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Wireless Personal Communications
An International Journal
© Springer Science+Business Media New York 2014
10.1007/s11277-014-1629-y
Heterogeneous HEED Protocol for Wireless
Sensor Networks
Satish Chand 1 , Samayveer Singh 1 and Bijendra Kumar1
Netaji Subhas Institute of Technology, Sector-3, Dwarka, New Delhi , 110078, India
Satish Chand (Corresponding author)
Email: schand86@hotmail.com
Samayveer Singh
Email: samayveersingh@gmail.com
Bijendra Kumar
Email: bizender@rediffmail.com
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.

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) ∗ xμ
A
∑ (x)μA
(x)μ
A

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 E1
5√
= +E1 α1 E0 = +E2 α2 E1
<⊖L
+α2 α1
n ∗ α2
<
α2
α1
1
n ∗ − 1⊖L
− ≥
α2
α1
1
1 − n ∗ ⊖L
1 − n ∗ < 0⊖L
<
1
n
⊖L
( − ) ≤ ∗ ( − )E2 E0
n ∗ (( ) − 1)5√
2
E2 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
d
2
d0
d ETXL
= L ∗ + L ∗ if d >ETXL ∈elec ∈mp ∗ d
4
d0
∈elec
2
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
∗ d
2
= L ∗ ( − 1) + L ∗ ∗( − 1) + L ∗ +L ∗Eh ∈elec
n
k
∈DA
n
k
∈elec ∈mp ∗d
4
n k n/k
n k n k
n/k
( − 1)
n
k
( − 1)
n
k
( − 1)
n
k

(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 ∗M re M nd M d
,Lre Lnd
Ld
,M re M nd M d

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.
,M re M nd M d
,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 and
HEED 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 time
Traffic 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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