Data fusion is reliable in achieving the computing and service demands of the applications in diverse real-time implications. In particular, security-based trust models rely on multi-feature data from different sources to improve the consistency of the solutions. The service providing solutions are relied on using the optimal decisions by exploiting the data fusion trust. By considering the significance of the security requirement in smart city applications connected with the Internet of Things, this manuscript introduces a rational attribute-based data fusion trust model. The proposed trust model relies on different timely attributes for identifying the reputation of the available service. This reputation is computed as the accumulative factor of trust observed at different times and details. The attributes and the uncertain characteristics of the service provider in the successive sharing instances are recurrently analyzed using deep machine learning to fuse uncertain-less data. This data fusion method reduces the uncertainties in estimating the precise trust during different application responses and service dissemination. The performance of the proposed method is verified using the metrics false positive, uncertainty, data loss, computing time, and service reliability.

1. Vol.:(0123456789)
1 3
Soft Computing (2021) 25:12275–12289
https://doi.org/10.1007/s00500-021-05910-2
FOCUS
Attribute‑based data fusion for designing a rational trust model
for improving the service reliability of internet of things assisted
applications in smart cities
S. Baskar1
· Rajalakshmi Selvaraj2
· Venu Madhav Kuthadi2
· P. Mohamed Shakeel3
Accepted: 24 May 2021 / Published online: 1 July 2021
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021
Abstract
Data fusion is reliable in achieving the computing and service demands of the applications in diverse real-time implications.
In particular, security-based trust models rely on multi-feature data from different sources to improve the consistency of the
solutions. The service providing solutions are relied on using the optimal decisions by exploiting the data fusion trust. By
considering the significance of the security requirement in smart city applications connected with the Internet of Things,
this manuscript introduces a rational attribute-based data fusion trust model. The proposed trust model relies on different
timely attributes for identifying the reputation of the available service. This reputation is computed as the accumulative fac-
tor of trust observed at different times and details. The attributes and the uncertain characteristics of the service provider in
the successive sharing instances are recurrently analyzed using deep machine learning to fuse uncertain-less data. This data
fusion method reduces the uncertainties in estimating the precise trust during different application responses and service
dissemination. The performance of the proposed method is verified using the metrics false positive, uncertainty, data loss,
computing time, and service reliability.
Keywords Attribute analysis · Data fusion · Deep learning · IoT · Trust model
1 Introduction
Internet of Things (IoT) security provides authenticated
services for the devices by enabling trust management and
provides the authorized service. The trust management for
IoT trust includes two types of evaluation: distributed and
centralized beliefs (Wang et al. 2020). The distributed are
used to deploy trust between the service provider and user,
whereas centralized are used to achieve the initial authority
to maintain trust (Tariq et al. 2019). There are several trust-
based services deployed to protect IoT devices and ensures
reliable communication. Communication dispatch is based
on Quality of Services (QoS) and provides efficient, trust-
worthy data (Zhang et al. 2020). The data are acquired from
different IoT applications, where it processes trust manage-
ment based on prediction. They include the time-based trust
evaluation for IoT devices associated with the predictive
analysis (Wang 2019). The research is obtained by detect-
ing the particular user is trusted or not. Based on this, access
is provided for the user. In IoT, all the devices are connected
securely and protect from vulnerable attackers in the hetero-
geneous platform (Gomathi et al. 2020).
Data fusion is deployed to attain the trust-based service
for the end-user and the service provider in the IoT plat-
form. Multi-attribute data and information acquired from a
diverse environment require extensive analysis for adminis-
tering security (Tian et al. 2020). These analyses are evalu-
ated from the extracted data and process the trust-based
service to the application. The identification is the initial
stage to process the precise trust-related data to the third
party (Sheron et al. 2019). It is mostly used to improve the
communication and consumption of IoT devices by intro-
ducing various applications (Ding et al. 2019). Thus, data
Communicated by Vicente Garcia Diaz.
* S. Baskar
sbaskar@ieee.org
1
Department of Electronics and Communication, Karpagam
Academy of Higher Education, Coimbatore, India
2
Department of CS and IS, Botswana International University
of Science and Technology, Palapye, Botswana
3
Faculty of Information and Communication Technology,
Universiti Teknikal Malaysia Melaka, Melaka, Malaysia

2. 12276 S. Baskar et al.
1 3
fusion integrates the features of data and obtains the neces-
sary data from the user. The evaluation is based on a trust
degree to obtain data accuracy in the IoT platform (Mydhili
et al. 2019). It is determined by utilizing the time-based trust
evaluation that monitors the trust is provided to the valid
user or not. It is processed by observing the trustworthiness
of the user access to the system in the appropriate time inter-
val (Fissaoui et al. 2020). The co-relation of data is fused
to enhance the performance of the system. Commonly the
fusion is obtained based on decision-making techniques (Xu
and Viriyasitavat 2019).
The trust evaluation is necessary to identify the trust-
worthiness of the user and satisfies the requirements. It is
determined by proposing certain types of methods where
it derives which application needs what types of services
(Shala et al. 2020). The analysis is achieved by evaluating
the security and privacy to improve the trust between the
users. The evaluation is attained by identifying the user is
authorized to access the particular service are not (Xu et al.
2020). The service is provided to the requested user in the
appropriate time interval that improves secure communica-
tion (Tournier et al. 2020). The benefits are used to extract
the data features and analyzes the co-relation; by processing
this trust, the score is improved. The behavior of the data is
analyzed and monitors the time-based data processing in IoT
applications (Mabodi et al. 2020). This proposed work aims
to improve service reliability and decrease false positives,
uncertainty, data loss, and computing time. It is performed
by introducing and deep recurrent learning that is based on
time-based analysis. The trust score is matched with the trust
model and increases precision by utilizing data fusion.
2 Related works
2.1 Trust‑based methods
The trust-based reputation method is processed in Fog based
IoT (FIoT), which is evaluated by introducing context-aware
trust. Hussain et al. (2020) presented context-aware multi-
source confidence in FIoT and improved the user’s trust-
worthiness. In this work feedback crawler system is used to
obtain trust efficiently.
IoT-based Trust management is developed for service-
centric is proposed by Awan et al. (2019) to make reliable
decision making, trust-based data perception, and privacy
preservation. The author introduced a Holistic Cross-domain
trust management model (HoliTrust) to provide a degree of
trust. It is commonly used on multiple level authorities to
determine trust evaluation.
Truong et al. (2019) presented a trust-based relation-
ship by modeling Experience-Reputation (E-R), which is
deployed for Mobile Crowd-Sensing (MCS). This work aims
to obtain the effective trustworthiness of the user in the IoT
platform and enhance the QoS. Reliability is attained by
introducing a trust indicator.
Challagidad et al. (2020) address trustworthiness issues
for cloud-based Customers (CCs) and Cloud Service Provid-
ers (CSPs). To determine the trust between cloud-related
services, the author developed a Multi-dimensional Dynamic
Trust Evaluation Scheme (MDTES). This model is used to
distinguish trustworthy and non-trustworthy and provides
the services accordingly to the CCs.
An intelligent IoT platform is proposed by Amoon et al.
(2020) to monitor the communication among users and
sources. Role-based reputed access controls (RRAC) are
developed to alleviate malicious attacks in the IoT platform
and decrease the detection time, overheads, and data loss. It
is processed to enhance communication reliability by com-
bining malicious users and forged resources.
Service authentication is provided for IoT communication
is proposed by Alqahtani et al. (2020) to address energy con-
sumption. Trust-Based Monitoring (TBM) is used to obtain
secure communication among the agents. The objective of
this work is to reduce communication costs and improves
the detection process.
Service discovery is developed by Kalkan and Rasmussen
(2020) to decreases the computation cost and overhead that
occur during communication. A decentralized trust-based
framework is designed to provide trust aggregation, which
is obtained. It is processed by developing a Distributed Hash
Table (DHT) to avoid malicious data in the IoT network.
Fuzzy-IoT is proposed by Alshehri and Hussain (2018) to
detect the malicious behavior of the user. In this work, three
types of processing are acquired first is to see the attackers
using fuzzy logic. Second, based on this security message
is forwarded to the service provider. Finally, secure com-
munication is obtained between nodes in the IoT platform.
Chen (2018) presented a Massive IoT Integrated Applica-
tion that decreases the internal security threats in IoT serv-
ers. IoT-based RBAC is used to provide access and security
to IoT applications. Trust Evaluation (TE) is deployed for
both inter and intra-server in collaborative IoT service.
Trust Computational Model is developed based on a
machine learning algorithm for analyzing the services in
IoT. In this paper, a quantifiable trust assessment model is
designed to extract the trust-related values using aggregated
methods. The features are removed from the sensor data
and provide the trust value, which is processed based on the
decision-making technique.
2.2 Data fusion techniques
Xu et al. (2019) developed IncompFuse to enhance the accu-
racy level from previous data processing. It is acquired by
comparing the previous and pursuing reports and analyzing

3. 12277
Attribute‑based data fusion for designing a rational trust model for improving the service…
1 3
the resultant value. The efficient data are obtained by devel-
oping the incompatibility probability method.
Boulkaboul and Djenouri (2020) designed a Data Fusion
for the Internet of Things to increase the system’s energy and
performance. Here, decision-making is attained based on the
belief theory method. An adaptive weighted fusion method
is achieved based on the Dempster–Shafer (D–S) theory to
find the co-relation data.
In this paper, to improve the objective and subjective
quality of features, the author introduced a deep auto-
encoder learning and adaptive weighted D–S evidence
synthesis. For this processing, the extraction and identifica-
tion of features are obtained from that the classification is
derived. In this work, feature-based fusion is obtained from
the performance of the system.
Semi-Supervised Fusion Framework For Multiple Net-
works (SFMN) is addressed by Long et al. (2020) to ana-
lyze the large-scale social network. The proposed method is
deployed by evaluating multiple data and process the optimiza-
tion of data alignment. The numerous network data are fused
by deriving the gradient boosting decision tree algorithm.
3
Proposed rational attribute‑based data
fusion trust model
Trust management is obtained for the service provider and
user to share the information reliably. The trust is provided
to the user that explores the IoT service applications for
multi-feature data from different sources. Figure 1 por-
trays the proposed rational attribute-based data fusion trust
(RADFT) in the IoT application environment.
The RADFT model is designed between the IoT applica-
tions, users, and the services rendered by the cloud. A trusted
server is responsible for validating the effectiveness of the
sharing process at different time intervals. Based on the effec-
tive recommendations, the successive responses and service
reliability are improved. The objective of this proposed work
is to decreases the uncertainty attribute based on a timely
manner. By addressing this uncertainty, the precision for trust
scores increases at different time intervals. In this work, Deep
Recurrent Learning is used for data fusion where the attributes
are fused and improve the correlation of trust. The proposed
method objective is derived in the following Eq. (1).
(1)
𝕤
∏
𝕋
��
𝔄+d�
Uu∕𝔇
�
∗
√
(F + d�∕𝔞)
�
+ (ℜ + T), ∀ Max service reliability
�
𝔞
∑
d� E+F
�
∗
d�
∑
I
�
𝔄
𝕤+g0
�
+ C�
− 𝕥 ∀ Min computation time
��∑
ℙ+𝕤
d�∕lo
�
+
�
E
I+d�
�
∗
∑
𝔄
T − ℙ, ∀ Min False Positive
l0
∑
ℜ
�
d�
+ f0
�
−
��
𝔄
𝔞+F
�
∗
�
Uu
∑
E∕𝕤
��
+ d�
− l0, ∀ Data Loss
⎫
⎪
⎪
⎪
⎪
⎬
⎪
⎪
⎪
⎪
⎭
Fig. 1 RADFT in IoT environment

4. 12278 S. Baskar et al.
1 3
The above Eq. (1) states the proposed method objective
that includes service reliability improvement; computation
time, false-positive, and data loss are decreased. The first
derivation describes the service reliability that is represented
as ℜ, in this; the evaluation is obtained as
(
𝔄+d�
Uu∕𝔇
)
here the
attributes referred to as 𝔄 are acquired from the incoming
data from different IoT applications. The data are repre-
sented as d′
where several users use it Uu. Post to this pro-
cess, the data fusion is performed based on attributes in the
appropriate time that is denoted as 𝕥.
Here data fusion and features are termed as 𝔇 and F and
the time of processing the particular service is referred to
as 𝕤 based on time 𝕋. Thus, the computation, false-positive,
and data loss are addressed as C′
, ℙ and l0 are decreased in
the proposed work. Thus, the analysis of trust is evaluated
to achieve better precision where the study is termed as 𝔞.
The trust is represented as T where it is deployed by iden-
tifying the features of attributes in a different time interval.
The feature identification is described as I and extracts the
features denoted as E and decreases the uncertainty 𝔖. For
this processing, a trust model is developed to monitor the
different applications’ attributes in varying time intervals.
The following Eq. (2) is used to derive a trust-based model
and additional details acquiring from various sources.
The trust model is evaluated to measure the precision for
the proposed work, which acquires the data and its features
that analyze the resultant data. In the above Eq. (2), the trust
model is established and extracts the features in the allocated
time, represented as
�
𝔄+𝔞
∑ E
F
�
∗
�
g0+F
∑
𝔞 l0
�
where the data are
granted to the user that is denoted as g0. By computing the
trusted model, it processes the different types of attributes
that include service latency, loss, time, and several users. In
this number of a user is represented as U = {u1, u2, … , un}
where the attributes are processed at a particular time and
(2)
T =
� d�
�
s
��
𝔄 + 𝔞
∑ E
F
�
∗
�
g0 + F
∑
𝔞 l0
��
+
F
�
I
�
Uu + d�
�
∗
F
�
d�∕l0
g0
s
− 𝕋
�
∗
�
𝔄 + 𝔞
E∕I
�
+
�� 𝔄
�
s
�
g0 + Uu
l0∕t + d�
��
∗
��
F + I
𝔄∕𝔞
�⎞
⎟
⎟
⎠
− (𝕋 − t)
obtains certain features at a different time interval. The fol-
lowing Eq. (3) is formulated to obtain the different attributes
acquiring. The attributes are defined as data availability and
response.
The attributes are derived from extracting the features
from the IoT application, which is computed in the above
Eq. (3). By formulating these attributes based on the time the
trust is provided to the user request. If the latency and loss
of services are obtained by extracting the feature in the first
iteration, then in the forthcoming processing, the attributes
differ. By computing (𝔞 ∗ 𝕤) ∗ (𝕥) − 𝕋 the analysis is pro-
cessed in the allocated time intervals that are forwarded to
the user, which is represented as f0. In this case, the uncer-
tainty occurs, it is decreased to precise they attributed from
different IoT applications. The above equation is derived to
obtain the attributes by identifying its features; the following
Eq. (4) is calculated to determine the features.
The identification of features is formulated in the above
Eq. (4). In this, several attributes are acquired, and the fea-
tures are extracted. Thus by Computing
��
(𝔞∗s)
∑d�
f0
l0
�
here the
analysis is obtained for the service, and it examines the data
loss for the features. The features vary in this; the first itera-
tion extracted features are matched with the forthcoming
features. In this manner, the first and second processing is
determined to identify the features. The following Eq. (5) is
used to evaluate the extract the features from the different
iteration.
(3)
𝔄 =
√(
s
Uu∕l� + d�
)
∗
(
I +
F
E
)
+
( d�
∏
𝕋
(𝔞 ∗ s) + F
)
∗ (t) − 𝕋
+
[ d�
∑
l�
(
Uu
𝔞 + F
)
∗
(
f0
s + t∕g0
)]
+
√(
d�∕
F
l0
)
∗
(
(𝔞 + F) +
I
g0
)
(4)
I =
⎧
⎪
⎪
⎨
⎪
⎪
⎩
𝕤
∏
F
�
Uu
(d�−l0)∕𝔞
�
∗
��
(𝔞∗𝕤)
∑d�
f0
l0
�
+
�
𝔞+F
𝔄∕d�+𝕥
�
l0
∑
𝕥
�
𝕤 + 𝕋
𝔄
�
∗
��
𝔞
𝕋∕ d�
Uu
�
+
∑ �
g0+Uu
F
�

5. 12279
Attribute‑based data fusion for designing a rational trust model for improving the service…
1 3
The attributes features are extracted by computing the
above Eq. (5); here the extraction is based on the correlation
of data in an appropriate time interval. The process of ana-
lyzing the features in the proposed method is given in Fig. 2.
The categorizing of co-relation and non-co-relation data
is represented as 𝔊 and 𝔊0 by performing this trust, the
score is derived from the services. In this equation, the
acquiring of data is denoted as Ξ, and it is computed as
(
Ξ + f0
)
∗ (F + I) in this, the features are identified and
extracted from the pursuing data. The matching of differ-
ent attributes is attained based on time, and it is formu-
lated in the below Eq. (6).
(5)
E =
�
�
�
�
�
�
�
�
⎧
⎪
⎨
⎪
⎩
� 𝕤
�
𝔞
�
I
𝔄
∗ d�
�
−
�
l0 − I
∑
𝔞 𝕋
��
∗
𝔞
∫
d�
�
F + 𝕤
∑
𝔄
�
+
� d�
�
𝕤
�
f0 + d�
�
+ 𝕥
�
∗
�
F
Uu
+ C
�
��
−
�
arg
��
𝕤 + d�
�
∗
�
C�
− 𝕥
���
∗
I
�
𝔊
�
l0 + 𝕤
𝕥 − d�
�
+
⎧
⎪
⎨
⎪
⎩
𝔊0
�
d�
(𝕤 + 𝔄) +
�
Ξ + d�
�
∗
��
F
f0∕𝔄
�⎫
⎪
⎬
⎪
⎭
−𝕥 +
� Uu
�
I
�
Ξ + f0
�
∗ (F + I)
��
�
�
�
�
�
−
�
𝔊0 − 𝕥
�
(6)
𝔞 =
�
𝜑 +
�
𝕤 ∗
d� + I
∑
E l0
�
�l0 +
�
𝔖 + 𝔊0
∏
𝕤 d� + Ξ
�
∗
f0
�
𝕥
�
g0 − d�
�
�
�
�
�
�
�
𝕋
�
Uu
�
𝔄
F∕𝕤
�
+ Uu ∗
�
𝕤 + Ξ
∑I
𝔄
d�
�
�
�
�
�
�
�
��
d� + 𝕤
Uu
�
+
d�
�
I
�
d�
+ Ξ
∑
𝔊 − 𝔊0
�
+ (𝕋 − 𝕥)
⎫
⎪
⎬
⎪
⎭
In the above Eq. (6), the different types of attributes are
evaluated. The first derivation indicates the latency, which
is represented as 𝜑. It is associated with features, and it
determines latency, service loss, number of users, and time.
Based on these four, the analysis is attained and evaluated
for the different time intervals for this matching of features
are determined. Finally, the trust is obtained based on the
matched attribute, and the latency is evaluated as
�
𝕤 ∗ d�+I
∑
E l0
�
here the service is identified from the extracted features.
The service loss is achieved by determining
�
𝔖+𝔊0
∏
𝕤 d�+Ξ
�
in
this, the uncertainty is evaluated from the acquired data.
Here non-co-relation is obtained and determines the services
from different sources. Thus, the numbers of user evaluation
are formulated as Uu ∗
�
𝕤+Ξ
∑I
𝔄 d�
�
here the services are
acquired and process the data based on the attribute. For
every time number of users differs, that the trust score is not
precise; another attribute is time-based feature extraction. It
is computed as
�
d�+Ξ
∑
𝔊−𝔊0
�
+ (𝕋 − 𝕥)here the data are acquired
in the allocated time in this attribute changes for every time
interval. By placing Eqs. (5) and (6), the matching of the
attribute is evaluated in Eq. (7).
(7)
𝔞(E) =
�
�
�
g0 + Uu
F
�
∗
�
𝕤
I
+ 𝕥
��
∗
�
Ξ + d�
�
∗
⎛
⎜
⎜
⎝
��
F + 𝕤
f0∕𝔄
�
+
�
𝕤 + I
∑
F l0
�⎞
⎟
⎟
⎠
− g0
+
⎛
⎜
⎜
⎝
��
d� + 𝕤
Uu
�
+
�
𝔄
F∕𝕤
�⎞
⎟
⎟
⎠
−
� d�
�
𝕤
�
f0
F
+ d�
∗ 𝔄
�
+ 𝕥
�
+
� 𝕤
�
𝔞
�
I
𝔄 + I
∗ d�
�
−
�
𝕤 − I
∑
𝔞 d� + l0
��
+
F
�
𝕤
�
𝔊 + 𝕋
I∕𝔄
�
∗
⎛
⎜
⎜
⎝
��
𝕤 ∗ 𝔖
I∕𝔄 + d�
�
+ l0 ∗
�
𝕤 ∗ l0
∑𝔖
I
d�∕Uu
�
⎞
⎟
⎟
⎠
Fig. 2 Attribute analyzing process

6. 12280 S. Baskar et al.
1 3
The analysis is extracted by computing the above Eq. (7)
where
�
𝕤∗l0
∑𝔖
I (d�∕Uu)
�
is deployed in this, the service loss is
determined. Here the extracted features are selected for dif-
ferent attributes and analyzed. Post to this, data fusion is
evaluated for the trust model that provides an efficient trust
score. The following Eq. (8) is used to determine the data
fusion and obtains the trust score for the trust model devel-
oped above.
The data fusion for the trust model is represented as
𝔇(M) is attained by processing the above Eq. (8). It
achieves the acquired different data attributes observed on a
time interval. In this, the co-relation and non-co-relation
attributes are extracted by deriving this trust score is not
precise. And, that uncertainty occurs, to avoid this deep
recurrent learning is introduced to increases precision. Here,
the uncertainty is determined by processing
√(
𝔊
f0+d�∕Uu
)
in
this number of users, latency, time, and service loss are
matched. In Fig. 3, the process before deep learning in attrib-
ute analysis is presented.
By proposing this deep learning, the accurate data are
matched and attains the optimal resultant attribute. In the
above equation, the matching is attained, and it is repre-
sented as m′
, where the data fusion is derived as 𝔇. The
scope of data fusion is to integrate the co-relation attributes
that are extracted from different IoT applications.
(8)
𝔇(M) =
⎧
⎪
⎪
⎨
⎪
⎪
⎩
d�
∑
𝕤
�
Ξ ∗ 𝔞
Uu+I
�
+
�
d�Ξ
𝔊0∕l0
�
∗
I
∏
𝔞∕F
d�
(𝕤)
��
𝔊
f0+d�∕Uu
�
+ m�
∗ a(E) + 𝕥
4
Deep recurrent learning (DRL)
for uncertainty analysis
Recurrent learning is processed to enhance the trust score for IoT
services by analyzing the trust based on different attributes. The
trust score is not attained optimally by deploying the data fusion
method for the above trust model. And, this recurrent network
performs the processing based on the hidden layer and attains the
co-relation attributes. The scope of using recurrent learning is to
perform better data fusion and achieves the trust score. In Table1,
the FPR and trust score for different features are presented.
From the table, it is seen that as precision increases, com-
puting time and trust score increase. However, the loss and
FPR reduce. The computing time increases due to the multiple
validating instances observed from the multi-attribute data.
Fig. 3 Pre-process before
learning
Table 1 Trust score and FPR
Features Precision Loss (%) Comput-
ing time
(s)
Trust score FPR
2 0.922 7.319 0.45 0.681 0.09
4 0.923 6.857 0.457 0.727 0.079
6 0.93 6.508 0.459 0.739 0.079
8 0.932 6.477 0.5 0.804 0.078
10 0.941 6.34 0.56 0.822 0.077
12 0.943 5.999 0.571 0.846 0.076
14 0.948 5.153 0.768 0.883 0.076
16 0.953 4.723 0.838 0.889 0.074
18 0.955 4.68 0.858 0.931 0.071
20 0.958 4.576 0.896 0.961 0.067
22 0.96 4.523 1.006 0.963 0.062
24 0.969 4.286 1.038 0.977 0.062

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Recurrent learning is employed to recognize the trust
score based on prediction based on the trust model. For
every time interval, the obtained attributes differ to obtain
the co-relation features of attribute the DRL is proposed.
This processing is based on the prediction of characteristics
from IoT service applications. The following Eq. (9) is used
to obtain the prediction-based DRL for attribute extraction.
The above Eq. (9) represents the prediction to attain the
trust score, for this trust model is considered for data fusion.
By formulating
(
𝔞+T
𝔄∕M
)
the trust is analyzed from the trust
model and provides the particular trust value which does
not have a higher trust score. For this, the prediction based
attribute is defined to evaluate the trust value where it is
represented as
√(
F+d�
M∕𝕤
)
+ 𝔳. By processing, this prediction
of varying attributes is obtained, which is denoted as 𝔳. In
Fig. 4a, the recurrent learning process is illustrated. The
hidden layer process is presented in Fig. 4b
The predictions of attributes are referred to as Φ. The
recurrent learning prediction is processed, and it classifies
uncertainty and trust equality, which is represented in the
below Eq. (10).
(9)
Φ =
d�
�
𝕥
�
Ξ ∗
𝕤
F
�
+
��
𝔞 + T
𝔄∕M
�
∗
d�
�
𝕤
(E + F)
�
+ (𝔄 + I) −
⎡
⎢
⎢
⎣
��
𝔞 + F
∑
d�(Ξ)
�
∗
𝔄
�
F
�
d�
− l0
I∕𝕤
�
+(𝔊 + F) ∗
�
𝔊0 − 𝔊
𝕥
��
−
d�
�
Uu
�
𝔞 ∗
g0
𝕤
Ξ
�
+
��
F + d�
M∕𝕤
�
+ 𝔳
(10)
𝔇 =
F
∑
𝕤
(𝔳 + 𝔄) ∗ M +
��
d�+Uu
Ξ
�
∗
∏ �
C�
+ 𝔄
�
− 𝕥
�
− 𝔊0 = 𝔖
��
E+F
Uu+d�
�
+
�
𝔳
∑
𝕤
F ∗ 𝔄
𝔞+I
∗
d�
∏
E
(𝔄 + I) − M
�
+ Φ(F) ∗ E = 𝔊
⎫
⎪
⎪
⎬
⎪
⎪
⎭
The data fusions are derived to evaluate the uncertainty
and equality computed in the above Eq. (10). The first
derivation is the uncertainty that is calculated by includ-
ing the computation time. The processing is not co-related
with the features which are represented as
(
C�
+ 𝔄
)
− 𝕥.
The uncertainty is determined by not matching the features
promptly. The second derivation determines the full equal-
ity of features where the predictions of the trust model are
deployed.
Here the relevant trust values are achieved by comput-
ing the processing in mentioned time, and it is formulated
as (𝔄 + I) − M + Φ(F). This hidden layer is performed
to address the uncertainty attributes in DRL, and it is cal-
culated in the following (11). In this DRL, consider one
hidden layer is processed based on this input is different
attributes. It is analyzed based on the prediction of attrib-
utes that are compared with the trust model.
The hidden layer is processed to obtain the optimal
data fusion for attributes, and it is referred to as H. The
layers on the hidden layer are represented as 𝔛 the input
is processed in the first layer where it acquires the input
attributes. The second layer evaluates the attributes based
on time and computes the feature matching with the trust
model. If the trust score is not reliable, it is set as the
training data and process with the rest of the features.
By performing this, prediction of attributes is defined
and improves the precision. The following Eq. (12) is to
decrease computing time and data loss.
(11)
H =
⎧
⎪
⎪
⎨
⎪
⎪
⎩
𝔛1 =
�
𝔄 + 𝔞
𝕤
�
− (I + F)
𝔛2 = 𝔛1 + T −
�
Uu + d�
�
∗ C�
⋮
𝔛n = 𝔛2 + m�
(M) ∗ Φ − 𝔛n−1
Fig. 4 a Recurrent learning
process, b hidden layer process

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The computing time and data loss are decreased by evalu-
ating the above Eq. (12); in this, the analysis is observed
in the mentioned time. The computing time is formulated
as (𝔞(F) − 𝕥) thus the analyses are determined for the
attributes. Thus, the data loss is addressed by deriving
(Ξ + Φ) − (𝕤 + T) here the trust is performed to decreases the
data loss. Based on the trust value, the trust score calculation
is determined where it decreases the false positive from the
attribute, and it is deployed in the following Eq. (13).
Trust values are evaluated by performing data fusion, for-
mulated in the above Eq. (13). In this
(
m�+d�
F∕E
)
determines the
matching process of features and extracts the features for
forthcoming attributes. Table 2 presents the matching ratio
for different time intervals.
The less is the uncertainty, the matching ratio is high and
hence the trust score. The high is the analysis of the features
extracted without hesitations; the fore-mentioned factors are
high. The trust model achieves high matching through dif-
ferent uncertainty instance verification. In Fig. 5, the overall
process of the trusted update based on DRL is illustrated.
The analyzed attributes are classified for FPR, precision,
and uncertainty during the recurrent process. The uncer-
tainty is analyzed at different intervals and recurrent
instances, as shown in Fig. 5. This helps to reduce the FPR
and improve the trust score. Thus, the trust model data
fusion is matched based on the DRL prediction by process-
ing the hidden layer. The false positive is decreased that is
computed as
(
𝔳 + 𝔄
𝕤
)
− ℙ; here, false positive is denoted as
ℙ. Finally, the trust score is achieved, and the data fusion is
processed promptly to improve the precision that is com-
puted in the below Eq. (14).
(12)
C�
�
l0
�
=
� 𝔞
�
Uu
�
F +
I
𝕤
�
∗
d�
�
𝔊
�
F
𝕋 + E
�
− Ξ
�
− (𝔞(F) − 𝕥) +
⎡
⎢
⎢
⎣
��
d� + I
E∕𝔖
�
∗
F
�
Ξ
(Ξ + Φ)
+
�
𝔄
∑
d� Uu
��
− (𝕤 + T) ∗
�
g0 + d�
�
(13)
T(𝔇) =
[ 𝕤
∑
𝔄
(I + 𝔊) ∗
(
F
𝔞
+ 𝕤
)
]
+
∏
(
Ξ
Uu + d�
)
−
[
(M + 𝕥) ∗
√(
F + I
𝔞 + g0∕C�
+ d�
)]
+
∑
𝔳
(
𝔄 + 𝕤
𝕥
)
∗
[(
m�
+ d�
F∕E
)
+
∏
(
𝔳 +
𝔄
𝕤
)]
− ℙ
In the above Eq. (14), the data fusions for the attributes
are evaluated based on a timely manner, and it is processed
by computing
(
Ξ ∗ d�
Uu+I
)
here the identification of data is
attained. Based on DRL, the predictions are deployed to
obtain better data fusion related to different attributes from
IoT applications. Thus, the above equation assures the objec-
tive and improves the precision of the trust score. Here, by
computing
(
Uu+𝕤
d�+𝔊∕Ξ
)
+ T − C�
(
l0
)
the service reliability is
enhanced for the proposed method.
4.1 Performance analysis
4.1.1 Simulation setup
This section briefs the performance analysis of the proposed
RADFT through experimental analysis. For a formal analy-
sis, the IoT environment is modeled with 240 IoT devices/
applications and a cloud server in the opportunistic network
environment (ONE) simulator. The cloud provides heteroge-
neous services from the stored data of size 1 TB. In this, the
response rate is varied between 100 and 1100 for the differ-
ent service requests. The applications consume a bandwidth
of 5–10 Mbps for service acceptance. Similarly, the data
accumulated from the cloud is 2000, for which a maximum
of 24 features are extracted and analyzed.
4.1.2 Performance metrics
For the trust model, the metrics considered are false-positive
rate (FPR), uncertainty factor, data loss, and service reli-
ability. Contrarily, the metrics computing time, precision,
matching, and loss are used for the data fusion process.
(14)
𝔇(𝕥) =
⎧
⎪
⎨
⎪
⎩
��
d�+Uu
Ξ+𝔞
�
+
�
Ξ ∗ d�
Uu+I
��
∗
�
𝔳 + 𝔄
𝕤
�
=
�
𝔊+𝕋
I∕𝔄
�
+
𝔄
∏
𝔳
(𝕤 + Φ) ∗
�
Uu+𝕤
d�+𝔊∕Ξ
�
+ T − C�
�
l0
�
Table 2 Matching ratio
Time
interval (s)
Features Uncertainty Trust score Matching %
5 1 0.099 0.671 84.154
10 8 0.065 0.804 88.724
15 12 0.089 0.846 92.318
20 18 0.075 0.931 93.115
25 20 0.073 0.961 93.893
30 23 0.099 0.973 96.467

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4.1.3 Comparative analysis
For verifying the consistency of the proposed trust model,
the methods FSP-TM (Alshehri and Hussain 2018), TruSD
(Kalkan and Rasmussen 2020), and TBM (Alqahtani et al.
2020) are used. For verifying the data fusion process, the
methods IncompFuse (Xu et al. 2019) and SFMN (Long
et al. 2020) are considered in the comparative study.
4.2 False positive rate
The false-positive rate for the proposed method shows a
lesser value by comparing it with the other three methods.
The false positive is related to the behavior termed as
trusted, yet there is no trusted service. In Fig. 6, the false-
positive varies with different trust-based services in IoT
applications. By deriving
�
𝔄+𝔞
∑ E
F
�
∗
�
g0+F
∑
𝔞 l0
�
the attributes are
acquired from the IoT applications, and from that, the fea-
tures are extracted. In this manner, the components are asso-
ciated with latency, several users, and other data types. The
analysis is processed to achieves the trust-based service and
communicate reliably. The processing represents the evalu-
ation
√(
F+I
𝔄∕𝔞
)
where the features are identified and ana-
lyzed in a particular interval of time. By processing these,
the false positive decreases concerning several trusted
services in IoT applications. The analyses are extracted on
different time instances, as the attributes vary for time.
4.3 Uncertainty factor
In Fig. 7, the uncertainty for the proposed method shows
lesser value by comparing it with the existing methods. It is
derived by evaluating
∏d�
𝕋 (𝔞 ∗ 𝕤) + F here the features are
attained and processes the services at the mentioned time.
In this, the data are acquired evaluated to overcome the
latency and loss of services. By Computing
�∑d�
𝕤
�
f0 + d�
�
+ 𝕥
�
∗
�
F
Uu
+ C
�
�
the forwarding of data is
attained to the end-user. Here, trust-based services are
obtained for the extracted features from the different applica-
tions. Thus, the evaluation is obtained by decreasing the
computing time of the services. By processing
�
𝔖+𝔊0
∏
𝕤 d�+Ξ
�
the
non-co-relation data are acquired. From that, the uncertainty
is calculated, and it is derived by processing the services. If
the data acquired in the initial time differs from the data
acquired simultaneously in this condition, the uncertainty
decreases. At different time intervals, the attribute is defined
appropriately and processes the data by making prediction-
based methods. The forthcoming and previous attributes are
defined and analyzed.
Fig. 5 DRL process for trust
update

10. 12284 S. Baskar et al.
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Fig. 6 FPR for # IoT devices and trust
Fig. 7 Uncertainty factor for # service responses, trust, and FPR

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Attribute‑based data fusion for designing a rational trust model for improving the service…
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4.4 Data loss
The data loss for the proposed method is less for varying IoT
devices and uncertainty, as shown in Fig. 8. The data loss
decreases if uncertainty decreases, whereas it is performed
for the different IoT devices. By processing
√(
𝕤∗𝔖
I∕𝔄+d�
)
+ l0
the service is determined based on this; the uncertainty is
evaluated. Here, the derivations are computed to obtain bet-
ter communication and exchange of data. Thus, the evalua-
tion is based on
d�
∑
𝕤
�
f0
F
+ d� ∗ 𝔄
�
+ 𝕥 where the service is
determined and processed on the appropriate time interval.
The data loss is addressed by formulating
(
d�+Ξ
𝔊0∕l0
)
in this, the
data acquiring are attained. In this processing, the uncer-
tainty is computed for different attributes of data and
decreases the data loss. By evaluating
(
d�−l0
I∕𝕤
)
+ (𝔊 + F) the
features are identified, and services are derived to the
requesting user in IoT. To avoid this prediction-based model
is developed to decreases the data loss, whereas a timely
manner is derived from this processing.
4.5 Service reliability
In Fig. 9, the service reliability is evaluated and attained to
process the attributes by computing
(
Uu+𝕤
d�+𝔊∕Ξ
)
+ T − C�
(
l0
)
.
In this derivation, the trust is provided to the user having the
co-relation data value and acquires the attributes from the
source applications. The service is obtained based on
extracted features; thus, it includes latency, number of users,
data loss, and time. Based on these factors, the service is
Fig. 8 Data loss for # IoT devices and uncertainty
Fig. 9 Service reliability for FPR and uncertainty

12. 12286 S. Baskar et al.
1 3
obtained and processed, including the data fusion related to
different attributes from the IoT application. The data fusion
is brought to prove the trust to the relevant service user and
process effective communication between them. The
exchanges of trust are deployed for the co-relation data, and
it attains the computing time decreases. In this, the data loss
is addressed, and it is evaluated by determining the Ξ ∗ d�
Uu+I
where the data acquiring is provided for the number of users
that satisfy the trust. Trust-based detection is obtained by
determining
(
𝔊+𝕋
I∕𝔄
)
where the trust is identified in the par-
ticular time interval, in Table 3, the comparative analysis for
the trust model is presented.
4.6 Computing time
In Fig. 10, the computing time for the proposed method
shows lesser time to analysis the features by deriving
(
m�+d�
F∕E
)
. In this processing, the matching of data is obtained
by evaluating the extracted features. In this, the extraction
process is based on a timely manner where it acquires the
co-related data. By formulating
(
𝔳 + 𝔄
𝕤
)
the data are
obtained for varying time intervals and processes the trust-
based services. It acquires the data that represents the fea-
tures based on computing
√(
F+I
𝔞+g0∕C�
+d�
)
in this identifica-
tion of features are deployed. Here the evaluation is
associated with computing time and data. The feature set of
data are analyzed to provide effective analysis and obtains
the processing data by determining (𝕤 + T) ∗
(
g0 + d�
)
. In
this, the trust-based user is provided access and evaluates the
data service to the IoT applications. In Eq. (12), the comput-
ing time of service is assessed and processes the data
appropriately.
4.7 Precision
The precision for the proposed method is shown in Fig. 11
determines concerning data and features. The attributes are
defined for processing the services and obtain better accu-
racy for the proposed method. It is achieved if the uncer-
tainty and data loss decreases by providing trust based on
the data fusion method. It is computed by evaluating
Table 3 Comparative analysis
for different factors
Metrics Variations FSP-TM TRUSD TBM RADFT Findings
FPR Trust=0.8 0.1497 0.1061 0.0927 0.0783 11.36% Less
Data loss (%) FPR=0.07 8.94 8.06 6.81 4.34 10.79% Less
FPR IoT devices=200 0.0838 0.0748 0.0711 0.0645 7.24% Less
Uncertainty factor IoT devices=200 0.0357 0.026 0.0221 0.0163 6.98% Less
Uncertainty factor Trust=0.8 0.0602 0.0501 0.0271 0.0223 7.05% Less
Service reliability 0.9069 0.9265 0.9461 0.9488 13.38% High
Data loss (%) 8.94 8.23 7.03 6.31 10.54% Less
Service reliability 0.9087 0.9273 0.9321 0.9422 11.7% High
Fig. 10 Computing time for # data and # features

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Attribute‑based data fusion for designing a rational trust model for improving the service…
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√(
E+F
Uu+d�
)
∗ (𝔄 + I) the extracted features are obtained and
process the data, and provide the service to the number of
users. By determining the service, the non-co-related data
associated with features are computed from the trust model.
The formulation of service is evaluated by processing
Φ(F) ∗ E + M in this, the prediction-based model is pro-
cessed. Here the predictions are obtained to compare the
trust model with the open data. The varying data are
obtained and extracts the features by computing
(
𝔊0−𝔊
𝕥
)
here
the co-relation and non-correlation are derived to improve
the precision. Thus, data fusion is observed to process co-
relation data.
4.8 Matching and loss
In Fig. 12, the comparison is obtained for the three methods,
where the varying features are extracted and processed at the
appropriate time. It is derived by computing Eq. (6), where
the different attributes are attained based on time. Thus,
matching increases for varying features from the various IoT
applications, the data loss decreases. It is processed by for-
mulating
𝕤
∏
𝔞
�
I
𝔄
∗ d�
�
where the data are identified and ana-
lyzed the services. By determining
(
ℜ − C�
∗ T
)
+ M thus
includes the processing of trust-based services. In Table 4,
the comparative analysis for the data fusion is presented.
Fig. 11 Precision for # data and # features
Fig. 12 Matching and loss for # features

14. 12288 S. Baskar et al.
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5 Conclusion
In this manuscript, a rational attribute-based data fusion
trust model is designed to improve the service reliability
of IoT applications. The proposed trust model uses data
fusion and deep recurrent learning to improve the reli-
ability of IoT services to the connected applications. In
this data fusion model, different data attributes from a
diverse environment are acquired, and the uncertainties are
reduced. The predicted trust model is recurrently analyzed
with certain attribute data to improve the service’s reliabil-
ity by reducing delays. This deep recurrent learning helps
to identify the uncertainties and false-positive rates during
different time instances of attribute analysis. The process
of data fusion using certain features helps to improve the
trust score based on the outcome of the learning process.
Therefore, the trust of the service is retained at a high level
before and after dissemination. The proposed trust model
achieves fewer false positives, uncertainty, and computing
time by improving service reliability.
Acknowledgements Authors would like to thank Department of Sci-
ence and Technology (DST), New Delhi, India, for the funding to carry
out the Research work- DST/TDT/AGRO-20/2019 and 22-01-2020
from Karpagam Academy of Higher Education, Coimbatore, India and
the dataset collection has been supported from Botswana International
university of science and technology (BIUST), Botswana.
Author contributions S.Baskar—Conceptualization, Methodology,
Writing- Original draft preparation. Rajalakshmi Selvaraj—Data cura-
tion. Venu Madhav Kuthadi—Visualization, Investigation. P. Mohamed
Shakeel—Writing—Reviewing and Editing.
Funding None.
Declarations
Conflict of interest The authors declare that they have no conflict of
interest. This research did not receive any specific grant from funding
agencies in the public, commercial, or not-for-profit sectors.
Ethical approval This article does not contain any studies with animals
performed by any of the authors.
Informed consent Informed consent was obtained from all individual
participants included in the study.
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1.107 0.987 0.717 10.51% Less
Precision 0.9345 0.952 0.9562 10.36% High
# Features
Computing time
(s)
1.0397 0.93 0.7372 8.255% Less
Precision 0.9408 0.9585 0.9716 10.975% High

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