Wireless personal communication

Vol.:(0123456789)
Wireless Personal Communications
https://doi.org/10.1007/s11277-021-08583-0
1 3
Optimized Energy Management Model on Data Distributing
Framework of Wireless Sensor Network in IoT System
Venu Madhav Kuthadi1
· Rajalakshmi Selvaraj1
· S. Baskar2
· P. Mohamed Shakeel3
·
Abhishek Ranjan4
Accepted: 4 May 2021
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021
Abstract
Data Dissemination is an essential transmitting method for a sensor network to the end-
users across any set of interconnected frameworks. WSN is often used within an IoT sys-
tem, in other words. As in a mesh network, a wide collection of sensors can collect data
individually and send data to the web via an IoT system through a router. The conventional
defined solution for data dissemination in Wireless Sensor Networks (WSN) does not
include the wide range of new applications built on the Internet of Things (IoT)systems.
Hence, it is observed that searching for an appropriate transmission link while distributing
data with optimized utilization of energy is a significant challenge in the IoT communica-
tion infrastructure. Therefore, in this paper, an Optimized Energy Management Model for
Data Dissemination (OEM-DD) framework has been proposed to optimize energy during
data transmission efficiently across all sensor network nodes in the IoT system. The effi-
ciency of the data dissemination across an interconnected network has been achieved by
introducing a Non-adaptive routing approach in which data is distributed effectively from
a single source to various points. Besides, Non-adaptive routing involves the dispersed col-
laboration system and the priority task planning principle combined with an integer frame-
work for the efficient energy processing and grouping of data in the sensor’s network. Opti-
mization of the energy management model through Non-adaptive routing allows low power
consumption and minimal energy usage for each sensor node in the IoT system to improve
the transfer and handling of data in severe interruption. The experimental results show that
the suggested model enhances the data transmission rate of 96.33% with less energy con-
sumption of 20.11% in WSN, which is the subset of IoT systems.
Keywords Internet of things · Wireless sensor networks · Energy efficiency · Data
transmission rate · Data dissemination
* Venu Madhav Kuthadi
venumadhav.kuthadi@yahoo.com
Extended author information available on the last page of the article

V. M. Kuthadi et al.
1 3
1
Introduction to Data Dissemination in Wireless Sensor Network
The IoT environment consists of technological components that include sensor networks
and low-cost IoTsystemswith better data computation capacity in the interconnected frame-
work. IoT [1] links different sensor nodes to the internet and networking devices, including
a vast proportion of specific built-in tools for data distribution in wireless communication.
In IoT systems, sensors management models are used to collect real-time data computa-
tion in the interconnected framework. Further, WSN[2, 3] is the subset of IoT systems that
have established the connection between several nodes through static and dynamic routing
procedures, which is considered one of the promising methods for smart IoT networks. In
IoT systems, all sensor nodes utilize several routing procedures to configure the intercon-
nection framework during data dissemination.WSN has numerous applications, including
environmental control, activity recognition, threat detection, health care, and social com-
munication. Based on the innumerable application, these sensor networks [4]can be dis-
tributed around the sensing field in such environments and execute important tasks such as
pulse detection, data gathering, and dissemination. The sensors’ detecting and processing
capacities enable WSN [5] in the IoT system to continue expanding in the communication
framework. Conventional WSNrouting procedures change significantly, contributing less to
energy optimization during data distribution in the IoT system.
Further, The dissemination of data [6] allocates or transmits numerical or any other
information to the IoT system’s end node. Data dissemination in the IoT system of WSN
involves the delivery of essential information [7]to the communication framework. Data
dissemination is the procedure for conveying messages via specified networks and restric-
tions to maintain different user segments in IoT systems. Data dissemination in WSN in
the IoT system [8]is one of the most critical processes for analyzing the data gathered from
various networks to the practical domain analysis.
The data dissemination is the information operation, which increases IoT system access
by different media to clients based on quantitative function. Wireless Sensor Network’s
primary aim is to optimize energy usage [9], even though transmission of energy has made
substantial advancement. Besides, In IoT systems, several communication models are com-
monly used in channels whereby sensor [10] entities nearer to the drain or ground sta-
tion have transferred several packages than far apart, contributing to the phenomenon. The
length of the life span of WSN in the IoT system is substantially reduced due to the ineffi-
cient energy management model. Thus, the balance of energy usage[11], typically referred
to as load balance, must be considered the core research area in several emerging systems.
The energy transfer in wireless technology [12] is calculated mainly by the transmitting
range and frame rate, as obtained by data dissemination. Therefore, The sensor nodes
ensure that information [13] must be gathered to the networks using several routing proce-
dures to deliver optimized energy levels.
The remaining work is given as follows; Section 2 discusses conventional background
studies’ insights on various data dissemination framework in wireless sensor networks.
Section 3 discusses the OEM-DD and the Non-adaptive routing approach. It shows how the
data is distributed effectively from a single source to various points with optimized energy
management. Section 4 Analysisexperimental results show that the suggested model
enhances the data transmission rate of 96.33% with less energy consumption of 20.11%
in WSN, which is the subset of IoT systems. Section 5 concludes the research with future
extension.

Optimized Energy Management Model on Data Distributing Framework…
1 3
2
Background Study on Data Dissemination in Wireless Sensor
Network
This section discusses several works that have been carried out by various researchers;
P. K. Poonguzhal et al.[14] developed the Design of Mutated Harmony Search Algo-
rithm (DMHSA) for Data Dissemination in WSN. The emphasis seems to be on devel-
oping a method based on a meta-heuristic designed routing mechanism for a distributed
system using a mutated harmony search algorithm (MHSA), to continuously study the
fixing of clusters to increase the energy efficiency.
Hazem Jihad Badarneh et al. [15] developed An efficient indexing framework for data
dissemination (EIF-DD) in WSN. The authors’ proposed method efficiently boosts the
indexation packages of arbitrary sensing devices within a Dynamic-Coalition system.
The structure suggested and reduced distributed packages by creating complex coali-
tions measurement of significance.
Mahdi Mousavi et al. [16] developed Energy and Social Cost Minimization for Data
Dissemination in Wireless Networks: Centralized and Decentralized Approaches (ESM-
DD). In the developed method, each acquiring node selects its nodes, and thus, costs are
allocated as per the strength of its chosen nodes. Mixed-Integer-Liner Program(MILP)
is used to achieve a unified optimum solution with more energy consumption and less
data efficiency than the distribution method.
S. M. Amini et al. [17] reported an Energy-efficient data dissemination algorithm
based on virtual hexagonal cell-based infrastructure and multi mobile sink (EE-HCM)
for wireless sensor networks. The specific idea behind the concept is to have the respon-
sibility to empower specific systems of detecting the present position of sink node that
update sensor node positions for network devices to those sensor node chosen by a vir-
tual hexagonal backend with more energy consumption and less data efficiency.
Wei Liu et al. [18] introduced A Neighbor-Based Probabilistic Broadcast Protocol
for Data Dissemination (NB-PBP) in Mobile IoT Networks. Therefore, the presented
method suggests disseminating data in the smartphone IoT devices through effective
telecast security rules. The introduced scheme utilizes the nearby awareness of sensor
nodes to identify a retransmit delay, giving priority to various packets as per the earn-
ings. A communication variable adjusting for the introduced scheme’s mobile nodes’
different network sections is implemented with less data efficiency.
AntoninoOrsino et al. [19] developed Caching-Aided Collaborative D2D Operation
for Predictive Data Dissemination (CAC-PDD) in Industrial IoT. In this work, the chal-
lenges have been met for disseminating information in typical production automation
dynamics with the proposal to use shifting industrial equipment as caching aids with
more energy consumption. The different information is implemented in dissemination
methods and then creates a quality of high-rate data services.
Moonseong Kim et al. [20] developed A Robust Energy Saving Data Dissemination
Protocol (RES-DD) for IoT-WSNs. The shortest way among sensors must be regarded
to spread the data over the whole of the IoT-WSNs, to reduce energy usage. The SPMS
(Shortest Path Mined) framework uses the Bellman-Ford approach to construct routing
tables and the forward multi-hop information to reduce energy usage and time. Because
of this feature, routing updates are complicated to avoid high traffic, which leads to
less data efficiency. In comparison, any sensors on the shortest distance would induce a
server loss of SPMS.

1 3
Hazem Jihad Badarneh et al. [21] introduced an efficient indexing framework for data
dissemination in wireless sensor networks (IFD-WSN). By developing custom-coali-
tions using the related measure, the introduced system reduces the distribution pack-
ets’ efficiency. Concerning the average latency routing and the packets distribution ratio,
the DC System is superior. Despite construction time, the number of nodes and over-
sight space costs could greatly surpass the R-tree, Decompose-tree, and Grid- related
DBSCAN with Cluster Forest.
Wang, X et al. [22] developed a home energy management system (HEMS) to plan
washing machine, water heater, electricity, dishwasher, air conditioner and climate con-
trol. The system consists of a residential energy system with renewables and a battery
storage unit. Compared to the normal domestic energy management system, the simula-
tion results declare the efficiency of the systems.
As observed from the literature study, searching for an appropriate transmission link
while distributing data with optimized energy utilization is a significant challenge in
the IoT communication infrastructure. Further effective data management in the distrib-
uted network has been considered in this research for the IoT system. Based on the firm
discussion, the OEM-DD framework has been utilized to optimize energy during data
transmission efficiently, which have been discussed as follows,
3
An Optimized Energy Management Model for Data Dissemination
(OEM‑DD)
An OEM-DD in WSN has been utilized to efficiently transmit data from all nodes in
the IoT system’s sensor network with less energy consumption. Data dissemination effi-
ciency is achieved by Non-adaptive routing that involves the dispersed collaboration
system and the priority task planning principle approaches. The introduced method’s
flow structure is shown in the Fig. 1; Here, the data dissemination includes the non-
adaptive routing with the energy management model to achieve a higher data transmis-
sion rate and less energy consumption rate.
Figure 1
Flow diagram of OEM-DD

1 3
3.1 Non‑Adaptive Routing
The efficiency of data dissemination distribution utilizes a Non-adaptive routing with
an excellent source of route discovery. In Non-adaptive routing, the sensor nodes are
separated by the center’s smallest distance into various IoT systems. D1 is a group of
nodes which has one-hops minimal distances of the bth node is denoted as f
dm
b
, while
L1 is a group of nodes in D1
. The routing protocol has K pairs and L1 × KL + 1 nodes.
Though this model is set accordingly, a sensed data should be obtained in terms of the
f
dm
b
as per the collaboration of all of the prior sensor nodes in the IoT system. The data
has been transferred from node-1 D1 to node-2 by the routing procedure with the dis-
tance of F
dm
b
, and M
dm
b
Based on the nearest pair of nodes with the transmission of data,
as illustrated in Fig. 2. The forwarded possibility on account of the modelling technique
f
dm
b
to continue, the information recorded on the transmission in the IoT system is shown
in Eq. (1)
As obtained from Eq. (1), S
dm
b
represents the possibility of the transmitted data obtained
during transmission from source to end-users. dm represents the sensor nodes with m rout-
ers as their least paths to the origin, f
dm
b
denotes the b-th node of dm . M
dm
b
denotes the near-
est pair of dm , F
dm
b
represent the pair of nodes with higher importance than f
dm
b
.
(1)
S
dm
b
= 1 −
⋃
uM
dm
b
(
1 −
⋃
xF
dm
b
(
1 − qdm+1
)
)
Fig. 2 The transmission of data
by Non-adaptive routing protocol
1 2
1 − +1
Node1
Routing
protocol Node2

1 3
The efficiency of the data dissemination across an interconnected network has been
achieved by introducing a Non-adaptive routing approach in which data is distributed
effectively from a single source to various points, as shown in Eq. (1). Given the concept
of a Non-adaptive routing protocol, it is explained that D1 … D8 are the nodes with the dis-
tance of f1 … f8 The path of data transmission in the IoT system’s sensor network is shown
in Fig. 3.
As inferred from Fig. 3.
(a) The nodes of a sensor with greater importance than f
dm
b
have been distributed the
information throughout the transmission. In other words, it has just transmitted the
information for the arbitrary node x𝜖F
dm
b
.
(b) the transmitted information has not been provided with the neighbours of f
dm
b
, to per-
sist nodes at u𝜖M
dm
b
and does not accept the transmitted information. For selective node
u𝜖M
dm
b

, whose diffusion information on all nodes in F
dm
b
is unlikely to achieve, and it is
shown in Eq. (2)
By correlating and observing Eqs. (1), (2), data transmission by a non-adaptive routing
protocol is achieved effectively since transmitted information has been provided with the
neighbours based on the effective path calculation based on diffused information. Here,
𝛾u denotes the possibility of nodes, which has obtained the production of relevant infor-
mation from F
dm
b

. This includes at least one node in F
dm
b
has dispersed the transmission
information to a particular node efficiently. Therefore the possibility of who receives the
transmission from M
dm
b
is for all f
dm
b
neighbours. This implies that in uM
dm
b
at least one node
has not received transmitted information from the node F
dm
b
. US
dm
b
is the possibility of the
(2)
�
𝛾u =
⋃
xF
dm
b
�
1 − qdm+1
�
𝛼 =
⋃
uM
dm
b
̂
𝛾u
1
2
3
4
5
6 7
8
1, 2
2, 3
2, 4
1, 5
6, 8
5, 1
Figure 3
The concept of Non-adaptive routing protocol

1 3
transmitted information would be processed by a specific node f
dm
b
and the usability of the
data qdm+1 must be provided as shown in Eq. (3)
As obtained from Eq. (3), q
bm
ab
represents the information transmission proportion of the
node dm−1 and dm . M
dm
b
denotes the nearest pair of dm , F
dm
b
represents the pair of nodes with
higher importance than f
dm
b

. The arbitrary node is denoted as x𝜖F
dm
b
and the persist nodes
are represented as u𝜖M
dm
b
.
The occurrence of the transmitted information obtained by f
dm
b
is referred to as X. The
activity of transmitted information is passed on by f
dm−1
b
is represented as Yb
. The transmit-
ted information obtained from f
dm
b
results from the collaborative transmission of nodes in
dm−1
. The possibility of activity X blends the complete possibility formula as shown in
Eq. (4)
The probability of effective data transmission is obtained from Eqs. (3)and (4), and the
data is equally distributed between all nodes of the sensor. The chances of receiving trans-
mitted data through f
dm
b
are distributed through f
dm−1
b
to Q
(
X|Yb
)

. The information supply
proportion of the node f
dm
b
to f
dm−1
b
equals. The possibility of Yb is likelihood corresponds to
the possibility of f
dm−1
b
information transmitted and it is explained in Eq. (5)
As obtained from Eq. (5), the possibility of information transmitted from a node is
denoted as q
dm
ab
f
dm−1
b
. US
dm
b
is the possibility of the transmitted information would be pro-
cessed by a specific node f
dm
b

. The preserved retransmitted nodes proportion must be based
on the transmission schemes, and it is shown in Eq. (6)
The preserved transmission node is used to know the proper distribution of data between
all nodes of the sensor, and this can be achieved by Eqs. (5) and (6), PR denotes the pre-
served retransmitted nodes, M
dm
b
denotes the nearest pair of dm , F
dm
b
represent the pair of
nodes with higher importance than f
dm
b
. Tm represents the time taken for the complete trans-
mission process. The uniformly distributed random function is described as follows,
(3)
US
dm
b
=
Tm−1
�
b=1
US
dm−1
b
q
bm
ab
⎧
⎪
⎨
⎪
⎩
1 −
�
uM
dm
b
⎛
⎜
⎜
⎝
1 −
�
xF
dm
b
�
1 − qdm+1
�⎞
⎟
⎟
⎠
⎫
⎪
⎬
⎪
⎭
(4)
Q(X) =
Tm−1
∑
b=1
Q
(
X|Yb
)
Q
(
Yb
)
=
Tm−1
∑
b=1
q
dm
ab
f
dm−1
b
(5)
US
dm
b
=
Tm−1
∑
b=1
US
dm−1
b
Tm−1
∑
b=1
q
dm
ab
f
dm−1
b
(6)
PR = 1 −
∑M
m=1
∑Tm
b=1
(1 −
⋃
uM
dm
b
�
1 −
⋃
xF
dm
b
�
1 − qdm+1
��
∑M
m=1
Tm
(7)
U
dm
b
=
{
1 f
dm
b
Distributed information on transmission
0 f
dm
b
stored retransmitted information

1 3
As obtained from Eq. (7), (0–1) allocation is followed by f
dm
b

, the uniformly distributed
random function is described as U
dm
b

. The amount of nodes that transmit the diffused infor-
mation is represented as U, and it can be evaluated from the Eq. (8),
As inferred from Eq. (8), the possibility of information transmitted from a node is
denoted as f
dm−1
b

, the requirement of U is obtained in Eq. (9)
The uniformity and the possibility of data responsible for data transmission are
obtained from Eqs. (7), (8) and (9). In which P
dm
a denotes the transmission chance that the
data obtained would be further delivered, the uniformly distributed random function is
described as U
dm
a . Tm represents the time taken for the complete transmission process, dm
represents the nodes with m routers as their least paths to the origin. Even though data
transmission undergoes little congestion and disturbance, the OEM-DD method involves
a dispersed collaboration system. In a dispersed collaboration system, the information and
assignment phase is used to distribute data uniformly, discussed as follows.
3.1.1 Dispersed Collaboration System
The dispersed collaboration system helps to improve data dissemination in the sensor
network of the IoT system. The routing network f
dm
b
initially recognizes the time limit t
dm
b
for the neighbour condition. The one-step neighbours are set to f
dm
b
and initializes M
dm
b
.
When f
dm
b
recognizes r𝜖M
dm
b
node, the transmitted data has been obtained, and changes
M
dm
b
= M
dm
b
− (r) from one step neighbour collection. The endpoint group f
dm
b
is often
described by M
dm
b
. f
dm
b
is about to transmit information for M
dm
b
= 𝜑 when t
dm
b
ends. For
M
dm
b
≠ 𝜑
, it means the transmission of information to all single neighbouring nodes of f
dm
b
,
it is not necessary for f
dm
b
to participate in the communication. f
dm
b
validate the available
resources, f
dm
b
leaves the system for the distribution of information. Alternatively, the vari-
able t
dm
b
would be reset. f
dm
b
j continues to identify and update the goal set for node status in
M
dm
b
.
In a dispersed collaboration system, let M indicate a memory that can store f informa-
tion with node indexes {1,2… r} to describe the M method as shown in Eq. (10)
The updating of data is obtained from Eq. (10); here, E and A are the units with m and
N values that are enhanced and adjusted. Through enhance period (
TE
) and the upgrade
period (
TA
) features area like. The period of f components in the node is predicted to be
(8)
U =
M
∑
m=1
Tm
∑
a=1
U
dm
b
∗ f
dm
b
∗ f
dm−1
b
(9)
R(U) =
M
∑
m=1
Tm
∑
a=1
R
(
Udm
a
)
=
m
∑
m=1
Tm
∑
a=1
Pdm
a
(10)
U = {E, A}
reduce
TE, TA𝜖 f ∶→ {M}
belong to
E = {1, 2, … r}
A = {1, 1, … l}, n ≠ l
⎫
⎪
⎪
⎪
⎬
⎪
⎪
⎪
⎭

1 3
either separately or simultaneously very little for the entire r deposited in M as shown in
Eq. (10) for different r and l . Let 𝛽 indicate the ultimate specific array of the transmitting
information, which means {1, 2, … r}, alternatively {1, 2, … l} where A𝜖E
. This implies
that every typical transmission is needed for A in E for significant data assignment n the
IoT system. Therefore, to overcome the collaboration of data assignment, the consideration
of the transmission phase is developed and discussed as follows,
a. Phase of Transmission
Considering the stages of communication, routing, and collecting is crucial to increas-
ing the IoT system’s sensor nodes. The transmission of data with the array of transmitting
{1,2, … r} is shown in Fig. 4a.
The assignment phase is increased by utilizing an examination focused on a broad
faith framework of two steps of E and A, and the data assignment for these two stages
Fig. 4 Transmission and assignment phase for E and A stages

1 3
is shown in Fig. 4b. Therefore, it is necessary to distinguish homogeneous and non-
homogeneous phase representation for U = (AE) . The system feature for allocating the E
and A-stages are illustrated in Fig. 4b indicated as shown in Eq. (11)
By assigning the data to two different stages, data transmission is more effec-
tive, as shown in Eq. (11), allocating the proportions for E and A𝜖U
. According to
the sign of series for U(E), n m and l = r . If n = m , then U(E) = (1, 2m
)𝜖n = l and
U(A) =
(
2m
− 1, 2m−1
)
𝜖A𝜖l
. The processed phase is routed, and the stage for U(E) and
U(A) for n = l is illustrated in Fig. 5. In summary, the nodes that obtained the infor-
mation are applied to communication infrastructure. After the assignment phase, there
should be correct priority wise planning of data for equal distribution, which has been
discussed as follows,
3.1.2 Priorities Task Planning
During data dissemination, priority task planning is commonly used in which the target
preparation of distribution applicants is to optimize one or even more tasks to the loca-
tion to satisfy the below priorities such as:
• The duration to be spread to the desired location,
• The estimated transmission time to the desired location, and
• The output in the area, etc.
(11)
U(E) =
�
[1, (2m
− 1)], if n = l
�
1, (M−1)
(m−1)
2m
�
, if n l
else
U(A) =
� �
2m
− 1(M−1)
(m−1)
2m−1
A𝜖E
�
𝜑, otherwise
⎫
⎪
⎪
⎪
⎬
⎪
⎪
⎪
⎭
1 2
S D
3
4 5
Data
packets
Path1
Path2
Fig. 5 The priority task planning

1 3
The priority task planning is used to deliver data dissemination for a smaller proportion
of routing edges, as shown in Fig. 5 with S-source and D-destination node based on path-1
and path-2. The edge that is certain to communicate is negotiating between two neighbours
due to the extreme secret terminal issue. Indeed, the networks across multiple nodes are not
quite the same neighbours in one path; individuals can transfer information simultaneously.
The priorities task planning is defined in algorithm 1.
Algorithm1: Priorities task planning
Input
If the priority planning is not required in
Edge identity = the edge that transmitted the information is sure to move on
Else, priority preparation would be required in
Highest number=0,
Highest identity=1
For b=1:
If
highest identity=b
end if
edge identity=
end for
end if
output edge identity
As observed in the algorithm 1. The priority task planning must be combined with the
distribution of network bandwidth. The nearest array of fb is represented as M
(
fb
)
. The
number of edges carried in M
(
fb
)
is represented as
|
|
|
M
(
fb
)|
|
|
.The set of edges that could
transmit the disseminated data is referred to as A. The group of edges not receiving the
transmitted information is referred to as L. As per the aim of priority task planning for
transmitting information, the mathematical model of priority programming should involve
many edges with fewer providers, as shown in Eq. (12)
The highest priority of data for transmission is obtained from Eq. (12), in which the
nearest array of fb is represented as M
(
fb
)

. The number of edges carried in M
(
fb
)
is repre-
sented as
|
|
|
M
(
fb
)|
|
|

. The set of edges that could transmit the disseminated data is referred to
as A. The group of edges not receiving the transmitted information is referred to as L. The
above Algorithm has stated priority wise distribution of data. Furthermore, data
(12)
∏
fb𝜖A
max
fb
|
|
|
M
(
fb ∪ L
)|
|
|

1 3
transmission between sensor nodes consumes more energy, to optimize that energy, an
energy management model is developed in the IoT system,
3.2 The Energy Management Model
The energy management model combined with an integer framework for the efficient
energy processing and grouping of sensors measurement techniques in the IoT core net-
work. Optimization of the energy management model allows low power consumption and
minimal energy usage for each node to improve the transfer and handling of severe inter-
ruption data. Energy is used for the transfer and dissemination of each datum. It depends
on the particular application domain if overall energy usage or network life is of significant
interest. To calculate overall power consumption, the energy used for all nodes within the
network needs a fairly straightforward blending feature.
3.2.1 Integer Framework
The integer framework approach is used to disseminate the directed graph’s dataph, i.e., In
these specific trees, the circles lead towards the node m that all data points have a precisely
same pathway to the node m
. The current end address can be used for a total directed graph
that connects it to the central database, yet distribution along those same paths is excluded
from the energy models.
A sum of L(r) calculations at the endpoint nodes should be obtained for each chan-
nel r for the integer framework scenario. It is believed that the stream diagram G(A, 𝜖)
is included of the pair of edges A and couple of vector 𝜖 ≤ A2
∕[(a, a) ∶ a𝜖A
]. The pair
of vector moving in and out from the specified edge a𝜖A that is represented as 𝜕i
(a)
and 𝜕o
(a)
. The pair of edges A consists of 3 interconnected subgroups: a pair of sen-
sor node (X), a couple of accumulation edges (Y), a pair of the target node, and the
service provider pair (S). The dissemination node and the data transmission between
the source node to target nodes are shown in Fig. 6. Each target edge has one outward
vector connected to the service provider 𝜕o
(b) = [(b, S)], b𝜖B
. Here b is the target edge;
S is the service provider. The vector event occurring on the service provider from the
Source
node
Targetnode
Dissemination
node
Service
provider
Fig. 6. The integer framework

1 3
target edges 𝜕i
(S) = 𝜑, 𝜕O
(S) = [(b, S) ∶ b𝜖B]
. The source edges o𝜖X create, transfer, and
accumulate frames. Accumulate edges a𝜖Y transfer, get frames still do not generate on
their own. The target edges b ← B gather the data packets and pass to the drain S. The
drain collects the data packets and accumulates these data, though it does not give the
data packets. Every channel c𝜖C has a pair of sensor edges X(C), X(C)𝜖X , a pair of accu-
mulator edges Y(c)(Y(c)(Y ∩ X)∕X(c)
, a pair of target edges B(c)(B(c)𝜖, B)
. X(c), Y(c),
and B(c) are collectively separate. Notice that all edges in Y(c), although most of them
function as an accumulator for channel c for specific channel source nodes. The edges
in V(c) can gain access to data for each channel c𝜖C . After gathering and distribution of
data, the mapping and accumulation of data are required, which is discussed as follows,
a. Optimizing the Mapping and Accumulation
The optimization is described for minimal actual energy sorting and collection for
the integer framework case and shown in Eq. (13)
As inferred from Eq. (13), c, C pair of information accumulation channel, Uc rep-
resents the overall distribution of energy cost experienced by edges, Vc denotes the
overall production of energy costs experienced by edges. o indicates the pair of source
edges for the channel, go
c
denotes the edge message is obtained in strand c or not through
the intermediate nodes, R(c) denotes the amount of data required to meet the channel.
Ho
re
represents the motion in the channel from edge o , 𝜕i
(c, a) that denotes the pair of
incoming vector, 𝜕o
(c, a) denotes the pair of the outgoing vector. Equation (13) ’s func-
tional form aims to reduce the total energy used for distribution and accumulation by
all edges. It enables a specific variable information channel to illustrate the limitations.
Even restrictions try to ensure the system can gather enough sensory information to
reach the channel. The motion preservation limits the pressure of each useful sensor as
it is shown in Eq. (14),
The sorting of data through each node or the sensor’s channel is obtained from Eqs. (13)
and (14). 𝜕o
(c, a), 𝜕0
(c, s) denote the pair of outgoing vector, go
c
denotes the edge message is
obtained in strand c or not through the intermediate nodes, Jre denotes whether the vector
is used for the channel. The amount of accumulation of data in each channel tca , tco gives
the overall distribution of energy cost experienced by nodes of the sensor Uc, Vc
. A quality
motion is carried out from source edge to S along the target vector. Rules obtained from
Eq. (14) ensure that every vector that flows are currently used in a directed graph that can
be denoted as K(c) = (f𝜖 ∈ (c) ∶ Hre = 0.
(13)
min
∑
c𝜖C
�
Uc + Vc
�
∑
o𝜖X(c)
go
c ≤ R(c)
∑
f𝜖𝜕i(c,a)
Ho
re =
∑
f𝜖𝜕o(c,a)
Ho
re
⎫
⎪
⎪
⎬
⎪
⎪
⎭
(14)
∑
f𝜕0(c,s)
Ho
re = go
c
Ho
re ≥ Jre
∑
f𝜕0(c,a)
Jre ≥ 1
⎫
⎪
⎪
⎬
⎪
⎪
⎭

1 3
Restrictions
∑
f𝜕0(c,a)
Jre ≥ 1 cause accumulation expenses if at least two streams are to
be consolidated and a server generates in the case of edges of the source vector is shown
in Eq. (15)
As obtained from Eq. (16), tca denotes the amount of channel calculation at the node
a, tco denote the amount of channel calculation at edge o. It should be remembered that
non-negative tca is imposed by the operational limits. Eventually, restriction calcu-
lates the amounts of data transmitting in all the ranges of the directed graph, and it is
explained in Eq. (16)
The restriction and the amount of data transmission is inferred from Eqs. (16),(17),
Uc represents the overall distribution of energy cost experienced by edges, Vc denotes
the overall production of energy costs experienced by edges. tca denotes the amount of
channel calculation at the edge a, all edges in Y(c), function as an accumulator for chan-
nel c for specific channel source nodes, D and A are the parameters scaling operation. It
has to be noticed that the restrictions on the motion preservation stay valid for all
c𝜖C, o𝜖X(c),
∑
f𝜕o(c,o)
Ho
re = 0
. The sensory networks do not add because the parameter is
limited to
(
𝜕0
(c, o) ≠ 1
)
.
The corresponding integer’s framework can build an adequate objective function, as
shown in Eq. (17)
As obtained from Eq. (17), here o(f) =
∑
cC
H∗
re represents the total amount of vector in
the directed graph. Nm denotes the duration of each slot, 𝜎f denotes separate parameters
for system normalization, A++ denotes the pair of non-zero actual indicators. One can
recognize 𝜎∗
=
(
𝜎∗
f
, f𝜖m
)
and assume that there survives a pair even outside the latest
list. If a is put to the record (A = A ∩ (a,
)
), the latest pair vector can have another one
restriction that is equal to a
, and the present best option 𝜎∗
eliminates the particular
limit. The latest pair model is designed to solve the initial double set intersecting
through the additional dual limitations is shown in Fig. 7a and b. The significant specu-
lation issue is being used for creating the a-pair mentioned, as shown in Eq. (18).
(15)
tca ≤
∑
f𝜖𝜕o(c,a)
Jre − 1
tco ≥
∑
f𝜖𝜕o(c,a)
Jre + go
c
− 1
⎫
⎪
⎬
⎪
⎭
(16)
Uc = D
∑
f𝜖∈(c)�𝜕o(c,s)
Hre
Vc = A
∑
u𝜖x(c)∩Y(c)
tca
⎫
⎪
⎬
⎪
⎭
(17)
min
∑
m𝜖M
Nm
�
𝜎f ≤ o
� ∑
m𝜖M(f)
Nm ≤ o(f)f𝜖x
Nm𝜖A++m𝜖M
⎫
⎪
⎬
⎪
⎭

1 3
As inferred from Eq. (19), 𝜎∗
f
denotes separate parameters for system normalization,
Bf denotes the vector f is available in a-pair obtained by the cost issue. The functional
form f introduces a unique a-set, which utilizes the particular limit of the existing dou-
ble approach α, and it is explained in Eq. (19)
(18)
max
∑
f𝜖X
𝜎∗
f
Bf
∑
f𝜖𝜕o(a)𝜕i(a)
Bf ≥ 1
⎫
⎪
⎬
⎪
⎭
(a)
(b)
Energy Transfer model
Dissemination model
Fig. 7 The energy usage for the transfer and dissemination of data

1 3
The above Eq. (19) enables an unused node Ba or a specific intercepting 𝜕i
(a) or out-
bound network to be transmitted or received at the moment q(a,u) And guarantee that the
created a-pair B(a,u) contains a node when it distributes.
In Eq. (19), where q(a,u) is the received power of edge a, and the signal to
noise interference ratio 𝛾 limits for the information transfer is represented. In
an attempt for an integer framework method to be accomplished, bi-linear-
ity can be treated as usual by non—zero steady alternatives that are denoted as
Are = Br ∗ Cu;Are Br, Are Cu, Are Br + Cu − 1
. The optimal limits for the above
statement are shown in Eq. (20)
As obtained from Eq. (20), Br denotes, the vector r is available in the a-pair is obtained
by the cost issue, Cu
, the vector u is available in the a-pair is obtained by the cost issue. x
represents a pair of binary values (0,1). u denotes the pair of edges in the sensor network.
The complete energy usage for transfer and dissemination is reduced by the optimization
method and ensures that adequate sensor data is received for each target edges. The energy
usage for the transfer and dissemination of information is shown in Eq. (21),
The energy usage for the transfer and dissemination of information is inferred from
Eq. (19), (21), ast
denotes the evaluation from the source edge is obtained by the target
edge, A, B represents the energy usage for transfer and dissemination of data. Br denotes,
the vector r is available in the a-pair is obtained by the cost issue, Cu
, the vector u is avail-
able in the a-pair is obtained by the cost issue. The motion management limitations involve
a flow of data from every node of the source to every node of target d to accumulate the
original’s measures. The flow of data from source to target edges or node is shown in
Eq. (22),
As obtained from Eq. (22), Mst
f
denotes the flow of data from the source node to the
target node, f𝜖𝜕i
(u) represents the data flow towards the node, f𝜖𝜕o
(u) denotes the data
flow away from the node. ast
denotes the target edge obtains the evaluation from the source
edge. The carry flow needs to be included throughout the ideal parameters with the binary
variables (0,1). The binary value is assigned to one if the vector Mst
f
allow the transmission
of data, and the value is assigned to 0 if the vector Mst
f
doesn’t allow the transmission of
data.
(19)
⎧
⎪
⎨
⎪
⎩
∑
f𝜖𝜕i(a)
Bf = Ba
1
𝛼
q(a, u)B(a,u) ≤ (𝛾 +
∑
v𝜖V(x)(a,u)
q(a,u)Ba)B(a,u)
(20)
{
Br𝜖x f𝜖x;Are = Br ∗ Cu
Cu𝜖x u𝜖U(x);Are Br + Cu − 1.
(21)
min
A + B; Are = Br ∗ Cu
∑
s𝜖x
ast
L
�
(22)
∑
f𝜖𝜕i(u)
Mst
f
=
∑
f𝜖𝜕o(u)
Mst
f
∑
f𝜖𝜕i(t)
Mst
f
= ast
⎫
⎪
⎬
⎪
⎭

1 3
The limitation for the assignment of binary values for transmission and dissemination of
data with the source and target node is shown in Eq. (23) and illustrated in Fig. 8.
As Eq. (23) obtained, the limitation for the transmission and dissemination of data is
received. Bf represents the vector that takes accumulated calculations, Bs
f
represents the vec-
tor that takes evaluation from source edges. The accumulation requirements are laid down as
shown in Eq. (24)
Accumulation requirements and the vector entering and leaving the source and target node
are obtained from Eq. (23), (24). s𝜖X
, denotes the data obtained from the source node, t𝜖T
denotes the data obtained from the target node. Bs
f
represents the vector that takes evaluation
from source edges. In general, a guarantee about the calculation on several vectors entering
the node can be obtained from Eq. (25)
The data entering the source node is obtained from Eq. (25), Ass,
u
denotes the calculation
from the source are accumulated at the edge, f𝜖𝜕o
(u) denotes the data flow away from the
node. Bs
f
represents the vector that takes evaluation from source edges.
Every node receiving at least validation adds and causes a value of transmission that is
comparable to the accumulation of the messages, and it is shown in Eq. (26)
(23)
⎧
⎪
⎨
⎪
⎩
Mst
f
Bf
Bf =
∑
s𝜖x
∑
t𝜖T
Mst
f
Mst
f
Bs
f
(24)
Bf ≥
∑
s𝜖X
∑
tT
Mst
f
Mst
f
≥ Bs
f
Bs
f
≥
∑
t𝜖T
Mst
f
⎫
⎪
⎬
⎪
⎭
(25)
⎧
⎪
⎨
⎪
⎩
∑
f𝜕o(u)
Bs
f
≥ 1
Ass,
u
≤ Bs
f
+
∑
f,𝜕o(u)∕(f)
Bs,
f,
Fig. 8 Dissemination of data with the source and target node

1 3
As obtained from Eq. (26), ha denotes the energy needed for data dissemination, Bf rep-
resents the vector that takes accumulated calculations. The energy costs within each node
at the highest prices of any bridge where a packets is transmitted is shown in Eq. (27)
The energy management from each node of the sensor is obtained from Eq. (26), (27),
Ast
represents the data transmission from the source to the target node, hs and denotes the
energy needed for data transmission from the source node. vs
Denotes the vector that evalu-
ates from the source node.
The optimization of the energy management model allows low power consumption and
minimal energy usage for each node to improve the transfer and handling of data in severe
interruption.
To optimize the network’s lifespan, the energy management model suggests the lowest
energy optimization model for the most insufficient energy use of various nodes, which
utilizes almost all energy. The node would then shut down the batteries and provides a
time calculation for system failures. The lowest power consumption and the lowest energy
consumption is shown in Fig. 9 with the input data of Ha, Hu and energy obtained at the
incoming and outgoing node f𝜖𝜕o
(u), f𝜖𝜕i
(u).
The data packets transfer to disseminate calculations to many targets complicates frame-
work reduction considerably more than those of the OEM-DD optimization model. The
following expansion focuses on the specification for the lowest energy efficiency that is
explained in Eq. (28)
(26)
⎧
⎪
⎨
⎪
⎩
∑
a𝜖A
ASS,
u 1
ha
∑
f𝜖𝜕o(u)
Bf − 1
(27)
hs
∑
f𝜖𝜕o(u)
Bf + vs
− 1
us Ast
Hu d(f)Bf
⎫
⎪
⎬
⎪
⎭
(28)
A Ha
Ha =
∑
c𝜖C
Hca
Hca = Bhca + q
∑
f𝜖𝜕i(u)
Hre + p
∑
f𝜖𝜕o(u)
Hre
⎫
⎪
⎬
⎪
⎭
( )
( )
Fig. 9 The Lowest energy optimization model

1 3
As inferred from Eq. (28), Ha denotes the energy needed for data dissemination, here
q + p = c, where q is the expense (cost) of sending data from the edge, p is the expense of
obtaining data at the edge. The average A indicates the energy usage by the nodes with the
highest consumption over a measuring interval. The energy usage for each node to improve
the transfer and handling of data in severe interruption is shown in Eq. (29)
The energy usage for each node to improve the transfer and handling of data in severe
interruption is shown in Eq. (28) (29), c, C denote the pair of information accumula-
tion channel, Gu denotes the energy requirement for data transmission through multiple
channels. The energy costs within each node where packets are transmitted are shown in
Eq. (30)
As inferred from Eq. (30), A indicates the energy usage by the nodes with the highest
consumption over a measuring interval, hu, Hu represent the pair of data available on the
channel, Gu denotes the energy requirement for data transmission through a single channel.
OEM-DD framework has been utilized to maximize energy efficiency during data trans-
fer through all IoT system sensor network nodes based on the firm mathematical discus-
sion. Further, routing strategy has gained reliability concerning the propagation of data
through an integrated network, and its simulation analysis is discussed as follows,
4 Results and discussion
The proposed method, The OEM-DD in WSN, has been validated based on the data trans-
mission rate, the energy usage during transmission. The nodes are spread randomly over a
particular area of a sensor field. The number of sensor nodes in the curved sector ranges
from 5 to 30 in-depth for various broadcasting networks. The present study has taken the
wireless sensor data in the following link https://data.world/datasets/sensors. The entire
structure in the IoT system’s sensor networks focuses on the probability distribution sys-
tem, depending on the routing procedure’s quality. The packet delivery rate for each trans-
mission is calculated by the Eq. (31)
As inferred from Eq. (31), the packet delivery rate is denoted as q(y) and the gap
between the two nodes is marked y . Tp
Represents the power of transmission. 𝜌 denoted the
standard deviation, 𝛼 indicates the delay of the route. The packet delivery rate of the OEM-
DD is shown in Fig. 10. The target edges b ← B gather the data packets and pass them to
the drain S.
(29)
A Gu
Hca = Bhca
Ha =
∑
c𝜖C
Hre
⎫
⎪
⎬
⎪
⎭
(30)
�
A Gu
Ga = Dhu + qHu +
∑
f𝜖𝜕o(u)
Bf
(31)
q(y) =
1
2
√
2
4
∗
1
𝜌
log
rp(2 ∗ 3.14)y𝛼
Tp

1 3
Here, The source edges o𝜖X create, transfer, and accumulate frames. Accumulate edges
a𝜖Y transfer, get frames still do not generate on their own. The target edges b ← B gather
the data packets and pass to the drain S. The drain collects the data packets and accumu-
lates these data, though it does not give the data packets. Hence, it increases the significant
packet delivery ratio than conventional methods.
a. Data Transmission Rate
The occurrence of the transmitted information obtained by f
dm
b
is referred to as X.
The activity of transmitted information is passed on by f
dm−1
b

, is represented as Yb . The
transmitted information obtained from f
dm
b
results from the collaborative transmis-
sion of nodes in dm−1
. The possibility of activity X blends the complete potential and
the chances of receiving transmitted data through f
dm
b
are distributed through f
dm−1
b
to
Q
(
X|Yb
)

. The data transmission rate is obtained from the probability distribution func-
tion, as explained in Eq. (3), (4). The data transmission rate of OEM-DD is shown in
the Fig. 11 in comparison with conventional methods. The routing applicant f
dm
b
initially
recognizes the time limit t
dm
b
for the neighbour condition to achieve the distributed data
transmission rate.
The preserved transmission node is used to know the proper distribution of data between
all nodes of the sensor, and this can be achieved by Eqs. (5) and (6), PR denotes the pre-
served retransmitted nodes, and Tm represents the time taken for the complete transmission
process. The uniformly distributed random function is obtained from Eq. (7), (0–1) alloca-
tion is followed by f
dm
b

, the uniformly distributed random function is described as U
dm
b
The
distributed data transmission (DDT) is shown in Fig. 12.
The concept of a Non-adaptive routing procedure is developed to find the data transmis-
sion rate with D1 … D8 nodes and the distance of f1 … f8
. The transmission of data by a
non-adaptive routing procedure is achieved in an effective manner with 𝛾u This includes at
Fig. 10 Packet delivery rate

1 3
least one node in F
dm
b
has dispersed the transmission information to a particular node effi-
ciently with improved performance factors. Therefore the possibility of who receives the
transmission from M
dm
b
is for all f
dm
b
neighbors. It is believed that the accumulation channel
G(A, 𝜖)
∑
f𝜕0(c,s)
Ho
re = go
c is included of the pair of edges, A, and a couple of vectors
Fig. 11 The data transmission rate of OEM-DD
Fig. 12 The distributed data transmission (DDT) rate of OEM-DD

1 3
𝜖 ≤ A2
∕[(a, a) ∶ a𝜖A
]. The pair of vector moving in and out from the specified edge a𝜖A is
represented as 𝜕i
(a) and 𝜕o
(a
). The significant investments issue is being used for creating
the a-pair mentioned as max
∑
f𝜖X
𝜎∗
f
Bf
. The performance of OEM-DD is shown in Table1.
The energy costs within each node where packets are transmitted Ha =
∑
c𝜖C
Hre to each
stream, Uc represents the overall distribution of energy cost experienced by edges, Vc
denotes the overall production of energy costs experienced by edges. Every channel c𝜖C
has a pair of sensor edges X(C), X(C)𝜖X
, a pair of accumulator edges
Y(c)(Y(c)𝜖(Y ∩ X)∕X(c)
, a pair of target edges B(c)(B(c)𝜖, B)
. X(c), Y(c), and B(c) are
collectively separate. One can notice that all edges in Y(c). However, most of them
function as an accumulator for channel c for specific channel source nodes. The energy
cost of all these nodes plays a vital role in data dissemination. The OEM-DD’s energy
cost rate is shown in Fig. 13 depicts an improved ratio than conventional methods.
It is believed that the stream diagram G(A, 𝜖) is included of the pair of edges A and cou-
ple of vector 𝜖 ≤ A2
∕[(a, a) ∶ a𝜖A
]. The energy usage A Ha for each node to improve the
transfer and handling of data in severe interruption with Ga = Dhu + qHu +
∑
f𝜖𝜕o(u)
Bf . indi-
Table1
The performance of OEM-DD
Nodes a-pair generation
(%)
Accumulation (%) Time taken for trans-
mission (%)
Delay of route
50 88.44 91.23 55.33 41.22
100 87.48 89.34 49.09 43.55
150 90.22 87.33 52.11 42.90
200 92.33 95.11 47.88 41.01
Fig. 13 The energy cost rate of the OEM-DD

1 3
cates the energy usage by the nodes with the lowest consumption of energy over a measur-
ing interval, Hca = Bhca + q
∑
f𝜕i(u)
Hre + p
∑
f𝜕o(u)
Hre denote the energy needed for data dis-
semination, q
∑
f𝜕i(u)
Hre q is the expense (cost) of sending data from the edge, p
∑
f𝜕o(u)
Hre is
the expense of obtaining data at the edge. The average Ha =
∑
cC
Hca indicates the energy
usage by the nodes with the lowest energy consumption over a measuring interval. The
energy consumption rate of OEM-DD is shown in Fig. 14.
Are = Br ∗ Cu is the vector r is available in the a-pair is obtained by the cost issue and
Br𝜖x f𝜖x
, the vector u is available in the a-pair is obtained by the cost issue. Br + Cu − 1repre-
sents a pair of binary values (0,1), and the complete energy use for transfer and dissemination
(efficiency) is reduced by the optimization method Cu𝜖xu𝜖U(x) and ensures that adequate sen-
sor data is received for each target edges. The energy usage for the transfer and dissemination
of information is obtained by
∑
s𝜖x
ast
L . A + B; Are = Br ∗ Cu
. The energy efficiency and
consumption of the proposed OEM-DD are shown in Table 2.
The proposed BTFM achieves the highest data transmission rate when compared to other
existing Neighbor-Based Probabilistic Broadcast Procedure for Data Dissemination (NB-
PBP), Caching-Aided Collaborative D2D Operation for Predictive Data Dissemination (CAC-
PDD), Robust Energy Saving Data Dissemination Procedure (RES-DD), and Indexing Frame-
work for Data Dissemination in Wireless Sensor Networks (IFD-WSN).
Fig. 14 The energy consumption rate of the OEM-DD
Table 2 The energy efficiency
and consumption of OEM-DD
Methods RES-DD IFD-WSN OEM-DD
Energy efficiency 67.33 78.90 79.88
Energy consumption (%) 76.22 65.22 20.11

1 3
5
Conclusion and Future Perspective
In this article, the OEM-DD is designed for effective data transmission from all sensor net-
work nodes with lower electricity consumption. WSN does not have the specified solution for
disseminating data with various new applications developed on IoT systems. The optimization
of the model energy management makes it possible for each node to optimize the transmis-
sion and handling of information during unnecessary interruptions with low power consump-
tion and low energy use. The experimental results show that the suggested model enhances
the data transmission rate of 96.33% and less energy consumption of 20.11% in sensor nodes
of IoT systems for wireless sensor networks. In the Future, adaptive learning methods are
planned to further integrate into OEM-DD to improve low-power reconfigurable applications’
performance.
References
1. Kassab, W. A., Darabkh, K. A. (2020). A-Z survey of Internet of Things: Architectures, protocols,
applications, recent advances, future directions, and recommendations. Journal of Network and Com-
puter Applications., 18, 102663
2. Zhang, X., Yue, W. T. A. (2020). perspective on “Transformative value of the Internet of Things and
pricing decisions.” Electronic Commerce Research and Applications., 12, 100967
3. Deebak, B. D., Al-Turjman, F. (2020). A hybrid secure routing and monitoring mechanism in IoT-
based wireless sensor networks. Ad Hoc Networks., 97, 102022
4. Boni, K. R., Xu, L., Chen, Z., Baddoo, T. D. (2020). A security concept based on scaler distribution
of a novel intrusion detection device for wireless sensor networks in a smart environment. Sensors.,
20(17), 4717
5. Muzammal, M., Talat, R., Sodhro, A. H., Pirbhulal, S. (2020). A multi-sensor data fusion enabled
ensemble approach for medical data from body sensor networks. Information Fusion., 1(53), 155–164
6. Alsulami, O. Z., Alahmadi, A. A., Saeed, S. O., Mohamed, S. H., El-Gorashi, T. E., Alresheedi, M.
T., Elmirghani, J. M. (2020). Optimum resource allocation in optical wireless systems with energy-
efficient fog and cloud architectures. Philosophical Transactions of the Royal Society A., 378(2169),
20190188
7. Koh, S., Lee, M., Brotzman, L. E., Shelton, R. C. (2020). An orientation for new researchers to
key domains, processes, and resources in implementation science. Translational behavioral medicine.,
10(1), 179–185
8. Haseeb, K., Islam, N., Saba, T., Rehman, A., Mehmood, Z. (2020). LSDAR: A light-weight struc-
ture-based data aggregation routing protocol with secure internet of things integrated next-generation
sensor networks. Sustainable Cities and Society., 54, 101995
9. Frei, M., Deb, C., Stadler, R., Nagy, Z., Schlueter, A. (2020). Wireless sensor network for estimating
building performance. Automation in Construction., 111, 103043

10. Rehan, W., Fischer, S., Chughtai, O., Rehan, M., Hail, M., Saleem, S. (2020). A novel dynamic
confidence interval based secure channel prediction approach for stream-based multichannel wireless
sensor networks. Ad Hoc Networks, 108, 102212

11. Xu, X., Zhang, X., Khan, M., Dou, W., Xue, S., Yu, S. (2020). A balanced virtual machine sched-
uling method for energy-performance trade-offs in cyber-physical cloud systems. Future Generation
Computer Systems., 1(105), 789–799

12. Guo, J., Li, X., Lv, Z., Yang, Y., Li, L. (2020). Design of real-time video transmission system for
drone reliability. MSE., 790(1), 012004

13. Tewari, A., Gupta, B. B. (2020). Security, privacy, and trust of different layers in the Internet-of-
Things (IoT) framework. Future generation computer systems., 1(108), 909–920

14. Poonguzhali, P. K., Ananthamoorthy, N. P. (2020). Design of mutated harmony search algorithm for
data dissemination in wireless sensor network. Wireless Personal Communications., 111(2), 729–751

15. Badarneh, H. J., Mansoor, A. M., Rahman, A. U., Ravana, S. D. (2020). An efficient indexing frame-
work for data dissemination in wireless sensor networks. Computers Electrical Engineering., 87,
106777

1 3

16. Mousavi, M., Klein, A. (2020). Energy and social cost minimization for data dissemination in wire-
less networks: centralized and decentralized approaches. IEEE Transactions on Vehicular Technology.,
69(5), 5521–5534

17. Amini, S. M., Karimi, A., Esnaashari, M. (2020). Energy-efficient data dissemination algorithm
based on virtual hexagonal cell-based infrastructure and multi-mobile sink for wireless sensor net-
works. The Journal of Supercomputing., 76(1), 150–173

18. Liu, W., Nakauchi, K., Shoji, Y. (2018). A neighbor-based probabilistic broadcast protocol for data
dissemination in mobile IoT networks. IEEE Access., 6(6), 12260–12268

19. Orsino, A., Kovalchukov, R., Samuylov, A., Moltchanov, D., Andreev, S., Koucheryavy, Y.,
Valkama, M. (2018). Caching-aided collaborative D2D operation for predictive data dissemination in
industrial IoT. IEEE Wireless Communications., 25(3), 50–57

20. Kim M, Park S, Lee W. (2018). A Robust Energy Saving Data Dissemination Protocol for IoT-WSNs.
KSII Transactions on Internet Information Systems. 12(12).

21. Badarneh, H. J., Rahman, A. U., Mansoor, A. M., Ravana, S. D. (2020). An efficient indexing frame-
work for data dissemination in wireless sensor networks. Computers Electrical Engineering., 87,
106777

22. Wang, X., Mao, X., Khodaei, H. (2021). A multi-objective home energy management system based
on internet of things and optimization algorithms. Journal of Building Engineering, 33, 101603
Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and
institutional affiliations.
Venu Madhav Kuthadi obtained his Bachelor’s degree in Computer
Science and Engineering from Nagarjuna University, India in 1998. He
obtained his Master’s degree in Computer Science from JNT Univer-
sity, India in 2001.He completed his PhD in computer science in 1998
and also obtained Doctorate in engineering from University of Johan-
nesburg in 2018. He worked as senior lecturer in the department of
Applied Information systems, University of Johannesburg from March
2000 to January 2017. Currently working as a Associate Professor in
the Department of CS IS, BIUST, Botswana. Kuthadi research work
was on the network security to generate a security pattern to safeguard
data that is transmitted over a network by introducing an adaptive pre-
processing technique using Principal Component Analysis (PCA) and
Hyperbolic Hopfield Neural Network (HHNN) to make streaming data
efficient. Kuthadi got more than 50 publications in peer reviewed Jour-
nals, 2 Text books and more than 20 international conference proceed-
ings and successfully supervised 10 masters and 3 Ph.D. students.
Kuthadi is editor for International Journal of Advanced Engineering
and Global Technology and reviewer for reputed Journals.
Rajalakshmi Selvaraj is working as a senior lecturer in the Department
of CS IS, BIUST, Botswana. Selvaraj has earned her doctorate in the
area of Network Security using honeypot. At present she is involved in
a project to develop a security system for the honey pot architecture in
order to solve the problem of attackers attacking honey pot. Selvaraj
got Ph.D in computer science. Her earlier work has included use of
association rule in data mining –A new way proposed in mixture of
frequent itemset mining and association rule in data stream to mine the
association rules based on utility. Dr Selvaraj collaborated her projects

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with research scholars globally. She is a member of various committees promoting research including IEEE,
ACM, and CSI etc. Dr Selvaraj has published over 45 articles in refereed international journals, conference
proceedings and book chapters, supervising good number of master and PhD students, Selvaraj got CCNA
certification from cisco as well.
S. Baskar Assistant Professor in the Department of Electronics and
Communication Engineering/Center for Interdisciplinary Research,
Karpagam Academy of Higher Education, Coimbatore, Tamilnadu,
India. He completed his Ph.D degree in Wireless Sensor Networks in
Anna University, Chennai and obtained his M.E (VLSI design) from
S.N.S College of Technology, Coimbatore in 2013 and B.E (Electronics
and Communication Engineering) from S.N.S College of Technology,
Coimbatore in 2009. His expertise in IoT, Wireless sensor
networks,deep learning and VLSI leads him to teach subjects in elec-
tronics, low power VLSI, human mission interface and pervasive Com-
puting. He worked as a research assistant in the departments of Nano
Science and Technology and Pervasive computing Technology. His
research interest includes Wireless sensor networks, Low power VLSI,
IoT, Material Science. He published more than 30 research articles in
the international journals, book chapters and Conferences. So far, he
guided 20 M.E/M.Tech/B.E projects.He is an Young Scientist Awardee
by Department of Science and Technology, Government of India.
P. Mohamed Shakeel received his Master of Science in Information
Technology in 2007 from Nehru Memorial College, Master of Busi-
ness Administrations from, Bharathi Dasan University, Trichy in 2009
and Master of engineering in Computer Science and Engineering from
Karpagam University in 2013, respectively. Presently he is doing
extended research in Universiti Teknikal Malaysia Melaka, Malaysia.
His research interests includes Medical Image processing, Networking,
and Cloud IoT.
Abhishek Ranjan obtained his Bachelor’s degree in Information Tech-
nology and his Master’s degree in Computer Science and Engineering
from Biju Patnaik University of Technology, Odisha, India in 2006 and
2008 respectively. He completed his PhD in Electrical and Electronic
Engineering from University of Johannesburg in 2018. He worked as a
Dean for Faculty of Computing, Manager Internal Quality Assurance
Department, Module Leader and Senior Lecturer at Botho University.
Currently working as a Dean and Head of Institution at Botho Univer-
sity, Lesotho Campus. Ranjan research interest area is MANET and
Network Security. Ranjan presented in various national and interna-
tional conferences and published in national and international
journals.

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Authors and Affiliations
Venu Madhav Kuthadi1
· Rajalakshmi Selvaraj1
· S. Baskar2
· P. Mohamed Shakeel3
·
Abhishek Ranjan4
1
Department of CS IS, Botswana International University of Science and Technology, Palapye,
Botswana
2
Department of Electronics and Communication, Karpagam Academy of Higher Education,
Coimbatore, India
3
Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka,
Melaka, Malaysia
4
Faculty of Computing, Botho University, Gaborone, Lesotho

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