Fuzzy clustering and energy resourceful routing protocol (FCER2P) for smart dust

BOHR International Journal of Smart Computing
and Information Technology
2022, Vol. 3, No. 1, pp. 1–8
DOI: 10.54646/bijscit.2022.21
www.bohrpub.com
REVIEW
Fuzzy clustering and energy resourceful routing protocol
(FCER2
P) for smart dust
D. Rajesh1* and G. S. Rajanna2
1PDF Scholar, Srinivas University, Mangalore, Karnataka, India
2Professor, Srinivas University, Mangalore, Karnataka, India
*Correspondence:
D. Rajesh,
rajeshd936@gmail.com
Received: 05 January 2022; Accepted: 26 January 2022; Published: 06 February 2022
Sensor nodes in smart dust are used to track and identify data that is being transferred to a sink. The accessibility
of node energy poses a significant challenge for smart dust and may have an impact on the long-term viability of
the network. As a result, constructing smart dust must take into account the need for algorithms and techniques
that enable the most use of scarce resources, especially energy resources. For instance, routing algorithms are
distinctive algorithms because they have a clear and direct connection with network longevity and energy. The
offered routing techniques use clustering each round and single-hop data delivery to the sink. A Fuzzy Clustering
and Energy Resourceful Routing Protocol (FCER2P) that reduces smart dust energy consumption and lengthens
lifespan of the network is proposed in this research. Using a specified threshold, FCER2P proposes a new
cluster-based fuzzy routing mechanism that can make use of clustering and multiple hop routing capabilities
simultaneously. This research is innovative in that it adjusts multi-hop connectivity by anticipating the optimum
intermediate node for aggregating and the sink, eliminates clustering each round while taking a fixed threshold
into account, and eliminates clustering per round altogether. When choosing the intermediary node to employ,
some fuzzy parameters, including residual energy, the number of neighbors, and the distance to the sink, are taken
into account.
Keywords: energy utilization, Fuzzy Clustering, Routing Protocol, smart dust
1. Introduction
Multiple with minimal processing capacity are widely
used in smart dust, which collects atmospheric data and
communicates with base stations (BS). In recent times,
improvements in embedded system design have reduced the
size, mass, and price of sensors while boosting processing
speed and delivering more precise data. Smart dust is
best employed for observing and tracking a variety of
applications, as exposed in Figure 1. As network nodes have
minimum energy, data transfer consumes the majority of
energy in smart dust. Therefore, it is essential to create a
structure that uses the smallest amount of energy possible
when sending information to the BS. One strategy to use
less energy is to design network topology with a hierarchical
system. These network nodes are arranged in a hierarchy
with multiple levels, with nodes in each layer sharing the
same properties. Clustering is one method for creating
the hierarchical system. To guarantee that all sensors are
gathering information from their physical surroundings,
network elements are subdivided into different clusters (1).
Since smart dust are typically dynamic, energy consumption
may decrease as that of the network’s lifespan increases by
choosing the most effective method for transferring packets
from node to sink.
In smart dust, there really are three distinct routing
techniques, namely, flat, hierarchical, and based on
geography. Smart dust structure is seen in Figure 2. The
rotation and flooding algorithms, in which every network
node has the same function, use flatter routing. This method
1

2 Rajesh and Rajanna
FIGURE 1 | Smart dust application.
allows nodes to interact with one another by gathering
environmental data (2). A much more flexible hierarchy
routing is an additional technique of data transmission to the
BS. Since they have the most resources and are in charge of
collecting and combining the data obtained from the nodes,
headers are frequently used in hierarchical routing (3).
According on how the network is set up, it is then assembled
and sent in one or more hops to the BS for treatment before
being received by the user. The cost of transmitting data to
the sink rises as the length here between nodes as well as
the sink widens. Energy can be saved, and the lifespan of
the header node increased when hierarchical routing is used
with the right intermediate node. Similar to what is being
said, utilizing multi-hop broadcasts increases connection,
range, and stability. There have been many methods for
determining this header node, but determining a header
node using a fuzzy system lowers the computation time. The
primary goal of the proposed A Fuzzy Clustering and Energy
Resourceful Routing Protocol (FCER2P) is to develop an
easy-to-implement energy-efficient routing mechanism that
utilizes clustering with a pre-determined threshold. In the
FCER2P, many hops and choosing the best intermediate
node are accomplished. The intermediate node is selected
from the listed header locations based on the "Remoteness
to BS" and "outstanding node energy" concept in order.
The suggested FCER2P clustering are dependent on the
assertive header node, which differs from other widely
used techniques in that clustering is not performed each
round. Certain clustering algorithms choose their headers at
random, which reduces the possibility of using the effective
concentration as a cluster head (CH). A header sensor node
is picked based on optimal fuzzy attribute parameters in
the suggested FCER2P. The present node has indeed been
recognized; hence, no additional nodes are chosen in the
next rounds. This approach results in a negligible amount of
conflicts and the transmission of control messages.
Existing fuzzy-based clustering methods for smart dust
are reviewed in the remaining parts of the article, which
are described in Section 2. The planned FCER2P system
is covered in detail in Section 3. Section 4 discusses the
simulation findings and comparability of the suggested
FCER2P with the evaluation findings. The results and
upcoming research are shown in Sections 5 and 6.
2. Related work
The clustering methodology Multi-Objective Fuzzy
Clustering Algorithm (MOFCA) (4) is built on a fuzzy
inference system, and it uses fuzzy variables such as
"remaining energy," "range to BS," and "intensity of every
node." This technique uses an energy-based competition
perimeter to select the CH. This technique was designed to
address the "energetic hole" and "hot region" issues. Using
the parameters such as Half Nodes Active (HNA), Foremost
Node Dead (FND), and Overall Available Energy, this
method calculates the lifespan of WSNs (TRE). Depending
on its location from the BS, the CHS diameter calculation
is determined using the residual power from the nodes.
When employing effective procedures to handle WSN
environments, the intensity variables are used as input
variables for the fuzzy logic that has been built. To evaluate
the MOFCA optimization technique with extra clustered
methodologies, including Low Energy Adaptive Clustering
Hierarchy (LEACH) (5), CH Election utilizing Fuzzy logic
(CHEF) (6), Energy-Efficient Unequal Clustering (EEUC)
(7), and Energy-Aware Unequal Clustering with Fuzzy
(EAUCF) (8), a comparison analysis has been performed as a
component of an innovative assessment process. In instance
1, the nodes are distributed almost uniformly using MOFCA.
Depending on its location, BS of an area of interest (AOI),
broadcast form, and BS placement are affected. Scenario 2
demonstrates that, with the exception of LEACH (9) and
CHEF (10) implementations, multi-hop networking does
have a maximum competitive radius. The BS is the AOI’s
center, and the nodes are arranged nearly uniformly. In
instances 3 and 4, nodes are deliberately generated in a
non-uniform dispersion. Though, with the latter installation,
the BS is outside of the AOI. Nodes in situation 4 have their
x and y positions changed by about 5 m, allowing for a
broadband network.
The MOFCA methodology (11) performs better compared
to the other three techniques, with an effectiveness that
is 57% higher than LEACH (5), according to the FND
measurement results. The execution effectiveness of CHEF
(12), EEUC (7), and EAUCF (13) is 29, 10, and 8% less than
MOFCA. The deployment of WSNs and emissions reduction

10.54646/bijscit.2022.21 3
FIGURE 2 | Smart dust hierarchical structure.
0
50
100
150
200
250
DSQLRA SVPA FCER2P
Network Lifespan
HND FND
FIGURE 3 | Network life of cluster approaches.
are more adjustable if transmission lines are installed. In the
studies, the MOFCA computation efficiency (4) was logically
preferable when contrasted to other cases. This technique’s
use of asymmetrical clustering, which produces balancing
energy usage, is just one of its advantages. One issue with the
method is the implementation of clustering in that round.
A fuzzy system-based clustering technique called EAUCF
has indeed been suggested to prolong a network’s lifetime.
This methodology partially uses the techniques to improve
node selection while also using a fuzzy system to rotate the
CH on a recurring basis, which improves performance over
previous methods. In real networks, this approach increases a
network’s lifespan, however, in node mobility, it has no effect.
One of the shortcomings of the method is the complexity
of the procedure due to the requirement to approximate the
sizes of both clusters prior to clustering. Such a method also
has the drawback of selecting a CH node without taking
"node density" into consideration, which could lead to the CH
being a node with few neighbors. Consistency is one benefit
of this method. A methodology that uses the fuzzy system
to determine the CH’s contentment is Energy-Efficient
Fuzzy Logic-Based Clustering Technique for Hierarchical
Routing Protocols in WSNs (FL-EEC/D) (14). Furthermore,
it equalizes energy use among clusters and resolves the hot
zone problem. The multi-hop communication system that
relies on Fibonacci sequences is used to address this. The
fuzzy logical interpretation is used by Fuzzy Logic for Multi-
hop WSNs (FLCAMN) methodology (15) to choose the right
CHs. The networking life is examined by FND and Half
Nodes Dead (HND) measurements. a comparison based on
various algorithms, including LEACH (5), Energy-Aware
Multi-Hop Multi-Path Hierarchical Routing Protocol for
WSNs (EAMMH), and Distributed Fuzzy Logic Algorithm
(DFLC) (16). At the 91st phase, the FLCMN-based FND
outscored LEACH (5), EAMMH (12), DFLC (11), and
FLCMN (10) by using average remaining energy as its input
variable. This illustrates how well nodes’ leftover energy is
used in conjunction with that of their nearby nodes. As
seen in Figure 3, FLCMN utilizes leftover energy from the
broadcaster’s WSNs to eventually prolong its lifespan (17).
A fuzzy scheme hierarchical proposed technique is
Fuzzy Logic-Based Energy Efficient Clustering Hierarchy
FIGURE 4 | FCER2P protocol.

FIGURE 5 | Membership function of remaining energy input variables.
FIGURE 6 | Membership function of neighbor‘s quantity input values.
(FLECH) (18). Throughout this technique, "remaining
energy," "focusing rate," and "length to the BS" are used
to select the cluster. In various cases, the effectiveness of
FLECH is compared to that of the segmentation-based
visual processing algorithm (SVPA) (15), the disseminated
scheduling scheme, and QoS-limited routing scheme for
wireless sensor networks (DSQLRA) (2). The FLECH
method performed better than other techniques in the
simulation by consuming less energy and extending longevity
through round data collection in the network. This strategy
has the advantage of selecting the CH in a deterministic and
weighted manner. This method’s potential for clustering in
every round is one of its drawbacks.
3. Proposed methodology
3.1. Fuzzy system
In terms of cluster analysis, choosing a central role is among
the most crucial choices you will have to make. Selecting
the best smart dust node to act as a smart dust CH can
significantly reduce energy consumption while extending
the system’s lifetime. The CH node has been chosen
using a variety of strategies up to this moment, includes
probability selection, unambiguous selection, simulated
annealing, and the use of fuzzy logic inside the selection
method. Fuzzy systems reduce the computation uncertainties
and complexity in WSNs (12). Fuzzy logic is a form of
cross-logic where every other statement’s proper value can
range from zero to one. A fuzzy scheme continuously
converts a dataset into a fuzzy non-linear transformation.
Every logical input variable is transformed into a group
of fuzzy values using the application "fuzzy builder." Every
fuzzy result is converted into a real corresponding value in
the defuzzer section. This component analyzes fuzzy values
and carries out procedures in accordance with rules. This
method includes the steps of creating a classification for
input variables, using fuzzy expressions, and producing a
final output, among many other things.
3.2. System model
The suggested FCER2P is predicated on the following
hypotheses:
1. All nodes have a similar beginning energy, and they all
have a homogeneous mixture.
2. The placement of nodes in a network is
randomly selected.
3. The very same time is assumed to be shared by all
participating nodes.
4. Every BS and node are static.
5. The Euclid algorithm is used to determine the range.
6. This header data is delivered to BS in numerous hops
and, under unusual circumstances, in a single hop.
7. The neighbours of a node are nodes located at a radius
of R from that node.
The energy usage prototypical for sending "1"-bit
information packets between transmitter and receiver spaced
"d" apart from one another is as follows:
E(l, d){l ∗Eelec + l ∗ Efs∗d2
if d < d 0 , l ∗ Eelec + (1)
l∗ Emp∗ d 4
if d < d 0
The cost of d0 is premeditated as follows:
d0 =
√
e f s/e m p (2)
Eelec is the quantity of energy used by the pivot during
communication for every bit of data sent and received.
The parameter yields energy usage for outdoor transmitting,
while the variable yields energy usage for multi-hop
communication. The indicator, which would be computed
using the equation below, obtains the energy needed by the
receiver to obtain data.
ERX = l ∗ Eelec (3)
The steps in the proposed FCER2P methodology are shown
in Figure 4.

10.54646/bijscit.2022.21 5
3.3. Proposed FCER2P
There are two components to the planned FCER2P.
Clustering, the use of the fuzzy system for energy-
efficient routing.
By using a pre-determined threshold, different types of
clustering, and providing a mutual mechanism for sending
packet to BS, it minimizes the clusters in smart dust to
improve the performance of smart dust. We will examine the
proposed FCER2P and its operation in the remaining sections
of this study. The suggested FCER2P has the following
properties in general:
1. During each round, fuzzy system-based distributed
clustering, uneven clustering, and no categorization
are used to cut down on power usage and the quantity
of control signals sent out.
2. To determine the optimal node related on leftover
energy and wherever it ought to be located directly
in clustering, every cluster has a unique set of fuzzy
input variables.
3. Setting a preset cutoff for overall maximum power has
been studied as a way to decrease the regularity of
header nodes re-clustering.
4. With such a multi-hop approach, the algorithms
choose the best route for communications to go from
every header node toward the BS.
Reduced energy consumption within the sensor network
is among the most crucial things to take into account
when comparing clustering methods. By consuming less
energy, the network’s efficiency can be increased. Hence,
every node’s neighbors count is thought of as an additional
fuzzy variable in clustering because the clustering intensity
rises as the number of nearby nodes increases. The network’s
energy usage stays constant when the dispersion inside
the cluster’s node is symmetrical. The very first cluster
is therefore analyzed in cycles 1, 4, and 7, before entire
cluster is generated and managed, with leftover energy and
neighbor’s quantity of every node being observed as fuzzy
input variables.
The first cluster’s fuzzy system receives input from every
node’s energy and size of the network. Every node in just
this cluster has a chance that ranges from 0 to 1, according
to the fuzzy rules connected to it, as shown in Table 1 and
Figure 10. The significance level is also broadcasted by every
node over the range of its message after it has been calculated,
and the recipient compared it with the values it has obtained
from its neighbors.
The CH is indeed the node with the highest priority in
its neighborhood range, and to make sure that everyone
is aware of its presence, it communicates its condition to
each other node within its signal radial distance. Delivering
a member connection request, the receiving node contacts
FIGURE 7 | The output variable’s threshold values for a probability in
the cluster.
the CH node, and if the obtained signal is robust, the
receiving node is chosen as a cluster participant. A smart
dust node accepts the membership application with the
smallest ID number whenever it receives two or more CH
notifications. The readers may be curious as to how the
primary clustering would indeed be chosen if there were
other equally likely candidates, given that clustering is part
of the suggested FCER2P contents. or "Is it feasible that the
decisions differ in similar scenarios given that every node
chooses autonomously?" Take into account that this option
is likely when responding to these inquiries. There is no
restriction on the node combinations in which an eligible
smart dust node broadcasts smart dust CH communication
to a neighboring smart dust node if multiple nodes in the
vicinity possess the same likelihood of being competitors
for CH. The recipient of this message accepts a qualifying
node with such a low ID. The node only with a lower
allocated ID is selected as the CH out of the aggregate of
107 potential nodes for every node that has the same chance,
per the evaluation. To conserve energy, routing methods
for WSNs are being developed. Efficiency can be enhanced
and the lifespan of the network increased by launching an
ideal routing plan and applying the right number of hops
dependent on length among nodes as well as BS. Data
delivery to the BS is accomplished in this study using a multi-
hop approach. This node combines the incoming data in
the suggested FCER2P by concluding clustering during every
round as well as transmitting sensory contextual information
to CH. Through the CH node, it sends information in multi-
hop toward the BS. Each CH node is picked from the list of
CHs based on the ability requirements. The "remoteness to
BS (D)" and "outstanding energy (E)" of the smart dust node
are combined to provide the performance metric (M). These
following standards must be met in order to choose a smart
dust CH node:
M(CH(i)) = D(CH(i))/E(CH(i)) (4)
The competitiveness radius (R), which must be determined
in order to select a head node from the present CHs, is as

FIGURE 8 | Proposed multi-hop FCER2P.
0
50
100
150
200
250
300
350
400
0 100 200 300 400
Lifetime
DSQLRA SVPA FCER2P
FIGURE 9 | Duration of the network.
follows:
R = (CH(i))/2 (5)
For instance, the smart dust CH with the greatest suitability
metric is considered CH(i) if CH(j) smart dust nodes and
CH(k) smart dust nodes are located inside the competing
radius (R) to also be considered CH(i) (i). This technique
uses single or multiple routing to transfer information to the
BS. Let us say that there are no other CH nodes within the
CH node’s competition radius (R) that have an appropriate
proficiency measure (M) (i). In that situation, the single-hop
method informs the BS. The suggested FCER2P is depicted as
a multi-hop in Figure 8.
4. Results and discussion
To see if the proposed approach is more scalable in respect
of node quantity, node density, and BS placement, it is
contrasted to the DSQLRA (2) and SVPA (15) methodologies
in the same scenarios. The strategies were assessed using
the MATLAB program according to the network lifetime
measure, which includes the FND, HND, and LND variables
in addition to the amount of energy consumed per cycle as
well as the quantity of lifeless smart dust nodes every cycle.
The work space in this experiment measures 100 × 100 m2.
All through the ecosystem, 300 smart dust nodes with just
an energy difference of 0.5 joules are dispersed arbitrarily.
Using only a star topology, the position of BS is beyond the
workplace, and the length from the node to BS is 130 miles
longer. The outcomes of comparing methods in terms of
network lifespan are shown in Figure 9. In respect of FND,
HND, and LND, the suggested FCER2P performs better than
alternative techniques.
As shown in Figures 10, 11, it seems that sending multi-
hop data from each CH toward the BS, as well as reducing
the amount of clustering instances, lowers the amount of
control alerts given, while maintaining a balanced energy

10.54646/bijscit.2022.21 7
0
2000
4000
6000
8000
10000
12000
0 100 200 300 400
Dead Nodes
DSQLRA SVPA FCER2P
FIGURE 10 | Amount of dead nodes in every cycle.
0
50
100
150
200
250
300
350
400
450
0 100 200 300 400 500 600
Remaning Energy
DSQLRA SVPA FCER2P
FIGURE 11 | The quantity of remaining energy in every cycle.
usage and plummeting quantity of lifeless nodes per cycle.
Additionally, compared to other algorithms, the effectiveness
of the suggested approach is more reliable. Because they
use the CH as well as are clustered with different cluster
sizes, the DSQLRA and SVPA techniques were chosen and
compared to FCER2P. In other terms, we made an effort
to compare FCER2P in a completely fair manner to several
new and reliable techniques. The prevention of grouping in
every round and the utilization of a cutting-edge routing
protocol are among FCER2P standout features. The lifetime
of the network variables was assessed by taking into account
the variety of nodes as well as the position of a BS in the
middle of the working environment in order to successfully
evaluate FCER2P to other approaches. In comparison to
other approaches, the results show that FCER2P performs the
best in terms of lengthening the lifespan of the network. The
suggested FCER2P has a lifespan of the network of 687, which
is a 65% enhancement over the better network lifespan of 417
achieved by other techniques.
5. Conclusion
This research concentration is on extending the lifespan
of WSNs and energy conservation. Moreover, it aims
to minimize the transmission of control signals. In a
MATLAB simulation, we applied our suggested clustering-
related routing strategy with a preset cutoff and multi-
hop propagation. Fuzzy Clustering and Energy Resourceful
Routing Protocol (FCER2P) applications were tested to
determine how well they scaled in terms of smart dust node
count, network magnitude, and BS place. The investigation
shows that the suggested FCER2P technique optimizes the
FND, HND, and LND factors; minimizes the number of
control packets broadcast; and uses less energy. With a pre-
defined threshold, various clustering strategies, multi-hop
routing with the right intermediate node, as well as other
parameters, the network’s lifespan is extended due to the
absence of clustering in every round. Network efficiency
is enhanced by employing the CH node’s peak energy in
combination with a pre-determined threshold.

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