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http://www.iaeme.com/IJMET/index.asp 325 [email protected]
International Journal of Mechanical Engineering and Technology (IJMET) Volume 8, Issue 9, September 2017, pp. 325–336, Article ID: IJMET_08_09_035
Available online at http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=8&IType=9
ISSN Print: 0976-6340 and ISSN Online: 0976-6359
© IAEME Publication Scopus Indexed
PERFORMANCE EVALUATION OF NSGA-III
BASED ENERGY EFFICIENT PROTOCOL FOR
WIRELESS SENSOR NETWORKS
Er. Harjot Kaur
Research scholar, Shri Venkateshwara, University, Gajraula, India
Dr. Gaurav Tejpal
Professor, Shri Venkateshwara, University, Gajraula, India
Dr. Sonal Sharma
Assistant Professor, Uttaranchal University, Dehradun, India
ABSTRACT:
Energy efficiency has recently turned out to be primary issue in wireless sensor
networks. Sensor networks are battery powered, therefore become dead after a certain
period of time. Thus, improving the data dissipation in energy efficient way becomes
more challenging problem in order to improve the lifetime for sensor devices. The
clustering and tree based data aggregation for sensor networks can enhance the
network lifetime of wireless sensor networks. Non-dominated Sorting Genetic
Algorithm (NSGA) -III based energy efficient clustering and tree based routing
protocol is proposed. Initially, clusters are formed on the basis of remaining energy,
then, NSGA-III based data aggregation will come in action to improve the inter-
cluster data aggregation further. Extensive analysis demonstrates that proposed
protocol considerably enhances network lifetime over other techniques.
Keywords: Wireless Sensor Networks, Ant Colony Optimization, Energy Efficient,
Particle swarm optimization.
Cite this Article: Er. Harjot Kaur, Dr. Gaurav Tejpal and Dr. Sonal Sharma,
Performance Evaluation of NSGA-III Based Energy Efficient Protocol for Wireless
Sensor Networks, International Journal of Mechanical Engineering and Technology
8(9), 2017, pp. 325–336.
http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=8&IType=9
1. INTRODUCTION
Modern progresses in digital electronics [2], micro-electro-mechanical system, and wireless
communications have empowered the growth of small-sized sensor nodes, which have low-
power, low-cost and are multifunctional. These sensor nodes have capability to sense and
communicate. Wireless sensor networks [1] are made up of a large number of sensor nodes,
Er. Harjot Kaur, Dr. Gaurav Tejpal and Dr. Sonal Sharma
http://www.iaeme.com/IJMET/index.asp 326 [email protected]
densely deployed either inside the region or very near to it. Working of WSN is shown in
Figure 1. Energy conservation is the major matter in wireless sensor network. Limited power
nodes which cannot be replaced can be carried by sensor nodes. In WSN, sensor nodes sense
data and transmit it to the base station. Since data from neighbouring sensor nodes [3] may be
redundant, it becomes complex for base station to process large amount of data. Moreover,
sensor nodes have their own energy. Due to redundant transmissions and loss of energy,
lifetime of sensor nodes can decrease. To increase lifetime, data aggregation [4] is performed.
Data aggregation means to collect and aggregate data [5] from multiple sensors to eliminate
redundancy and conserve energy.
Figure 1 Working of Wireless sensor network
Wireless sensor networks have a wide range of applications in areas [2] such as security,
military and health. For instance, a doctor can monitor the physiological data about a patient
remotely. The current health condition of the patient is better understood by the doctor.
Foreign chemical agents can be detected in the air and water with the help of sensor network.
Pollutant’s type, amount and location can be identified.
Lin et al. (2015) [4] utilized evolutionary game theory to select CH to reduce the hot-spot
problem. In this method, size of cluster is optimized through optimal cluster size algorithm.
The appropriate selection of CHs reduces the energy consumption and enhances the life of
network. Gong et al. (2015) [5] designed a routing protocol ETARP (i.e., Energy Efficient
Trust-Aware Routing Protocol for Wireless Sensor Networks) to reduce the energy
consumption and increase the security during communication among nodes in WSNs. The
selection of route between sensor nodes is based on utility theory. Shi et al. (2015) [6]
addressed the issue of mobile sinks like route maintenance in WSNs by introducing dynamic
layered routing protocol. The distribution frequencies and scopes of routing updates are
minimized using the combination of dynamic anchor selection and dynamic layered Voronoi
scoping.
Performance Evaluation of NSGA-III Based Energy Efficient Protocol for Wireless Sensor Networks
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Leu et al. (2015) [7] utilized Regional Energy Aware Clustering with Isolated Nodes
(REAC-IN) algorithm to select CHs based on weight. Weight is calculated considering each
sensor’s residual energy and regional average energy of every sensor in all clusters. Shen et
al. (2015) [8] solved the problem of delay in message transmission in underwater WSNs using
Location-Aware Routing Protocol (LARP). In this method, position knowledge of sensor
nodes is used to facilitate message transmission. Bouyer et al. (2015) [9] used fuzzy C-means
(FCM) algorithm to create optimum number of CHs in LEACH algorithm to reduce the
energy and prolong the network life-time. Cai et al. (2015) [10] proposed Bee-Sensor-C
routing protocol inspired from Bee Sensor (i.e. bee-inspired routing protocol) that can form
clusters dynamically and transmit the data in parallel fashion.
Shankar et al. (2016) [11] used hybrid Particle Swarm Optimization (PSO) and Harmony
Search Algorithm (HSA) to select CH efficiently utilizing minimum energy. Zahedi et al.
(2016) [12] presented the problem of uneven distribution of CHs, unbalanced clustering, and
their scope to limited applications of WSNs. They used fuzzy c-means clustering algorithm to
create balanced clusters and Mamdani fuzzy inference system to select suitable CHs. Fuzzy
rules are optimized through swarm intelligence algorithm based on firefly algorithm.
Sabet and Naji (2016) [13] implemented the multi-level route-aware clustering (MLRC)
technique to save energy in decentralized clustering protocols. The main advchromosomeage
of this protocol is that it creates a cluster and routing tree, simultaneously, to reduce an
unnecessary generation of routing control packets.
Naranjo et al. (2017) [14] presented Prolong- Stable Election Protocol (P-SEP) to elect the
CHs among heterogeneous nodes in fog-supported WSNs to increase the life of network.
Xenakis et al. (2017) [15] utilized simulated annealing technique to control the topology by
maximizing the network coverage and lifetime of WSNs as objective functions. Nayak and
Vathasavai (2017) [16] utilized type-2 fuzzy logic in WSNs to make a decision for CH
efficiency. Ouchitachen et al. (2017) [17] implemented IMOWCA (Improved Multi-Objective
Weighted Clustering Algorithm) for the selection of CHs. Residual energy is used to select
the best performing node for further communication with BS. Base Station Genetic Algorithm
is utilized to balance the energy among different clusters.
Elshrkawey et al. (2017) [18] addressed the issues of LEACH protocol like improper
selection of CH, formation of unbalanced clusters, and continuous transmission of updating
data. They used threshold value to elect CHs, sensor nodes send their updated data in their
allotted time, and modified TDMA scheduling is utilized to break steady state phase. Rani et
al. (2017) [19] used E-CBCCP protocol to cache the data at CH and relay node to evade the
communication of same data packets. Control packets are used to inform all sensor nodes that
data packets are same and do not transmit the data packets. Laouid et al. (2017) [20] designed
an approach to select the best route based on hop count and residual energy of each sensor
node to maximize the life of network.
Ez-zazi et al. (2017) [21] utilized adaptive coding scheme considering channel state and
distance between inter nodes to scrutinize the trade-off between energy efficiency and
reliability. Huang et al. (2017) [22] used public transportation vehicles as mobile sinks to
gather data. To balance the energy consumption, an energy-aware routing and energy-aware
unequal clustering algorithms are used. Zhao et al. (2017) [23] utilized layer-based diffusion
particle swarm optimization approach to optimize the position of sink and sensor to sink route
to maximize the lifetime of WSNs.
In this paper, we propose improved method for General Self-Organized Tree based
Energy Balance routing protocol (tree-based). In present tree-based protocol routing tree is
manufactured where tree centered routing is performed to transmit knowledge to the bottom
section however in that if the parent node dies the topography must be repair again that'll
Er. Harjot Kaur, Dr. Gaurav Tejpal and Dr. Sonal Sharma
http://www.iaeme.com/IJMET/index.asp 328 [email protected]
consume a lot of power and there might be loss of knowledge also. To prevail around the
problem of sign delay and knowledge reduction in the system because of the nodes
disappointment in the root to sink, cluster based aggregation process can be utilized. In big
system, well- organized sign of knowledge to the sink requires obtaining the maximum route
according to how many trips; therefore, knowledge can be aggregated at group head which is
to be transmitted to the bottom station. The clustering strategy may minimize knowledge
redundancy and reduce the congestive routing traffic in knowledge transmission. Following
the clustering tree centered routing at the cluster-heads it is required to obtain the shortest
route between the source and the sink, but the smallest route issue is NP-Hard in nature [22].
1.1. Contribution:
Following are our main contributions in this research paper:
1. First of all, we have evaluated the performance of some well-known existing energy
efficient protocols for WSNs.
2. Based upon the comparative analysis we have found that effective inter-cluster data
aggregation using met heuristic techniques can improve the network lifetime further.
3. We have designed and implemented well-known NSGA-III based clustering tree-
based protocols to enhance the results further.
4. Extensive analysis has also been done to evaluate the effectiveness of the proposed
technique.
Rest of the paper is organized as follows: In Section 2, network energy model is described
for WSNs. Section 3, describes the proposed technique with suitable mathematical
formulation. Experimental Set-up and results are demonstrated in Section 4. Concluding
remarks are demonstrated in Section 5.
2. NETWORK ENERGY MODEL
In this research work, we have randomly deployed WSN with “N” sensor nodes in M*N
network field. All nodes even including the sink are stationary in nature. Each node has its
own unique identification number. Each node monitors the given environment and
communicates data with sink. Whenever communication is done given node have to spent
some energy based upon the distance (D) with sink. All the communication links are
symmetric in nature.
2.1. Energy Model
Whenever a node sends or receives it has to spend some energy based upon two channel
propagation models called free space (D� power loss) for the purpose of one-hopordirect
transmission and the multipath fading channel (D� power loss) for packet transmission via
multihop. Therefore, energy consumption model can be mathematically defined as follows:
ET�� L, D�LE ����� + Lε��� �D�, D < D�,E ����� + Lε����D�D ≥ D�, (1)
Here?? Is the size of data packet, �� � is free space energy loss, ??mp is multipath energy
loss. D�, Is a threshold distance which determine which energy model will be used. It can be
calculated as follows:
D� = ! "#$% &"'��� (2)
Performance Evaluation of NSGA-III Based Energy Efficient Protocol for Wireless Sensor Networks
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2.2 CH formation
In this section level-based clustering will be discussed. CHs are formed using energy aware
threshold function. Which means nodes who has more energy will have more probability to
become CHs.
Each node generates random value and try to become CH. If random value is less than
evaluated Threshold (T(i)), then it will become CH, become member node otherwise. T(i) can
be mathematically evaluated as follows
( )� = * +,-./,+,-01.3+4 5 .,+,- 67
∗ 9) 1�
9:;< 1� For all nodes if E = r�>0 (3)
Here r represents the current round in WSNs network lifetime, E = r� is the current energy
of given node i. E@AB Represents average remaining energy which is evaluated using eqn. (4)
E@AB = ∑DE ��F for every node i (4)
N is the number of total nodes.
3. NSGA-III BASED TREE-BASED TECHNIQUE
In this section, we propose an NSGA-III-tree-based based routing to develop shortest path
among available CHs and sink. NSGA-III is a well-known metaheuristic technique which can
find optimal path between given set of nodes with sink as destination. The actual design in the
proposed NSGA-III may be identified inside Algo 1. Initial, a collection of reference point is
created, which is denoted G = HIJ, I�,…ILM.For an N objective problem, IO PQ 1,2. . . T is an N
dimensional vector represented by IO, T = IOJ, IO�,….IOU�VW , where IO,L ≥ X, T = 1,2. . , N and ∑ IO,LULYJ = 1. Next, the original population using N users is definitely arbitrarily produced.
Intended for an ideal
point Q∗ it is somtimes so time − consuming in order to determine exact, Qh∗ , therefore it
is really estimated with the bare minimum cost discovered to date to get intent objective ih̅, and is kept up to date in the search. Actions 5-21 are generally iterated before the
broadcasting is satisfied. In Step 6, an offspring population klW is created by utilizing the same
genetic operators with those in NSGA-III. Along with such as, klW it is integrated with e
present population mlW as well as form a different population. nlW .Thereafter,nlW . Is normalized
applying an ideal point Q∗. Following normalization, the particular clustering user is used to
split the particular users inside nlW into a set of T clusters oOpJ, oOp� where the cluster oOpP is
definitely depicted by the reference point IL. Then, a non-dominated organizing dependant on
importance (not Pareto-dominance) works to help classify nlW in unique q non-domination
levels iJ, i�, rNs tu uN�. Dominance, which is a key principle in q NSGA-III, will be
presented later. The moment non-dominated-sorting have been accomplished, right now their
maiming steps complete the population slots inmvwlWxJ making use of 1 level at one time,
commencing fromih. Compared with both equally NSGA-II and NSGA-III, we just at
random select answers in the last recognised level ih in q NSGA-III, due to the fact
dominance offers stressed out both equally unity plus diversity. Certainly, several tactics to
enhance the diversity could also be used likewise in Step 18.
Er. Harjot Kaur, Dr. Gaurav Tejpal and Dr. Sonal Sharma
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Algorithm 1 Proposed NSGA-III based path selection
1: G ← z{N{|r}{ ~{i{|{No{ mu�N}t � 2: m GN�}�rP��{ mu��Pr}�uN �
3: � ← GN�}�rP��{ Gs{rP mu�N} � 4: }́ ← q 5: while the termination criterion is not met do
6: klW Make Offspring Population (mvw }́)
7: ��sr}{ Gs{rP mu�N} klW � 8: nlW ← mvwlW ∪ klW : Normalize ( nl,W Q∗)
10: oOp ← Clustering ( nlW Q∗�
11: iJ, i� … } ← q Non-dominated-sort. (nlW , oOp)
12: mvw }́ + 1) ;
13: � ← 1
14: while ǀmvw }́ + 1ǀ + ǀ i� ≤ � do
15: mvw }́ + 1 ← mvw }́ + 1 ∪ i�
16 � ← � + 1
17 end while
18: Random Sort i��
19: mvw }́ + 1 ← mvw }́ + 1i�[1� − ǀmvw }́ + 1�] 20 }́ ← }́ + 1
21: end while
Although, NSGA-III borrows a number of tips associated with NSGA-III such as flexible
normalization as well as association operations, that they are different in some aspects. 1st, we
all embrace a less complicated procedure that can create numerous guide factors, additional
ensuing in a human judgements populace size. Second, a normalization plus association
(clustering) procedures take place in several occasions this show up in Formula 1, along with
the execution data is also a small modified. Over most, we all exchange the first Pareto
prominence included in NSGAIII while using the recommended dominance, that could nicely
steadiness a convergence plus range around many-objective optimization. In the examples
below subsections, the important procedures associated with NSGA-III should be referred to
in detail, along with the motivation in it will also be pointed out.
Performance Evaluation of NSGA-III Based Energy Efficient Protocol for Wireless Sensor Networks
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4. EXPERIMENTAL SET-UP AND RESULTS
The MATLAB simulation tool is used for simulation purpose. It evaluates the performance of
the proposed technique with existing technique i.e. tree-based on the following metrics i.e.
stability period, network lifetime, residual energy (average remaining energy), and throughput
by taking 100 sensor nodes. Other parameters for simulation are adapted from the tree-based.
The sensors distributed arbitrarily in a 100×100 area with base station at (100m, 100m).
Table 1 shows the various simulation parameters for comparative analysis.
Table 1 WSNs Set-up
Parameter Value
Area(x,y) 150,150
Base station(x,y) 50,150
Nodes(n) 200
Probability(p) 0.1
Initial Energy 0.5
transmiter_energy 50 ∗ 10/�
receiver_energy 50 ∗ 10/�
Free space(amplifier) 10 ∗ 10/J�
Multipath(amplifier) 0.0013 ∗ 10/J�
Effective Data aggregation 5 ∗ 10/�
Maximum lifetime 3000
Data packet Size 4000
Table 2 First Node Dead
Initial Energy Existing Proposed
0.11 154 289
0.12 166 321
0.13 179 343
0.14 193 368
0.15 207 383
0.16 220 439
0.17 235 465
0.18 249 483
0.19 266 495
0.20 275 545
Table 2 has shown the comparison among Existing protocol and proposed with respect
first node dead time. It has been clearly shown that the numbers of rounds for first node dead
in case of the proposed are quite more than the existing protocol.
Table 3 Last Node Dead
Initial Energy Existing Proposed
0.11 343 549
0.12 375 598
0.13 407 653
0.14 439 699
Er. Harjot Kaur, Dr. Gaurav Tejpal and Dr. Sonal Sharma
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0.15 467 753
0.16 497 798
0.17 526 848
0.18 559 904
0.19 592 948
0.20 622 999
Table 3 has shown the comparison among existing protocol and proposed protocol with
respect last node dead time. It has been clearly shown that the numbers of rounds for last node
dead in case of the proposed are quite more than the existing protocol.
Table 4 Residual Energy
Initial Energy Existing protocol Proposed protocol
0.11 0.0185 0.048224
0.12 0.0216 0.052484
0.13 0.0260 0.056582
0.14 0.0302 0.061433
0.15 0.0351 0.065578
0.16 0.0397 0.070420
0.17 0.0444 0.074733
0.18 0.0510 0.079170
0.19 0.0570 0.082786
0.20 0.0629 0.087936
Table 4 has shown the comparison among existing protocol and proposed with respect
residual energy. It has been clearly shown that the residual energy in case of the proposed is
quite more than the existing protocol.
Table 5 Packets Sent To Bs
Initial Energy Existing protocol Proposed protocol
0.11 28.2190 130.9470
0.12 31.7870 142.1930
0.13 34.4050 154.4180
0.14 33.8050 166.6400
0.15 36.5740 178.7760
0.16 38.5410 190.7800
0.17 42.0450 202.4600
0.18 42.3800 215.9240
0.19 47.1580 224.1180
0.20 47.7430 238.4770
Table 5 has shown the comparison among existing protocol and proposed with respect
packets sent to BS. It has been clearly shown that the number packets sent to BS in case of the
proposed are quite more than the existing protocol.
Figure 2 has shown the comparison among existing protocol and proposed with respect
first node dead time. It has been clearly shown that the numbers of rounds for first node dead
in case of the proposed are quite more than the existing protocol. It has clearly verified that
the proposed algorithm is comparatively better than the existing techniques
Performance Evaluation of NSGA-III Based Energy Efficient Protocol for Wireless Sensor Networks
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Figure 2 First Node Dead
Figure 3 has shown the comparison among existing protocol and proposed with respect
last node dead time. It has been clearly shown that the numbers of rounds for last node dead in
case of the proposed are quite more than the existing protocol. It has clearly verified that the
proposed algorithm is comparatively better than the available techniques.
Figure 3 Last node dead time
Figure 4 has shown the comparison among existing protocol and proposed with respect to
number of packets transferred between the base stations to cluster head as well as between
cluster head to member nodes in each round. It has been clearly shown that the packets with
in case of the proposed are quite more than the existing protocol. It has clearly confirmed that
the proposed algorithm is comparatively better than the available techniques.
Er. Harjot Kaur, Dr. Gaurav Tejpal and Dr. Sonal Sharma
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Figure 4 Packets Sent To BS
Figure 5 has shown the comparison among existing protocol and proposed with respect to
average remaining energy i.e. residual energy. It has been clearly shown that the residual
energy in case of the proposed are quite more than the existing protocol. It has clearly
confirmed that the proposed algorithm is comparatively better than the existing techniques.
Figure 5 Average Remaining Energy (Residual Energy)
5 CONCLUSIONS
This paper proposes a hybrid protocol which utilizes clustering; NSGA-III based clustering
protocol for WSNs. It decomposes the sensor network into numerous segments thus called
clusters and cluster heads are chosen in every cluster. Then, tree based data aggregation come
in action and collects sensing information directly from cluster heads by utilizing short
distance communications. The NSGA-III optimization evaluates the shortest path among
sinks and cluster heads. The use of compressive sensing reduces the packet size which is
Performance Evaluation of NSGA-III Based Energy Efficient Protocol for Wireless Sensor Networks
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going to be transmitted in the sensor network. The MATLAB simulation tool is used for
simulation purpose. It evaluates the performance of the proposed technique with existing
technique i.e. tree-based on the following metrics i.e. stability period, network lifetime,
residual energy (average remaining energy), and throughput. Extensive analysis indicate that
the proposed technique outperforms others.
There is no conflict of interest regarding the publication of this paper.
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