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Recent Advances in Computer Science and Communications, 2020, 12, 1-00 1

RESEARCH ARTICLE

2213-2759/20 $65.00+.00 © 2020 Bentham Science Publishers

Cost-Effective Cluster-based Energy Efficient Routing for Green Wireless Sensor Network

Sandeep Verma1,*, Neetu Sood1 and Ajay Kumar Sharma2

1Electronics and Communication Engineering, Dr BR Ambedkar National Institute of Technology, Jalandhar, India; 2I. K.G. Punjab Technical University, Kapurthala, India

Abstract: Background: The green Information and Communications Technologies (ICTs) have brought a revolution in uplifting the technology efficiently to facilitate the human sector in the best possible way. Green Wireless Sensor Network (WSN) tactically focuses on improving the survival period of deployed nodes (as they have a limited battery) in any target area.

Objectives: To address this concern, the main objective is to improve the routing in WSN. The cluster-based routing helps in acquiring the same with the appropriate Cluster Head (CH) selection. The use of energy heterogeneous nodes that normally comprise of high energy nodes, puts a lot of financial burden on the users as they incur a huge cost, thus becoming a bottleneck for the growth of green WSN. Therefore , another objective of the study is to reduce this cost involved in the net-work.

Method: A cost-effective routing protocol is proposed that introduces energy-efficient CH selec-tion by incorporating parameters namely, node density, residual energy, the total energy of net-work and distance factor. Thus, the proposed protocol is termed as Cost-Effective Cluster-based Routing Protocol (CECRP) as it performs remarkably better with only two energy level nodes as compared to state-of-the-art protocols with three levels nodes.

Results and Conclusion: It can be encapsulated from the simulation results that CECRP outper-forms state-of-the-protocols on different performance metrics. Furthermore, it is comprehended from the simulation results that CECRP proves to be 33.33% more cost-effective as compared to the competent protocols, hence CECRP favors the green WSN.

A R T I C L E H I S T O R Y

Received: July 05, 2019 Revised: August 14, 2019 Accepted: October 12, 2019 DOI: 10.2174/2666255813666200312145504

Keywords: Cost-Effective; Green WSN; CECRP; Cluster-based routing; Cluster head selection.

1. INTRODUCTION

The Wireless Sensor Networks (WSNs) have shown to be a promising sensing technology but been suffering from the bottleneck of limited energy resources [1, 2]. WSNs comprise numerous spatially distributed sensor nodes, which are basically exploited to monitor the environment for vari-ous factors namely, humidity, temperature, vibration, pres-sure or any detection of moving object [3]. These sensor nodes have the capability to undergo wireless communica-tion with other nodes or sink in single-hop or multi-hop manner [4]. Due to such sensing and wireless communica-tion capabilities, there are numerous applications of WSNs which are only limited to the human imagination. The devel-opment in MEMS (Micro-Electro-Mechanical Systems)

*Address correspondence to this author at the Department of Electronics and Communication Engineering, Dr BR Ambedkar National Institute of Technology, Jalandhar-144011, India; Tel: +919914374003; E-mail: sandeepv.ec.13@nitj.ac.in

technology has helped in the production of small-sized and low-cost sensor nodes that can be deployed over large areas for monitoring [5].

Once nodes are deployed in unattended areas, it is infea-sible to replace the batteries of these nodes that get depleted due to the wireless communication. Once the battery is dead, it becomes non-operational and it does not collect any data; furthermore, the network suffers from connectivity loss. Therefore, it is essential to design a model for energy-efficient communication within nodes or between nodes and sink [6]. These energy-efficient techniques help in acquiring enhanced network lifetime which is defined as ‘the number of rounds completed till all nodes are dead’ [7].

Large area monitoring inevitably requires multi-hop communication to avoid long haul communication which eventually devours energy of a high magnitude. Cluster-based routing has been promising in dealing with large area networks by facilitating networks with enhanced scalability,

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uniform load balancing, reducing the number of data trans-missions, etc. [8].

1.1. Heterogeneous WSN When nodes deployed are of the same configuration, the

network is said to be homogeneous, whereas the network deployed with different energy levels of nodes are heteroge-neous networks [9]. The heterogeneity in a node can be of different genres namely, sensing, computational, coverage and energy [10]. Many researchers have proposed various heterogeneous routing protocols that have achieved en-hanced stability period of the network as compared to homo-geneous routing protocols. So, in this work, energy hetero-geneity of the nodes is taken into consideration.

A network can be made heterogeneous by incorporating different energy levels of sensor nodes. It starts from the two energy levels (i.e., normal and advanced nodes) wherein advanced nodes carry more stock of energy as compared to normal nodes in a protocol known as Stable Election Proto-col (SEP) [11]. The number of energy levels of nodes is fur-ther increased to three levels (normal, intermediate and ad-vanced nodes) in EEHC (Energy Efficient Heterogeneous Clustered) scheme [12]. In order to acquire the enhanced stability period, the number of heterogeneous nodes is fur-ther enhanced to four levels in BEENISH (Balanced Energy Efficient Network Integrated Super Heterogeneous) protocol [13]. These protocols focus on proposing the energy-efficient method of Cluster Head (CH) selection for energy-efficient routing. It is noteworthy to mention that a CH has the re-sponsibility to collect the data from the cluster nodes and after aggregating the data, it forwards it to the sink. The in-clusion of different parameters for CH selection namely, energy and distance has helped in acquiring a better network performance. However, very few techniques have incorpo-rated node density factor that helps in selecting a node to be CH that is surrounded by a greater number of nodes as com-pared to any other node.

1.2. Problem Definition and Objectives

A. Problem Definition: In the quest of acquiring a higher stability period and network lifetime, many researchers follow the approach of increasing the number of energy levels of the nodes in the network [14]. However, the cost factor involved in incorpo-rating these higher energy nodes is overshadowed. The increase in the cost of the nodes tends to exert a huge financial burden on the users working for a par-ticular application for a green WSN [15]. Further-more, CH selection is another important concern which must be addressed so that the survival period of CH could be improved. Therefore, there is a re-quirement of energy-efficient CH selection that compensates for the decreased level of energy heter-ogeneity.

B. Objectives: In consideration of the problem defined above, the following objectives are framed. a. The primary objective is to reduce the effective

cost of the network for employing energy heter-ogeneity in nodes, while still acquiring the en-hanced network performance.

b. Another objective is to select CH by including the essential parameters along with mostly ex-ploited parameters which are residual energy and distance to the sink. It is done to elongate the survival of CH node.

In order to compensate for both the cost factor and ener-gy-efficient CH selection, the following contributions are reported.

1.3. Major Contributions

There are two significant contributions reported in this manuscript that are highlighted as follows.

a) In this work, Cost-effective Cluster-based Routing Pro-tocol (CECRP) is proposed that selects a node with re-spect to CH on the basis of parameters namely, residual energy, the total energy of the network, ‘distance be-tween the node and the sink’ and node density factor.

b) CECRP is made cost-effective by incorporating the net-work with only two energy levels of nodes that too with very less stock of initial energy as compared to the com-petitive protocols namely, DRESEP (Distance-based Residual Energy-efficient Stable Election Protocol) [16], SEECP (Stable Energy Efficient Clustering Protocol) [17] and TERP (Threshold-sensitive Energy-efficient Routing Protocol) [18]. CECRP (proposed work) still outperforms the aforementioned protocols on the benchmark of different performance metrics namely, stability period, network lifetime and network’s remain-ing energy.

1.4. Novelty Analysis of Proposed Work

In the development of energy-efficient routing techniques in WSN, the various levels of energy heterogeneous nodes are considered for network stability and longevity. However, the attention of the research, towards the increased cost of the network due to the different levels of energy heterogene-ous nodes, is still missing.

Therefore, this is the first-ever attempt towards handling the financial burden i.e., increased network cost due to the different levels of energy heterogeneous nodes. Another striking feature of the proposed work is that it not only re-duces the financial expenditure occurring due to the various heterogeneous nodes but also uplifts the network perfor-mance by the proposed CH selection technique. Hence, the network performance is not compromised with the reduced number of levels of energy heterogeneous nodes.

The rest of the manuscript is organized as follows. Sec-tion 2 discusses the related work in the field of heterogene-ous WSN and the methods of their CH selection. Section 3 presents the proposed protocol CECRP and the discussions about the set-up and steady-state phase are covered. The simulation results are presented in Section 4 and finally, the conclusion and future scope are discussed in Section 5.

2. RELATED WORK

A plethora of research has reported the advancements made in cluster-based routing techniques. The nodes are as-

Cost-Effective Cluster-based Energy Efficient Routing Recent Advances in Computer Science and Communications, 2020, Vol. 12, No. 1 3

sumed to be homogeneous, but due to different morphologi-cal and operational factors, the nodes always differ in their initial energy before coming to any operation. Cluster-based routing has helped in abating the number of data transmis-sions in the network [19]. In this work, the heterogeneous routing protocols focusing on clustering, have been studied [20]. SEP [11] was the first protocol that decided upon the CH selection on the basis of different weighted probabilities . It failed to operate for multi-level heterogeneous networks. DEEC (Design of a distributed Energy-Efficient Clustering) algorithm [21] selected CH by defining the ratio of residual energy to the average energy. It outperformed SEP, however, it suffered from the penalization of advanced nodes which resulted in the frequent selection of advanced nodes as CH. DDEEC (Developed Distributed Energy-Efficient Cluster-ing) [22] worked for the avoidance of penalization of ad-vanced nodes. They introduced the energy threshold for de-ciding upon the CH selection. Simulation results showed the significant improvement made by DDEEC as compared to DEEC. When the improvement over the stability period was reported at the two levels, EEHC [12] introduced the CH selection at three energy levels. It considered the energy fac-tor but the distance factor was not incorporated that made it inefficient. Furthermore, EEHC still suffered from the penal-ization effect on the high energy nodes. The number of ener-gy-levels of nodes was increased to four in the case of BEENISH. It again suffered from the penalization of nodes that reduced its performance. The sink mobility was intro-duced in iBEENISH (Improved BEENISH) [23] that im-proved the performance of BEENISH protocol and also avoided the penalization effect. However, the increase of energy levels of nodes up to four had exerted a high financial burden thereby making it highly expensive pertaining to any application.

Many researchers have focused on mitigating the hot-spot problem to reduce the energy consumption of sensor nodes [24-26]. Further for uniform energy consumption, Cooperative transmission for the selection of hop device was exploited [26]. In our recently published work [28-30], we proposed cluster based routing technique specifically de-signed for the harsh environment applications. Some crucial QoS (Quality of Service) aware routing techniques have been reported by some researchers [31-32]. Furthermore, while handling QoS parameters, the delay is investigated as it occurs while data transmission is in progress for multi-hop communication [33-34]. To deal with such a problem for the large area network, researchers opted for a zone concept in WSN [35-36]. Some of the techniques that focused on net-work longevity with energy-efficient clustering are reported [19] [32][35].

Aforementioned studied protocols are pro-active proto-cols suitable for attended applications. Whereas, some proto-cols are developed to work for unattended applications namely, forest fire detection, volcanic eruptions, etc. [36]. TSEP (Threshold-sensitive stable election protocol) [37] worked based on the threshold value i.e., hard threshold and soft threshold, to preserve the energy of nodes. DRESEP [16] selected CH on the basis of not only energy but also the distance factor. It adopted dual-hop communication by in-corporating a pre-fixed radius of a circular region defined across the sink that ultimately decides for dual-hop or single

hop. The dual-hop communication leads to the energy hole problem. The advancement to DRESEP was made in SEECP [17] that fixed the number of CHs in the network and worked towards acquiring the stability of the network. SEECP fixed the circular radius on the basis of physical ge-ometry. Though the simulations result of SEECP outper-formed DRESEP, it still suffered from the energy hole prob-lem. PSEP (Prolong Stable Election Protocol) [38] selected CH on the basis of energy parameter and worked to achieve a higher stability period. The fuzzy-based techniques have been proposed for network longevity and some of the work related to the sensing heterogeneity is also discussed [39, 40].

TERP [18] selected CH on the basis of Boolean Differen-tial Evolution schemes that optimize the CH selection. The protocol is still not energy-efficient as it neither considers the node density factor nor it attempts any effort for the hotspot problem.

2.1. Research Gap

The heterogeneous protocols discussed above have pro-posed one or the other solutions to efficiently select the CH [41]. None of the protocols have given any consideration to the cost incurred by incorporating heterogeneous nodes of higher energy resources. The inclusion of node density factor will surely lessen the communicating distance of nodes from the other nodes in the cluster. Considering all these im-portant issues, CECRP is proposed that improves the CH selection. After reviewing the configuration of different sen-sor nodes, it is comprehended that with the increase in ener-gy values of any node, the cost of that particular node is in-creased significantly. Therefore, a research focus towards the enhanced cost of the network is missing rather researchers have been focusing more on the energy efficiency of the sen-sor nodes.

3. CECRP

This section discusses the working operation of CECRP by reporting set up and steady-state phase which are dis-cussed as follows.

3.1. Network Assumptions

a) There are few following network assumptions which are taken into consideration for the opera-tion of CECRP. The sensor nodes of two energy levels namely, normal and advanced nodes are deployed in the network and kept stationary along with sink for the entire run of the network. The energy of advanced nodes is more than the normal nodes. These sensor nodes do not have any GPS equipment installed on their devices i.e., these nodes are located unaware.

b) After random deployment of nodes, it is as-sumed for non-availability of recharging source. So, once a node is dead, it cannot be recharged.

c) As soon as the nodes are deployed, they get connected to each other by the exchange of mes-sages. Sink broadcasts HELLO message in the

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network, to which nodes acknowledge by send-ing their unique IDs. Then those IDs are broad-casted by the sink in the network so as to con-nect each node with one another.

d) The radio interference, signal attenuation or in-tervention made by different objects and any damage caused to the nodes under harsh envi-ronmental conditions are not taken into consid-eration.

e) The connection between nodes is symmetrical. f) The sink is assumed to be enriched with unlim-

ited power supply. g) The security aspects for the wireless communi-

cation by the nodes are beyond the scope of this manuscript.

h) The term ‘green’ is used in the manuscript to emphasize on the energy efficient as well as the cost-effective approach adopted for WSN.

When the data transmission is carried out in the network, as a matter of fact when nodes are switched on for operation, the energy consumption takes place by following the radio energy model.

3.2. Radio Energy Model

In this paper, the fundamental radio energy consumption model is used for both the protocols as shown in Fig. (1). It decides for the amount of depletion of energy for all the nodes that communicate in the network. As can be seen in Fig. (1), the transmit and receive electronics are used that are held responsible for the switching on and off the circuit of transmitter and receiver.

The energy consumed while the network is in operation is demonstrated by the set of eq. (1-5). The computational energy is considered to be negligible. So, while formulating the energy model, the energy depleted during communica-tion is taken into consideration.

The eq. (1-2) describe the consumption of energy by the node while pursuing data transmission at a different value of distance. The energy consumed in the transmission of f-bit data at the distance d is represented by !!" (f, d) and given as follows.

E!" f, d = f×E!"# + f×E!"#×d!   for d≤do (1)

E!" f, d = f×E!"# + f×E!"#×d!  for d>do (2)

The term d used in the above equations represents the distance between the source and destination nodes or be-tween nodes and sink. !!"# represents the energy consumed for switching on and off the transmitter and receiver circuit-ry. The threshold distance is represented by ‘do’ and is ex-pressed as in eq. (3).

d! =!!"#!!"#

(3)

The characteristics of transmitter amplifiers are given by !!"# and !!"# where !!"# is for free space energy model (power loss d2) and !!"# accounts for the energy consumed for multi-path energy model (power loss d4).

The energy consumed while receiving the data per bit is given by eq. (4).

E!"(f) = f×E!"!# (4)

The CH which performs the data aggregation consumes energy as given by the eq. (5).

E!"(f) = m×f×E!" (5)

The amount of energy used for free space model is given by !!"# and the energy consumed in the reception of f-bit data is represented by !!". !!" is the energy consumed in data aggregation of 1-bit data. Moreover, !!"(!) is the energy expenditure during data aggregation of received f-bit data of m number of data packets.

3.3. Operation of CECRP

The network of CECRP is operated in a similar way to any clustering protocol under two phases namely, set up and steady-state phase. 3.3.1 Set up phase

The set-up phase includes network formation and CH se-lection which is discussed as below. 3.3.1.1. Network Formation in CECRP

The total number of advanced and normal nodes is given by a set of equations (6-13). In these equations, the propor-tion of a number of advanced nodes is represented by m frac-tion of total number of nodes which is denoted by n. The

 Fig. (1). Radio Energy Dissipation Model.

Transmit Electronics Tx Amplifier Receive

Electronics

d

f bit packet f bit packet

Etx.(f,d)

Emp *f*d2 (f,d)

Erx (f,d)

Eelec*f Eelec*f

Cost-Effective Cluster-based Energy Efficient Routing Recent Advances in Computer Science and Communications, 2020, Vol. 12, No. 1 5

total number of advanced and normal nodes is represented by !!"# and !!"# respectively.

N!"# = n×m (6)

N!"# = n×(1 −m) (7)

The advanced nodes are α times higher in energy as compared to normal nodes as shown in eq. (8). !!"# and !!"# represent the energy of all advanced and normal nodes, respectively.

The total energy of the network is given by !! which is computed in eq. (12). The step-wise computation of the en-ergy of the network is done as follows.

E!"# = E!× 1 + α ×n×m (8)

E!"# = E!× 1 −m ×n (9)

E! = E!"# + E!"# (10)

E! = E!× 1 + α ×n×m +  E!× 1 −m ×n (11)

E! = n×E!×(1 +m×α) (12)

The notations α and m are the energy and number frac-tions of advanced nodes, respectively. The energy of normal nodes given by !!. !! is computed in eq. (8-12), as it is to be incorporated in the threshold formula for CH selection. These equations give the laconic description of the heteroge-neous structure of proposed protocols. 3.3.1.2. CH Selection in CECRP

The CH selection in DRESEP protocol followed the same criteria as in NEAP [42] protocol but it added distance parameter to its threshold formula. The values of the proba-bilities and threshold for each type of nodes are discussed as follows.

P! =!

!!!×! (13)

P! =!(!!!)!!!×!

(14)

The notations used in above eq. (13-14) are defined as follows. !! and !!" represent the probabilities for normal and advanced nodes, respectively.

P is the optimum probability of a node to become CH in the network that ultimately helps in constructing the optimal number of clusters in the network. The threshold values for normal and advanced nodes are computed in equations (15-16), respectively. The rest of the terms are the same as de-fined in the above subsection.

In eq. (15-16), the notations !!"# and !!" are used to de-fine the average Euclidean distance of all the nodes from the sink and distance of a node from the sink, respectively.

!!"#$% is the node density of a node computed by dis-covering the node which is at the least distance from the oth-er nodes. !!"# is the residual energy of the node which is updated after every round and !! is the total energy of the node which is computed from the eq. (12). The current num-ber of rounds is represented by r.

3.3.2. Steady-state phase

This phase follows the same procedure as of DRESEP by following the dual-hop communication. The radius is fixed at 30 m distance from the sink. The main focus of this manu-script is the energy-efficient selection of CH that could im-prove the network performance. So, the steady-state phase is the same as that of DRESEP protocol.

3.4. Methodology of CECRP

The whole operational methodology for CECRP is shown in Fig. (2). The steps considered for the methodology of CECRP are discussed below.

a. In the set up phase, the heterogeneous nodes are de-ployed with two energy levels in a network where the sink is located in the middle of the network.

b. The probability for each type of nodes i.e., normal or advanced nodes is determined by using eq. (13-14).

c. Simultaneously a random number is generated for each node going through the selection race of CH. The threshold value (T.V.) for the particular is com-puted by using one of eq. (15-16). Thereafter, T.V. is compared with a random number (R.N.), if the R.N. value is found to be less than the T.V., a node is said to be cluster member node else it is declared as CH.

d. In steady-state phase, cluster member nodes send da-ta to the sink and for the selected CH, distance (de-noted by D) is calculated from the sink. If D is less than Radius denoted by R, CH sends data to the sink directly, else it is forwarded to the nearest CHs in-side the region.

T N! = !!

!!!! !×!"# !!!

× !!"#!!"

×N!"#$%×!!"#!!

(15)

T A! = !!

!!!! !×!"# !!!

× !!"#!!"

×N!"#$%×!!"#!!

  (16)

e. Energy (denoted by E) is checked for each ith node, where i ranges from 1 to N (total number of nodes i.e., 100). If the energy of the ith node becomes zero, that node is said to be dead (denoted by D_N (i)). If D_N(i) is equal to N then the whole network is said to be dead and the network stops functioning. Oth-erwise, the D_N(i) is incremented to 1 and the next node is considered for the whole process of threshold computation.

3. RESULTS AND DISCUSSION

In this section, the simulation analysis of CECRP is dis-cussed. The simulation is done using MATLAB software version 2015a [43]. The simulation parameters for simula-tion of CECRP are kept same as that of TERP. However, the energy of normal and advanced nodes is assigned as 0.5 Joule and 1 Joule, respectively. The normal and advanced nodes are 80 and 20 in number, respectively.

6 Recent Advances in Computer Science and Communications, 2020, Vol. 12, No. 1 Verma et al.

Fig. (2). Working of CECRP.

The radio energy parameters have the same value for CECRP as possessed by the other protocols. The simulation parameters are given in Table 1. The reason for considering the protocols TERP, SEECP and DRESEP for the perfor-mance evaluation is the higher stability period and the en-hanced network lifetime acquired by these protocols. The simulation analysis of CECRP is done on the benchmark of different performance metrics.

3.1. Simulation Assumptions

There are some assumptions that are considered while performing the simulation analysis.

a. The parameters considered for the simulation analy-sis are given in Table 1.

b. While implementing the proposed routing technique in the simulation tool, the physical obstruction to the message signal is not considered.

c. It is assumed that the mathematical model of the proposed work is easy to implement in MATLAB Software.

d. The ideal scenario for implementation is taken into consideration. The physical medium through which the signal is sent is assumed to be free from any con-straint of the physical medium.

e. The number of iterations occurring in the proposed work is considered to be the number of rounds get-ting covered by the proposed technique.

Calculate Threshold value (T.V.) by using equations (15-

16)

If (T.V.) >R.N.

Node is termed as

Cluster Member

Node is selected as

CH

Tx Data to CH based on TDMA

schedulling

Calculate probability value according to eq.

(13) or eq. (14)

Set up Phase

No

Yes

No

Steady State Phase

Generate a random

number (R.N.)

For every type of node

Determine distance of a sink from selected CH

Tx Data to sink

If Energy (i) <=0(i=1 to N)

Start

Random Deployment of heterogeneous

nodes

YesIf D_N(i)<N(Total nodes)

Node is dead (D_N (i))

No

Stop

D_N(i)=D_N(i)+1

Yes

Cost-Effective Cluster-based Energy Efficient Routing Recent Advances in Computer Science and Communications, 2020, Vol. 12, No. 1 7

f. Signal attenuation due to the scattering and reflection is not considered in this work while simulation .

3.2. Simulation Analysis

The simulation analysis presents the simulation results acquired for the different performance metrics. These are explained as follows.

a) Stability Period: The stability period is the number of rounds covered until any node is depleted of its energy or it is dead. The simulation results show that

the stability period for CECRP is 7552 rounds as compared to 5063, 4610 and 3877 rounds of TERP, SEECP and DRESEP protocols, respectively as shown in Fig. (3). CECRP improves the stability pe-riod of TERP, SEECP and DRESEP by 49.14%, 63.8% and 94.76%, respectively.

The reason for such improvement is the addition of node proximity factor that reduces the distance among the CHs and the cluster member nodes. That ultimately reduces the energy consumption caused due to data transmission to CHs from the far located cluster member nodes. Furthermore, the

Table 1. Simulation parameters.

Network Parameters Values

Network Area Size 100 ×100 m2

Number of Nodes (N) 100

Initial energy of nodes (in Joules) (!!) 1

Energy heterogeneity Node Type 2-level; normal and advanced nodes

Energy fraction of advanced nodes (!) !=0.5

Number of advanced nodes fraction (m) m=0.2

Energy required for running transmitter and receiver E!"# 50nJ/bit

Threshold distance (!!) 87m

Amplification energy required for smaller distance ! ≤ !! (!!"#) 10pJ/bit/m2

Amplification energy required for larger distance ! > !! (!!") 0.0013pJ/bit/m4

Energy consumption incurred while data aggregation (!!") 5nJ/bit/signal

Fig. (3). Performance comparison of CECRP against other protocols. (A higher resolution / colour version of this figure is available in the electronic copy of the article).

8 Recent Advances in Computer Science and Communications, 2020, Vol. 12, No. 1 Verma et al.

factors of residual energy, distance to the sink and total en-ergy, help in the selection of the appropriate node for the role of CH. The inclusion of all these factors not only reduces the effective distance of the CH nodes from the cluster member nodes, consequently, the survival period of every node is elongated.

b) Network Lifetime: The number of rounds complet-ed till all nodes are depleted of their energy is termed as network lifetime. The network lifetime of CECRP is found to be 18294 rounds whereas the network lifetime of TERP, SEECP and DRSEP protocols for 19169, 4620 and 16749 rounds, is respectively as observed from Figs. (3 and 4).

As the energy is preserved for all types of nodes, the nodes are able to sustain for a longer period thereby enhanc-ing network lifetime. It is to be noted that the network life-time of CECRP is very competitive to TERP but slightly lower. It is due to the load balancing in the network that re-duces the instability period and thereby reduces the network lifetime with some rounds. However, the loss of information is very insignificant in the last stages of network operation. In other protocols, for example in TERP, the network relia-bility is compromised with the increased instability period. Therefore, it is always in the interest of the researchers that they must exploit their techniques in a way that they have reduced instability period so that once the whole network is dead, the new network could be deployed in the same field area.

c) Network’s remaining energy: It is very interesting to note down the fact that the initial energy of the network is kept at 105 Joules but in case of other

comparative protocols, it is 140 Joules as shown in Fig. (5). Even though the network energy of CECRP is very less initially, still it is able to cover a compet-itive number of rounds as compared to other proto-cols. It is due to energy-efficient CH selection pro-posed in the network. The reason behind the lower value of initial energy in the proposed protocol is the reduced number of energy heterogeneous nodes. In comparison to the TERP and other protocols, only level-two energy heterogeneous nodes are consid-ered that account for the less energy as compared to the competent protocols. When the proposed work considers the involvement of crucial factors for CH selection, the effective energy of the nodes is pre-served. The effective distance of the CH nodes from their respective cluster members is reduced compre-hensively which ultimately reduces the energy con-sumption of the network.

d) Cost-effective HWSN: It is one of the essential met-rics that is predominantly targeted in this work. It is to be noted that for CECRP, the total energy of the network is taken as 105 Joules, whereas the total en-ergy of TERP and other protocols is taken as 140 Joules. The aggregated performance of CECRP is given in Table 2. The energy consumed by CECRP is 33.33% less than the competitive protocols. Con-sequently, it reduces the cost of the network by 33.33%. As the network performance is achieved by the CECRP in terms of stability period and network lifetime, therefore, it can be comprehended that CE-CRP is one of the most cost-effective heterogeneous protocols as compared to TERP, SEECP and

Fig. (4). Alive Nodes vs rounds comparison of CECRP against other protocols. (A higher resolution / colour version of this figure is availa-ble in the electronic copy of the article).

Cost-Effective Cluster-based Energy Efficient Routing Recent Advances in Computer Science and Communications, 2020, Vol. 12, No. 1 9

DRESEP protocols. With such a gigantic reduction in the cost factor, this method is expected to relax the financial burden on the users and will help them to enhance the scalability of the network for the given application.

e) Aggregated improvement by CECRP: The per-formance of the protocol CECRP against other pro-tocols is shown in Table 3.

Table 2. Aggregated percentage improvement by CECRP.

Protocols Stability Period (%) Cost saving (%)

TERP 49.14

33.33 SEECP 63.18

DRESEP 94.76

Table 3. Overall improvement of CECRP against other pro-tocols.

P.M. DRESEP SEECP TERP CECRP

FND 3877 4610 5063 7552

HND 7836 4615 8503 12132

LND 16749 4620 19169 18295

FND, HND, LND, P.M. are abbreviated for First Node Dead, Half Node Dead, Last Node Dead and Performance Metrics, respectively.

It is evident from Table 3 that the protocol CECRP has outperformed the other protocols in terms of FND and HND as it covers 2489 and 3629 more rounds as compared to TERP. However, the LND is 874 rounds less as compared to TERP. It is one of the added advantages to the network per-taining to applications where the instability period should be as minimum as possible. As given in Table 3, the instability period (Instability Period=FND-LND) of the CECRP is found to be 10743 rounds, whereas in case of TERP, it is 14106 rounds. In the applications such as forest fire detec-tion and military applications, it is imperative to have the least instability period. Therefore, the CECRP outperforms the other protocols in terms of the above discussed perfor-mance metrics.

CONCLUSION

Green WSN has been promising in assigning different roles to nodes for data dissemination according to their ener-gy stocks. A node with a high value of energy costs more than that of low value and due to the same reason, more lev-els of energy heterogeneous nodes to be deployed make the entire network quite expensive. To resolve this concern, in this paper, CECRP is proposed that renders a cost-effective network. It incorporates energy-efficient CH selection with only two levels of energy heterogeneous nodes deployed in the network. The point worth noting is that these deployed nodes incur less cost due to their low value of energy stock, still outperforming TERP, SEECP and DRESEP protocols. The parameter of node proximity helps in lessening the dis-tance of cluster member nodes from the CH that consequent-ly helps in energy preservation of nodes, eventually enhanc-

Fig. (5). Network’s remaining energy comparison of CECRP against other protocols. (A higher resolution / colour version of this figure is available in the electronic copy of the article).

10 Recent Advances in Computer Science and Communications, 2020, Vol. 12, No. 1 Verma et al.

ing the stability period and network lifetime. It is observed through the simulation that CECRP improves stability period by 49.14%, 63.8% and 94.76% as compared to TERP, SEECP and DRESEP protocols, respectively.

In future, the work can be extended to two modules; in first, the mobility of sink will be introduced to acquire high throughput, thereby reducing the delay caused in data deliv-ery. In the second phase, the proposed work can be extended for the data transmission scenario with multiple data sinks around each periphery of the network. Quality of service parameters (QoS) can be analyzed for thorough investigation of the proposed work.

CONSENT FOR PUBLICATION

Not applicable.

AVAILABILITY OF DATA AND MATERIALS  Not applicable.  

FUNDING  None.  

CONFLICT OF INTEREST

The authors declare no conflict of interest, financial or otherwise.

ACKNOWLEDGEMENTS

Declared none.

REFERENCES [1] I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci,

"Wireless sensor networks: a survey", Comput. Netw., vol. 38, no. 4, pp. 393-422, 2002.

http://dx.doi.org/10.1016/S1389-1286(01)00302-4 [2] I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, "A

survey on sensor networks", IEEE Commun. Mag., vol. 40, no. 8, pp. 102-114, 2002.

http://dx.doi.org/10.1109/MCOM.2002.1024422 [3] H.M. Ammari, N. Gomes, W.I. Grosky, M. Jacques, B. Maxim,

and D. Yoon, Review of applications of wireless sensor networks.. Wirel. Sens. Netw. Curr. Status Future Trends, 2012, p. 1.

[4] M. Healy, T. Newe, and E. Lewis, Wireless sensor node hardware: A review.SENSORS.. IEEE, 2008, pp. 621-624.

[5] J.N. Al-Karaki, and A.E. Kamal, "Routing techniques in wireless sensor networks: a survey", IEEE Wirel. Commun., vol. 11, no. 6, pp. 6-28, 2004.

http://dx.doi.org/10.1109/MWC.2004.1368893 [6] A.A. Abbasi, and M. Younis, "A survey on clustering algorithms

for wireless sensor networks", Comput. Commun., vol. 30, no. 14, pp. 2826-2841, 2007.

http://dx.doi.org/10.1016/j.comcom.2007.05.024 [7] S. Tyagi, and N. Kumar, "A systematic review on clustering and

routing techniques based upon LEACH protocol for wireless sensor networks", J. Netw. Comput. Appl., vol. 36, no. 2, pp. 623-645, 2013.

http://dx.doi.org/10.1016/j.jnca.2012.12.001 [8] D.N. Sandeep, and V. Kumar, "Review on Clustering, Coverage

and Connectivity in Underwater Wireless Sensor Networks: A Communication Techniques Perspective", IEEE Access, 2017.

http://dx.doi.org/10.1109/ACCESS.2017.2713640 [9] S. Tanwar, N. Kumar, and J.J. Rodrigues, "A systematic review on

heterogeneous routing protocols for wireless sensor network", J. Netw. Comput. Appl., vol. 53, pp. 39-56, 2015.

http://dx.doi.org/10.1016/j.jnca.2015.03.004 [10] M. Yarvis, N. Kushalnagar, H. Singh, A. Rangarajan, Y. Liu, and S.

Singh, "Exploiting heterogeneity in sensor networks", Proceedings of 24th Annual Joint Conference of the IEEE Computer and Com-munications Societies, vol. vol. 2, , 2005pp. 878-890

[11] S. Verma, N. Sood, and A.K. Sharma, "Genetic Algorithm-based Optimized Cluster Head selection for single and multiple data sinks in Heterogeneous Wireless Sensor Network", Appl. Soft Comput., 2019.105788

http://dx.doi.org/10.1016/j.asoc.2019.105788 [12] G. Smaragdakis, I. Matta, and A. Bestavros, SEP: A stable election

protocol for clustered heterogeneous wireless sensor networks.. Boston University Computer Science Department, 2004.

[13] D. Kumar, T.C. Aseri, and R.B. Patel, "EEHC: Energy efficient heterogeneous clustered scheme for wireless sensor networks", Comput. Commun., vol. 32, no. 4, pp. 662-667, 2009.

http://dx.doi.org/10.1016/j.comcom.2008.11.025 [14] T.N. Qureshi, N. Javaid, A.H. Khan, A. Iqbal, E. Akhtar, and M.

Ishfaq, "BEENISH: Balanced energy efficient network integrated super heterogeneous protocol for wireless sensor networks", Pro-cedia Comput. Sci., vol. 19, pp. 920-925, 2013.

http://dx.doi.org/10.1016/j.procs.2013.06.126 [15] S. Singh, S. Chand, and B. Kumar, "Energy efficient clustering

protocol using fuzzy logic for heterogeneous WSNs", Wirel. Pers. Commun., vol. 86, no. 2, pp. 451-475, 2016.

http://dx.doi.org/10.1007/s11277-015-2939-4 [16] N. Mittal, and U. Singh, "Distance-based residual energy-efficient

stable election protocol for WSNs", Arab. J. Sci. Eng., vol. 40, no. 6, pp. 1637-1646, 2015.

http://dx.doi.org/10.1007/s13369-015-1641-x [17] N. Mittal, U. Singh, and B.S. Sohi, "A stable energy efficient clus-

tering protocol for wireless sensor networks", Wirel. Netw., vol. 23, no. 6, pp. 1809-1821, 2017.

http://dx.doi.org/10.1007/s11276-016-1255-6 [18] N. Mittal, U. Singh, and B.S. Sohi, "A novel energy efficient stable

clustering approach for wireless sensor networks", Wirel. Pers. Commun., vol. 95, no. 3, pp. 2947-2971, 2017.

http://dx.doi.org/10.1007/s11277-017-3973-1 [19] K. Thangaramya, K. Kulothungan, R. Logambigai, M. Selvi, S.

Ganapathy, and A. Kannan, "Energy aware cluster and neuro-fuzzy based routing algorithm for wireless sensor networks in IoT", Comput. Netw., vol. 151, pp. 211-223, 2019.

http://dx.doi.org/10.1016/j.comnet.2019.01.024 [20] N. Wang, and Z. Hong, "An Energy-Efficient Topology Control

Algorithm for Earth Building Monitoring Using Heterogeneous Wireless Sensor Networks", IEEE Access, vol. 7, pp. 76120-76130, 2019.

http://dx.doi.org/10.1109/ACCESS.2019.2921727 [21] L. Qing, Q. Zhu, and M. Wang, "Design of a distributed energy-

efficient clustering algorithm for heterogeneous wireless sensor networks", Comput. Commun., vol. 29, no. 12, pp. 2230-2237, 2006.

http://dx.doi.org/10.1016/j.comcom.2006.02.017 [22] B. Elbhiri, R. Saadane, and D. Aboutajdine, "Developed Distribut-

ed Energy-Efficient Clustering (DDEEC) for heterogeneous wire-less sensor networks", 5th International Symposium on I/V Com-munications and Mobile Network (ISVC), 2010pp. 1-4

http://dx.doi.org/10.1109/ISVC.2010.5656252 [23] M. Akbar, N. Javaid, M. Imran, N. Amjad, M.I. Khan, and M.

Guizani, "Sink mobility aware energy-efficient network integrated super heterogeneous protocol for WSNs", EURASIP J. Wirel. Commun. Netw., vol. 2016, no. 1, p. 66, 2016.

http://dx.doi.org/10.1186/s13638-016-0552-1 [24] "ECH: An Enhanced Clustering Hierarchy Approach to Maximize

Lifetime of Wireless Sensor Networks", IEEE Access, vol. •••, pp. 1-1, 2019.

[25] R. Elkamel, A. Messouadi, and A. Cherif, "Extending the lifetime of wireless sensor networks through mitigating the hot spot prob-lem", J. Parallel Distrib. Comput., vol. 133, pp. 159-169, 2019.

http://dx.doi.org/10.1016/j.jpdc.2019.06.007 [26] A.L. Kakhandki, and S. Hublikar, "Energy efficient selective hop

selection optimization to maximize lifetime of wireless sensor net-work", Alex. Eng. J., vol. 57, no. 2, pp. 711-718, 2018.

http://dx.doi.org/10.1016/j.aej.2017.01.041

Cost-Effective Cluster-based Energy Efficient Routing Recent Advances in Computer Science and Communications, 2020, Vol. 12, No. 1 11

[27] S. Verma, N. Sood, and A.K. Sharma, "Design of a novel routing architecture for harsh environment monitoring in heterogeneous WSN", IET Wirel. Sens. Syst., vol. 8, no. 6, pp. 284-294, 2018.

http://dx.doi.org/10.1049/iet-wss.2018.5025 [28] S. Verma, N. Sood, and A.K. Sharma, "QoS provisioning-based

routing protocols using multiple data sink in IoT-based WSN", Mod. Phys. Lett. A, vol. •••, 2019.1950235

http://dx.doi.org/10.1142/S0217732319502353 [29] S. Yahiaoui, M. Omar, A. Bouabdallah, E. Natalizio, and Y.

Challal, "An energy efficient and QoS aware routing protocol for wireless sensor and actuator networks", AEU Int. J. Electron. Commun., vol. 83, pp. 193-203, 2018.

http://dx.doi.org/10.1016/j.aeue.2017.08.045 [30] W.A. Jabbar, W.K. Saad, and M. Ismail, "MEQSA-OLSRv2: A

Multicriteria-Based Hybrid Multipath Protocol for Energy-Efficient and QoS-Aware Data Routing in MANET-WSN Convergence Scenarios of IoT", IEEE Access, vol. 6, pp. 76546-76572, 2018.

http://dx.doi.org/10.1109/ACCESS.2018.2882853 [31] S. Maurya, V.K. Jain, and D.R. Chowdhury, "Delay aware energy

efficient reliable routing for data transmission in heterogeneous mobile sink wireless sensor network", J. Netw. Comput. Appl., vol. 144, pp. 118-137, 2019.

http://dx.doi.org/10.1016/j.jnca.2019.06.012 [32] J. Lin, P.R. Chelliah, M. Hsu, and J. Hou, "Efficient Fault-Tolerant

Routing in IoT Wireless Sensor Networks Based on Bipartite-Flow Graph Modeling", IEEE Access, vol. 7, pp. 14022-14034, 2019.

http://dx.doi.org/10.1109/ACCESS.2019.2894002 [33] F.A. Khan, M. Khan, M. Asif, A. Khalid, and I.U. Haq, "Hybrid

and Multi-Hop Advanced Zonal-Stable Election Protocol for Wire-less Sensor Networks", IEEE Access, vol. 7, pp. 25334-25346, 2019.

http://dx.doi.org/10.1109/ACCESS.2019.2899752 [34] S. Kumar, V. Kumar, O. Kaiwartya, U. Dohare, N. Kumar, and J.

Lloret, "Towards Green Communication in Wireless Sensor Net-

work: GA Enabled Distributed Zone Approach", Ad Hoc Netw., vol. •••, 2019.101903

http://dx.doi.org/10.1016/j.adhoc.2019.101903 [35] A.S. Toor, and A.K. Jain, "Energy Aware Cluster Based Multi-hop

Energy Efficient Routing Protocol using Multiple Mobile Nodes (MEACBM) in Wireless Sensor Networks", AEU Int. J. Electron. Commun., vol. 102, pp. 41-53, 2019.

http://dx.doi.org/10.1016/j.aeue.2019.02.006 [36] A. Kashaf, N. Javaid, Z.A. Khan, and I.A. Khan, "TSEP: Thresh-

old-sensitive stable election protocol for WSNs", 10th Internation-al Conference on Frontiers of Information Technology (FIT), 2012pp. 164-168

http://dx.doi.org/10.1109/FIT.2012.37 [37] P.G.V. Naranjo, M. Shojafar, H. Mostafaei, Z. Pooranian, and E.

Baccarelli, "P-SEP: A prolong stable election routing algorithm for energy-limited heterogeneous fog-supported wireless sensor net-works", J. Supercomput., vol. 73, no. 2, pp. 733-755, 2017.

http://dx.doi.org/10.1007/s11227-016-1785-9 [38] S. Singh, S. Chand, and B. Kumar, “An energy efficient clustering

protocol with fuzzy logic for WSNs,” in 5th International Confer-ence-Confluence The Next Generation Information Technology Summit.. Confluence, 2014, pp. 427-431.

[39] S. Singh, S. Chand, and B. Kumar, "Multilevel heterogeneous network model for wireless sensor networks", Telecomm. Syst., vol. 64, no. 2, pp. 259-277, 2017.

http://dx.doi.org/10.1007/s11235-016-0174-2 [40] M. Golsorkhtabar, F.K. Nia, M. Hosseinzadeh, and Y. Vejdanpar-

ast, "The novel energy adaptive protocol for heterogeneous wire-less sensor networks", 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT), vol. vol. 2, 2010pp. 178-182

http://dx.doi.org/10.1109/ICCSIT.2010.5563781 [41] H. Moore, MATLAB for Engineers.. Pearson, 2017.

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