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Wireless Pers Commun (2014) 77:2117–2139DOI 10.1007/s11277-014-1629-y

Heterogeneous HEED Protocol for Wireless SensorNetworks

Satish Chand · Samayveer Singh · Bijendra Kumar

Published online: 6 February 2014© Springer Science+Business Media New York 2014

Abstract One of the important protocols for increasing the network lifetime in wirelesssensor networks (WSNs) is hybrid energy efficient distributed (HEED) protocol. This pro-tocol considers two parameters for deciding the cluster heads, i.e., residual energy and nodedensity and has been designed for the homogeneous WSNs. In this paper, we consider theimplementation of HEED for a heterogeneous network. Depending upon the type of nodes,it defines one-level, two-level, and three-level heterogeneity and accordingly the implemen-tation of HEED is referred to as hetHEED-1, hetHEED-2, and hetHEED-3, respectively. Wealso consider one more parameter, i.e., distance and apply fuzzy logic to determine the clusterheads and accordingly the hetHEED-1, hetHEED-2, and hetHEED-3 are named as HEED-FL, hetHEED-FL-2, hetHEED-FL-3, respectively. The simulation results show that as thelevel of heterogeneity increases in the network, the nodes remain alive for longer time andthe rate of energy dissipation decreases. And also, increasing the heterogeneity level helpssending more packets to the base station and increases the network lifetime. The increasein the network energy increases the network lifetime manifold. In fact, using fuzzy logic,the network lifetime increases by 114.85 % that of the original HEED without any increasein the network energy. Thus, the hetHEED-FL-3 provides the longest lifetime (387.94 %increase) in lifetime at the cost of 19 % increase in network energy), sends maximum numberof packets to the base station, and has minimum rate of energy dissipation.

Keywords Sensor networks · Clustering · Network lifetime · Rounds · Load balancing ·Membership function · Fuzzy logic · Heterogeneity

S. Chand (B) · S. Singh · B. KumarNetaji Subhas Institute of Technology, Sector-3, Dwarka, New Delhi 110078, Indiae-mail: [email protected]

S. Singhe-mail: [email protected]

B. Kumare-mail: [email protected]

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1 Introduction

The wireless communication is one of the important types of communication that requiresno fixed infrastructure. There are many situations where wireless communication can bedeployed such as volcano, battlefield monitoring, old building structure. It is generally usedwhere the normal cabling is difficult or financially impractical. The wireless communicationdone using the sensor devices is called wireless sensor communication and the resultantnetwork is called the wireless sensor network (WSN). The WSNs are easily deployable,maintenance free, and provide fault-tolerant platform for gathering data from the environ-ment [1]. They are cost effective also because the sensors are very cheap devices and do notrequire any infrastructure such as lying cabling. The sensors, also called motes or actuators[2], have an ability to sense the physical environment for an event that may include sound,humidity, light, temperature, vibration, etc. They collect data by measuring the comprehen-sive conditions in their surroundings and transmit it to sink that in turn either processes itor forwards to the data processing centre using internet. Currently, the wireless systems dealwith the integration of low-power communication, sensing, energy storage, and computation[2]. In a WSN, the communication can be done using either single hop or multihop. In singlehop (also called peer to peer communication), the sensor nodes directly communicate withany other sensor node or with the base station. In multihop communication, there may be asequence of hops while communicating to the base station from a sensor node. Deploymentof sensors in a WSN can be deterministic or random depending on the application. They canbe stationary or location-aware, homogeneous or heterogeneous in nature. Since the sensorsare not supported by external battery, their energy must be used very efficiently in order tomonitor the area for longer time. One possible solution to have longer lifetime of a WSNis to use more sensors, but it may increase collision and, in that case, a suitable schedulingmechanism need be employed. Other solution of prolonging the lifetime of a WSN is toemploy the heterogeneity in sensor nodes [3]. There are three common types of resourceheterogeneity in a sensor node, namely, computational, link, and energy heterogeneity. Incomputational heterogeneity, the heterogeneous node has more resources such as powerfulmicroprocessor and relatively more memory so that it can provide complex data processingand longer-term storage. In link heterogeneity, the heterogeneous node has high-bandwidthand long-distance network transceiver so that it can provide more reliable data transmis-sion. Energy heterogeneity means that the sensor nodes have different levels of energy. Thecomputational and link heterogeneities implicitly depend on the energy as these types ofnodes consume more energy. Thus, the energy based heterogeneity may be considered as themost dominating heterogeneity in WSNs. It has been reported that providing heterogeneityin sensor nodes prolongs the network lifetime, improves reliability of data transmission, anddecreases the latency of data transportation. There have been several protocols for WSNs,which may be classified into different categories. One of the important categories of proto-cols consists of clustering or hierarchical protocols such as low energy adaptive clusteringhierarchy (LEACH) [4] and its different modifications such as LEACH-C, LEACH-M [4,5],threshold sensitive energy efficient sensor network protocol (TEEN) [6], adaptive periodicthreshold-sensitive energy efficient sensor network protocol (APTEEN) [7], power-efficientgathering in sensor information systems [8], stable election protocol (SEP) [9], energy effi-cient clustering scheme (EECS) [10], deterministic energy efficient clustering (DEEC) [11]protocol and hybrid energy efficient distributed (HEED) [12]. Among these types of proto-cols, the HEED is one of the most popular protocols as the cluster heads in this protocolare decided based on the residual energy and degree of nodes. The degree of nodes distrib-utes load among the cluster heads. In other protocols, the cluster heads are selected based

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Heterogeneous HEED Protocol for Wireless Sensor Networks 2119

on the residual energy only and no load balancing is done. In this paper, we discuss theHEED protocol for deploying the underlying network as our heterogeneous network modelin order to increase the lifetime. Our model can describe one- level, two-level, and three-levelheterogeneity and, accordingly, we may call the implementation of HEED as hetHEED-1,hetHEED-2, and hetHEED-3. The one-level heterogeneity assumes all sensor nodes in a WSNto have equal amount of energy, for which the original HEED is implemented. We may alsocall it as homogeneous HEED. The two-level and three-level heterogeneity assume the sensornodes in a WSN to be equipped with two and three energy levels, respectively, for whichwe call the implementation of HEED as hetHEED-2 and hetHEED-3 protocols. The originalHEED considers two parameters—residual energy and node density to determine the clusterheads. In hetHEED-1, hetHEED-2, and hetHEED-3, we consider the same two parametersto determine the cluster heads so that we can compare their performance with respect to het-erogeneity. We also consider one more parameter, i.e., distance between a sensor and sink, inaddition to residual energy and node density and apply the fuzzy logic to calculate the prob-ability in order to decide the cluster heads. The resultant HEED implementation is namedas HEED-FL (original HEED with fuzzy logic), hetHEED-FL-2 (hetHEED-2 with fuzzylogic), and hetHEED-FL-3 (hetHEED-3 with fuzzy logic). Increasing the energy in networkin order to make them heterogeneous increases the network lifetime, which is at much higherside, especially in case of hetHEED-3 (74.2 % energy increase leads to 213.38 % increase innetwork lifetime). Using fuzzy logic in HEED without increasing any energy in the networkincreases the network lifetime by 114.85 % of that of the original HEED. Increasing theheterogeneity level with fuzzy logic increases the network lifetime manifold. For example,the 19 % increase in the network energy enhances the network lifetime by 387.94 %.

The rest of the paper is organized as follows. Section 2 reviews the related literature.Section 3, discusses the fuzzy system including its different components—fuzzifier, fuzzyrulebase, fuzzy inference engine, and defuzzifier. In Sect. 4, a heterogeneous model for WSNsis discussed that is used to simulate hetHEED-1, hetHEED-2, and hetHEED-3, HEED-FL,hetHEED-FL-2, and hetHEED-FL-3. In Sect. 5, we discuss cluster formation, data collectionand data transmission. The simulation results are given Sect. 6 and, finally, the paper isconcluded in Sect. 7.

2 Literature Review

The routing protocols for WSNs may be categorized into different classes based on theapplications such as location based, data-centric, mobility based, multipath based, QoS based,and hierarchical [13]. The location based protocols utilize the position information of nodesto relay the data of the desired regions rather than the whole network. Some of the importantlocation based protocols are minimum energy communication network [14], greedy anti-voidrouting [15], and geographical and energy aware routing [16]. In the data centric routingprotocols, also called flat-based, all nodes in WSN use flood based data transferring scheme.Some of the important data centric based protocols include sensor protocols for informationvia negotiation [17], directed diffusion [18], and Rumor routing [19]. The multipath routingprotocols such as sensor-disjoint multipath protocol [20] use multiple paths to enhance thenetwork performance. In QoS based routing protocols, the network makes balance betweenthe energy consumption and data quality besides the QoS metrics such as delay, energy,bandwidth while delivering the data to base station or sink. Some of the QoS based protocolsinclude sequential assignment routing [21], stateless protocol for real-time communication insensor networks [22], and energy-aware routing [23]. The hierarchical protocols, also called

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clustering protocols, cluster the sensor nodes. These protocols generally work in two phases.In first phase, the cluster heads are selected and, in second phase, routing/data transmissionis performed. The low energy adaptive clustering hierarchy (LEACH) [4] is the very firstclustering protocol that forms the clusters based on the received signal strength. In thisprotocol, the data is transmitted through cluster heads, whose numbers are predetermined.The cluster heads are changed randomly over the time so that the cluster heads (sensor nodes)do not become dead by draining up their entire energy. There have been discussed differentvariants of LEACH such as LEACH-C, LEACH-M, LEACH-V [4,5].

Manjeshwar and Agarwal discuss a TEEN protocol [6] that uses hierarchical structure.This protocol responds to the sudden changes in the sensed attribute, a physical parameterabout which a user is interested and thus it is useful for time-critical applications. The TEENprotocol has been modified as APTEEN protocol [7] that is meant for both time-critical eventsand periodic data collections. Lindsey and Raghavendra discuss power efficient gathering insensor information systems protocol [8], an improved version of LEACH, that uses chains ofsensor nodes. The data is transmitted from all sensor nodes through their respective chains toa single node, called cluster head. The cluster head aggregates the data to remove the duplicityand then transmits it to the base station or sink. It outperforms the LEACH; however, dueto excessive delay, it is not suitable for large networks. Smaragdakis et al. discuss SEP[9], an extension of LEACH, that uses hierarchical clustering and heterogeneity unlike theLEACH. In this protocol, a node becomes cluster head on the basis of weighted electionprobabilities of each node according to their respective energies. The EECS protocol [10]elects the cluster heads with more residual energy through local radio communication. It isused for periodical data gathering applications using WSNs. It uses load balancing and energyefficiently. However, it requires global knowledge of distances between the cluster-heads andbase station. Li et al. discuss DEEC [11] for two-level and multi level heterogeneous WSNs.This protocol selects cluster heads using the ratio of residual energy of each node and theaverage energy of the network. The nodes having high initial and residual energies havemore chance of becoming cluster heads. The nodes nearer to the sink require spending moreenergy than those farther because of the extra burden of the nodes within the neighborhoodof the base station. Thus, smaller clusters are formed using the nearer nodes to balance theload among the cluster heads that fall in different regions and vice versa. This concept hasbeen discussed by Eshghi and Haghighat [24].

The HEED [12] protocol selects cluster heads based on their residual energy and nodedegrees. The node degree helps balancing the load among the cluster heads. In this protocol,the clustering process is carried out in terms of iterations and, in every iteration, the nodesnot covered by any cluster head double their probability of becoming a cluster head. It haslow overhead in terms of processing cycles and message exchanged. This protocol does notassume any distribution of nodes or location awareness. It also achieves fairly uniform clusterhead distribution across the network and prolongs the network lifetime besides supportingdata aggregation. A variant of HEED protocol, called integrated HEED (iHEED) [25], hasintegrated data aggregation in the multihop routing by considering data aggregation operatorssuch as AVG or MAX. It can serve both source and data driven applications. Another variantof HEED by Huang and Wu [26] discusses a constant time clustering mechanism that may betermed as an extended probabilistic algorithm for HEED protocol. In this algorithm, the nodeshaving high energy participate in cluster head election and the remaining are eliminated; thus,requiring less rounds for selecting cluster heads. Another variant of HEED, called Misensehierarchical cluster based routing algorithm (MiCRA) [27], maintains the balanced energyconsumption of nodes so that the network lifetime increases. The paper [28] discusses theHEED for heterogeneous network model; however, no new heterogeneous network model is

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Inference Engine

Knowledge Base

Fuzzification

Defuzzification

Membership

functions

Fuzzy

Rules

Battery Power

Node Density

Distance

Probability

Inputs

Output

Fig. 1 Fuzzy logic based system

discussed in that paper. It uses two-level and multilevel heterogeneous model from [11] andthree-level heterogeneous model from [3]. Its performance is poorer than that of ours. In [29],similar work has been discussed as in [28], but it considers the nodes movable unlike in [28]that has static nodes. As regard to performance of [29], our proposed hetHEED protocolsperform better and same is the case with HEED-FL, hetHEED-FL-2 and hetHEED-FL-3. Innext section, we discuss fuzzy system as it is needed for finding the cluster heads.

3 Fuzzy System

The system based on fuzzy logic consists of four parts: fuzzifier, fuzzy knowledge base,fuzzy inference engine, and defuzzifier as shown in Fig. 1.

The inputs to the system are crisp numbers. The fuzzifier transforms these crisp valuesinto fuzzy values and stores in a fuzzy set by applying a suitable fuzzification function.The fuzzy rules are of the form IF-THEN, which are stored in fuzzy rulebase, also calledknowledgebase.

The output of the fuzzifier and the rules from the knowledgebase are given to the fuzzyinference engine as inputs for simulating human reasoning process by making fuzzy inference.The output of the fuzzy inference engine is provided to the defuzzifier that converts the fuzzyvalues into crisp values. The defuzzifier calculates the centroid and uses it to calculate theprobability. The centroid is computed as follows:

Centroid =∑

µA (x) ∗ x∑

µA (x)(1)

where, µA (x) denotes the membership function of set A.We have used Mamdani model [30] for inference engine because it is most widely used

in applications due to its simplicity. We consider three input parameters in our fuzzy systemthat include battery power, node density, and distance between a sensor and the sink. Each ofthe input variables has three membership functions, i.e., the battery power has low, medium,and high; the node density has sparsely, medium, and densely; the distance has near, medium,and far. The membership function corresponding to the output variable, i.e., probability has 9values—very weak, weak, little weak, lower medium, medium, higher medium, little strong,strong, very strong (Fig. 2).

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Battery Power Node Density Distance

Probability

Input Linguistic Nodes

Input Term Nodes

RuleNodes

Output Term Nodes

Output Linguistic Nodes

Layer 1

Layer 2

Layer 3

Layer 4

Layer 5

Fuzzifier

Defuzzifier

Inference Engine

Fig. 2 Layered fuzzy scheme

The membership functions for battery power consists of one full and two half trapezoidal;for node density, two trapezoidal and one triangular; for distance, two half trapezoidal andone triangular; and for output probability, two half trapezoidal and seven triangular, as shownin Fig. 3a–d, respectively (Table 1).

We use fuzzy model for selecting the cluster heads. The node corresponding to maximumprobability is chosen as the cluster head. In next section, we discuss our heterogeneousnetwork model.

4 Proposed Heterogeneity Network Model

Before discussing our network model, we outline the basic assumptions made for WSN inour work:

(a) All sensor nodes and base station are stationary after deployment; each is identified bya unique ID.

(b) Nodes are location-unaware, i.e. not equipped with GPS-capable antennae.(c) All nodes have similar capabilities (processing/communication), but different in terms

of energies.(d) Nodes are left unattended after deployment, meaning thereby battery recharge is not

possible.(e) There is only one BS, located at the centre in the network, has a constant power supply;

thus has no energy, memory and computation constraints.(f) Each node has the ability to aggregate data; as a result several data packets can be

compressed as one packet.

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Fig. 3 Fuzzy sets correspondingto fuzzy inputs and outputparameters. a Fuzzy set for fuzzyinput variable: battery power.b Fuzzy set for fuzzy inputvariable: node density. c Fuzzyset for fuzzy input variable:distance. d Fuzzy set for fuzzyoutput variable: probability

(a)

(b)

(c)

(d)

(g) The distance between nodes can be computed based on the received signal strength.(h) Nodes have the capability of controlling the transmission power according to the distance

of receiving nodes and the node failure is considered due to energy depletion.

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Table 1 Fuzzy rule base

Battery power Node density Distance Probability

Low(0) Sparsely(0) Near(0) Little weak(2)

Low(0) Sparsely(0) Medium(1) Weak(1)

Low(0) Sparsely(0) Far(2) Very weak(0)

Low(0) Medium(1) Near(0) Lower medium(3)

Low(0) Medium(1) Medium(1) Little weak(2)

Low(0) Medium(1) Far(2) Weak(1)

Low(0) Densely(2) Near(0) Medium(4)

Low(0) Densely(2) Medium(1) Lower medium(3)

Low(0) Densely(2) Far(2) Little weak(2)

Medium(1) Sparsely(0) Near(0) Medium(4)

Medium(1) Sparsely(0) Medium(1) Lower medium(3)

Medium(1) Sparsely(0) Far(2) Little weak(2)

Medium(1) Medium(1) Near(0) Higher medium(5)

Medium(1) Medium(1) Medium(1) Medium(4)

Medium(1) Medium(1) Far(2) Lower medium(3)

Medium(1) Densely(2) Near(0) Little strong(6)

Medium(1) Densely(2) Medium(1) higher medium(5)

Medium(1) Densely(2) Far(2) Medium(4)

High(2) Sparsely(0) Near(0) Little strong(6)

High(2) Sparsely(0) Medium Higher medium(5)

High(2) Sparsely(0) Far(2) Medium(4)

High(2) Medium(1) Near(0) Strong(7)

High(2) Medium(1) Medium(1) Little strong(6)

High(2) Medium(1) Far(2) Higher medium(5)

High(2) Densely(2) Near(0) Very strong(8)

High(2) Densely(2) Medium(1) Strong(7)

High(2) Densely(2) Far(2) Little strong(6)

(i) The radio link is symmetric such that energy consumption of data transmission fromnode A to node B is the same as that of transmission from node B to node A.

Now, we discuss a three level heterogeneous network model. This model describes a WSN thatconsists of three types of sensor nodes based on their energy levels. The nodes having moreenergy are supposed to be costlier than those having less energy. Because of the high cost, thenodes having maximum energy are assumed to be minimum in numbers. The nodes havingminimum energy level are the cheapest ones and hence they can be deployed abundantly.We assume that the WSN has N number of nodes out of which � ∗ N nodes have minimumenergy, where 0 ≤ � ≤ 1. We may call them as the normal nodes and the energy of thesetypes of nodes denoted as E0. The �2 ∗ N nodes have more energy than the normal nodes. Wemay call these nodes as the advance nodes and denote the node energy by E1. The remaining(N − (� ∗ N + �2 ∗ N)) nodes have the maximum energy, denoting the node energy by E2.These nodes may be called as super nodes. Thus, we have the inequalities for the number ofnodes and their energy levels.

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� ∗ N > �2 ∗ N >(N − (� ∗ N + �2 ∗ N

))and E0 < E1 < E2

The total energy of the network, Tenergy , is given by the following expression.

Tenergy = � ∗ N ∗ E0 + �2 ∗ N ∗ E1 + (1 − � − �2) ∗ N ∗ E2 (2)

We will show that this model (2) can describe one-level, two-level, and three-level hetero-geneity depending on the value of θ. The bounds of θ are 0 and 1. When θ = 0, we have onlyone term in (2) as the first two terms in (2) become zero. For θ = 0, Tenergy in (2) containssuper nodes only, which signifies one-level heterogeneity. We may also call it homogeneitybecause the network contains only a single type of nodes. In this case, a node in the net-work has E2 energy. We impose suitable constraints so that the model contains normal nodesrather the super nodes in case of one-level heterogeneity. This can be obtained by definingthe following relation:

� = E2 − E0

n ∗ f(E1, E2)(3)

where n is a positive integer greater than 1 and f is a function of E1 and E2. In a very simpleform, we can have f either (E2 + E1) or (E2 − E1). The value of θ in (3) should be in theconsonant with the condition: E0 < E1 < E2.

Now, we will show that this model can describe two-level heterogeneity, i.e., the networkcontains only two types of nodes. For this, we find the value of θ, which is given by thesolution of the following equation:

1 − � − �2 = 0 (4)

Equation (4) is not an arbitrary; it basically diminishes the third term in (2), making thusthe model of two-level heterogeneity. Using (4), the model in (2) contains two types of

nodes: normal and advance nodes. Equation (4) has two solutions:((√

5)

− 1)

/2 and((√

5)

+ 1)

/2. Since θ is upper-bounded by 1 and((√

5)

+ 1)

/2 > 1, the valid solution

of (4) is((√

5)

− 1)

/2. For θ =((√

5)

− 1)

/2, the model (2) contains two types of

nodes that have energies E0 and E1.For three-level heterogeneity, we need to determine the range of �. The upper bound

of the range is((√

5)

− 1)

/2. Let the lower bound of � be θL that is to be determined.

The range of θ for three-level heterogeneity is �L < � <((√

5)

− 1)

/2. Taking f as

(E2 − E1) and θ from (3), we have

�L <E2 − E0

n ∗ (E2 − E1)<

((√5)

− 1)

/2 (5)

Let E1 = α1 + E0 and E2 = α2 +E1. From (5), we have

�L <α2 + α1

n ∗ α2

It can be written asα2

α1<

1

n ∗ �L − 1

Or

− α2

α1≥ 1

1 − n ∗ �L(6)

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Since LHS of inequality (6) is negative, we should have

1 − n ∗ �L < 0

This gives

1

n< �L (7)

From (5) can be written as

(E2 − E0) ≤n ∗

((√5)

− 1)

2∗ (E2 − E1) (8)

The inequality may be written as

n ∗((√

5)

− 1)

∗ E1 − 2 ∗ E0 ≤(

n ∗((√

5)

− 1)

− 2)

∗ E2 (9)

In this way, we have shown that the energy model in (2) can describe one-level, two-leveland three-level heterogeneity in a WSN.

5 Cluster Formation and Data Transmission

In this section, we discuss in general cluster formation, data collection, data aggregation, andthen data transmission to the base station. In our network of sensors, a sensor acts either as acluster head or simply a cluster member. We discuss the computation of the energy spent bythe cluster head and the cluster members in a cluster in collecting or transmitting the data.The energy spent in transmitting L-bit message by a sensor depends on the distance [4,5].

For short distance d , the energy consumed ETXS is given by

ETXS = L ∗ ∈elec + L ∗ ∈fs ∗ d2 if d ≤ d0 (10)

For long distance d , the energy consumed ETXL is given by

ETXL = L ∗ ∈elec + L ∗ ∈mp ∗ d4 if d > d0 (11)

where ∈elec signifies energy dissipated per bit per m2 and ∈fs signifies the energy to run thetransmitter or receiver circuitry and ∈mp are transmitter-amplifier model parameters.

The first terms in (10) and (11) signify the energy spent by the transmitter circuitry that isbasically related to the digital coding, modulation, filtering, etc. and the second terms signifythe energy spent in actual transmission of the message data of L-bits. We generally refer thistotal energy as the energy spent in transmission. The distance is short or long is decided onthe value of d0, also called as threshold, whose value is given by [4,5]

d0 =√

∈ f s

∈mp=

√10 ∗ 10−12

0.0013 ∗ 10−12 = 87.70 (12)

This threshold value is maximum and in practice less value of d0 is considered, e.g., 70, 75,85, etc.

The energy spent in receiving L-bits data is given by [4,5]

ERx = L ∗ ∈R (13)

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The energy spent in sensing L-bits data is given by [4,5]

ESx = L ∗ ∈S (14)

Here, ∈R and ∈S each are equal to ∈elec (i.e., ∈elec =∈R =∈S).The head node receives the data from several sensors, which are meant for monitor-

ing/sensing some activity. It is quite likely that the duplicate data may be received by thecluster head from different sensors as they are monitoring the same activity.

The energy spent in aggregating L-bits data is given by [4,5]

EDA = L ∗ ∈DA (15)

where, ∈DA = 5 nJ per message bit.Normally, the number of clusters are predetermined, say, 5 % and so, of the total nodes in

the network. Once the cluster head are decided, these heads broadcast advertisement mes-sage to all sensors. Depending upon the received signal energy (assuming residual energyis the only parameter for deciding cluster heads), each sensor node decides its cluster headand informs its decision to the cluster head that corresponds to the maximum received sig-nal energy. In our work, the cluster heads are selected based on the residual energy andthe node degree. The very first time, the residual energy of a node is equal to its initialenergy and after each iteration (iteration is defined later), it gets reduces by the amountspent in sensing or data transmission, etc. The degree of a node is the number of nodes inits sensing range. The energy spent by the cluster head to broadcast advertisement is givenby (10) as it is the short distance and the energy spent by a sensor node to inform its clus-ter head is also given by (10). In this way, the clusters are formed. It may be mentionedhere that there is very small probability to be two cluster heads within each other’s clus-ter range [12]. The sensors are inexpensive devices; they are normally deployed in abun-dant. All sensors gather data for (or sense) the same activity taken/taking place in thegiven area, there is a possibility of the same data collection by multiple sensors. Sinceall sensors send their data to their respective cluster heads, a cluster head may get dupli-cate data that need be discarded. The non-head nodes sense the area/collect the data byspending the energy according to (14) and send the sensed/collected data to their clusterheads by spending the energy according to (10). The head nodes receive the data from theirrespective cluster members and send it to the sink. The energy spent by the head nodesin receiving the data from cluster members is given by (13) and the energy spent by thehead nodes aggregating the data (removing the duplicate data) is given by (15) and theenergy spent by the head nodes in sending the received data to the sink is given by (11).Collecting the data from cluster members and sending to sink by a cluster head, we termit as iteration. The energy spent by the network containing total n nodes out of which kas the head nodes in an iteration consists of the energy spent by the cluster members insensing the data and sending it to their respective cluster heads and the energy spent bythe cluster heads in receiving the data from their respective cluster members, aggregatingthe data and then sending it to the sink. This data may be termed as one frame. Thus, inan iteration, one frame data is collected/sensed from the area and sent to the base station(sink).

The energy spent by a single non-head sensor is given by, assuming each message size ofL bits, for a single frame data (i.e., per iteration) is

Enh = L ∗ ∈elec +L ∗ ∈fs ∗ d2 (16)

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Fig. 4 Clusters with their cluster heads shown in different colors. (Color figure online)

The energy spent by a cluster head for a single frame data is given by

Eh = L ∗ ∈elec

(n

k− 1

)+ L ∗ ∈DA ∗

(n

k− 1

)+ L ∗ ∈elec +L ∗ ∈mp ∗d4 (17)

For simplicity, we uniformly divide n nodes into k clusters; each consists of n/k sensors,assuming n is divisible by k. In case n is not divisible by k, some clusters have one nodemore than other clusters and accordingly (17) can be modified for such clusters. Among n/k

sensors, one sensor is cluster head and the remaining(

nk − 1

)sensors are cluster members.

The first term in (17) signifies the energy spent by the cluster head in receiving the data from(nk − 1

)cluster members. The second term specifies the energy spent in aggregation of the

data received from(

nk − 1

)cluster members. The last two terms signify the energy spent

by the cluster head in transmission of the message to base station/sink. Figure 4 shows aninstance of clusters formed in three-level heterogeneity for non-fuzzy implementation. Inthis figure, the normal, advance, and super nodes have been denoted by circular (◦), star (*),and plus (+) marks, respectively. The sink or base station has been marked as X, situatedat the center of the region. The members of a cluster including cluster head, that has beenexplicitly pointed by in each cluster, are shown by the same color. In case of fuzzy implemen-tation, such types of clusters are formed; however, for repeated nature we have not showedthem.

The current cluster heads have sent the frame data to the sink and these head nodes aremarked as non-member, i.e., they cannot be considered to be selected as cluster heads till allthe sensor nodes in the network have become the cluster heads. In this way, one iteration iscomplete. In next iteration, another set of sensors from the unmarked sensors are selected ascluster heads depending on the residual energy and the node degree in case of non-fuzzy and

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for the fuzzy case the cluster heads depend upon the residual energy, node degree, and thedistance. The probability for fuzzy implementation is computed as follows:

Probabili t y = a ∗ Lre + b ∗ Lnd + c ∗ (Dm − Ld)

a ∗ Mre + b ∗ Mnd + c ∗ Md(18)

where a, b, and c are weights of residual energy, node density, and distance, respectively.Lre, Lnd , and Ld signify level values for residual energy, node density, and distance, respec-tively, and Mre, Mnd , and Md are maximum level values for residual energy, node density,and distance, respectively. In our work, the residual energy has values low, medium, and high,which correspond to level values as 0, 1, and 2, respectively. The node density has sparsely,medium, and densely that correspond to level values as 0, 1, and respectively. The distancehas values near, medium, far and the corresponding level values are 0, 1, 2. Thus, the valuesof Mre, Mnd , and Md each are 2 and each of Lre, Lnd , and Ld can have values as 0 or 1, or 2(because we have considered three membership functions, i.e. low, medium, high in case ofbattery power; sparsely, medium, densely in case of node density; and near, medium, far fordistance). For giving equal weightage to each of the parameters, i.e., residual energy, nodedensity, and distance, we can have a = b = c = 1. However, the residual energy normallyhas more weightage than the node density and distance, each. In literature, the values of a,b, c have been taken as 2, 1, 1, respectively. Selection of a cluster head (or rotating clusterhead) amongst the nodes is based on the probabilities.

These newly cluster heads form their clusters by broadcasting advertisement message.Once their clusters are formed, the cluster members collect the data and send to their clusterheads. The cluster heads aggregate the received data and then send to the base station/sink, itis the second frame. The second iteration is complete. We carry out further iterations as longas there are unmarked sensors (which have not been cluster heads). The number of iterationswhen all sensors have become cluster heads forms a round. It may happen that some of thesensors have exhausted their energies. These sensors will not able to sense/collect the data andhence will not participate in further clustering process. Such nodes are called dead nodes. AWSN contains redundant sensors, whose sole purpose is to monitor a given area for activities.Even if some sensors are dead, the remaining sensors will participate in clustering processand hence in data collection. When all sensors have depleted their energies, no clusteringprocess will take place and hence no data collection. This determines the network life timein our case.

In our work, a pre-specified percentage of the number of sensors nodes are taken as theinitial cluster heads that form their respective clusters by broadcasting advertisement messageand receiving responses from the nodes wishing to be cluster member. The cluster formationprocess is called TCP (cluster process). Each of the cluster heads collects the sensed datafrom their cluster members, aggregates it, and then sends the aggregated data to the basestation. Collecting, aggregating data, and sending the aggregated data by a cluster head tothe base station forms TNO (network operation) and the current cluster heads are marked asnon-candidates. The non-candidates will not participate in selection of cluster heads unlessall sensors have become cluster heads. This entire process starting from the cluster selectionto the data transmission by the cluster heads to base station, i.e., (TCP + TNO) forms a singleiteration. The iterations are carried out till all sensors have not become cluster heads in someiteration. The number of iterations, when all clusters have become cluster heads, forms oneround. In the beginning of a round, no sensor is non-candidate. The iterations and hence therounds are performed till there is a cluster. In other words, even if there is a single sensor thathas not depleted its energy, it will form a cluster of itself that perform data collection/sensing

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and sending it to the base station. Thus, load balancing is done automatically as it takes careof all sensors.

6 Results and Discussions

In this section, we discuss the implementation of HEED protocol for our heterogeneitynetwork model and call it as hetHEED. We have shown in the previous section that ournetwork model is capable to define one-level, two-level, and three-level heterogeneity of thewireless sensor networks. Accordingly, we call the implementation of HEED as one-levelHEED or hetHEED-1, two-level HEED or hetHEED-2 and three-level HEED or hetHEED-3heterogeneity, respectively. The heterogeneity affects the cluster head selection that in turnaffects the network lifetime. In original HEED, the probability for selecting a cluster headhas been calculated based on the residual energy and neighbor density of nodes. Since ourproposed protocol hetHEED is based on the original HEED, we use the same parameters forcluster head selection. In literature, one more parameter, i.e., the distance between a sensor andsink has also been considered for computing probability. The distance between the sink anda sensor can be computed based on the received signal energy. We incorporate this distanceparameter in our hetHEED and apply fuzzy logic to calculate the probability for cluster headselection and the corresponding hetHEED are denoted as HEED-FL, hetHEED-FL-2, andhetHEED-FL-3, respectively.

In our simulations, we consider random deployment of 100 number of sensor nodes ina square field of dimension 100×100 m2. We discuss simulation results for various valuesof �. For θ = 0, there are only normal nodes in the network and the corresponding WSNhas one-level heterogeneity. We may also call this network as homogeneous network and theimplementation of HEED is the original HEED protocol, which we denote as hetHEED-1.The value of � = (

√5 − 1)/2 defines a WSN that consists of normal and advance nodes

and the corresponding network is said to have two-level heterogeneity. The implementationof HEED for these types of networks is denoted by hetHEED-2.

For three-level heterogeneity, � should assume values in accordance with the inequality0 < � < (

√5−1)/2. The corresponding network has three types of nodes, namely, normal,

advance, and super nodes. The energy of a super node, E2, is computed from the values ofE0, E1 and � using (2) that must satisfy the inequality E0 < E1 < E2.

The hetHEED with fuzzy logic, i.e., HEED-FL, hetHEED-FL-2, and hetHEED-FL-3 usesame number of nodes (of respective types) in the network as the hetHEED-1, hetHEED-2,and hetHEED-3. The number of nodes in the network is taken as 100, which are assumed to benormal node for hetHEED-1 and HEED-FL. For hetHEED-2 and hetHEED-FL-2, numbersof normal and advance nodes are 61 and 39, respectively. For hetHEED-3 and hetHEED-FL-3, the number of normal, advance, and super nodes are 51, 26, and 23, respectively. It maybe noted that the number of normal, advance, and super nodes cannot be taken arbitrarily.We need to consider total number of nodes out of which the model parameter � determinestheir respective numbers. We may consider arbitrary value of the initial energy of a normalnode also the advance node, but the energy of a super node cannot be taken arbitrary, it isdetermined by (2).

For all three level of heterogeneity with and without fuzzy logic, we have carried outsimulations for large number of input parameters, i.e., by taking different energy levels ofthe normal nodes, advance nodes, super nodes, and various values of θ. In all cases, wegot similar types of results for each type of heterogeneity. However, we have shown resultsgraphically for one-level heterogeneity by taking the energy of a normal node as 0.5 J; two-

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Table 2 Simulation parameters

Description Symbol Value

N. of Sensors N 100

Sink position Sp (50, 50)

Threshold distance d0 70 m

Cluster radius Cr 25 m

Energy consumed by the amplifier to transmitat a shorter distance

∈fs 10 nJ/bit/m2

Energy consumed by the amplifier to transmit at a longer distance ∈mp 0.0013 pJ/bit/m4

Energy consumed in the electronics circuit totransmit or receive the signal

Eelec 50 nJ/bit

Energy for data aggregation ∈DA 5 nJ/bit/signal

Message size L 4,000 bits

Initial energy E0 0.5 J

1000 2000 3000 4000 5000 6000 7000

10

20

30

40

50

60

70

80

90

100

Number of Rounds

Num

ber

of A

live

Sens

or N

odes hetHEED-FL-3 level

hetHEED-FL-2 levelHEED-FLOriginal HEED (hetHEED -1 level)hetHEED-2 levelhetHEED-3 level

Fig. 5 Number of alive nodes versus number of rounds

level heterogeneity by taking the energies of normal and advanced nodes as 0.5 and 0.6 J,respectively; for three-level heterogeneity by taking the energies of normal, advance, andsuper nodes as 0.5, 0.6, and 2.0 J, respectively. The energy E2 = 2.0 J corresponds to the valueof θ = 0.51 and n = 2. The energy for a super node in the hetHEED-FL-3 has been taken asE2 = 0.8 J for θ = 0.51 and n = 3. We may mention that in the hetHEED the cluster headshave been decided based on two parameters (residual energy and node density), whereas inthe hetHEED with fuzzy logic the cluster heads have been decided based on three parameters(residual energy, node density, and distance). The input parameters used in our simulationsare summarily given in Table 2. The simulation time is 900 s, data packet size is 512 bitsand the bandwidth is taken as 1 Mbps.

We have computed the simulation results for getting different output parameters forhetHEED and hetHEED with fuzzy logic. Figure 5 shows how the energies of nodes getdrained with respect to the number of rounds for hetHEED-1 (original HEED), hetHEED-2,hetHEED-3, hetHEED-FL (original HEED with fuzzy logic), hetHEED-FL-2, hetHEED-FL-3. It is evident from the graphs shown in Fig. 5 that the nodes in the hetHEED-l die earlier

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Table 3 Number of rounds whenfirst and last nodes are dead(simulation time: 900 s, packetsize: 512 bits, bandwidth: 1Mbps)

Protocols First node dead Last node dead

Original HEED 490 1,200

hetHEED-2 level 500 1,476

hetHEED-3 level 502 4,262

HEED-FL 400 2,922

hetHEED-FL-2 level 998 5,278

hetHEED-FL-3 level 998 6,636

0 1000 2000 3000 4000 5000 6000 70000

10

20

30

40

50

60

70

80

90

100

No of Rounds

Tot

al E

nerg

y C

onsu

mpt

ion

Original HEED (hetHEED-1 level)hetHEED-2 levelhetHEED-3 levelHEED-FLhetHEED-FL-2 levelhetHEED-FL-3 level

Fig. 6 Residual energy in network versus number of rounds

than those of the hetHEED-2 and the nodes in hetHEED-2 die earlier than the hetHEED-3.In other words, increasing the level of heterogeneity increases the network lifetime. The het-HEED with fuzzy logic keeps some nodes alive for large number of rounds. The nodes in thehetHEED-2 die earlier than the hetHEED-FL-2. It indeed performs better than the hetHEED-3. Same is the case for hetHEED-3 and hetHEED-FL-3. In fact, in all the hetHEED protocols,the nodes die much earlier than the corresponding to the hetHEED with fuzzy logic protocols.Among all these, hetHEED-FL-3 performs the best as far as the number of alive nodes isconcerned. It is to mention that in the hetHEED-3 the energies have been taken as 0.5, 0.6,and 2.0 J, respectively, for the normal, advance and super nodes, whereas in the hetHEED-FL-3 the energies are as 0.5, 0.6, and 0.8 J, respectively, for the corresponding nodes. Eventaking less energy of the super nodes, the hetHEED-FL-3 performs much better than thehetHEED-3 (refer Fig. 5). We have also obtained the results for network lifetime (number ofrounds) when the first node has become dead and the last node has become dead as shownin Table 3. As evident from Table 3, as the level of heterogeneity increases, the number ofrounds increases in almost all the cases for the both first node dead and last node dead.

We have also computed simulation results how the energy of the network is dissipated withrespect to the number of rounds for the hetHEED and hetHEED with fuzzy logic for all levelsand they are shown in Fig. 6. As evident from Fig. 6, the energy of the network dissipatesrapidly for the hetHEED-1 as well as hetHEED-2 with respect to number of rounds. As thelevel of heterogeneity increases, the rate of energy consumption decreases. The HEED-FL(original HEED with fuzzy logic) performs better than the hetHEED-1 and hetHEED-2 both.

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0 500 1000 1500 2000 2500 3000 3500 4000 4500 50000

0.5

1

1.5

2

2.5

3

3.5

4

4.5x 104

Number of Rounds

Num

ber

of P

acke

ts S

ent t

o B

ase

Stat

ion

hetHEED -3hetHEED-FL- 2 levelHEED-FLOriginal HEED (hetHEED -1)hetHEED -2hetHEED-FL- 3 level

Fig. 7 Number of packets sent to base station with respect to number of rounds

The hetHEED-FL-2 performs better than all levels of the hetHEED in spite of the fact thatthe hetHEED-3 has more network energy than that of the hetHEED-FL-2. Thus, the rate ofenergy consumption is much slower in case the hetHEED with fuzzy logic than the hetHEEDfor all levels of heterogeneity.

Figure 7 shows the simulation results in terms of the number of packets sent to the basestation (BS) with respect to the number of rounds. The number of packets sent to the basestation increases as the number of rounds increases. This behavior is depicted in Fig. 7. Wealso observe that as the level of heterogeneity increases, the more number of packets are sentto the base station. In case of fuzzy logic implementation of the hetHEED, more packetsreach to the base station. Only the hetHEED-3 is able to send the packets for large number ofrounds (greater than 1,800 rounds), whereas the HEED-FL can send packets to base stationfor large number of round in spite of the less energy. Thus, in our proposed protocol, thenodes remain alive for longer time, more packets are sent to the base station, and the rate ofenergy consumption decreases, as the level of heterogeneity increases. We now discuss effectof the energy increase in the network on its lifetime for our proposed heterogeneous networkmodel. We have computed the increase in network lifetime with respect to that of the originalHEED for hetHEED-2, hetHEED-3, HEE-FL, hetHEED-FL-2, and hetHEED-FL-3, whichare given below.

hetHEED-2 level:

Number of sensor nodes = 100 (=61+39).Number of normal nodes =61;Number of advance nodes = 39;Energy of a normal sensor node = 0.5 J.Energy of an advance sensor node = 0.6 J.Total network energy = 61 × 0.5 + 39 × 0.6 = 53.9 J.Network lifetime= 1,476 h.Percentage increase in network energy = 7.8Percentage increase in network lifetime = 8.5

hetHEED-3 level:

Number of sensor nodes= 100 (=51 + 26 + 23).

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Number of normal nodes = 51;Number of advance nodes = 26;Number of super nodes = 23;Energy of a normal sensor node = 0.5 J.Energy of an advance sensor node = 0.6 J.Energy of a super sensor node = 2.0 J.Total network energy = 51 ×0.5+26 × 0.6 + 23 × 2 = 87.1 J.Network lifetime= 4,262 h.Percentage increase in network energy = 74.2Percentage increase in network lifetime = 213.38

HEED-FL (Original HEED with fuzzy logic):

Number of sensor nodes = 100.Energy of a sensor node = 0.5 J.Total network energy = 50 J.Network lifetime for original HEED = 1,360 hNetwork lifetime for HEED-FL = 2,922 hPercentage increase in network energy = 0.0Percentage increase in lifetime= 114.85.

hetHEED-FL-2 level:

Number of sensor nodes = 100 (=61+39).Number of normal nodes = 61;Number of advancel nodes = 39;Energy of a normal sensor node = 0.5 J.Energy of an advance sensor node = 0.6 J.Total network energy = 61×0.5+39×0.6 = 53.9 J.Network lifetime= 5,278 h.Percentage increase in network energy = 7.8Percentage increase in network lifetime = 288.08

hetHEED-FL-3 level:

Number of sensor nodes = 100 (=51+26 +23).Number of normal nodes = 51;Number of advance nodes = 26;Number of super nodes = 23;Energy of a normal sensor node = 0.5 J.Energy of an advance sensor node = 0.6 J.Energy of a super sensor node = 0.8 J.Total network energy = 51×0.5+ 26 × 0.6 + 23 × 0.8 = 59.5 J.Network lifetime = 6,636 h.Percentage increase in network energy = 19Percentage increase in network lifetime = 387.94

We observe from Table 4 that increasing the energy in network increases its lifetime inmuch proportion. This increase is very high in case of the hetHEED with fuzzy logic. In fact,without increasing in the network energy, the lifetime increases by 114.85 % when fuzzylogic is used. We have also computed other performance results that include throughput,traffic load, and aggregate delay as defined below.

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Table 4 Percentage increase innetwork energy andcorresponding increase innetwork lifetime for hetHEEDand HEED with fuzzy logic

Variant Increase in net-workenergy (%)

Increase in net-work lifetime(%)

hetHEED-2 level 7.8 8.50

hetHEED-3 level 74.2 213.38

HEED-FL 0.0 114.85

hetHEED-FL-2 level 7.8 288.08

hetHEED-FL-3 level 19.0 387.94

20 30 40 50 60 70 80 90 1000

5

10

15

20

25

30

35

40

45

Number of Sensors

Thr

ough

put (

Kbp

s)


Fig. 8 Throughput versus number of sensors

Let ts and tr denote the time instances when a particular packet is generated at source andreceived as destination. The total delay is defined as their difference, i.e.,

Total Delay = tr − ts

The aggregate delay is given by

Aggregate Delay = Total Delay

Total Receive packets

The throughput and traffic load are given by

Throughput = Total Receive packets ∗ packet size ∗ 8

Total Simulation time

Traffic Load = Total packets Sends ∗ packet size

The throughput, traffic load, and aggregate delay are shown in Figs. 8, 9, and 10, respectively.We observe from Fig. 8 that increasing the number of sensors increases the throughput of thenetwork. Furthermore, as the level of heterogeneity increases, the throughput also increases.For using fuzzy logic, the increase is higher than that of non-fuzzy implementation. The

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20 30 40 50 60 70 80 90 1000

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

Number of Sensors

Tra

ffic

Loa

d (K

bps)


Fig. 9 Traffic load versus number of sensors

20 30 40 50 60

Number of Sensors70 80 90 100

0

0.05

0.1

0.15

0.2

0.25

0.3

Agg

rega

te D

elay

(Se

c)


Fig. 10 Aggregate delay versus number of sensors

traffic load has also similar behaviour as shown in Fig. 9. In both graphs, the increase ratecomparatively much higher for hetHEED-3 level as compared to other cases. The aggregatedelay lies in a very small range except for the hetHEED-3 level as shown in Fig. 10.

7 Conclusions

In this paper, the HEED protocol has been discussed for the heterogeneous wireless sensornetwork. In this work, we have incorporated different level of heterogeneity, namely, one-

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level, two-level, and three-level heterogeneity in terms of the node energy and accordingly theimplementation of HEED has been named as hetHEED-1 (original HEED), hetHEED-2, andhetHEED-3, respectively. We have also implemented all these levels of heterogeneity usingfuzzy logic that considers distance in addition to the residual energy and node density forselecting cluster heads. Increasing heterogeneity level increases network lifetime in muchproportion as compared to the increase in the network energy. In fact, using fuzzy logicfor original HEED, the network lifetime increases by 114.85 % without any increase in thenetwork energy.

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Satish Chand did his M.Sc. in Mathematics from Indian Instituteof Technology, Kanpur, India and M.Tech. in Computer Science fromIndian Institute of Technology, Kharagpur, India and Ph.D. from Jawa-harlal Nehru University, New Delhi, India. Presently he is working asa Professor in Computer Engineering Division, Netaji Subhas Instituteof Technology, Delhi, India. Areas of his research interest are Multime-dia Broadcasting, Networking, Video-on-Demand, Cryptography, andImage processing.

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Samayveer Singh received his B.Tech. in Information Technologyfrom Uttar Pradesh Technical University, Lucknow, India in 2007 andhis M.Tech. in Computer Science & Engineering from National Insti-tute of Technology, Jalandhar, India, in 2010. He is pursuing his PhDin the Department of Computer Engineering, Netaji Subhas Institute ofTechnology, New Delhi, India. His research interest includes wirelesssensor networks.

Bijendra Kumar did his Bachelor of Engineering from H.B.T.I. Kan-pur, India. He has done his Ph.D. from Delhi University, Delhi, Indiain 2011. Presently he is working as an Assistant Professor in ComputerEngineering Division, Netaji Subhas Institute of Technology, Delhi,India. His areas of research interests are Video applications, water-marking, and Design of algorithms.

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