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Wireless Personal Communications

An International Journal

© Springer Science+Business Media New York 201410.1007/s11277-014-1629-y

Heterogeneous HEED Protocol for WirelessSensor Networks

Satish Chand 1 , Samayveer Singh 1 and Bijendra Kumar 1

Netaji Subhas Institute of Technology, Sector-3, Dwarka, New Delhi , 110078, India

Satish Chand (Corresponding author)Email: [email protected]

Samayveer SinghEmail: [email protected]

Bijendra KumarEmail: [email protected]

Published online: 6 February 2014

Abstract

One of the important protocols for increasing the network lifetime in wireless sensor networks

(WSNs) is hybrid energy efficient distributed (HEED) protocol. This protocol considers two

parameters for deciding the cluster heads, i.e., residual energy and node density and has been

designed for the homogeneous WSNs. In this paper, we consider the implementation 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 implementation of HEED is referred to as hetHEED-1,

hetHEED-2, and hetHEED-3, respectively. We also consider one more parameter, i.e., distance and

apply fuzzy logic to determine the cluster heads 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 the level of heterogeneity increases in the network, the nodes remain alive for

longer time and the rate of energy dissipation decreases. And also, increasing the heterogeneity level

helps sending more packets to the base station and increases the network lifetime. The increase in 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 increase in 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 number of packets to the base station, and has minimum

rate of energy dissipation.

mailto:[email protected]



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Keywords Sensor networks – Clustering – Network lifetime – Rounds – Load balancing –

Membership function – Fuzzy logic – Heterogeneity

Satish Chand

did his M.Sc. in Mathematics from Indian Institute of Technology, Kanpur, India and M.Tech. in

Computer Science from Indian Institute of Technology, Kharagpur, India and Ph.D. from Jawaharlal

Nehru University, New Delhi, India. Presently he is working as a Professor in Computer Engineering

Division, Netaji Subhas Institute of Technology, Delhi, India. Areas of his research interest are

Multimedia Broadcasting, Networking, Video-on-Demand, Cryptography, and Image processing.

Samayveer Singh

received his B.Tech. in Information Technology from Uttar Pradesh Technical University, Lucknow,

India in 2007 and his M.Tech. in Computer Science & Engineering from National Institute of

Technology, Jalandhar, India, in 2010. He is pursuing his PhD in the Department of Computer

Engineering, Netaji Subhas Institute of Technology, New Delhi, India. His research interest includes

wireless sensor networks.



Bijendra Kumar

did his Bachelor of Engineering from H.B.T.I. Kanpur, India. He has done his Ph.D. from Delhi

University, Delhi, India in 2011. Presently he is working as an Assistant Professor in Computer

Engineering Division, Netaji Subhas Institute of Technology, Delhi, India. His areas of research

interests are Video applications, watermarking, and Design of algorithms.

1 Introduction

The wireless communication is one of the important types of communication that requires no fixed

infrastructure. There are many situations where wireless communication can be deployed such as

volcano, battlefield monitoring, old building structure. It is generally used where the normal cabling is

difficult or financially impractical. The wireless communication done using the sensor devices is called

wireless sensor communication and the resultant network is called the wireless sensor network

(WSN). The WSNs are easily deployable, maintenance free, and provide fault-tolerant platform for

gathering data from the environment [1]. They are cost effective also because the sensors are very

cheap devices and do not require 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 comprehensive

conditions in their surroundings and transmit it to sink that in turn either processes it or forwards to the

data processing centre using internet. Currently, the wireless systems deal with 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 single hop (also called peer to peer

communication), the sensor nodes directly communicate with any other sensor node or with the base

station. In multihop communication, there may be a sequence of hops while communicating to the base



station from a sensor node. Deployment of sensors in a WSN can be deterministic or random

depending on the application. They can be stationary or location-aware, homogeneous or

heterogeneous in nature. Since the sensors are not supported by external battery, their energy must be

used very efficiently in order to monitor the area for longer time. One possible solution to have longer

lifetime of a WSN is to use more sensors, but it may increase collision and, in that case, a suitable

scheduling mechanism need be employed. Other solution of prolonging the lifetime of a WSN is to

employ the heterogeneity in sensor nodes [3]. There are three common types of resource

heterogeneity in a sensor node, namely, computational, link, and energy heterogeneity. In

computational heterogeneity, the heterogeneous node has more resources such as powerful

microprocessor and relatively more memory so that it can provide complex data processing and

longer-term storage. In link heterogeneity, the heterogeneous node has high-bandwidth and long-

distance network transceiver so that it can provide more reliable data transmission. Energy

heterogeneity means that the sensor nodes have different levels of energy. The computational and link

heterogeneities implicitly depend on the energy as these types of nodes consume more energy. Thus,

the energy based heterogeneity may be considered as the most dominating heterogeneity in WSNs. It

has been reported that providing heterogeneity in sensor nodes prolongs the network lifetime,

improves reliability of data transmission, and decreases 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 protocols consists of clustering or hierarchical protocols such as low energy

adaptive clustering hierarchy (LEACH) [4] and its different modifications such as LEACH-C,

LEACH-M [4, 5], threshold sensitive energy efficient sensor network protocol (TEEN) [6], adaptive

periodic threshold-sensitive energy efficient sensor network protocol (APTEEN) [7], power-efficient

gathering in sensor information systems [8], stable election protocol (SEP) [9], energy efficient

clustering scheme (EECS) [10], deterministic energy efficient clustering (DEEC) [11] protocol and

hybrid energy efficient distributed (HEED) [12]. Among these types of protocols, the HEED is one of

the most popular protocols as the cluster heads in this protocol are decided based on the residual

energy and degree of nodes. The degree of nodes distributes load among the cluster heads. In other

protocols, the cluster heads are selected based on the residual energy only and no load balancing is

done. In this paper, we discuss the HEED protocol for deploying the underlying network as our

heterogeneous network model in order to increase the lifetime. Our model can describe one- level,

two-level, and three-level heterogeneity 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

WSN to have equal amount of energy, for which the original HEED is implemented. We may also call

it as homogeneous HEED. The two-level and three-level heterogeneity assume the sensor nodes in a

WSN to be equipped with two and three energy levels, respectively, for which we call the

implementation of HEED as hetHEED-2 and hetHEED-3 protocols. The original HEED considers

two parameters—residual energy and node density to determine the cluster heads. In hetHEED-1,

hetHEED-2, and hetHEED-3, we consider the same two parameters to determine the cluster heads

so that we can compare their performance with respect to heterogeneity. We also consider one more

parameter, i.e., distance between a sensor and sink, in addition to residual energy and node density



and apply the fuzzy logic to calculate the probability in order to decide the cluster heads. The resultant

HEED implementation is named as HEED-FL (original HEED with fuzzy logic), hetHEED-FL-2

(hetHEED-2 with fuzzy logic), and hetHEED-FL-3 (hetHEED-3 with fuzzy logic). Increasing the

energy in network in order to make them heterogeneous increases the network lifetime, which is at

much higher side, especially in case of hetHEED-3 (74.2 % energy increase leads to 213.38 %

increase in network lifetime). Using fuzzy logic in HEED without increasing any energy in the network

increases the network lifetime by 114.85 % of that of the original HEED. Increasing the heterogeneity

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, fuzzy rulebase, fuzzy inference

engine, and defuzzifier. In Sect. 4, a heterogeneous model for WSNs is 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 collection and data transmission. The simulation

results are given Sect. 6 and, finally, the paper is concluded in Sect. 7.

2 Literature Review

The routing protocols for WSNs may be categorized into different classes based on the applications

such as location based, data-centric, mobility based, multipath based, QoS based, and hierarchical

[13]. The location based protocols utilize the position information of nodes to relay the data of the

desired regions rather than the whole network. Some of the important location based protocols are

minimum energy communication network [14], greedy anti-void routing [15], and geographical and

energy aware routing [16]. In the data centric routing protocols, 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 information via negotiation [17], directed diffusion [18], and Rumor

routing [19]. The multipath routing protocols such as sensor-disjoint multipath protocol [20] use

multiple paths to enhance the network performance. In QoS based routing protocols, the network

makes balance between the 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

protocols include sequential assignment routing [21], stateless protocol for real-time communication in

sensor networks [22], and energy-aware routing [23]. The hierarchical protocols, also called

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 transmission is performed.

The low energy adaptive clustering hierarchy (LEACH) [4] is the very first clustering protocol that

forms the clusters based on the received signal strength. In this protocol, 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 different variants 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 parameter about which a user is

interested and thus it is useful for time-critical applications. The TEEN protocol has been modified as

APTEEN protocol [7] that is meant for both time-critical events and periodic data collections. Lindsey

and Raghavendra discuss power efficient gathering in sensor information systems protocol [8], an

improved version of LEACH, that uses chains of sensor nodes. The data is transmitted from all sensor

nodes through their respective chains to a single node, called cluster head. The cluster head aggregates

the data to remove the duplicity and then transmits it to the base station or sink. It outperforms the

LEACH; however, due to 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 the

LEACH. In this protocol, a node becomes cluster head on the basis of weighted election probabilities

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 is used for periodical data gathering

applications using WSNs. It uses load balancing and energy efficiently. However, it requires global

knowledge of distances between the cluster-heads and base 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 the average energy of the network. The nodes having high initial and

residual energies have more chance of becoming cluster heads. The nodes nearer to the sink require

spending more energy than those farther because of the extra burden of the nodes within the

neighborhood of the base station. Thus, smaller clusters are formed using the nearer nodes to balance

the load among the cluster heads that fall in different regions and vice versa. This concept has been

discussed by Eshghi and Haghighat [24].

The HEED [12] protocol selects cluster heads based on their residual energy and node degrees. 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 nodes not covered by any cluster head

double their probability of becoming a cluster head. It has low overhead in terms of processing cycles

and message exchanged. This protocol does not assume any distribution of nodes or location

awareness. It also achieves fairly uniform cluster head distribution across the network and prolongs the

network lifetime besides supporting data aggregation. A variant of HEED protocol, called integrated

HEED (iHEED) [25], has integrated data aggregation in the multihop routing by considering data

aggregation operators such as AVG or MAX. It can serve both source and data driven applications.

Another variant of HEED by Huang and Wu [26] discusses a constant time clustering mechanism that

may be termed as an extended probabilistic algorithm for HEED protocol. In this algorithm, the nodes

having 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 Misense hierarchical cluster

based routing algorithm (MiCRA) [27], maintains the balanced energy consumption of nodes so that

the network lifetime increases. The paper [28] discusses the HEED for heterogeneous network model;



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however, no new heterogeneous network model is discussed in that paper. It uses two-level and

multilevel heterogeneous model from [11] and three-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 protocols perform better and same is the case with HEED-FL, hetHEED-

FL-2 and hetHEED-FL-3. In next 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.

Fig. 1

Fuzzy logic based system

The inputs to the system are crisp numbers. The fuzzifier transforms these crisp values into 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 called knowledgebase.

The output of the fuzzifier and the rules from the knowledgebase are given to the fuzzy inference 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 fuzzy values into crisp values. The

defuzzifier calculates the centroid and uses it to calculate the probability. The centroid is computed as

follows:

where, denotes the membership function of set A.

We have used Mamdani model [30] for inference engine because it is most widely used in applications

Centroid =∑ (x) ∗ xμA

∑ (x)μA

(x)μA



due to its simplicity. We consider three input parameters in our fuzzy system that include battery

power, node density, and distance between a sensor and the sink. Each of the 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 9 values—very weak, weak, little weak,

lower medium, medium, higher medium, little strong, strong, very strong (Fig. 2).

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 and one triangular; and

for output probability, two half trapezoidal and seven triangular, as shown in Fig. 3a–d, respectively

(Table 1).



Fig. 3

Fuzzy sets corresponding to fuzzy inputs and output parameters. a Fuzzy set for fuzzy input variable:battery power. b Fuzzy set for fuzzy input variable: node density. c Fuzzy set for fuzzy input variable:distance. d Fuzzy set for fuzzy output variable: probability



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)



(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

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)

We use fuzzy model for selecting the cluster heads. The node corresponding to maximum probability is

chosen as the cluster head. In next section, we discuss our heterogeneous network model.

4 Proposed Heterogeneity Network Model

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

All sensor nodes and base station are stationary after deployment; each is identified by a

unique ID.

Nodes are location-unaware, i.e. not equipped with GPS-capable antennae.

All nodes have similar capabilities (processing/communication), but different in terms of

energies.

Nodes are left unattended after deployment, meaning thereby battery recharge is not possible.

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.

Each node has the ability to aggregate data; as a result several data packets can be

compressed as one packet.

The distance between nodes can be computed based on the received signal strength.

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.

The radio link is symmetric such that energy consumption of data transmission from node 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 that

consists of three types of sensor nodes based on their energy levels. The nodes having more energy



(2)

(3)

(4)

are supposed to be costlier than those having less energy. Because of the high cost, the nodes having

maximum energy are assumed to be minimum in numbers. The nodes having minimum 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 nodes have minimum energy, where 0 1. We may

call them as the normal nodes and the energy of these types of nodes denoted as . The

nodes have more energy than the normal nodes. We may call these nodes as the advance nodes and

denote the node energy by . The remaining nodes have the

maximum energy, denoting the node energy by . These nodes may be called as super nodes.

Thus, we have the inequalities for the number of nodes and their energy levels.

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

We will show that this model (2) can describe one-level, two-level, and three-level heterogeneity

depending on the value of . The bounds of are 0 and 1. When 0, we have only one term in

(2) as the first two terms in (2) become zero. For 0, in (2) contains super nodes only,

which signifies one-level heterogeneity. We may also call it homogeneity because the network contains

only a single type of nodes. In this case, a node in the network has energy. We impose suitable

constraints so that the model contains normal nodes rather the super nodes in case of one-level

heterogeneity. This can be obtained by defining the following relation:

where n is a positive integer greater than 1 and is a function of and . In a very simple form,

we can have . The value of in (3) should be in the

consonant with the condition: .

Now, we will show that this model can describe two-level heterogeneity, i.e., the network contains

only two types of nodes. For this, we find the value of , which is given by the solution of the following

equation:

Equation (4) is not an arbitrary; it basically diminishes the third term in (2), making thus the 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: and . Since is upper-

bounded by 1 and , the valid solution of (4) is . For

, the model (2) contains two types of nodes that have energies and

.

For three-level heterogeneity, we need to determine the range of . The upper bound of the range is

⊖ ∗ N ≤ ⊖ ≤E0 ∗ N⊖2

E1 (N − (⊖ ∗ N + ∗ N))⊖2

E2

⊖ ∗ N > ∗ N > (N − (⊖ ∗ N + ∗ N)) and < <⊖2 ⊖2 E0 E1 E2

Tenergy

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

θ θ θ =θ = Tenergy

E2

⊖ =−E2 E0

n ∗ f( , )E1 E2

f E1 E2

f either ( + ) or ( − )E2 E1 E2 E1 θ

< <E0 E1 E2

θ

1 − ⊖ − = 0⊖2

(( ) − 1)/25√ (( ) + 1)/25√ θ

(( ) + 1)/2 > 15√ (( ) − 1)/25√θ = (( ) − 1)/25√ E0

E1

⊖

(( ) − 1)/2√



(5)

(6)

(7)

(8)

(9)

. Let the lower bound of be that is to be determined. The range of for

three-level heterogeneity is . Taking as and

from (3), we have

Let and . From (5), we have

It can be written as

Or

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

This gives

From (5) can be written as

The inequality may be written as

In this way, we have shown that the energy model in (2) can describe one-level, two-level and 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, and then data

transmission to the base station. In our network of sensors, a sensor acts either as a cluster head or

simply a cluster member. We discuss the computation of the energy spent by the 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].

(( ) − 1)/25√ ⊖ θL θ

< ⊖ < (( ) − 1)/2⊖L 5√ f ( − )E2 E1 θ

< < (( ) − 1)/2⊖L−E2 E0

n ∗ ( − )E2 E15√

= +E1 α1 E0 = +E2 α2 E1

<⊖L+α2 α1

n ∗ α2

<α2

α1

1n ∗ − 1⊖L

− ≥α2

α1

11 − n ∗ ⊖L

1 − n ∗ < 0⊖L

<1n

⊖L

( − ) ≤ ∗ ( − )E2 E0n ∗ (( ) − 1)5√

2E2 E1

n ∗ (( ) − 1) ∗ − 2 ≤ (n ∗ (( ) − 1) − 2)5√ E1 ∗ E0 5√ ∗ E2



(10)

(11)

(12)

(13)

(14)

(15)

For short distance , the energy consumed is given by

For long distance , the energy consumed is given by

where signifies energy dissipated per bit per m and to run the

transmitter or receiver circuitry and are transmitter-amplifier model parameters.

The first terms in (10) and (11) signify the energy spent by the transmitter circuitry that is basically

related to the digital coding, modulation, filtering, etc. and the second terms signify the energy spent in

actual transmission of the message data of L-bits. We generally refer this total energy as the energy

spent in transmission. The distance is short or long is decided on the value of , also called as

threshold, whose value is given by [4, 5]

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

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

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

Here, and each are equal to (i.e., .

The head node receives the data from several sensors, which are meant for monitoring/sensing some

activity. It is quite likely that the duplicate data may be received by the cluster head from different

sensors as they are monitoring the same activity.

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

where, 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 message to all

sensors. Depending upon the received signal energy (assuming residual energy is the only parameter

for deciding cluster heads), each sensor node decides its cluster head and informs its decision to the

cluster head that corresponds to the maximum received signal energy. In our work, the cluster heads

are selected based on the residual energy and the node degree. The very first time, the residual

energy of a node is equal to its initial energy and after each iteration (iteration is defined later), it gets

d ETXS

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

d ETXL

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

∈elec2 signifies the energy∈fs

∈mp

d0

= = = 87.70d0∈fs

∈mp

− −−−

√ 10 ∗ 10−12

0.0013 ∗ 10−12

− −−−−−−−−−−−−√d0

= L ∗ERx ∈R

= L ∗ESx ∈S

∈R ∈S ∈elec = = )∈elec ∈R ∈S

= L ∗EDA ∈DA

= 5∈DA



(16)

(17)

reduces by the amount spent in sensing or data transmission, etc. The degree of a node is the number

of nodes in its sensing range. The energy spent by the cluster head to broadcast advertisement is given

by (10) as it is the short distance and the energy spent by a sensor node to inform its cluster head is

also given by (10). In this way, the clusters are formed. It may be mentioned here that there is very

small probability to be two cluster heads within each other’s cluster range [12]. The sensors are

inexpensive devices; they are normally deployed in abundant. All sensors gather data for (or sense) the

same activity taken/taking place in the given area, there is a possibility of the same data collection by

multiple sensors. Since all sensors send their data to their respective cluster heads, a cluster head may

get duplicate data that need be discarded. The non-head nodes sense the area/collect the data by

spending the energy according to (14) and send the sensed/collected data to their cluster heads by

spending the energy according to (10). The head nodes receive the data from their respective cluster

members and send it to the sink. The energy spent by the head nodes in receiving the data from cluster

members is given by (13) and the energy spent by the head nodes aggregating the data (removing the

duplicate data) is given by (15) and the energy 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 term it as iteration. The energy spent by the network containing total nodes out of which

as the head nodes in an iteration consists of the energy spent by the cluster members in sensing the

data and sending it to their respective cluster heads and the energy spent by the cluster heads in

receiving the data from their respective cluster members, aggregating the data and then sending it to

the sink. This data may be termed as one frame. Thus, in an 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 of L bits, for a

single frame data (i.e., per iteration) is

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

For simplicity, we uniformly divide nodes into clusters; each consists of sensors, assuming

is divisible by . In case is not divisible by , some clusters have one node more than other

clusters and accordingly (17) can be modified for such clusters. Among sensors, one sensor is

cluster head and the remaining sensors are cluster members. The first term in (17)

signifies the energy spent by the cluster head in receiving the data from cluster members.

The second term specifies the energy spent in aggregation of the data received from

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 an instance of clusters formed in three-level

heterogeneity for non-fuzzy implementation. In this figure, the normal, advance, and super nodes have

n

k

= L ∗ +L ∗Enh ∈elec ∈fs ∗ d2

= L ∗ ( − 1) + L ∗ ∗( − 1) + L ∗ +L ∗Eh ∈elecnk

∈DAnk

∈elec ∈mp ∗d4

n k n/k

n k n k

n/k

( − 1)nk

( − 1)nk

( − 1)nk



(18)

been denoted by circular , star (*), and plus ( ) marks, respectively. The sink or base station has

been marked as X, situated at the center of the region. The members of a cluster including cluster

head, that has been explicitly pointed by in each cluster, are shown by the same color. In case of fuzzy

implementation, such types of clusters are formed; however, for repeated nature we have not showed

them.

Fig. 4

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

The current cluster heads have sent the frame data to the sink and these head nodes are marked as

non-member, i.e., they cannot be considered to be selected as cluster heads till all the sensor nodes

in the network have become the cluster heads. In this way, one iteration is complete. In next iteration,

another set of sensors from the unmarked sensors are selected as cluster heads depending on the

residual energy and the node degree in case of non-fuzzy and for the fuzzy case the cluster heads

depend upon the residual energy, node degree, and the distance. The probability for fuzzy

implementation is computed as follows:

where a, b, and c are weights of residual energy, node density, and distance, respectively. ,

and signify level values for residual energy, node density, and distance, respectively, and

, and 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

(∘) +

P robability =a ∗ + b ∗ + c ∗ ( − )Lre Lnd Dm Ld

a ∗ + b ∗ + c ∗Mre Mnd Md

,Lre Lnd

Ld

,Mre Mnd Md



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 distance has values near, medium, far and the

corresponding level values are 0, 1, 2. Thus, the values of , and each are 2 and

each of , and can have values as 0 or 1, or 2 (because we have considered three

membership functions, i.e. low, medium, high in case of battery power; sparsely, medium, densely in

case of node density; and near, medium, far for distance). For giving equal weightage to each of the

parameters, i.e., residual energy, node density, and distance, we can have a b c 1. However,

the residual energy normally has 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 cluster head) 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 cluster heads. The cluster

heads aggregate the received data and then send to the base station/sink, it is the second frame. The

second iteration is complete. We carry out further iterations as long as there are unmarked sensors

(which have not been cluster heads). The number of iterations when all sensors have become cluster

heads forms a round. It may happen that some of the sensors have exhausted their energies. These

sensors will not able to sense/collect the data and hence will not participate in further clustering

process. Such nodes are called dead nodes. A WSN 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 process and hence in data collection. When all sensors have depleted their

energies, no clustering process will take place and hence no data collection. This determines the

network life time in our case.

In our work, a pre-specified percentage of the number of sensors nodes are taken as the initial cluster

heads that form their respective clusters by broadcasting advertisement message and receiving

responses from the nodes wishing to be cluster member. The cluster formation process is called T

(cluster process). Each of the cluster heads collects the sensed data from their cluster members,

aggregates it, and then sends the aggregated data to the base station. Collecting, aggregating data, and

sending the aggregated data by a cluster head to the base station forms T (network operation) and

the current cluster heads are marked as non-candidates. The non-candidates will not participate in

selection of cluster heads unless all sensors have become cluster heads. This entire process starting

from the cluster selection to the data transmission by the cluster heads to base station, i.e., (T T

forms a single iteration. The iterations are carried out till all sensors have not become cluster

heads in some iteration. The number of iterations, when all clusters have become cluster heads, forms

one round. In the beginning of a round, no sensor is non-candidate. The iterations and hence the

rounds are performed till there is a cluster. In other words, even if there is a single sensor that has not

depleted its energy, it will form a cluster of itself that perform data collection/sensing and sending it to

the base station. Thus, load balancing is done automatically as it takes care of all sensors.

,Mre Mnd Md

,Lre Lnd Ld

= = =

CP

NO

CP +)NO



6 Results and Discussions

In this section, we discuss the implementation of HEED protocol for our heterogeneity network model

and call it as hetHEED. We have shown in the previous section that our network model is capable to

define one-level, two-level, and three-level heterogeneity of the wireless sensor networks.

Accordingly, we call the implementation of HEED as one-level HEED or hetHEED-1, two-level

HEED or hetHEED-2 and three-level HEED or hetHEED-3 heterogeneity, respectively. The

heterogeneity affects the cluster head selection that in turn affects the network lifetime. In original

HEED, the probability for selecting a cluster head has been calculated based on the residual energy

and neighbor density of nodes. Since our proposed protocol hetHEED is based on the original HEED,

we use the same parameters for cluster head selection. In literature, one more parameter, i.e., the

distance between a sensor and sink has also been considered for computing probability. The distance

between the sink and a sensor can be computed based on the received signal energy. We incorporate

this distance parameter in our hetHEED and apply fuzzy logic to calculate the probability for cluster

head selection and the corresponding hetHEED are denoted as HEED-FL, hetHEED-FL-2, and

hetHEED-FL-3, respectively.

In our simulations, we consider random deployment of 100 number of sensor nodes in a square field

of dimension 100 100 m . We discuss simulation results for various values of . For ,

there are only normal nodes in the network and the corresponding WSN has one-level heterogeneity.

We may also call this network as homogeneous network and the implementation of HEED is the

original HEED protocol, which we denote as hetHEED-1. The value of

defines a WSN that consists of normal and advance nodes and the corresponding network is said to

have two-level heterogeneity. The implementation of HEED for these types of networks is denoted by

hetHEED-2.

For three-level heterogeneity, should assume values in accordance with the inequality

. The corresponding network has three types of nodes, namely, normal,

advance, and super nodes. The energy of a super node, , is computed from the values of

and using (2) that must satisfy the inequality .

The hetHEED with fuzzy logic, i.e., HEED-FL, hetHEED-FL-2, and hetHEED-FL-3 use same

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 be normal node for

hetHEED-1 and HEED-FL. For hetHEED-2 and hetHEED-FL-2, numbers of 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 may be 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 determines their respective numbers. We may consider

arbitrary value of the initial energy of a normal node also the advance node, but the energy of a super

node cannot be taken arbitrary, it is determined by (2).

× 2 ⊖ θ = 0

⊖ = ( − 1)/25√

⊖0 < ⊖ < ( − 1)/25√

E2

,E0 E1 ⊖ < <E0 E1 E2

⊖



For all three level of heterogeneity with and without fuzzy logic, we have carried out simulations for

large number of input parameters, i.e., by taking different energy levels of the normal nodes, advance

nodes, super nodes, and various values of . In all cases, we got similar types of results for each type

of heterogeneity. However, we have shown results graphically for one-level heterogeneity by taking

the energy of a normal node as 0.5 J; two-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, and super nodes as 0.5, 0.6, and 2.0 J, respectively. The energy 2.0 J

corresponds to the value of 0.51 and n 2. The energy for a super node in the hetHEED-FL-3

has been taken as 0.8 J for 0.51 and n 3. We may mention that in the hetHEED the

cluster heads have been decided based on two parameters (residual energy and node density),

whereas in the 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 simulations

are summarily given in Table 2. The simulation time is 900 s, data packet size is 512 bits and the

bandwidth is taken as 1 Mbps.

Table 2

Simulation parameters

Description Symbol Value

N. of Sensors N 100

Sink position (50, 50)

Threshold distance 70 m

Cluster radius 25 m

Energy consumed by the amplifier to transmit at a shorter distance 10 nJ/bit/m

Energy consumed by the amplifier to transmit at a longer distance 0.0013 pJ/bit/m

Energy consumed in the electronics circuit to transmit or receive the signal 50 nJ/bit

Energy for data aggregation 5 nJ/bit/signal

Message size L 4,000 bits

Initial energy 0.5 J

We have computed the simulation results for getting different output parameters for hetHEED and

hetHEED with fuzzy logic. Figure 5 shows how the energies of nodes get drained with respect to the

θ

E2 =θ = =

E2 = θ = =

Sp

d0

Cr

∈fs 2

∈mp 4

Eelec

∈DA

E0



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 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 hetHEED with fuzzy logic keeps some nodes alive for large

number of rounds. The nodes in the hetHEED-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 is concerned. 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. Even taking less energy

of the super nodes, the hetHEED-FL-3 performs much better than the hetHEED-3 (refer Fig. 5). We

have also obtained the results for network lifetime (number of rounds) when the first node has become

dead and the last node has become dead as shown in Table 3. As evident from Table 3, as the level of

heterogeneity increases, the number of rounds increases in almost all the cases for the both first node

dead and last node dead.

Table 3

Number of rounds when first and last nodes are dead (simulation time: 900 s, packet size: 512 bits,bandwidth: 1 Mbps)

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



Fig. 5

Number of alive nodes versus number of rounds

We have also computed simulation results how the energy of the network is dissipated with respect to

the number of rounds for the hetHEED and hetHEED with fuzzy logic for all levels and they are shown

in Fig. 6. As evident from Fig. 6, the energy of the network dissipates rapidly for the hetHEED-1 as

well as hetHEED-2 with respect to number of rounds. As the level 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. The hetHEED-FL-2 performs better than all levels of the

hetHEED in spite of the fact that the hetHEED-3 has more network energy than that of the hetHEED-

FL-2. Thus, the rate of energy consumption is much slower in case the hetHEED with fuzzy logic than

the hetHEED for all levels of heterogeneity.

Fig. 6

Residual energy in network versus number of rounds



Figure 7 shows the simulation results in terms of the number of packets sent to the base station (BS)

with respect to the number of rounds. The number of packets sent to the base station increases as the

number of rounds increases. This behavior is depicted in Fig. 7. We also observe that as the level of

heterogeneity increases, the more number of packets are sent to the base station. In case of fuzzy logic

implementation of the hetHEED, more packets reach to the base station. Only the hetHEED-3 is able

to send the packets for large number of rounds (greater than 1,800 rounds), whereas the HEED-FL

can send packets to base station for large number of round in spite of the less energy. Thus, in our

proposed protocol, the nodes remain alive for longer time, more packets are sent to the base station,

and the rate of energy consumption decreases, as the level of heterogeneity increases. We now

discuss effect of the energy increase in the network on its lifetime for our proposed heterogeneous

network model. We have computed the increase in network lifetime with respect to that of the original

HEED for hetHEED-2, hetHEED-3, HEE-FL, hetHEED-FL-2, and hetHEED-FL-3, which are given

below.

Fig. 7

Number of packets sent to base station with respect to number of rounds

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 7.8

Percentage increase in network lifetime 8.5

hetHEED-3 level:

Number of sensor nodes 100 ( 51 26 23).



Number of super nodes 23;



Energy of a super sensor node 2.0 J.

Total network energy 51 0.5 26 0.6 23 2 87.1 J.




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 h

Network lifetime for HEED-FL 2,922 h


Percentage increase in lifetime 114.85.

hetHEED-FL-2 level:

Number of sensor nodes 100 ( 61 39).


Number of advancel nodes 39;


=

=

= = + +

=

=

=

=

=

=

= × + × + × =

=

=

=

=

=

=

=

=

=

=

= = +

=

=

=




Total network energy 61 0.5 39 0.6 53.9 J.




hetHEED-FL-3 level:

Number of sensor nodes 100 ( 51 26 23).



Number of super nodes 23;



Energy of a super sensor node 0.8 J.

Total network energy 51 0.5 26 0.6 23 0.8 59.5 J.


Percentage increase in network energy 19


We observe from Table 4 that increasing the energy in network increases its lifetime in much

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 fuzzy logic is used. We

have also computed other performance results that include throughput, traffic load, and aggregate

delay as defined below.

=

= × + × =

=

=

=

= = + +

=

=

=

=

=

=

= × + × + × =

=

=

=



Table 4

Percentage increase in network energy and corresponding increase in network lifetime for hetHEED andHEED with fuzzy logic

Variant Increase in network energy (%) Increase in network 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

Let and denote the time instances when a particular packet is generated at source and received

as destination. The total delay is defined as their difference, i.e.,

The aggregate delay is given by

The throughput and traffic load are given by

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 the network.

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 traffic load has also similar

behaviour as shown in Fig. 9. In both graphs, the increase rate comparatively much higher for

hetHEED-3 level as compared to other cases. The aggregate delay lies in a very small range except

for the hetHEED-3 level as shown in Fig. 10.

ts tr

Total Delay = −tr ts

Aggregate Delay =Total Delay

Total Receive packets

Throughput =Total Receive packets ∗ packet size ∗ 8

Total Simulation timeTraffic Load = Total packets Sends ∗ packet size



Fig. 8

Throughput versus number of sensors

Fig. 9

Traffic load versus number of sensors



1.

2.

Fig. 10

Aggregate delay versus number of sensors

7 Conclusions

In this paper, the HEED protocol has been discussed for the heterogeneous wireless sensor network.

In this work, we have incorporated different level of heterogeneity, namely, one-level, two-level, and

three-level heterogeneity in terms of the node energy and accordingly the implementation of HEED has

been named as hetHEED-1 (original HEED), hetHEED-2, and hetHEED-3, respectively. We have

also implemented all these levels of heterogeneity using fuzzy logic that considers distance in addition

to the residual energy and node density for selecting cluster heads. Increasing heterogeneity level

increases network lifetime in much proportion as compared to the increase in the network energy. In

fact, using fuzzy logic for original HEED, the network lifetime increases by 114.85 % without any

increase in the network energy.

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