PERFORMANCE EVALUATION OF NSGA-III BASED ENERGY EFFICIENT PROTOCOL FOR WIRELESS SENSOR … ·...

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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


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


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



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, ε�� 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)



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.



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.



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



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



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.



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



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.

REFERENCES

[1] Arora, Vishal Kumar, Vishal Sharma, and Monika Sachdeva. A survey on LEACH and

other’s routing protocols in wireless sensor network. Optik-International Journal for Light

and Electron Optics 127, no. 16 (2016): 6590-6600.

[2] Azharuddin, Md, Pratyay Kuila, and Praschromosomea K. Jana. Energy efficient fault

tolerchromosome clustering and routing algorithms for wireless sensor networks.

Computers & Electrical Engineering 41 (2015): 177-190.

[3] García Villalba, Luis Javier, Ana Lucila Sandoval Orozco, Alicia Triviño Cabrera, and

Claudia Jacy Barenco Abbas. Routing protocols in wireless sensor networks. Sensors 9,

no. 11 (2009): 8399-8421.

[4] Lin, Deyu, Quan Wang, Deqin Lin, and Yong Deng. An energy-efficient clustering

routing protocol based on evolutionary game theory in wireless sensor networks.

International Journal of Distributed Sensor Networks 11, no. 11 (2015): 409503.

[5] Gong, Pu, Thomas M. Chen, and Quan Xu. ETARP: an energy efficient trust-aware

routing protocol for wireless sensor networks. Journal of Sensors 2015 (2015).

[6] Shi, Lei, Zheng Yao, Baoxian Zhang, Cheng Li, and Jian Ma. An efficient distributed

routing protocol for wireless sensor networks with mobile sinks. International Journal of

Communication Systems 28, no. 11 (2015): 1789-1804.

[7] Leu, Jenq-Shiou, Tung-Hung Chiang, Min-Chieh Yu, and Kuan-Wu Su. Energy efficient

clustering scheme for prolonging the lifetime of wireless sensor network with isolated

nodes. IEEE communications letters 19, no. 2 (2015): 259-262.

[8] Shen, Jian, Hao-Wen Tan, Jin Wang, Jin-Wei Wang, and Sung-Young Lee. A novel

routing protocol providing good transmission reliability in underwater sensor networks.

Sensors 16, no. 1 (2015): 171-178.

[9] Bouyer, Asgarali, Abdolreza Hatamlou, and Mohammad Masdari. A new approach for

decreasing energy in wireless sensor networks with hybrid LEACH protocol and fuzzy C-

means algorithm. International Journal of Communication Networks and Distributed

Systems 14, no. 4 (2015): 400-412

[10] Cai, Xuelian, Yulong Duan, Ying He, Jin Yang, and Changle Li. Bee-sensor-C: an

energy-efficient and scalable multipath routing protocol for wireless sensor networks.

International Journal of Distributed Sensor Networks 11, no. 3 (2015): 976127.

[11] Shankar, T., S. Shanmugavel, and A. Rajesh. Hybrid HSA and PSO algorithm for energy

efficient cluster head selection in wireless sensor networks. Swarm and Evolutionary

Computation 30 (2016): 1-10.

[12] Zahedi, Zeynab Molay, Reza Akbari, Mohammad Shokouhifar, Farshad Safaei, and Ali

Jalali. Swarm intelligence based fuzzy routing protocol for clustered wireless sensor

networks. Expert Systems with Applications 55 (2016): 313-328.

[13] Sabet, Maryam, and Hamidreza Naji. An energy efficient multi-level route-aware

clustering algorithm for wireless sensor networks: A self-organized approach. Computers

& Electrical Engineering 56 (2016): 399-417.

[14] Naranjo, Paola G. Vinueza, Mohammad Shojafar, Habib Mostafaei, Zahra Pooranian, and

Enzo Baccarelli. P-SEP: a prolong stable election routing algorithm for energy-limited



heterogeneous fog-supported wireless sensor networks. The Journal of Supercomputing

73, no. 2 (2017): 733-755.

[15] A. Xenakis, F. Foukalas, G. Stamoulis, I. Katsavounidis. Topology control with coverage

and lifetime optimization of wireless sensor networks with unequal energy distribution.

Computers & Electrical Engineering, June 2017.

[16] Nayak, Padmalaya, and Bhavani Vathasavai. Energy Efficient Clustering Algorithm for

Multi-Hop Wireless Sensor Network Using Type-2 Fuzzy Logic. IEEE Sensors Journal

17, no. 14 (2017): 4492-4499.

[17] Hicham Ouchitachen, Abdellatif Hair, Najlae Idrissi, Improved multi-objective weighted

clustering algorithm in Wireless Sensor Network, Egyptian Informatics Journal, Volume

18, Issue 1, March 2017, Pages 45-54,

[18] Mohamed Elshrkawey, Samiha M. Elsherif, M. Elsayed Wahed, An Enhancement

Approach for Reducing the Energy Consumption in Wireless Sensor Networks, Journal of

King Saud University - Computer and Information Sciences, Available online 7 April

2017, ISSN 1319-1578.

[19] Shalli Rani, Syed Hassan Ahmed, Jyoteesh Malhotra, Rajneesh Talwar, Energy efficient

chain based routing protocol for underwater wireless sensor networks, Journal of Network

and Computer Applications, Volume 92, 15 August 2017, Pages 42-50, ISSN 1084-8045.

[20] Abdelkader Laouid, Abdelnasser Dahmani, Ahcène Bounceur, Reinhardt Euler, Farid

Lalem, Abdelkamel Tari, A distributed multi-path routing algorithm to balance energy

consumption in wireless sensor networks, Ad Hoc Networks, Volume 64, September

2017, Pages 53-64.

[21] Imad Ez-zazi, Mounir Arioua, Ahmed El Oualkadi, Pascal Lorenz, On the performance of

adaptive coding schemes for energy efficient and reliable clustered wireless sensor

networks, Ad Hoc Networks, Volume 64, September 2017, Pages 99-111, ISSN 1570-

8705.

[22] Han, Zhao, Jie Wu, Jie Zhang, Liefeng Liu, and Kaiyun Tian. A General Self-Organized

Tree-Based Energy-Balance Routing Protocol for Wireless Sensor Network. pp. 1-2,

2014.

[23] Bosire Kombo Obiero, Medium Access Control Protocol For Wireless Body Area

Networks Efficient and Reliable Design. International Journal of Computer Engineering &

Technology, 8(2), 2017, pp. 30–37.

[24] Ankit Thakkar, Ketan Kotecha, A New Enhanced Leach Routing Protocol for Wireless

Sensor Network Based on Gaussian Distribution of Random Numbers, International

Journal of Advanced Research in Engineering and Technology (IJARET), Volume 4,

Issue 7, November - December 2013, pp. 207-215

[25] Basavaraj S. Mathapati, Siddarama. R. Patil and V. D. Mytri, Power Control With Energy

Efficient and Reliable Routing Mac Protocol for Wireless Sensor Networks, International

Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 1, January-

June (2012), pp. 223-231

[26] Vijay Ukani, Tanish Zaveri, Sameer Kapadia, Vaqmac: Video Aware Qos Mac Protocol

For Wireless Video Sensor Networks, International Journal of Electronics and

Communication Engineering & Technology (IJECET), Volume 5, Issue 4, April (2014),

pp. 103-115

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