=Paper= {{Paper |id=Vol-3149/short3 |storemode=property |title=A Novel Minimized Energy Routing Technique for IoT Assisted WSN (short paper) |pdfUrl=https://ceur-ws.org/Vol-3149/short3.pdf |volume=Vol-3149 |authors=Hazem N. Abdulrazzak,Aya A. Hussein,Alexander Kuchansky |dblpUrl=https://dblp.org/rec/conf/ttsiit/AbdulrazzakHK22 }} ==A Novel Minimized Energy Routing Technique for IoT Assisted WSN (short paper)== https://ceur-ws.org/Vol-3149/short3.pdf

A Novel Minimized Energy Routing Technique
for IoT Assisted WSN
Hazem N. Abdulrazzaka, Aya A. Husseinb, and Alexander Kuchanskyc
a
Al-Rafidain University College, Baghdad, Iraq
b
Gilgamesh Ahliya University-GAU Baghdad, Iraq
c
Kyiv National University of Construction and Architecture, Povitriflotskyi ave., 31, 03037, Kyiv, Ukraine

Abstract
The problem of routing in WSN (Wireless Sensor Network) is to minimize the energy
consumption during data transmission, the IoT (Internet of Things) monitoring system use the
horizontal clustering of WSN to achieve this goal. The goal of this work is to create multi
clusters with multi cluster head to communicate with sink node, the sink node directly connects
to IoT server. A set of clusters has been created by dividing the WSN area in to 5 clusters
horizontally, in each cluster the CH (Cluster Head) collects the data from all sensor nodes and
communicate with sink node. The energy consumption is calculated based on wireless radio
model and proposed clustering algorithm. The total energy consumption, normalized average
energy and residual energy of proposed protocol is better than the two existing protocols that
compared, the two protocols are PEGASIS (Power-Efficient Gathering in Sensor) and IEEPB
(Improved Energy- Efficient PEGASIS- Based protocol). The results show that the H-IEEPB
(Horizontal Improved Energy- Efficient PEGASIS- Based protocol) has an improvement in
energy consumption and minimize it more than 10% and 25% compared with PEGASIS and
IEEPB respectively, the residual energy and the normalized average energy also get good
results compared with the others.

Keywords 1
WSN, IoT, IEEPB, PEGASIS, Clustering, H-IEEPB

1. Introduction

Routing protocols in wireless sensor network represent the backbone of any reliable communication
in sensor network. Therefore, the goal is how can enhance and improve the behavior of wireless sensor
network by improving the routing protocols to overcome any deficiencies or constraints exist in this
type of network. There are many techniques and many algorithms proposed by different authors to
improve many kinds of routing protocols. In this paper, we are focusing on the chain routing protocols.
chain based routing protocols which is already one of the hierarchal routing protocols types [1] to ensure
energy efficiency broadcasting [2]. Thus, based on chain based routing protocols an improvement is
achieved on IEEPB protocol by create multi chain and sink node.
The object of this study is proposing a new routing technique and divide the network area in to
deferent clusters, these clusters has unequal number of nodes, all nodes in each cluster collect its data
and forwarding it’s to CH. All Cluster Heads communicate with the unlimited power node called sink
node.
The subject of this study is the routing method that increase the transmission time as well as reduce
the energy consumption in all network devices. Section 3 represent the system model briefly.
The purpose of the work is to design a new topology of sensor network with IoT monitoring system
based on horizontal clusters of chain routing protocol, the WSN area has been divided in to 5 regions
and the proposed protocol is H-IEEPB.

Emerging Technology Trends on the Smart Industry and the Internet of Things, January 19, 2022, Kyiv, Ukraine
EMAIL: hazem.n.it@mail.com (H. N. Abdulrazzak); aya.ayad.it@gmail.com (A. A. Hussein); kuczanski@gmail.com (A. Kuchansky)
ORCID: 0000-0003-1827-431X (H. N. Abdulrazzak); 0000-0002-7126-5925 (A. A. Hussein); 0000-0003-1277-8031 (A. Kuchansky)
©️ 2022 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)

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2. Problem Statement
Clustering in WSNs is an excellent way to reduce the energy consumption of the sensor nodes, as it
depends on the batteries on its work, so a mechanism must be suggested to reduce its energy
consumption. In fact, the clustering technique leads to Collect and combine data to reduce the number
of messages sent and reduce the transmission.

3. Review of the Literature

In [3] the author presents a survey on different hierarchical routing protocols and showing the
taxonomy criteria of hierarchical-based routing protocols in wireless sensor networks. The most
information that is related to the types of routing protocols based on the network architecture, clustering
attributes, protocol operation, path establishment, and others. The author focus on the chain based
routing protocol with the importance of choosing the right cluster properties and sensor capabilities
with taking more than one protocol as an example by giving a quick review for each protocol.
In [4] the author suggests a protocol that offering a chain into the clustering concept. This suggestion
generates a new chain based routing protocol PEGASIS. PEGASIS protocol is chain based routing
protocol that constructs chains between the neighboring nodes to collect the data and send it to the next
node in an accumulative way to aggregate the data in a cluster head or can be called leader node and
forward it to sink node.
The authors in [5-9] propose an improved protocol over PEGASIS protocol to Multi-Chain
PEGASIS protocol by using the concepts of relative distance and this led to allows sink node to generate
the location information table and construct the multi-chain topology without the need for GPS.
Multipath routing for the directional diffusion routing protocol from the source to the destination node
with a certain probability of selecting one path among all possible paths was proposed in [10-14].
Authors in [15-16] advocated load balancing energy schemes for wireless sensor networks and
energy harvesting for the overall network. They utilized Ant Colony Optimization for multiple path
finding and claimed satisfying results. The study used an electromagnetic antenna-based approach for
energy harvesting to gain high performance of IoT and WSNs. Numerous studies have been carried out
on minimum energy consumption.

4. System Model

The proposed system in this paper is built in the wireless radio model and clustering algorithm was
proposed.
For WSN connected to IoT system the energy consumption in transmission side and receiving side
can be calculated as in (1) and (2) based on Figure 1.

𝜅(𝑒𝑒𝑙𝑒𝑐. + 𝑒𝑎𝑚𝑝. ∗ 𝑑 2 ), 𝑑 < 𝑑0
𝐸𝑇𝑋 = { (1)
𝜅(𝑒𝑒𝑙𝑒𝑐. + 𝑒𝑎𝑚𝑝. ∗ 𝑑 4 ), 𝑑 ≥ 𝑑0
Where ETX is the energy consumption in transmission side, κ is the data size that can be send through
wireless channel, d0 is the threshold distance, d is the distance between two sensor nodes , eamp. and
eelec. is he required energy for transmitter amplifier in free space, (equal to 100 pJ/bit/m2) and the
energy consumed by the radio to run the transmitter or receiver circuitry, (equal to 50nJ/bit)
respectively.

𝐸𝑅𝑋 = 𝜅 ∗ 𝑒𝑒𝑙𝑒𝑐. (2)
ERX is the energy consumption in transmission side.

The distance between two nodes showing in (3)
𝑑 = √(𝑥2 − 𝑥1 )2 + (𝑦2 − 𝑦1 )2 (3)

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Where x2 is the x dimension of node no.2, x1 is the x dimension of node no.1, y2 is the y dimension
of node no.2 and y1 is the y dimension of node no.1.

Figure 1: First-order radio model

To calculate the energy consumption in any node, the system selects the mode and compute it based
on (1) and (2), the consumption mode presented in (4) and (5):

𝐸 , 𝑇𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛 𝑚𝑜𝑑𝑒
𝐸𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 = { 𝑇𝑋 (4)
𝐸𝑅𝑋 , 𝑅𝑒𝑐𝑒𝑖𝑣𝑖𝑛𝑔 𝑚𝑜𝑑𝑒

𝐸 𝑇𝑜𝑡𝑎𝑙/𝑐𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 = ∑𝑛𝑖=1 𝐸𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 (𝑖) (5)

The residual energy Ei can be calculated as in (6) and (7) for total residual energy:

𝐸0 − 𝐸𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 (1) , 𝑖 = 1 (𝐹𝑖𝑟𝑠𝑡 𝑅𝑜𝑢𝑛𝑑)
𝐸𝑖 = { (6)
𝐸(𝑖−1) − 𝐸𝐶𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 (𝑖) , 𝑂𝑡ℎ𝑒𝑟 𝑅𝑜𝑢𝑛𝑑𝑠

Where E(i−1) is the residual energy for the previous sensor node, EConsumption (i) is the energy
consumption of sensor node and EConsumption (1) is the energy consumption of the sensor node in first
round

𝐸𝑇𝑜𝑡𝑎𝑙 = ∑𝑛𝑖=1 𝐸𝑖 (7)

Normalized average energy NE can be calculated as in (8):
𝐸𝑇𝑜𝑡𝑎𝑙
𝑁𝐸 = (8)
𝑛 ∗ 𝐸0
Where E0 is the initial energy

WSN area in this system is 200*200 so the clustering algorithm divide this area horizontally for 5
clusters. The cluster size is 200*40 and the propose algorithm as shown:

 Clustering Algorithm

M1, M2, M3, M4, M5 =0 // No. of nodes in each cluster
Load nodes locations // node.x & node.y

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for i=1to n
if node(i).y <= 40
M1=M1+1;
node(M1).x=node(i).x;
node(M1).y=node(i).y;
end if
if node(i).y > 40 && node(i).y <=80
M2=M2+1;
node(M2).x=node(i).x;
node(M2).y=node(i).y;
end if
if node(i).y > 80 && node(i).y <=120
M3=M3+1;
node(M3).x=node(i).x;
node(M3).y=node(i).y;
end if
if node(i).y > 120 && node(i).y <=160
M4=M4+1;
node(M4).x=node(i).x;
node(M4).y=node(i).y;
end if
if node(i).y > 160 && node(i).y <=200
M5=M5+1;
node(M5).x=node(i).x;
node(M5).y=node(i).y;
end if
end for
end

5. Experiments
The proposed work has been simulated using Matlab simulator to examine and investigate enhanced
IEEPB using proposed algorithm and the simulation parameters as shown in Table 1

Table 1
Simulation parameters
Parameters Values
E0 0.5 J
No. of nodes 100
No. of rounds 4000
Protocols PEGASIS, IEEPB, H-IEEPB
WSN area 200 m * 200m

The network topology of proposed work as shown in Figure 2.

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Figure 2: Proposed Clustering WSN

As in Figure 2 each cluster communicates with its CH, sink node connect with all cluster heads.
Clustering algorithm classify the sensor nodes in to 5 clusters and select the CH locations. Table 2 and
Table 3 are showing the number of nodes in each cluster and the cluster heads locations respectively:

Table 2
Number of Nodes / Cluster
Cluster No. No. of Nodes
1 25
2 20
3 17
4 18
5 20

Table 3
Cluster Heads Locations
Cluster No. Location
1 (0,20)
2 (0,60)
3 (0,100)
4 (0,140)
5 (0,180)

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6. Results
The simulation time of this experiment is 100 ms, as shown in Figure 2 the network area has been
divided in to 5 regions, the shortest path to connect all nodes was created.

 Alive Nodes comparison

Figure 3: Alive Nodes comparison

As shown the proposed protocol in this work H-IEEPB has batter result in Alive nodes, so in Figure
3 the proposed protocol nodes still working till round No.3999 and the first node die at round No.1395
compared with others is very good to get more time for network to work in full nodes, as shown in
Figure 4 and the network lifetime percentages in Table 4 respectively.

 Network lifetime for all protocols

Figure 4: Network Lifetime

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Table 4
Network Lifetime Percentage
Percentage PEGASIS IEEPB H-IEEPB
1% 78 151 1395
20% 762 1275 1684
40% 1198 1805 1777
60% 1373 1840 1841
80% 1467 1858 1867
100% 1808 1865 3999

 Residual Energy comparison

Figure 5: Residual Energy comparison

The residual energy comparison in Figure 5 explained that the residual energy of H-IEEPB is more than
10% energy saving compared with IEEPB protocol and more than 25% saving energy compared with
PEGASIS protocol, for 0.5 J initial energy and 100 nodes distribution the total energy is 50 J and for
example the total energy of H-IEEPB after 1000 round is 21.8927 J, while the total energy of IEEPB
and PEGASIS are 19.8664 J and 11.5491 J respectively.

 Normalized Average Energy comparison

The normalized average energy starting from 1 to 0, the proposed protocol success and save more
energy than others as shown in Figure 6.

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Figure 6: Normalized Average Energy comparison

 Energy Consumption comparison

The stability of energy consumption per node during the simulation rounds is shown in Figure 7, our
protocol has an average consumption 0.24 *10-3 J for all nodes till round no. 1867, at this round the
network lost 80% of its nodes as mentioned in network lifetime table. When the network work in the
last 20% the average energy consumption is increased for 200 rounds extra then decreased till end of
the simulation but the other protocols operations are stopped in rounds 1808 and 1865 for PEGASIS
and IEEPB respectively.

Figure 7: Energy Consumption comparison

7. Conclusions

In WSN routing protocols, the energy saving and reducing the energy consumption of nodes is a
main goal. IoT monitoring system in different applications need to work with WSN together. In this
paper the proposed routing protocol success in saving energy compared with others. The scientific
novelty is by dividing the area in to multi slides as a clusters, and select multi cluster heads to
communicate with sink node and IoT server. The practical significance is to make a flexibility in
distribute sensor nodes and create multi path at the same time so we can save energy and time also.

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Prospects for further research are to increase the number of clusters according to the area size and can
use different intelligent systems to improve this work.

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