing network is used to
transmit sensing data to the farm’s front-end integrated
microcontroller for fusion of the sensing data. After fusion of the
sensing data is completed, the processed data is transmitted to the
data processing center of the entire farm for encapsulation of the
blockchain algorithm. Data are sent to the cloud database for
storage after they are packaged by the system. At the same time,
the data in the cloud database can be managed and analyzed by
remote operators. Data encapsulation based on blockchain
technology is used to effectively prevent any data from being stolen
or destroyed by hackers. A complete blockchain encapsulation
database system is implemented in the experimental stage, and the
operator can use this system to encrypt the data that have been
fused at the remote end for blockchain technology. In the
encryption process of this blockchain, the security of data
processing can be specifically enhanced by the system. Finally,
each encapsulated block of data is securely stored in a private
cloud database. There is a function to check whether the data in
the cloud have been tampered with. A very innovative approach to
using blockchain technology is proposed in this paper to enhance
the security of data processing.
Index Terms—Wireless sensor networks (WANs), Internet of
Things (IoT), blockchain, cloud database, fusion.
T
I. INTRODUCTION
he proposed system uses blockchain technology to
enhance the security of sensor data transmission, which
is the most important goal of this research project. For a
smart farm that contains crops with high unit prices, the growth
parameters of the crops that are usually planted are the most
important confidential information in the industry. Often, a pot
of orchids may be worth from a few hundred yuan to a few
thousand yuan. Therefore, the information on the growth
parameters of high unit price crops on the farm is very
confidential. Currently, most smart farms use wireless sensor
networks (WSNs) systems to monitor various growth
conditions of crops.[1] [2] These systems also transmit various
environmental parameters related to plant growth to the farm's
central processing unit. Usually, the transmission of data may
only be encrypted with a simple password, which is easily
Manuscript received xx xx, 2022; revised xx xx, 2022 and xx
xx, 2022; accepted xx xx, 2022. Date of publication xx xx, 2022; date
of current version xx xx, 2022 This work was supported in part by the
Department of Information Technology, Takming University of Science and
Technology and Department of Electrical Engineering, National Chin-Yi
University of Technology.” Corresponding author: Wen-Tsai Sung.
cracked or tampered with by network hackers. Assuming that
the central control processing unit utilizes remote login
monitoring, the probability of the system being attacked is very
high. Therefore, it is only a matter of time before the system is
hacked. Currently, issues related to information security have
become the focus of attention. This project uses blockchain,
which is a very important technology. to enhance the security
of data transmission. To date, most researchers have applied
blockchain technology to finance because the blockchain
method is very rigorous, and it is difficult for hackers to tamper
with the existing ledger data. Therefore, blockchain technology
has been applied in the financial world for virtual currency. In
our method, blockchain technology is applied to enhance the
transmission of growth parameters of high unit price crops to
ensure that the data will not be tampered with or stolen by
hackers. Fig. 1 shows the difference between the use of
blockchain technology for virtual currency and this study.
Smart farms are used as the research basis for this study;
please refer to Fig. 2 for the system architecture of the proposed
approach. In the smart farm used in this study, the researchers
placed different kinds of environmental sensors inside the
planting platform. These sensors include air temperature and
humidity sensors, soil moisture sensors, illuminance sensors,
gas sensors, and soil pH sensors. When these sensors are used
to measure environmental parameters, the sensed data is
transmitted to integrated microcontrollers.[3][4] These
microcontrollers run on Windows or Linux operating systems.
Therefore, these microcontrollers perform different types of
sensing data fusion processes. The first step in the system is to
process the sensing data through these fusion processes. After
the data of various sensors have been integrated, the data is
encapsulated through the block chain algorithm through the
control unit center. Then, each packaged block is transmitted to
the cloud database. Remote operators can log in to the system
to query and view various data. The data can also be presented
through various graphics, which are convenient for users to
analyze and observe.[5][6]
II. THE DEVELOPMENT OF BLOCKCHAIN
In most of the current applications, the blockchain is almost
regarded as a decentralized ledger system, which is integrated
using cryptography, mathematical algorithms and economic
Sung-Jung Hsiao is with the Department of Information Technology,
Takming University of Science and Technology, Taipei City, 11451, Taiwan
(e-mail: sungjung@ gs.takming.edu.tw).
Wen-Tsai Sung. is with the Department of Electrical Engineering, National
Chin-Yi University of Technology, Taichung, 411030, Taiwan (e-mail:
songchen@ncut.edu.tw).
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models. This type of decentralized ledger system operates in a
peer-to-peer (P2P) network architecture and is used to combine
associated data blocks into a chain-like data structure according
to timestamps. The characteristics of this type of network
architecture result in data that cannot be tampered with.
Additional benefits include liquidated transactions, secure
encryption mechanisms and improved traceability.[7][8][9]
Zhou et al. proposed an optimized deployment mechanism of
blockchain under the architecture of the Internet of Things [10].
2
The proposal of this method is that the mechanism based on the
blockchain can effectively solve the security problems in the
wireless Internet of Things system. Xu et al. proposed a
lightweight and anti-attack bidirectional blockchain paradigm
for IoT [11]. Bataineh et al. proposed a novel secure blockchain
framework for IoT health applications [12]. Alrubei et al.
proposed the application of blockchain platform in IoT [13].
Mahrous et al. proposed the application of blockchain in IoT
digital forensics architecture [14].
Fig. 1. Comparison of current examples of blockchain technology.
Fig. 2. The system architecture of the proposed approach.
In blockchain development technology, the peer-to-peer technology, the blockchain network developed into a
network architecture, which was used as the network decentralized network architecture. However, the network
architecture of early blockchain technology, is the most architectures of the web applications that are commonly used
characteristic network. After the improvement of network today are all in the form of centralized servers and clients.
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While there may still be some network architectures that are
decentralized, only a few newer network architectures are
decentralized.[15][16]
The clear concept of a decentralized network architecture is
that no node will be instructed by other nodes to complete an
action, and there is no important central node in the entire
network architecture. The rights and obligations between all
nodes are equal, so when any node in the network stops working
or fails, the execution of the entire system is not affected.
Therefore, the system is very stable. The most important aspect
of the decentralized network architecture is its reduced
dependence on the "center" or "key node". In the current online
world, most of the system services are summarized in a
centralized network architecture, such as Facebook, Twitter,
and Google. Although these are internet-based service
companies, their internal network system architectures almost
all use decentralized networks. This architecture, which has
become the foundation of their base layer of service, is used to
improve computing power and data reliability and shorten the
service time. In terms of the above network architecture, a
network service system can include both a centralized
architecture and a decentralized architecture. For users of
network services, all data, resources, management rights and
responsibilities in system services are centralized in the
company's server. Assuming that the number of users continues
to increase, the internet company's centrally managed
infrastructure, servers, and network must also increase. If the
system fails, all users' data access will be affected. As
2
previously mentioned, these disadvantages are very clear. In
addition, if the servers of these internet-based companies are
accidentally compromised, the data stored in their databases
may be tampered with or stolen. These companies have
absolute "manipulation authority" over all user data, so users
are fearful that their data will be leaked.[17][18]
Blockchain technology is a "decentralized web of trust" that
creates a set of "public ledgers" on the internet. All users in the
network jointly "book" and "check" on the ledger to ensure the
authenticity and immutability of the information. Before
blockchain technology, all transactions required an
intermediary agency, such as a financial information company,
that was responsible for bank transfers and ATM, credit card
and other transactions. With the implementation of blockchain,
the original intermediary no longer exists. All cash flow
transaction processes use encrypted technology and a
decentralized public ledger to allow the computers of all
participants to book and confirm together, becoming a
decentralized transaction system. Since its development,
blockchain has undergone three stages of evolution. The first
stage is Blockchain 1.0 - Bitcoin, where the decentralization of
virtual currency was introduced. The second stage is
Blockchain 2.0 - Ethereum, where smart contract authentication
was introduced. The third stage is Blockchain 3.0 - IOTA,
where the Internet of Things was connected. Today's
blockchain technology covers smart contracts, remote
identification systems, digital currency payment processors,
smart wallets and exclusive digital currencies.[19][20]
Fig. 3. The evolution of blockchain technology.
data directly to the fusion center. The results, which are
III. DECISION FUSION RULES
presented later, are based on generalized simple assumptions
As shown in Fig. 4, a total of N sensors are randomly that can be adapted to different network structures. An example
distributed in a region of interest (ROI), where the ROI is a is presented below to illustrate how this method can be adapted
square whose area is equal to b2. The variable N is random and to complex practical applications.[21][22]
It is assumed that the sensor area is large and the signal decays
obeys the Poisson distribution, as shown in Eq. (1):
rapidly
with increasing distance to the target. Therefore, as
𝑁𝑁
−𝜆𝜆
𝜆𝜆 𝑒𝑒
p(N) =
, N = 0,⋯, ∞
(1)
shown
in
Fig. 5, only a small fraction of the sensors are able to
𝑁𝑁!
detect the signal from the target. The measurement data
A. Hierarchical Network Structure
obtained by most sensors are pure noise. Since the local
The proposed study focuses on the application aspects of decisions from these sensors do not convey enough information
wireless sensor networks. A network structure has been implied about the target, this approach is not only ineffective but also
in the previous discussion; that is, all sensors in the ROI send wastes energy. Moreover, when the network scale is very large,
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scalability problems exist. A reasonable solution is to use a 3level hierarchical network structure, as shown in Fig. 6. Nodes
that are relatively close to each other form a cluster, and each
cluster has its own cluster head, which acts as a local fusion
center and has more powerful computing and communication
capabilities. As shown in Fig. 5, each cluster is used to detect a
certain subregion in the ROI. These sensors send data to their
respective cluster heads rather than to the fusion centers, which
are farther away. Based on the data sent by the sensors in a
certain cluster or subarea to the corresponding cluster head, a
decision will be made on whether there is a target in the subarea.
This decision will then be sent to the fusion center, and the
fusion center will collect the information on whether there is a
target or an event in a specific area.[23]
2
area.
Fig. 5. Energy contours of a target located in the sensor
region, which consists of 9 cluster heads and their
corresponding subregions.
As long as the previous assumptions are still valid in every
cluster or subregion, an estimate of the detection performance
of the cluster head layer can be made.[24][25]
Fig. 4. Signal energy contours for targets located in the sensor
Fig. 6. The 3-level hierarchical sensor network structure.
Obviously, when N is given and the assumption condition is
B. System-Level False Alarm Rate
H0, 𝛬𝛬 obeys the binomial distribution with the majority (N,
In this section, the derivation of the detection performance of
𝑝𝑝𝑓𝑓𝑓𝑓 ). When N is large enough, 𝑃𝑃𝑟𝑟 {𝛬𝛬 ≥ 𝑇𝑇 | 𝑁𝑁, 𝐻𝐻0 } can be
the system version is presented, that is, the false alarm
approximated by the Laplace-De Moivre theorem as Eq. (3)
probability 𝑝𝑝𝑓𝑓𝑓𝑓 and the discovery probability 𝑝𝑝𝑑𝑑 at the fusion
center. In addition, the simulation results are compared with the 𝑃𝑃 {𝛬𝛬 ≥ 𝑇𝑇 | 𝑁𝑁, 𝐻𝐻 } =
𝑟𝑟
0
theoretical analysis. The false alarm rate at the fusion center 𝑝𝑝𝑓𝑓𝑓𝑓
𝑁𝑁
𝑇𝑇 − 𝑁𝑁𝑁𝑁𝑓𝑓𝑓𝑓
𝑁𝑁−1
𝑁𝑁 𝑖𝑖
is given by Eq. (2):
�1 − 𝑝𝑝𝑓𝑓𝑓𝑓 �
≈ 𝑄𝑄 �
�
(3)
� � � 𝑝𝑝𝑓𝑓𝑓𝑓
𝑖𝑖
�𝑁𝑁𝑝𝑝𝑓𝑓𝑓𝑓 (1 − 𝑝𝑝𝑓𝑓𝑓𝑓 )
𝑝𝑝𝑓𝑓𝑓𝑓 = ∑∞
(2)
𝑁𝑁=𝑇𝑇 𝑝𝑝(𝑁𝑁)𝑃𝑃𝑟𝑟 {𝛬𝛬 ≥ 𝑇𝑇 | 𝑁𝑁, 𝐻𝐻0 }
𝑖𝑖=𝑇𝑇
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It is known that the kurtosis coefficient of the Poisson
distribution is 3+(1/𝜆𝜆). When λ becomes larger, the kurtosis
coefficient of the Poisson distribution will be close to that of the
Gaussian distribution, which can be explained by the unique
properties of the Poisson distribution. A Poisson random
variable with mean λ can be considered the sum of M
independent and identically distributed Poisson random
variables with mean λ0=λ/M. Therefore, if a Poisson random
variable has a large mean λ, then it is obtained by adding a large
number (M) of independent and identically distributed Poisson
random variables with a constant mean λ0. Therefore, according
to the central limit theorem (CLT), its distribution will be close
to a Gaussian distribution. Therefore, when λ is large, the
probability mass of N will be concentrated around the mean (λ),
as illustrated in Fig. 7, which shows the probability mass
function of N when λ=1,000 and λ=10,000. According to this
characteristic of the Poisson distribution and using the
condition that both the mean and variance of the Poisson
distribution are λ, when λ is large, the following approximate
conclusions can be drawn, are shown in Eqs. (4) and (5).
𝑃𝑃𝑟𝑟 � 𝜆𝜆 − 6√𝜆𝜆 ≤ 𝑁𝑁 ≤ 𝜆𝜆 + 6√𝜆𝜆 � ≃ 1
(4)
or
−𝜆𝜆 𝑁𝑁
3 𝑒𝑒 𝜆𝜆
∑𝑁𝑁
𝑁𝑁1 𝑁𝑁!
≃1
where N1 =�𝜆𝜆 − 6√𝜆𝜆�, N3 =⌈𝜆𝜆 −6√𝜆𝜆�
(5)
Therefore, for a large λ, the typical value of N is also a large
number. The probability of this N taking a small value is
negligible. For example, when λ=10,000, 𝑃𝑃𝑟𝑟 { 𝑁𝑁 < 810} =
2.4 × 10−10 . When 𝜆𝜆 = 10,000 , 𝑃𝑃𝑟𝑟 {𝑁𝑁 < 9400} = 6.6 ×
10−10 . Therefore, when λ is large enough, Eq. (6) yields
∞
𝑁𝑁
𝑁𝑁=0
𝑁𝑁3
𝑖𝑖=𝑇𝑇
𝑁𝑁 𝑖𝑖
𝑁𝑁−𝑖𝑖
�1 − 𝑝𝑝𝑓𝑓𝑓𝑓 �
𝑃𝑃𝑓𝑓𝑓𝑓 = � 𝑝𝑝(𝑁𝑁) � � � 𝑝𝑝𝑓𝑓𝑓𝑓
𝑖𝑖
≃ �
𝑁𝑁=𝑁𝑁2
𝑇𝑇 − 𝑁𝑁𝑁𝑁𝑓𝑓𝑓𝑓
𝜆𝜆𝑁𝑁 𝑒𝑒 −𝜆𝜆
𝑄𝑄 (
)
𝑁𝑁!
�𝑁𝑁𝑁𝑁𝑓𝑓𝑓𝑓 (1 − 𝑝𝑝𝑓𝑓𝑓𝑓 )
𝑁𝑁3
= �
𝑁𝑁=𝑁𝑁2
𝑇𝑇 − 𝜇𝜇0
𝜆𝜆𝑁𝑁 𝑒𝑒 −𝜆𝜆
𝑄𝑄 �
� … … … … . (6)
𝑁𝑁!
𝜎𝜎0
where N2=max (T, N1), 𝜇𝜇0 ≜ 𝑁𝑁𝑁𝑁𝑓𝑓𝑓𝑓 , and 𝜎𝜎0 ≜
�𝑁𝑁𝑁𝑁𝑓𝑓𝑓𝑓 (1 − 𝑝𝑝𝑓𝑓𝑓𝑓 ). Since the Laplace-De Moivre approximation
in Eq. (3) is valid for sufficiently large N, this can be exploited
to derive Eq. (6). The importance of Eq. (5) is that it can
significantly reduce the computational load for calculating pfa
or pd. This is because it is enough to perform an accumulation
less than or equal to 12√𝜆𝜆 during the computation but not an
infinite number of accumulations.
C. System-level Detection Probability
From the nature of the problem discussed and the content
expressed, different local sensors have different 𝑝𝑝𝑑𝑑𝑖𝑖 , where 𝑝𝑝𝑑𝑑𝑖𝑖
is a function of 𝑑𝑑𝑖𝑖 . Therefore, under the assumption of H1, the
total number of discoveries (𝛬𝛬) no longer obeys the binomial
distribution, and it is difficult to derive the analytical formula
for the probability distribution of 𝛬𝛬. However, the system can
obtain 𝑃𝑃𝑑𝑑 by an approximation or simulation method. When the
number of sensors N is large, the system-level 𝑃𝑃𝑑𝑑 can be
obtained by approximating with the CLT, as shown in Eq. (7)
Fig. 7. Probability mass function of the Poisson distribution.
𝑇𝑇−𝑁𝑁𝑝𝑝̅ 𝑑𝑑
𝑃𝑃𝑟𝑟 {Λ ≥ T|N, 𝐻𝐻1 } ≅ 𝑄𝑄 �
�𝑁𝑁𝑁𝑁 −2
(7)
�
where the relevant calculations are shown in Eqs. (8), (9),
(10) and (11)
𝜎𝜎� 2 =
𝑝𝑝̅𝑑𝑑 =
𝑏𝑏/2
𝜋𝜋
∫ 𝐶𝐶(𝑟𝑟)𝑟𝑟𝑟𝑟𝑟𝑟 + (1 − 4 )𝛾𝛾
𝑏𝑏 2 0
𝑏𝑏
𝜋𝜋
2
2𝜋𝜋
2𝜋𝜋
� [1 − 𝐶𝐶(𝑟𝑟)]𝐶𝐶(𝑟𝑟)𝑟𝑟𝑟𝑟𝑟𝑟 + �1 − � 𝛾𝛾(1 − 𝛾𝛾)
𝑏𝑏 2 0
4
𝐶𝐶(𝑟𝑟) = 𝑄𝑄 �𝜏𝜏 − �
𝑃𝑃0
1+𝛼𝛼𝛼𝛼 𝑛𝑛
𝛾𝛾 = 𝑄𝑄 �𝜏𝜏 − �
�
𝑃𝑃0
1+𝛼𝛼(
√2𝑏𝑏 𝑛𝑛
)
2
(8)
(9)
(10)
�
Another approximate expression of 𝛾𝛾 is Eq. (12):
𝛾𝛾 = 𝑄𝑄(𝜏𝜏) = 𝑝𝑝𝑓𝑓𝑓𝑓
(11)
(12)
Here, Eq. (11) is used for approximation. However, when the
ROI is large, that is, when b is large, this difference can be
ignored. Using a derivation method such as Eq. (6), taking the
average of Eq. (7) according to the value of N, the system-level
𝑃𝑃𝑑𝑑 , which is given by Eq. (13), can be obtained.
𝑁𝑁3
𝑃𝑃𝑑𝑑 ≃ �
𝑁𝑁3
= �
𝑁𝑁=𝑁𝑁2
𝑁𝑁=𝑁𝑁2
𝑁𝑁 −𝜆𝜆
𝜆𝜆 𝑒𝑒
𝑁𝑁!
𝜆𝜆𝑁𝑁 𝑒𝑒 −𝜆𝜆 𝑇𝑇 − 𝑁𝑁𝑝𝑝̅𝑑𝑑
(
)
𝑁𝑁!
√𝑁𝑁𝑁𝑁 −2
𝑇𝑇 − 𝜇𝜇1
𝑄𝑄 (
)
𝜎𝜎1
(13)
where 𝜇𝜇1 ≜ 𝑁𝑁𝑝𝑝̅𝑑𝑑 , 𝜎𝜎1 ≜ √𝑁𝑁𝑁𝑁 −2 . Likewise, when 𝜆𝜆 is large,
the typical value of N is also large. Therefore, the Gaussian
approximation determined by the CLT in Eq. (7) is still valid.
D. Simulation Results
System-level 𝑃𝑃𝑑𝑑 and 𝑃𝑃𝑓𝑓𝑓𝑓 can also be estimated by simulation.
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The ROC curves of the receiver in Figs. 8-11 are obtained by
the approximation method and simulations under different
system parameters. Fig. 8 and Fig. 10 are the simulation results
obtained by 105 Monte Carlo samplings, while Fig. 9 and Fig.
11 are the simulation results obtained by 107 Monte Carlo
samplings. As seen from these figures, the results obtained by
the approximation method are very close to the results obtained
by the simulation method, even when the 𝑃𝑃𝑓𝑓𝑎𝑎 at the system level
is very low (Fig. 9 and Fig. 11).
written in the form of T = βλ, then Eq. (15) can be obtained.
𝑁𝑁
3
𝑃𝑃𝑓𝑓𝑓𝑓 ≃ ∑𝑁𝑁=𝑁𝑁
2
𝜆𝜆𝑁𝑁 𝑒𝑒 −𝜆𝜆
𝑁𝑁!
𝑄𝑄 �
�𝛽𝛽−𝑝𝑝𝑓𝑓𝑓𝑓 �√𝜆𝜆
� … … … … ..(15)
�𝑝𝑝𝑓𝑓𝑓𝑓 �1−𝑝𝑝𝑓𝑓𝑓𝑓 �
Similarly, Eq. (16) can be obtained according to Eq. (13).
𝑁𝑁
3
𝑃𝑃𝑓𝑓𝑓𝑓 ≃ ∑𝑁𝑁=𝑁𝑁
2
𝜆𝜆𝑁𝑁 𝑒𝑒 −𝜆𝜆
𝑁𝑁!
(𝛽𝛽−𝑝𝑝̅ 𝑑𝑑 )√𝜆𝜆
𝑄𝑄 �
�𝜎𝜎 −2
� … … … … ..(16)
Therefore, when λ→ ∞, if 𝛽𝛽 < 𝑝𝑝𝑓𝑓𝑓𝑓 , then 𝑃𝑃𝑓𝑓𝑓𝑓 =𝑃𝑃𝑑𝑑 = 1. If
𝑝𝑝𝑓𝑓𝑓𝑓 < 𝛽𝛽 < 𝑝𝑝̅𝑑𝑑 , then 𝑃𝑃𝑓𝑓𝑓𝑓 = 0 and 𝑃𝑃𝑑𝑑 = 1 . If 𝛽𝛽 > 𝑝𝑝̅𝑑𝑑 , then
𝑃𝑃𝑓𝑓𝑓𝑓 =𝑃𝑃𝑑𝑑 = 0. Therefore, if only the value of β is between 𝑝𝑝𝑓𝑓𝑓𝑓
and 𝑝𝑝̅𝑑𝑑 , the detection performance of the infinite sensor network
can be perfect when λ→ ∞, that is, 𝑃𝑃𝑓𝑓𝑓𝑓 = 0 and 𝑃𝑃𝑑𝑑 = 1. Fig. 12
and Fig. 13 show the curves of 𝑃𝑃𝑓𝑓𝑓𝑓 and 𝑝𝑝̅𝑑𝑑 as a function of λ.
As λ increases, 𝑃𝑃𝑑𝑑 gradually tends to 1, while 𝑃𝑃𝑓𝑓𝑓𝑓 gradually
tends to 0. The system adjusts the value of 𝛽𝛽 to satisfy 𝛽𝛽(𝑝𝑝𝑓𝑓𝑓𝑓 +
𝑝𝑝̅𝑑𝑑 )/2. In addition, λ is large enough. Therefore, even if SNR0
is small, the system can achieve good detection performance.
Fig. 8. The ROC curves obtained by approximation and
simulation. ( 𝜆𝜆 = 1000, 𝑛𝑛 = 2, 𝑏𝑏 = 100, 𝑎𝑎 = 200, 𝑃𝑃0 = 1000,
500, 100, corresponding, 𝜏𝜏 = 0.77, 0.73, 0.67).
Fig. 10. The ROC curves obtained by approximation and
simulation. (𝑃𝑃0 = 500, 𝑛𝑛 = 3, 𝑏𝑏 = 100, 𝛼𝛼 = 40, 𝜏𝜏 = 0.90)).
Fig. 9. The ROC curves obtained by approximation and
simulation (107 Monte Carlo simulations).
E. Incremental Analysis
Incremental analysis is very useful to analyze system
performance when the average number of sensors λ is large.
From Eq. (6), it is known that Eq. (14) can be given as:
max�𝑇𝑇, �𝜆𝜆 − 6√𝜆𝜆�� ≤ 𝑁𝑁 ≤ �𝜆𝜆 + 6√𝜆𝜆�
(14)
Fig. 11. The ROC curves obtained by approximation and
simulation. (107 Monte Carlo simulations).
Therefore, when λ → ∞ , if 𝑇𝑇 ≤ �𝜆𝜆 + 6√𝜆𝜆� , then N
approaches λ. Assuming that the system-level threshold can be
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∞
Pr{Λ = 𝑘𝑘 | 𝐻𝐻1 } = � 𝑝𝑝(𝑁𝑁)𝑃𝑃𝑟𝑟 { Λ = 𝑘𝑘 | 𝑁𝑁, 𝐻𝐻1 }
≃ ∑∞
𝑁𝑁=0
𝜆𝜆𝑁𝑁 𝑒𝑒 −𝜆𝜆
𝑁𝑁!
1
√2𝜋𝜋𝜎𝜎1
𝑁𝑁=0
2
2
𝑒𝑒 −[𝑘𝑘−𝜇𝜇1 (𝑁𝑁)] �[2𝜎𝜎1 (𝑁𝑁)] … … … … …(18)
(𝑁𝑁)
where, 𝜇𝜇1 (𝑁𝑁) = 𝑁𝑁𝑝𝑝̅𝑑𝑑 , 𝜎𝜎1 (𝑁𝑁) = √𝑁𝑁𝑁𝑁 −2 . Similarly, under the
assumption of 𝐻𝐻0 , for a larger 𝜆𝜆, Eq. (19) can be obtained.
Pr {Λ = 𝑘𝑘 | 𝐻𝐻0 }
∞
1
𝜆𝜆𝑁𝑁 𝑒𝑒 −𝜆𝜆
2
2
𝑒𝑒 −[𝑘𝑘−𝜇𝜇0 (𝑁𝑁)] �[2𝜎𝜎0 (𝑁𝑁)]
≃ �
𝑁𝑁! √2𝜋𝜋𝜎𝜎0 (𝑁𝑁)
𝑁𝑁=0
Fig. 12. Variations in the system-level discovery probability 𝑃𝑃𝑑𝑑
with λ. (𝑛𝑛 = 2, 𝑏𝑏 = 100, 𝛼𝛼 = 200, 𝜏𝜏 = 0.5).
(19)
where, 𝜇𝜇0 (𝑁𝑁) = 𝑁𝑁𝑝𝑝𝑓𝑓𝑓𝑓 , 𝜎𝜎0 (𝑁𝑁) = �𝑁𝑁𝑁𝑁𝑓𝑓𝑓𝑓 (1 − 𝑝𝑝𝑓𝑓𝑓𝑓 ). It can be
deduced that the likelihood ratio of Λ is Eq. (20).
𝐿𝐿(Λ) =
𝑃𝑃𝑃𝑃{Λ | 𝐻𝐻1 }
≃
𝑃𝑃𝑃𝑃{Λ | 𝐻𝐻0 }
𝑁𝑁
−[Λ−𝜇𝜇1 (𝑁𝑁)]2�[2𝜎𝜎2
1 (𝑁𝑁)]
∑∞
𝑁𝑁=0𝜆𝜆 �[𝑁𝑁!𝜎𝜎1 (𝑁𝑁)]𝑒𝑒
2 (𝑁𝑁)]
2
(𝑁𝑁)]
�
−[Λ−𝜇𝜇
[2𝜎𝜎
∞
0
0
∑𝑁𝑁=0𝜆𝜆𝑁𝑁 ⁄[𝑁𝑁!𝜎𝜎0 (𝑁𝑁)]𝑒𝑒
(20)
Therefore, the optimal fusion rule at the fusion center is the
test of the likelihood ratio, which is given by Eq. (21)
𝐿𝐿(Λ) ≤ 𝑇𝑇𝐿𝐿 (𝐻𝐻0 ), 𝐿𝐿(Λ) ≥ 𝑇𝑇𝐿𝐿 (𝐻𝐻1 )
Fig. 13. Variations in the system-level discovery probability
𝑃𝑃𝑓𝑓𝑓𝑓 with λ. (𝑛𝑛 = 2, 𝑏𝑏 = 100, 𝛼𝛼 = 200, 𝜏𝜏 = 0.5).
F. Optimality of Decision Fusion Rules
The decision fusion rule (i.e., the counting rule) proposed by
the previous system is actually a threshold test performed by
local sensors on the total number of detected targets, which is a
very intuitive method. It is necessary to compare this fusion rule
with the optimal decision fusion rule, which is proposed based
on the total number of detected targets.
The parameter Λ in the above formula is a random variable of
lattice type, that is, it takes values at equal intervals between 0
and N. Therefore, according to the CLT, for a large N, the
probability 𝑝𝑝𝑘𝑘 = 𝑃𝑃𝑟𝑟 {Λ = 𝑘𝑘⁄𝑁𝑁} is equal to a sample of
Gaussian density, as shown in Eq. (17):
Pr{Λ = 𝑘𝑘⁄𝑁𝑁 } ≃
2
2
1
𝑒𝑒 −(𝑘𝑘−𝜇𝜇) ⁄(2𝜎𝜎 )
√2𝜋𝜋𝜎𝜎
(𝑘𝑘 = 0, … , 𝑁𝑁)
(17)
Therefore, under the assumption of 𝐻𝐻1 , for a larger 𝜆𝜆, Eq. (18)
can be obtained.
(21)
Since the system-level 𝑃𝑃𝑓𝑓𝑓𝑓 is given, the NP decider needs to
be implemented by the counting rule mentioned above, and the
system-level threshold T can be found by Eq. (6) only by
knowing λ and τ. To obtain the optimal threshold, the value of
P0 must also be known. However, even without an optimal τ
value, the counting rule can still be implemented, and often a
better τ value can be obtained based on some prior knowledge
of P0. Therefore, it is not necessary to know the exact
information of P0 when implementing the counting rule, even
when estimating the detection performance at the system level.
For the optimal fusion rule, it is necessary to know the exact
values of α, P0 and b. Therefore, more information, especially
the signal power P0, must be known when using the optimal
fusion rule. However, the parameter P0 is unknown in most
cases. Moreover, due to its dependence on the signal power P0,
the optimal fusion rule is more sensitive to errors in the
estimation of P0. Therefore, the optimal fusion rule only has
theoretical significance. In some practical applications, its role
and robustness are not ideal. Therefore, estimating the value of
P0 in these applications is often difficult.
It can be seen from Eq. (20) that 𝐿𝐿(Λ) is a nonlinear
transformation of Λ . If 𝐿𝐿(Λ) is a monotonically increasing
transformation of Λ, then thresholding Λ and 𝐿𝐿(Λ) has the same
detection performance.
In Fig. 14 and Fig. 15, L is a function of Λ for different system
parameters. In all cases, 𝐿𝐿(Λ) is monotonically increasing with
respect to Λ, which means that the counting rule and the optimal
fusion rule are equivalent in detection performance. In addition
to the cases shown in Fig. 14 and Fig. 15, the relationship
between L and Λ for different system parameters is also studied.
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For all system parameters studied, 𝐿𝐿(Λ) is monotonically
increasing with respect to Λ.
5
In Fig. 16, the ROC curves obtained by simulating the
counting rule and the optimal fusion rule (based on 106 Monte
Carlo simulations) are given. The ROC curves of the counting
rule and the optimal fusion rule almost coincide.
IV. INTRODUCTION TO BLOCKCHAIN TECHNOLOGY
Fig. 14. The 𝐿𝐿(Λ) function. (λ = 1000, n = 2, b = 100, α =
200, 𝑃𝑃0 = 50, 100, 500, 1000, 2000, relatively, 𝜏𝜏 =
0.66, 0.67, 0.73, 0.77, 0.82).
The encryption technology currently used in the blockchain
uses the secure hash algorithm (SHA)-256 algorithm. Generally,
the operations of hash functions have a common feature.
Regardless of how many bits there are in the original data, as
long as the hash operation is performed, the length of the result
is fixed. Usually, this result is called a "digest". A digest is
unique, as if it were a human fingerprint. A hash function, also
known as a hash algorithm, is a method of creating a small
digital "fingerprint" from any kind of data. The hash function
compresses the message or data into a summary so that the
amount of data decreases and the format of the data is fixed.
Using this function, the data is shuffled and a fingerprint called
hash values, hash codes, hash sums, or hashes, is recreated. A
hash value is usually represented by a short string of random
letters and numbers. Good hash functions rarely have hash
collisions in the input domain. In hash tables and data
processing, it not suppressing that conflicts to distinguish data
can make database records more difficult to find.
Today, hash algorithms are also used to encrypt password
strings stored in databases. Since the hash value calculated by
the hash algorithm is irreversible (i.e., the calculated value
cannot be reversed back to the original value), passwords can
be effectively protected.
Fig. 15. The 𝐿𝐿(Λ) function. (n = 3, b = 100, α = 40, 𝑃𝑃0 =
500 𝜏𝜏 = 0.90).
Fig. 16: The ROC curves of the counting rule and optimal
fusion rule. (𝜆𝜆 = 1000, 𝑛𝑛 = 2, 𝑏𝑏 = 100, 𝑎𝑎 = 200, 𝑃𝑃0 =
1000, 500, 100, relatively, 𝜏𝜏 = 0.77, 0.73, 0.67).
Fig. 17. Example of a hash algorithm based on entering values
of different lengths.
Fig. 17 shows that when strings of different lengths are input,
a unique 64-character is output after calculation using the SHA256 algorithm. These results, which are similar to fingerprints,
are unlikely to be easily replicated.
Assume that the first three characters of the output by the
system are set as "666". In addition to the original input data,
other input values need to be added to the calculation using the
SHA-256 algorithm. These other input values are the values to
be mined by the system. As shown in Fig. 18, after combining
the original measurement data of the system with another
unknown data string, the hash algorithm calculates the result
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that the first three characters are "666". These unknown data
must be calculated by mining the system.
Fig. 18. The hash calculation state when the first 3 characters
of the output result are "666".
As shown in Fig. 19, the unknown data contain the block
number nonce, the timestamp and the hash value of the previous
block. The nonce value is the calculated value of the system that
is used to perform mining. When the nonce value of each block
is calculated, the hash value of the block conforms to the
specified "666" in the first 3 characters, which is an assumption
of the system development. Other characters can also be set by
6
the system, for example, 222, 33, abc, or 1111. However, the
more characters there are, the more time the system needs to
complete calculations.
Fig. 19. The unknown data contain the block number nonce,
the timestamp and the hash value of the previous block.
Fig. 20. System interface for the implementation of the proposed method.
V. SMART FARM BLOCKCHAIN TECHNOLOGY
IMPLEMENTATION
In practice, our research team has developed applications that
apply blockchain technology to encapsulate sensing data. For
information on the system operation interface of system, please
refer to the screen in Fig. 20. The left half of this figure shows
the system operation interface, which is where the input of
sensing data occurs. The right half of this figure shows the
system interface, which is where the operation of the cloud
database occurs.
In the interface shown in Fig. 21, the “Key” field represents
the key value in front of the hash value that the system wants to
calculate. In the example shown in Fig. 21, this key value is set
to "666". The "No." field represents the number field of this
block. The "Nonce" field represents the value mined by the
system. In this example, the nonce value is set to “14574”,
which allows the hash value of this block to meet the standard
set by the system. When the operator presses the "Mining"
button, the hash value of the block will be calculated by the
system. The nonce "14574" is mined through the hash value
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calculation of the block. The "Previous Hash" field indicates the
hash value of the previous block. The hash value of the previous
block of Fig. 21 is 64 "0" characters. Because this screen is the
first block of this system, there is no block in front of this
system, which is represented by "0".
After the block hash value is calculated by the system, the
operator can press the "Save" button to save the block hash
value, as shown by the screenshot in Fig. 22.
The operator can view the actual data in each encapsulated
block in real time, as shown in the screenshot in Fig. 23.
The proposed system normally packs block data every half
hour. The environmental parameters do not change much in a
short time. However, the sensor network system of the whole
farm monitors the environmental data of the whole farm at any
time. As shown in Fig. 24, after the operator inputs different
sensing data, the second block is encapsulated. In addition to
the hash value of Block 2, the value "3116" is calculated by the
system. This value is the nonce value of Block 2.
2
Fig. 21. Calculation of the hash value of the first block.
Fig. 22. Representation of pressing the "Save" button to save the calculated data.
Fig. 23. Real-time view of block data in the database.
Fig. 24. The second block data situation of the system
package.
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A function is designed within the proposed system to check
whether the block data have been tampered with. When the
system is running, this function is automatically executed. In
Fig. 25, the hash value field of its own block has been tampered
2
with by the operator to the value "6631c73f8d71295...", which
is obviously different from that specified by "666". When the
operator presses the "Check" button, the system immediately
notifies the operator that the data have been tampered with.
Fig. 25. Representation of the operator pressing the "Check" button to check whether the data have been tampered with.
Fig. 26 shows the situation where the proposed blockchain
technology data are encapsulated in the database system.
Operators can add, delete, update or search for any record data
in the blockchain database.
Fig. 26. The database operation interface of the proposed
blockchain system.
Table 1 shows the pseudocode of the proposed method based
on blockchain technology. This pseudocode is the algorithmic
process by which the system calculates the hash value of each
block. In this algorithmic process, the operator sets the first 3
characters of the hash value of each block to "666". The
operator can also set other characters. However, according to
the experimental results, the longer the character set is, the
larger the number of calculations and the more time it takes to
calculate the result.
TABLE I
BLOCKCHAIN TECHNOLOGY PSEUDOCODE
Input: The current block number, the previous block hash
value (if block number=1, the previous block hash value
=”0”), the current block sensing data, and the current
timestamp.
Output: The current block hash value and the nonce value
Method:
Step 1: Set the first three digits of the current block hash
value to 6 (set by the system), and then mine the nonce
value.
Step 2: nonce is incremented from 0 to a qualifying value
Step 3 while (the first three digits of the current block hash
value are equal to 6)
{
Step 3.1 Compute the current block hash value
Step 3.2 nonce + = 1
}
Step 4 Return the current block hash value and the nonce
value
End
Fig. 27 shows the comparison of the time used in the data
processing system using or not using (the tradition method)
blockchain technology, where the time spent refers to the
encapsulation time of each block. In addition to the comparison
of blockchain technology, this analysis includes a comparison
of the time required for the system to check for tampering.
When the number of data records is less than 10,000 records,
the computing time is within 5 seconds. When the number of
data records reaches 50,000 records, the computing time
significantly increases. In addition, the gap between the
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computing time required by the system using blockchain
technology and the computing time not using blockchain
technology increases.
2
perform data fusion between heterogeneous sensors. After data
fusion is completed, the fused data is encrypted with blockchain
technology. This encryption process will affect the operational
efficiency of the entire system in calculating the hash value of
the block. In the process of calculating the hash value of the
block, when the number of specified preceding characters is
greater, the overall calculation amount of the system will
become very large, and the system also needs more data
processing time. Therefore, the entire system using blockchain
technology needs to pay more attention to the process in the
calculation.
ACKNOWLEDGMENT
Fig. 27. Comparison of the time spent storing data using the
traditional method and the method using the blockchain in this
system.
Fig. 28 shows a comparison of the probability of tampering
with the sensing data when the system does or does not utilize
blockchain technology. When the data consist of 2,000 records,
the probability of data tampering increases to 18% of the whole.
When the data consist of 5,000 records, the probability data
tampering is 40%. The above situation shows that when the
amount of data in the system is larger, the probability of data
corruption is higher. In this case, it is a very important
contribution to the system that blockchain technology is utilized
to encapsulate data to strengthen the security of the data.
Fig. 28. Comparison of the probability of data tampering.
VI. CONCLUSION
A very innovative approach using blockchain technology is
proposed in this paper to enhance the security of data
processing. Currently, blockchain technology is usually used in
encrypted virtual currency transactions, such as bitcoin and
ether, in the financial industry. This technology is very rigorous,
and the proposed method can effectively improve the security
of the system by applying it to the data transmission of the
Internet of Things. Currently, issues related to information
security have become the focus of attention. In this paper,
blockchain technology is used to enhance the security of data
transmission, which is a very important key technology. First,
the crop environment sensing data from the farm is used to
This research was supported by the Department of
Information Technology, Takming University of Science and
Technology, Department of Electrical Engineering, National
Chin-Yi University of Technology. The authors would like to
thank the National Chin-Yi University of Technology, Takming
University of Science and Technology, Taiwan, for financially
supporting this research.
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Foundation. His research interests include Artificial
Intelligence Internet of Things (AIoT), Wireless Sensors
Network. (songchen@ncut.edu.tw)
Sung-Jung Hsiao is working with the
Department of Information Technology,
Takming University of Science and
Technology as an associate professor. He
received the BS degree in electrical
engineering from the National Taipei
University of Technology, Taiwan, in 1996
and the MS degree in computer science and
information engineering from National
Central University, Taiwan in 2003. Ph.D. degree in
department of Electrical Engineering, National Taipei
University of Technology, Taiwan in 2014. He has had the
work experience of research and design at the famous
computer company of Acer Universal Computer Co.,
Mitsubishi, and FIC. (sungjung@gs.takming.edu.tw)
Wen-Tsai Sung is working with the
Department of Electrical Engineering,
National Chin-Yi University of Technology
as a professor and Dean of Research and
Development. He received a PhD and MS
degree from the Department of Electrical
Engineering, National Central University,
Taiwan in 2007 and 2000. He has won the
2009 JMBE Best Annual Excellent Paper
Award and the dragon thesis award that sponsor is Acer
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