https://doi.org/10.31449/inf.v46i7.4272 Informatica 46 (2022) 47–54 47 
 
Fuzzy Data Aggregation Approach to Enhance Energy-Efficient 
Routing Protocol for HWSNs 
Asaad A Alhijaj
1
, Baida'a Abdul Qader Khudor
2
, Imad S. Alshawi
*3 
Email:  asaad.abdulhassan@uobasrah.edu.iq
1
, baidaa.khudur@uobasrah.edu.iq
2
, emad.alshawi@uobasrah.edu.iq
3 
*
Imad S. Alshawi 
Department of Computer Science, College of Computer Science and Information Technology,  
University of Basrah, Basrah, IRAQ 
Keywords: cluster partition, fuzzy data aggregation, HWSNs, routing, spider monkey optimization  
Received: July 5, 2022 
The sensor nodes' computing capability, communication capabilities, and power supply are severely 
constrained in WSNs, making sensor battery replacement or recharging difficult or even impossible. 
Therefore, energy is an important challenge to consider while creating WSNs. In hazardous 
circumstances, accurate data aggregation and routing are crucial, and the energy consumption of 
sensors must be closely controlled. Due to environmental conditions and short-distance sensors, 
however, there is a high possibility of duplicating data. Large datasets include a range of data, some of 
which are helpful while others are entirely unnecessary. This redundancy reduces performance in terms 
of redundant transmission and computation expense. Data aggregation, on the other hand, may reduce 
duplicate data in a network, hence reducing the volume of data sent and increasing the network's 
lifespan. In this context, two novel energy-conscious approaches called Fuzzy Data Aggregation with 
Spider monkey optimization (FDA-SMORP) for data aggregation in the cluster head and routing to the 
sink are presented. These strategies attempt to offset the energy consumption among all nodes in a 
wireless network such that these nodes exhaust all of their energy and die almost simultaneously. To 
demonstrate the efficacy of the suggested approaches in terms of minimizing delay caused by route 
planning, balancing energy usage, and extending network lifetime, the proposed methods are compared 
to some of the most well-known WSN systems. 
Povzetek: Razvit je sistem za nadzorovanje potrošnje energije v senzorskih brezžičnih omrežjih. 
 
1 Introduction 
A WSN has a large number of nodes that can sense 
changes in the real-world environment. All aspects of 
human existence may benefit from a wirelessly 
networked sensor, such as smart buildings, the Internet 
environment, battlefields, industry, healthcare, and 
agriculture, and these are just a few of the uses of 
WSN[1]. The life of the network decreases as the sensors 
run out of power. These problems can only be solved if 
energy is used in the most efficient way possible. 
Because nodes create comparable data when placed close 
to each other or sent to data at the same time, this can 
cause data redundancy issues. This reduces network life 
energy consumption during processing, sending, and 
receiving data. To solve this problem, instead of sending 
each felt value to the sink separately, the data is first 
collected and aggregated using aggregate functions such 
as sum, average, etc., and it is then passed through 
routing protocols to deliver the data to the sink[2], [3]. 
Data aggregation is the analysis of raw data 
attributes and the application of correlations. Using a 
data aggregation approach, sensor nodes turn 
unprocessed data into a digest before delivering it to the 
 
sink. Data aggregation minimizes transmission costs and 
network overloading as a consequence of the decreased 
size of the digest. We argue that data aggregation is a 
critical method for reducing energy consumption in 
WSNs [4], [5]. However, there are still several obstacles 
to overcome before data aggregation performance can be 
improved. Existing contributions describe many 
aggregation algorithms that organize sensor nodes based 
on raw data to aggregate information. Nevertheless, 
aberrant data frequently emerges in raw data. 
Consequently, data instability has a direct impact on the 
efficiency of such approaches[6], [7]. In WSNs, there are 
a lot of ways to reduce the amount of data; like that each 
sensor collects before sending it to the sink, or while 
aggregating data in the cluster head (CH). Or use a way 
for the data packets to be routed like an efficient 
clustering solution with data aggregation, employing 
several mobile sinks for heterogeneous WSN[8]. 
Several researchers have highlighted the problem of data 
aggregation with routing in WSNs [9], [10]. When it 
comes to WSNs in general, the most difficult problem is 
finding ways to improve energy efficiency so that the 
network can last much longer [11], [8]. 
Table 1 summarizes the related works with their 
methodology, performance, and results. So, the current 

48 Informatica 46 (2022) 47–54 A. A. Alhijaj et al. 
work proposes a new energy-conscious protocol for 
HWSNs called Fuzzy Data Aggregation with Spider 
monkey Optimization Routing Protocol (FDA-SMORP). 
The new protocol can combine two approaches Fuzzy 
Data Aggregation[11] with Spider monkey Optimization 
Routing Protocol [12]. So, FDA-SMORP is used to 
aggregate the sensing data inside the clusters by the 
cluster heads, which are used by the FDA, and to send 
the aggregated data through the optimal path to the sink 
for HWSNs by using the SMORP. 
 
Table 1: Summarization table on the related works 
 
Ref Methodology Performance/Results 
[13]  Ant Data 
Aggregation 
Algorithm 
 A population-based approach such as the ant colony system allows researchers to 
naturally traverse research space in optimization settings in pursuit of the most useful data, 
and it is via data aggregation that wsns may reduce their power consumption. 
 In each cluster head, the sink node sends a unique seed vector that accounts for network 
dispersion. Clusters transmit measurement data to the sink node through a multi-hop 
routing tree. 
[14]  Support 
Vector 
Machine 
 Fisher's 
Discrimination 
Ratio 
 His incremental support vector machine (SVM) training method aimed to eliminate 
unessential input.  
 Sets may be distinguished between data that has been aggregated and data that has been 
disseminated in a set by using Fisher's Discrimination Ratio (FDR).  
 The training of SVM is quicker since there are fewer data samples necessary. 
[15]  Mobile Sink 
Is For Data 
Aggregation 
 They represented solutions for effective data aggregation with several movable troughs 
in HWSNs. When using the statically sink-based technique, data packets are dumped over 
a multi-hop connection and sent throughout the network. As a result, the fixed basin is 
inefficient in terms of its use of energy.  
 A mobile sink is utilized to gather data, which uses less power and hence prolongs the 
network's lifetime.   
[16]  Naive Bayes 
Prediction 
 Data from wsns can be reduced using Naive Bayes Incremental Prediction, making the 
network last longer. And extract only the necessary data. 
[17], 
[18] 
 Particle 
Swarm 
Optimization 
 Data aggregation has been suggested by utilizing compressive sensing technology, 
where active sensor nodes are optimized to decrease the amount of duplicate data using 
particle swarm optimization. As a result, they are efficient in terms of their use of energy. 
[8]  Fuzzy Dstar-
Lite 
 The authors proposed Fuzzy Dstar-Lite as a routing technique for producing the 
optimum information routing for HWSNs. Additionally, it brings up the point of outdoing 
the obstruction example and elucidates the Unbalanced Energy Depletion (UED) problem 
in the network. 
[19]  Open-Pit 
Mining 
 Open mining is presented as a method for aggregating data that is both efficient and 
cost-effective to use.  
 This data mining method uses a lot of wsns. Each one has a center node around which 
many virtual pits collect and send data to the sink. 
[20]  Neural 
Network 
 Cosine 
Similarity 
 the Reduce duplicate data and eliminate outliers by using a neural network of self-
organized maps. 
  The use of cosine similarity in sensor node creation further simplifies the process based 
on the data's density and similarity. 
[21], 
[22] 
 Spider 
Monkey 
Optimization 
Routing 
Protocol 
 The researchers described a novel technique for clustering the HWSNs approach that 
employed an efficient way of selecting the head of the cluster nodes, the degree of sensor 
nodes, and the remaining energy. Additionally, the chaining technique is used to collect and 
send the information package. 
  They proposed a swarm-based intelligence method called SMORP that was used in the 
homogeneous WSNs and the heterogeneous HWSNs. This method is used to find the 
optimal path in the network based on a set of routing criteria. 
[12]  Fuzzy Data 
Similarity 
 A method called fuzzy data similarity (FDS) is presented to determine the similarity 
between two texts. To demonstrate the efficacy of the proposed method, the FDS was 
shown to be around 93% accurate. 
 Most comparable techniques employ distance measurements to evaluate the differences 
between a pair of objects, and the suggested algorithm is compared to one of the most used 
distance scales (Jaccard similarity, Cosine similarity, Overlap Coefficient). 
 

FDA to Enhance Energy-Efficient Routing Protocol for HWSNs Informatica 46 (2022) 47–54 49 
 
 
This paper is organized as follows: In Section 2 
presents a proposed smart data aggregation with a new 
routing protocol for HWSN. Section 3 shows the 
simulation results of the proposed method. Finally, the 
conclusion of this paper is presented in Section 4. 
2 FDA with SMORP for HWSNs 
The proposed method represents the process of 
aggregation and routing data for HWSNs. We assume 
that our network has two types of heterogeneous sensor 
nodes: the normal senses (N-sensor) and the high senses 
(CH-sensor). The N-sensors have limited resources, such 
as limited processing speed, storage capacity, and 
communication bandwidth. While the CH-sensors have 
high resources and represent the cluster heads in the 
network. The network is configured as follows: the N-
sensors are deployed randomly, while the CH-sensors are 
deployed carefully. The cluster partition method [24] is 
used, in this paper, to organize the HWSNs as orderly 
clusters.  
The SMORP selects the appropriate next hop to the 
sensor node based on the routing criteria (maximum 
remaining energy, fewest hops, and lowest traffic load). 
This work supposes: (i) All N-sensors have the same 
transmission range and begin with the same amount of 
battery power. Each N-sensor in (ii) is aware of its 
position, as well as that of its CH and neighbors. (iii). All 
CHs have the same transmission range and start-up 
power from the battery (iv). Each CH is aware of its 
position and also of its neighbors, namely the other CHs 
and the sink location. 
2.1 Network Model 
The goal of the proposed model is to ensure that when 
some of the sensors send an event at the same time, there 
is a high probability that the same event will repeat, 
increasing the amount of data that occupies high space 
and lowering energy in the network. CH's FDA is used to 
effectively aggregate data based on redundancy 
elimination, extract useful information, and then send it 
via an improved spider monkey protocol, which reduces 
the power consumption of sensor nodes and thus extends 
the life of the network. Figure 1 shows the data 
aggregation with routing in HWSNs. 
The routing protocol is one of the major concerns in 
extending the lifetime of HWSNs. If any sensor node (N-
sensor or CH-sensor) runs out of energy during the 
routing protocol, the information exchange between (N-
sensor and CH) and (CH and the sink) will likewise be 
broken. Typically, this results in a shortage of HWSNs 
over their lifetime. The amount of power each sensor in 
an HWSN gets affects how long it lasts, it is very 
important to save power in those sensors so that the 
network as a whole can last as long as possible. In this 
light, the SMORP is capable of extending the lifetime of 
HWSNs by lowering energy expenditures and evenly 
distributing energy usage. 
2.2 FDA-SMORP proposed 
FDA-SMORP is used to aggregate the sensing data 
inside the clusters by the cluster heads, the FDA has 
recommended a method for aggregating data that 
eliminates redundancies and extracts relevant 
information. A similarity measure in the context of data 
mining is a distance whose dimensions indicate object 
properties. Thus, if the distance between two data points 
is small, the objects will be highly similar, and vice 
versa[25]. The majority of aggregation techniques use 
distance measurements to evaluate the differences 
between a pair of items [11].  
After that, the SMO method evaluates a tree 
structure in the course of (N, Fit), where N is the 
candidate node set in the forwarding route and Fit is the 
fitness functions set that each candidate node n ∈ N is 
assigned a fitness function value fit(n). The tree node 
will explore depending on its fitness function.  
In SMORP, the created routing route is used 
repeatedly (rounds), and the status of each node along the 
way is evaluated to decide if the same path should be 
used for the next round. According to the previous 
assumption, the sink has access to current information on 
each node's battery energy, position coordinates, and 
network traffic load. Eq. (1) is used to determine the 
fitness of a contiguous node (n i). 
 
)) ( ), ( ), ( ( ) (
i i i i
n D n TL n RE fuzzy n fit        (1) 
 
Where RE(n i), TL(n i), and D(n i) are the remaining 
energy, traffic load, and the distance to the destination 
for node n i, respectively. All these parameters are the 
inputs that will calculate the fitness value to the node n. 
After that, the GLSM assesses the information gathered 
from all of LLSM's neighbor nodes and chooses the 
optimal node with the greatest probability P with the 
probability value specified by Eq. (2): 
 



N
j
j
i
i
n fit
n fit
n P
1
) (
) (
) (
                        
(2) 
 
 
Figure 1 : Data aggregation with routing in HWSNs 

50 Informatica 46 (2022) 47–54 A. A. Alhijaj et al. 
Where P(n i) is the probability associated with node n i, fit 
(n i) is the fitness associated with node n, and N is the 
number of neighbor nodes. 
Figure 2 shows how data is aggregated within the 
routing protocol FDA-SMORP in each cluster head 
effectively. 
3 Performance Evaluation of FDA-
SMORP  
The primary goal of this paper is to develop the SMORP 
[11]. In this paper, we assume that three sensors send the 
events at the same time. Thus, the network is optimized 
by the assembly process in each cluster. The simulation 
results for the proposed method are compared over three 
scenarios. 
3.1 Simulation Setting 
Simulations are carried out in MATLAB R2010a 
(version 7.10) under Windows 7 (32 bits). The 
experiments are performed on a PC (ThinkPad T410i, 
China) with an Intel 
R
 Core 
TM
 i3 Processor running at 
2.4 GHz and 2 GB of RAM. To make the network as 
realistic as possible, some parameters must be set in the 
system. Table 2 depicts a heterogeneous network with 
1000 N-sensors and 36 CHs randomly arranged within a 
300 m x 300 m square topographical area. Both systems 
are used the clustering method to group the N-sensors 
around CH-sensors. Also, they used a radio model [26] 
and exhausted their transmission cycles (2000). Each 
system produces a 2 KB packet length. All N-sensors and 
CH-sensors start with the same starting energy of (0.5 J) 
and (2.5 J) with a sensed transmission of (20 m) and (80 
m), respectively. The traffic load, in each node, is 
assumed to be generated randomly between [0...10] and 
[0...50] for the N-sensors and the CH-sensors, 
respectively. 
3.2 Simulation Results 
The life of HWSN can be extended by using a CH fuzzy 
data collection method called FDA with a routing 
protocol called SMORP that has been optimized in to 
increase energy efficiency.  To see how well it worked, it 
was tested in three different scenarios, if the same 
routing metrics and the same environment were used in 
both. 
To validate the operation of the proposed model, 
three scenarios are applied to the model. Assuming the 
 
 
 
 
Figure 2: The flow chart of the FDA-SMORP proposed 

FDA to Enhance Energy-Efficient Routing Protocol for HWSNs Informatica 46 (2022) 47–54 51 
 
packet size is 2k, and then considers setting a high 
similarity threshold. That is, the greater the similarity of 
the detected events, the less the amount of data 
transmitted to the sink. FDA proposed algorithm is put 
into every CH-sensor. Thus, we notice the effect of the 
algorithm on clusters only, instead of the N-sensors 
a) First Scenario 
In this scenario, assuming the head of the cluster receives 
different messages from the three sensors, the data is all 
aggregated and transmitted.  
b) Second Scenario 
In the second scenario, assuming that the head of the 
cluster receives two similar messages from the three 
sensors, the messages are aggregated removed from the 
similarity, and sent to the sink.  
c) Third Scenario 
In the third scenario, assuming that the head of the 
cluster receives three identical messages from the three 
sensors, the messages are aggregated removed from the 
similarity and sent to the sink. 
The network lifetime results obtained using three 
scenarios are compared by counting the number of 
sensors that remain alive after each data round. At this 
point, Figure 3 shows the proportion of CH sensors, 
which are still alive in each scenario. As a result, the 
performance of the third scenario outperforms the 
performance of both the first and second scenarios, 
meaning that the more the detected events are similar, the 
smaller the amount of data sent. In light of this, we note 
that the amount of energy consumed in the third scenario 
is small compared to the first and second scenarios based 
on the total number of nodes that are still alive in the 
network. Here, after sending (2000) packets to two 
sensors over the network, the result of the network life 
achieved in the third scenario is approximately (60%) 
more than in scenario two and approximately (80%) 
more than in scenario first. 
The percentage of energy remaining in the CH 
sensors varies with the number of transfer cycles 
depending on the system used. The third scenario 
outperforms the first and second proposed scenarios in 
terms of overall performance and efficiency. 
Figure 4 shows how the percentage of residual power 
for the CH sensors varies based on the transfer mode 
used. As you can see, the third scenario is better than the 
first and second scenarios by maintaining the stability of 
the network for as long as possible.  
4 Conclusion  
Many routing protocols have been used in WSNs for 
saving energy. Nevertheless, just saving energy is 
inadequate to prolong the life of the networks. Large 
datasets include a variety of information, some of which 
is useful, while others are completely superfluous. 
Assuming that some of the sensors transmit an event at 
the same time or when they are close to each other, this 
can cause data redundancy issues these problems can 
only be solved if energy is used in the most efficient way 
possible. As a result, two novel energy-conscious 
approaches called Fuzzy Data Aggregation with Spider 
monkey optimization (FDA-SMORP) for data 
Table 2: Simulation parameters 
 
Parameter        Value 
Area of topographical (meters) 300m x 300m 
Location of the sink (meters) (0, 150) 
Length of control packets 2k 
No. of transmission packets 
(rounds) 
2 x 10
3 
N-
sensors 
Number of nodes 1000 
Limit of transmission 
distance 
20 m 
Initial energy 0.5 J 
E elec 50 nJ/bit 
E amp 100 
pJ/bit/m
2 
Max. traffic in node’s 
queue 
10 
CHs 
No. of nodes 36 
Limit of transmission 
distance 
80 m 
Initial energy 2.5 J 
E elec 100 
nJ/bit 
E amp 200 
pJ/bit/m
2 
Max. traffic in node’s 
queue 
50 
 
 
 
Figure 3: The CH-sensors ratio remains alive (three N-
sensors sent at a time). 
 
 
Figure 4: The energy ratio of the remaining CH-sensors 
(three N-sensors sent at a time). 
 

52 Informatica 46 (2022) 47–54 A. A. Alhijaj et al. 
aggregation in the cluster head and routing to the sink are 
presented. These strategies attempt to offset the energy 
consumption among all nodes in a wireless network such 
that these nodes exhaust all of their energy and die 
almost simultaneously.  The simulation results of the 
proposed model indicate that FDA-SMORP 
outperformed in terms of greatly enhancing data latency 
reduction and lifetime maximization of the network. 
 
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54 Informatica 46 (2022) 47–54 A. A. Alhijaj et al.