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Akbar et al. EURASIP Journal on Wireless Communications and
Networking (2016) 2016:66
DOI 10.1186/s13638-016-0552-1
RESEARCH Open Access
Sink mobility aware energy-efficient
network integrated super heterogeneous
protocol for WSNs
Mariam Akbar1, Nadeem Javaid1*, Muhammad Imran2, Naeem Amjad1, Majid Iqbal Khan1
and Mohsen Guizani3
Abstract
In this paper, we propose Balanced Energy-Efficient Network Integrated Super Heterogeneous (BEENISH), improved
BEENISH (iBEENISH), Mobile BEENISH (MBEENISH), and improved Mobile BEENISH (iMBEENISH) protocols for
heterogeneous wireless sensor networks (WSNs). BEENISH considers four energy levels of nodes and selects cluster
heads (CHs) on the base of residual energy levels of nodes and average energy level of the network, whereas iBEENISH
dynamically varies the CHs selection probability in an efficient manner leading to increased network lifetime. We also
present a mathematical sink mobility model and validate this model by implementing it in BEENISH (resulting in
MBEENISH) and iBEENISH (resulting in iMBEENISH). Finally, simulation results show that BEENISH, MBEENISH, iBEENISH,
and iMBEENISH protocols outperform contemporary protocols in terms of stability period, network lifetime, and
throughput.
Keywords: Mobility management, Clustering, Heterogeneous, WSNs, Energy efficient, Routing, Super heterogeneous
1 Introduction
A wireless sensor network (WSN) is a collection of small
sized sensors (nodes) which are deployed in the area of
interest. These nodes are able to sense different param-
eters and can communicate with each other as well as
communicate with the sink (also called base station (BS)).
These nodes operate on small batteries and have limited
processing and limited wireless communication capabili-
ties [1, 2]. Nodes are independent when deployed in the
fieldbecausetheyareabletoself-configureandsurvive.
However, it is difficult to re-charge them. So, energy con-
sumption of these nodes should be minimum in order to
achieve an appreciable network lifetime. Routing proto-
cols play a key role in achieving longer network lifetime.
To achieve fault tolerance, usually, WSNs consist of hun-
dreds or even thousands of sensor nodes [3–5]. WSNs
are used in a variety of applications such as military
surveillance, patient monitoring, environmental monitor-
ing, traffic transportation, and vibration monitoring [6].
*Correspondence: nadeemjavaidqau@gmail.com
1COMSATS Institute of Information Technology, Islamabad, Pakistan
Full list of author information is available at the end of the article
Considering the reduced capabilities of nodes, commu-
nication with the BS could be conceived without routing
protocols. With this assumption, the flooding technique
[7] becomes prominent due to its simplicity in imple-
mentation. In this technique, a source node initiates a
data broadcast. Consecutive retransmissions of the source
data by neighboring nodes are then made to ensure data
delivery at the intended destination. However, this imple-
mentation simplicity brings about many drawbacks: nodes
receive multiple copies of the same data, different nodes
transmit copies of the same data, nodes have limited
resources but these do not limit their functionalities, etc.
In order to overcome these drawbacks, gossiping [8] has
been introduced. Instead of transmitting data to all neigh-
bors, the gossiping technique transmits data to selected
neighbors. This technique avoids implosion; however,
resource blindness and overlapping are still present. More
importantly, these drawbacks become more prominent as
the node density is increased. Due to drawbacks in exist-
ing techniques, development of energy-efficient routing
protocols is very much necessary now. Soon after network
establishment, routing protocols take charge of construct-
ing as well as maintaining routes between nodes and the
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Akbar et al. EURASIP Journal on Wireless Communications and Networking (2016) 2016:66 Page 2 of 19
BS. The ways in which routing protocols perform route
discovery and maintenance make these protocols suitable
for certain applications.
WSNs are of two types: homogeneous and heteroge-
neous. In the first type, all nodes when deployed in
the network are initially equipped with the same energy
levels. However, in the second type, nodes before the
start of network operation are equipped with different
energy levels. Low Energy Adaptive Clustering Hierar-
chy (LEACH) [9] and Hybrid Energy-Efficient Distributed
(HEED) protocols [10] are clustering routing protocols
which are especially designed for homogeneous WSNs. As
a consequence, these schemes do not perform well in het-
erogeneous networks, since they are designed solely for
homogeneous environments. On the other hand, in het-
erogeneous environments, LEACH and HEED are unable
to discriminate between nodes with high energy (that is,
they have a longer lifetime) and nodes which posses low
energy (die more quickly). On the other hand, cluster-
ing protocols for heterogeneous environments are effi-
ciently designed to tackle heterogeneity in the network
[4, 5]. Smaragdakis et al. [11] proposed a Stable Elec-
tion Protocol (SEP) for two-level heterogeneous WSNs.
SEP considers two types of nodes according to their
initial energies: normal and advanced. Advanced nodes
are equipped with more energy than the normal ones.
Distributed Energy-Efficient Clustering (DEEC) [12] and
Developed DEEC (DDEEC) [13] start from two energy
levels, whereas Enhanced DEEC (EDEEC) [14] starts from
three energy levels. Afterwards, these three approaches
will be generalize to support multi-energy levels.
In this paper, we propose BEENISH protocol, where a
network has four different energy levels of nodes: nor-
mal, advanced, super, and ultra-super. In this scheme,
normal nodes have the less initial energy level as com-
pared to ultra-super nodes that have the highest initial
energy level. Following the same principle as of LEACH
and DEEC, BEENISH rotates the CHs among nodes. In
BEENISH, selection of CHs follow the probability that is
the ratio between residual energy of each node and aver-
age energy of the network. Based on the residual energy,
BEENISH chooses different epochs for each of the nodes.
Nodes with higher energy are more often selected as CHs
as compared to the lower energy ones. In some cases,
ultra-super, super, and advanced nodes are more punished
than the normal ones in BEENISH. iBEENISH solves this
problem by dynamically adjusting the CH selection proba-
bility. Results show that BEENISH and iBEENISH achieve
longer stability periods, enhanced network lifetime, and
increased number of messages sent to the BS as compared
to DEEC, DDEEC, and EDEEC, respectively. The sink
mobility version of the proposed BEENISH and iBEEN-
ISH perform better than the non-sink mobility versions in
terms of the selected performance evaluation parameters.
This work is an extension of our previously published
protocol BEENISH [15].
The remainder of this paper is organized as follows.
Section 2 presents the related work. Section 3 presents
the four-level heterogeneous WSN model. Our proposed
routing schemes are described in Section 4. Section 5
contains the sink mobility model. Section 6 illustrates
the simulation results. Section 7 concludes our research
work.
2 Related work
In order to achieve energy efficiency at the network layer
in WSNs, many routing protocols have been proposed
(Table 1). These protocols decide the routing path for
delivering the data to the end station [16]. Generally, rout-
ing protocols can be divided into three categories: (i) flat
routing protocols, (ii) cluster-based routing protocols, and
(iii) location-based routing protocols. Nodes perform sim-
ilar tasks, there exists no structure for routing, nodes
are connected, and they forward data through connected
nodes, in the first category of routing protocols. Whereas
in the second category, network is divided into clusters
and nodes select CH and send data through CH. As the
name indicates, the third category exploits nodes’ location
for routing. Among these categories, cluster-based rout-
ing protocols are the most energy-efficient ones. There-
fore, category number two is our focal point in this
research work.
Heinzelman et al. [9] introduced a clustering algorithm
for homogeneous WSNs; LEACH, which is based on
probability nodes, select themselves as CHs. They divide
the protocol operation into two phases: setup phase and
steady state phase. In the setup phase, two tasks are
performed: selection of CHs and association of cluster
members with CHs. Every node generates a random num-
ber (between 0 and 1) and compares this random number
with a pre-defined threshold value. If the generated ran-
dom number is greater than the pre-defined threshold
value and the node has not been selected as CH for the last
1
prounds (pis the probability of CH selection), then it is
selected as the CH for the current round. The CH node(s)
sends an association message to the non-CH nodes to
inform them that it is the CH now. On reception, the
non-CH nodes respond to the nearest CH node by send-
ing a confirmation message. Soon after the setup phase,
the steady state phase begins with data transmission from
nodes to CHs and then from CHs to the BS. All these
transmissions and receptions are performed within the
allocated Time Division Multiple Access (TDMA) slots
while keeping in mind that these allocations are centrally
controlled by the BS.
In EACLE [17], the route selection is executed inde-
pendently after the CH selection. This two-phase control
approach increases the overheads and reduces the battery
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Akbar et al. EURASIP Journal on Wireless Communications and Networking (2016) 2016:66 Page 3 of 19
Table 1 Comparison of the state-of-the-art work
Technique Features Domain Flaws/deficiencies Results achieved
LEACH [9] Clustering algorithm Homogeneous WSNs Transmissions and receptions within the allocated TDMA
only, cluster heads are elected randomly hence optimal
number and distribution of cluster heads cannot be ensured
and cannot be used with large-scale WSNs
Better network lifetime, high data delivery
ratio
HEED [10] Hybrid but fully distributed
clustering scheme
Multi-hop WSN clustering
algorithm
More CHs are generated than the expected number
and this accounts for unbalanced energy consumption in
the network, significant overhead in the network causes
noticeable energy dissipation resulting in lower network
lifetime
Uniform CH distribution across the network
and load balancing; multi-hop fashion
between the CHs and the BS promote more
energy conservation and scalability
SEP [11] Hierarchically clustered
scheme
Heterogeneous network for
WSNs
Supports only static nodes, does not support more than two
levels of hierarchy in terms of energy
Weighted election probability for becoming
a CH, longer stability period scalable
DEEC [12] Clustering-based
algorithm
Heterogeneous aware network
for WSNs
Overhead and complexity of forming clusters in multiple
levels implementing threshold-based functions
CH selected on the basis of probability of
ratio of residual energy and average energy
of the network, a node having more energy
has more chances to be a CH. It prolongs the
lifetime of the network
DDEEC [13] Energy-aware adaptive
clustering based algorithm
Heterogeneous network for
WSNs
As the initial energy of nodes is reduced and as time
passed by, advanced nodes will have the same CH selection
probability like the normal ones.
Permits to balance CH selection on the basis
of residual energy, a node having more
energy has more chances to be a CH. It
prolongs the lifetime of the network.
EDEEC [14] Energy-aware adaptive
clustering-based
algorithm
Heterogeneous network for
WSNs
3 types of nodes involved, more level of complexity involved More data packets received at base station, a
node having more energy has more chances
to be a CH. It prolongs the lifetime of the
network and the stability period.
BEENISH [15] Multi-level energy-based
scheme
Heterogeneous network for
WSNs
4 types of nodes involved, more level of complexity
involved, ultra-super, super, and advanced nodes are more
punished than the normal ones
Longer stability periods, enhanced network
lifetime increased number of messages sent
to the BS
EDR [16] Data routing scheme Data centric routing for WSNs Limited to data centric routing, does not support
cluster-based routing, improper load balancing
Ability to use in both event-driven and
query-driven applications, ensuring shortest,
routing path, transmitting very less number
of packets, significant power savings
EACLE [17] Tree-rooted distributed
clustering scheme
Transmission power controlled
WSNs
Does not support outdoor wireless channel model, only a
single sink for hundred of sensors deployed
Avoids packet collision, facilitates packet
binding, energy-efficient
HRLS [19] Hierarchical location
service based scheme
Practical distributed location
serviced WSNs
More energy is consumed in the computation processes Provides sink location information in a
scalable and distributed manner, each sink
in HRLS distributively constructs its own
hierarchy of grid rings
TTDD [23] Geographic routing-based
scheme
Low power scheme WSNs for
efficient data delivery
Forwarding path is not the shortest path and may lead to
large latency for longer path; grid structure formation and
query flooding cost large energy consumption
sensor nodes can productively establish a
structure to set up forwarding information,
effective in high mobility scenarios, better
suited to event-detecting WSNs
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Akbar et al. EURASIP Journal on Wireless Communications and Networking (2016) 2016:66 Page 4 of 19
Table 1 Comparison of the state-of-the-art work (Continued)
Technique Features Domain Flaws/deficiencies Results achieved
DLA [27]] Localization-based
scheme
Spatially constrained WSNs More energy is consumed in the computation processes Position estimation performed by each node
in an iterative manner, constraints enable
nodes to update their positions on regular
intervals; for reducing energy consumption,
a stopping criteria for wireless transmissions
has been introduced
VGDRA [28] Grid-based dynamic
scheme
Dynamic route adjustment
technique for WSNs
Only a few nodes are able to adjust their data routes for data
delivery
Minimizes the remonstration cost of routes
and maintain optimal routes near the mobile
sink stop which minimizes the energy
consumption of nodes, improves network
lifetime
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Akbar et al. EURASIP Journal on Wireless Communications and Networking (2016) 2016:66 Page 5 of 19
power, which shortens the lifetime of the WSNs. To cope
with this problem, the authors proposed a clustering-
based routing protocol Power Aware Routing and Clus-
tering (PARC) Scheme for WSNs which reduces these
overheads.
HEED [10] is a distributed clustering algorithm which
stochastically selects the CHs. This hybrid approach
selects CHs on the basis of probability and minimizes
the energy cost by association mechanism. This algorithm
exploits the availability of multiple transmission power
levels of nodes and correlates the selection probability of
each node to its residual energy.
In [18], authors proposed a novel method for mobile
sink operations in which the probe priority of the mobile
sink is determined from data priority to increase the QoS.
They use the mobile sink to reduce the routing hot spot.
In the SEP protocol proposed in [11], every protocol
in which every sensor node in a heterogeneous two-level
hierarchical network independently selects itself as a CH
based on its initial energy.
Qing et al. [12] proposed the DEEC protocol in which
the CH selection is based on the probability of the ratio
of residual energy of nodes and the average energy of the
network. In this algorithm, a node with a higher energy
has more chances to be selected as CH.
The DDEEC, proposed in [13], selects CHs on the basis
of residual energy of nodes. Thus, this process makes the
advanced nodes more probable to be selected as CHs dur-
ing the initial rounds as compared to the normal nodes. As
the initial energy of nodes is reduced and as time passed
by, advanced nodes will have the same CH selection prob-
ability like the normal ones.
Saini and Sharma [14] proposed the EDEEC proto-
col which extends the concept of heterogeneity to three
energy levels by adding the concept of super nodes.
Authors in [19] introduced the Hierarchical Ring Loca-
tion Service (HRLS) protocol, a practical distributed loca-
tion service which provides sink location information in
a scalable and distributed manner. In contrast to existing
hierarchical-based location services, each sink in HRLS
distributively constructs its own hierarchy of grid rings.
In [20], authors proposed a new independent structure-
based routing protocol which implements a sink mobility
and provides scalability by exploiting k-level indepen-
dent grid structure for data dissemination from source
to destination. However, independent of the number of
movement of both sinks and events, the propped protocol
does not construct any additional routing structure.
Zhao et al. [21] proposed a framework to maximize the
lifetime of WSNs by using a mobile sink. They formulated
linear programming models for static as well as mobile
sinks. Within a predefined delay tolerance level, each node
does not need to send the data immediately as it becomes
available. Instead, a node can store data temporarily and
transmit it when the mobile sink arrives at the most favor-
able location for achieving extended network lifetime.
Authors in [22] focused on the upper bound of the total
distance traveled by the mobile sink. Authors believe that
the inter-transition distance between any two successive
positions of a mobile sink must be restricted to avoid data
loss. Also, considering the overhead on a routing tree con-
struction at each sojourn location of the mobile sink, it is
required that the mobile sink sojourns for at least a certain
duration at each of its sojourn locations.
In [23], Luo and Hubaux jointly considered sink mobility
and routing to maximize data collection during the net-
work lifetime. However, authors in [21–23] do not exploit
clustering, whereas we do so in order to prolong the net-
work lifetime. Mobility of actors has been exploited in
[24–26] to improve or recover connected coverage lost
duetofailureofanactor.
A distributed localization technique has been presented
in [27] for WSNs. In this technique, position estimation is
performed by each node in an iterative manner by solv-
ing spatially constrained local programs. On the bases of
range and estimated position(s), the defined constraints
enable the nodes to update their positions on regular
intervals. In order to reduce energy consumption of the
network, a stopping criteria for wireless transmissions has
been introduced.
A Virtual Grid-Based Dynamic Routes Adjustment
(VGDRA) scheme is presented in [28]. It minimizes the
remonstration cost of routes and maintain optimal routes
near the mobile sink stop. They design set of rules for
communication through which few nodes adjust their
routes for data delivery. Through this strategy, they min-
imize the energy consumption of remaining nodes. As
a result, this scheme improves network lifetime as com-
pared to the existing schemes.
Ourproposedschemeisahybridthatutilizestheben-
efits of both clustering and sink mobility. To maximize
the network efficiency, we consider a four-level hetero-
geneous network, where the network field is divided into
clusters. Cluster formation method and CH selection cri-
teria are defined in the next sections. The CH that has
minimum energy, mobile sink stops on its location and
directly gathers data from nodes. In this way CH mini-
mizes the energy consumption and stays alive for a longer
time.
3 Four-level heterogeneous WSN model
A WSN can have nodes with different initial energies.
Such kind of network is heterogeneous where initial ener-
gies of nodes are different. In our proposed scheme, we
consider four different energy levels of nodes. On the
bases of their energy, we call them normal, advanced,
super, and ultra-super. Where normal nodes’ energy is E0,
advanced nodes are of fraction mof normal nodes in the
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Akbar et al. EURASIP Journal on Wireless Communications and Networking (2016) 2016:66 Page 6 of 19
network, and their energy is atimes more than that of
the normal ones, i.e., E0(1+a). Super nodes have greater
energy as compared to the advanced nodes and they are of
fraction m0of normal nodes with energy btimes greater
as compared to normal nodes, E0(1+b). Similarly, ultra-
supernodesareoffractionm1of normal nodes with u
times more energy than normal nodes, E0(1+u). Total
number of ultra-super nodes presented in the network are
calculated as follows:
Tota l _Numultra_super =Nm1(1)
where Nis the total number of nodes in the network.
Supernodesinthenetworkarecalculatedas
Tota l _Numsuper =Nm0(2)
Advanced nodes in the network are computed by
Tota l _Numadvanced =Nm (3)
Whereas normal nodes are calculated as follows:
Tota l _Numnormal =N(1−m1−m0−m)(4)
Initial energy of the ultra-super nodes is calculated as
follows:
Eultra_super=Tota lultra_super ×E0(1+u)=Nm1E0(1+u)(5)
Initial energy of the super nodes is calculated as follows:
Esuper=Tota lsuper ×E0(1+b)=Nm0E0(1+b)(6)
The total initial energy of all advanced nodes is computed
by
Eadvanced=Tota ladvanced ×E0(1+a)=NmE0(1+a)(7)
Initial energy of the normal nodes is calculated as follows:
Enormal =Tota lnormal ×E0=N(1−m1−m0−m)E0(8)
Initial energy of the heterogeneous network is computed
by adding the energies of normal, advanced, super, and
ultra-super nodes. The total initial energy is given in Eqs.
(9) and (10) as follows:
Etotal =Eultra_super +Esuper +Eadvanced +Enormal (9)
Etotal =Nm1E0(1+u)+Nm0E0(1+b)
+NmE0(1+a)+N(1−m1−m0−m)E0
(10)
As compared to the homogeneous network with initial
energy E0, our proposed heterogeneous WSN contains
Nm1(1+u)+Nm0(1+b)+Nm(1+a)+N(1−m1−m0−m)
times more energy. Both networks have equal number of
nodes. Figure 1 presents the network model of BEENISH
When the network starts operation, nodes consume
different amounts of energy for transmission depending
upon the distance. Moreover, as compared to the nodes
in the cluster, CH has more load of transmission, as it
receives the data from member nodes and sends it to BS.
In this way, CHs consume more energy. After some time,
the residual energy of nodes present in the network may
vary. We conclude that after a certain time (rounds), a
homogeneous system becomes heterogeneous because of
difference in residual energy of nodes.
4 Proposed schemes: BEENISH and iBEENISH
This section presents the brief overview of our proposed
scheme BEENISH, then we describe iBEENISH. Selec-
tion criterion of CHs in BEENISH considers the residual
energy of nodes and the average energy level of the net-
work. Moreover, BEENISH considers a heterogeneous
network with four different energy level nodes (i.e., nor-
mal, advanced, super, and ultra-super).
Rotating epoch is defined with nithat represents num-
ber of rounds for a node siin which it can become a CH,
where i=1, 2, ....N. Energy consumption of CH (node) is
greaterthanmembernodesinacluster.Ifpopt represents
the optimal probability for the selection of CHs in a homo-
geneous network, then poptNisthenumberofCHsper
round are ensured on average. niis defined as ni=1
popt ,
in which each node siatleast once becomes CH. We are
considering different energy levels among nodes, so when
network operation starts, it follows LEACH criteria and
epoch niis kept constant for all nodes. Due to which there
is non-uniform energy distribution. Low energy node can
be selected as CH and drains its energy. As a result, less
energy nodes die before the nodes with greater energy. To
overcome this deficiency, BEENISH rotates the epoch on
the basis of nodes’ residual energy levels, Ei(r). As a result,
energy consumption is balanced because initially nodes
with high energy have high residual energy and they are
frequently selected as CHs as compared to normal ones.
More specifically, ultra-super nodes have high frequency
to become CH as compared to rest of the three levels.
After that, super nodes are more frequently selected as CH
as compared to the remaining two levels. Similarly, normal
nodeshavelessfrequencyofbecomingCHascompared
to the advanced nodes. In this way, the load distribution
on each node is almost uniform.
Let for epoch ni,pi=1
nidefines the probability of a
node to become a CH. Our proposed scheme chooses the
average probability pito be popt which ensures that there
are popt number of CHs in each round. Hence, all nodes
die at approximately the same time. If the energy levels of
nodes are different, then pi>popt for high energy nodes.
¯
E(r)represents the average energy of the network dur-
ing the rth round [12]. It is calculated as follows:
¯
E(r)=1
NEtotal 1−r
R(11)
Where, Rshows the total number of rounds from start of
the network till all the nodes die which is given as:
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Akbar et al. EURASIP Journal on Wireless Communications and Networking (2016) 2016:66 Page 7 of 19
Fig. 1 Network topology of BEENISH
R=Etotal
Eround
(12)
where Eround is the network’s energy consumption per
round and is calculated as
Eround =L2NEelec +NEDA +kεmpd4
toBS +Nεfsd2
toCH
(13)
where the number of clusters in every round are denoted
by k,CHpayscostintheformofdataaggregationenergy
EDA, distance between CH and BS is represented by dtoBS,
whereas, distance between node in a cluster and CH is
dtoCH.IfNnodes are randomly deployed in an M2region,
then
dtoCH =M
√2πk,dtoBS =0.765 M
2(14)
The value of kopt (that is optimal number of clusters in
the network) is given below. It is calculated by taking the
derivative of Eround with respect to kand setting it equal
to zero.
kopt =√N
√2πεfs
εmp
M
d2
toBS
(15)
The value of threshold probability is calculated in the
same manner as authors did in [9, 12]. Based on this value,
anodesidecides whether to become a CH or not. The
threshold probability is given below:
T(si)=pi
1−pirmod 1
Piif siG
0otherwise
(16)
where Gis the set of nodes which are eligible to be selected
as CHs. Set Grepresents the nodes that do not become
CH. These nodes choose a random number between 0 and
1. Then they compare chosen number to the threshold
T(si), if number is less than threshold value then node si
becomes CH for that particular round.
As we describe earlier that after certain rounds, the
homogeneous network becomes heterogeneous with mul-
tiple levels of energy. In BEENISH, initially we introduce
four level heterogeneous network and four types of nodes
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Akbar et al. EURASIP Journal on Wireless Communications and Networking (2016) 2016:66 Page 8 of 19
with different initial energies (normal, advanced, super
and ultra-super nodes). CH selection probability of nor-
mal, advanced, super and ultra-super is given below:
pBEENISH
i=
⎧
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎨
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎩
poptEi(r)
(1+m(a+m0(−a+b+m1(−b+u)))) ¯
E(r)for normal nodes
popt(1+a)Ei(r)
(1+m(a+m0(−a+b+m1(−b+u)))) ¯
E(r)for advanced nodes
popt(1+b)Ei(r)
(1+m(a+m0(−a+b+m1(−b+u)))) ¯
E(r)for super nodes
popt(1+u)Ei(r)
(1+m(a+m0(−a+b+m1(−b+u)))) ¯
E(r)for ultra−super nodes
(17)
The above expression shows that a node with high resid-
ual energy has high probability of becoming CH. This
strategy is energy efficient and distributes the load among
the nodes in a balanced manner. As a result, stability
period of the network increases. As it is possible that at
some stages during the network lifetime three types of
greater energy nodes (ultra-super, super, and advanced)
have the same energy as that of normal nodes, in this case,
ultra-super nodes have high frequency to be selected as
CH and they are penalized more than super and advanced
nodes. Similarly, in comparison to advanced nodes, super
nodes are more penalized. To avoid this conventional
approach for selecting CH, in the proposed scheme, prob-
ability of nodes to become CH varies with the varying
residual energy. Implementing the above mentioned strat-
egy of frequently penalizing the nodes with higher resid-
ual energies balances energy consumption resulting in
smooth behavior of the proposed schemes.
In this regard, our proposed iBEENISH protocol makes
some changes in the probabilities defined in the BEENISH
protocol. The difference is based on the absolute resid-
ual energy Tabsolute , which varies the probability according
to variation in the residual energy. If ultra-super, super,
and advanced nodes drain their energies and their resid-
ual energy become equal to the same energy level as that
of normal nodes, then the probability of becoming a CH
varies and all four kind of nodes will have the same proba-
bility. The selection probabilities of nodes to become CHs
in iBEENISH are given in Eq. (18):
Absolute residual energy level is denoted by Tabsolute and
its value is given in Eq. (19):
Tabsolute =zE0(19)
where the value of zlies in the range of [ 0, 1]. If z=
0andTabsolute =0, then the scheme working behind
is BEENISH. We run the simulation many times vary-
ing the value of z. The value of z=0.71 is showing
best results. The main objective is to obtain longer sta-
bility period. It is observed that it is not necessary that
all nodes with a higher energy level become CH. There is
always an optimal number of CHs in the network. Some-
times, it is also probable that normal nodes become CH.
In Fig. 2, we obtain best results for first dead node using
the parameters given in Table 2.
Tabsolute =0.71 ×E0(20)
Through value c, the number of CHs are optimized and
it is a positive integer. For both smaller and larger values
of c, our scheme works in a “direct communication” man-
ner. The reason behind this is that the majority of nodes
send their data directly to the BS. In direct communica-
tion, nodes send their data directly to BS. The nodes that
are far from the BS consume more energy in long-distance
transmissions. In order to avoid long-distance communi-
cation, we find the optimum value of cwhich provides the
best results in terms of the death of the first node. For this
purpose, we run simulations many times by varying value
of cbetween range [0, 1] and find that at c=0.02 net-
work shows better results in terms of the death of the first
node; Fig. 2 shows how caffects the round in which the
first node dies.
5 Sink mobility
The energy efficiency is the main objective in any WSN
so that the network lifetime and stability period can be
maximized. Nowadays, sink mobility is an effective way
to maximize the network lifetime and stability period of
the network. So, we introduce sink mobility in BEENISH
and iBEENISH protocols and then examine their effects.
We put a greater emphasis on the network stability period
piBEENISH
i=
⎧
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎨
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎪
⎩
poptEi(r)
(1+m(a+m0(−a+b+m1(−b+u)))) ¯
E(r)for Nrm nodes if Ei(r)>Tabsolute
popt(1+a)Ei(r)
(1+m(a+m0(−a+b+m1(−b+u)))) ¯
E(r)for Adv nodes if Ei(r)>Tabsolute
popt(1+b)Ei(r)
(1+m(a+m0(−a+b+m1(−b+u)))) ¯
E(r)for Sup nodes if Ei(r)>Tabsolute
popt(1+u)Ei(r)
(1+m(a+m0(−a+b+m1(−b+u)))) ¯
E(r)for Ult nodes if Ei(r)>Tabsolute
c×popt(1+u)Ei(r)
(1+m(a+m0(−a+b+m1(−b+u)))) ¯
E(r)for Nrm, Adv, Sup Ult nodes if
Ei(r)≤Tabsolute
(18)
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Akbar et al. EURASIP Journal on Wireless Communications and Networking (2016) 2016:66 Page 9 of 19
Fig. 2 Variation round of cand zuntil the first node dies
because a greater stability period gives better and reliable
data.
Sink mobility is divided into two classes: un-controlled
and controlled mobility. In the first technique, the sink is
able to move freely/randomly in the network, whereas, in
the second technique, the sink can follow only pre-defined
path throughout the network lifetime. Furthermore, con-
trolled mobility is of two types. The first one is non-
adaptive and non-flexible, which chooses fixed sojourn
locations of the sink for the whole network lifetime,
whereas the second technique is adaptive, robust, and
flexible because it chooses sojourn locations for mobile
sink in every round in order to maximize the network
lifetime. We implement this adaptive technique.
When the sink is static, the probability of getting cov-
erage holes in the network increases. After some time,
when network is operational, the energy of few nodes in
Table 2 Simulation parameters
Parameter Value
Network field 100 m ×100 m
Number of nodes 100
E00.5 J
Message size 4000 bits
Eelec 50 nJ/bit
Efs 10 nJ/bit/m2
Eamp 0.0013 pJ/bit/m4
EDA 5 nJ/bit/signal
d0(threshold distance) 70 m
popt 0.1
the network possibly becomes low which can lead toward
a coverage hole problem. Coverage holes are avoided in
WSN because those regions where nodes die are left unat-
tended and then it becomes difficult to monitor. Sink
mobility effectively minimizes the generation of cover-
age holes and balances the energy consumption among
the sensors. This is why we implement sink mobility in
BEENISH and iBEENISH and improve the stability period
of both of them. Sink mobility versions of BEENISH and
iBEENISH are MBEENISH and iMBEENISH, respectively.
5.1 System model
In our system model, the network follows the following
assumptions:
1. The considered WSN is proactive. All nodes in the
network generate equal amount of data per unit time.
2. Each data unit is of the same length.
3. All nodes have the same transmission range.
4. Each node has a unique pre-defined ID.
5. Protocol operation has rounds that are equal time
slots.
6. At the beginning of each round, new sink locations
are computed which remain fixed during that round.
7. Sinks have no energy constraint and able to move
from one sink location to another.
8. Sink moves to a location outside the network for
recharging fuel or electricity.
5.2 Issues to be tackled in sink mobility
A mobile sink is usually driven by fuel and/or electric-
ity; that is why, the total travel distance covered by the
sink throughout the network lifetime should be bounded.
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Akbar et al. EURASIP Journal on Wireless Communications and Networking (2016) 2016:66 Page 10 of 19
When a mobile sink moves from one sink location to
another, the probability of data loss is high; so, the distance
between two sink locations should be at minimum. Adap-
tive sink mobility requires that the sink must re-construct
the routing table or routing tree at each new location,
which takes a specific time. So, a mobile sink should reside
for a minimum amount of time at each sink location. The
transmission of data from nodes/CHs to a sink only occurs
when the sink is not moving, i.e., sink is located at any
sink location. Therefore, the sum of stop times in a mobile
sink tour should be maximized. There should be a max-
imum number of stop locations for a mobile sink so that
the throughput is maximized.
Another important issue that we elaborate on here
is the use of sink mobility in a clustered environment.
Most of the recent research works implement sink mobil-
ity in cluster-less topologies. The reason behind this is
non-compatibility between clustering and sink mobility.
If we apply the same sink mobility in a clustered envi-
ronment and a cluster-less environment taking all other
parameters constant, the comparison shows that the net-
work lifetime and stability period of a cluster-less protocol
is much better than the clustered protocol. We tackle
this issue efficiently and implement the sink mobility in
our clustered heterogeneous protocol MBEENISH and
iMBEENISH that results in prolonged network lifetime
and extended stability period [29].
5.3 Mobile sink model
In this subsection, we propose a mathematical model of
sink mobility mobile sink model (MSM) in which we take
a single sink that can move to certain sink locations in
every round. Sink locations are determined at the start of
every round in our proposed model, in order to increase
the network lifetime. Therefore, this model is an adaptive
sink mobility model.
Our proposed protocols, MBEENISH and iMBENISH,
select CHs on the basis of probability, and high energy
nodes have greater probability to become CHs. So, the
sink mobility mechanism in MSM includes selection of
sink locations in every round which are most feasible
toward the network lifetime maximization. In the start of
every round, the energies of all CHs are compared and
the five CHs with minimum energies are selected from
the set of CHs. After that, the locations of these five min-
imum energy CHs are chosen to be the sink locations for
that particular round. In our proposed protocols, the sink
is actually making transmission easy for those CHs which
are left with less energy than other existing CHs. When
the sink is at a sink location k∈γ0,itharvestsdatafrom
that minimum energy CH. If a node has a sink location
in its communication range then it sends the data to the
sink when it reaches that location; otherwise, it sends the
data to its nearest CH. The node waits for the sink at
the closest sink stop, in case where more than one sink
location is in its communication range. The same hap-
pens with the CHs in every round; each CH checks for
the sink locations and finds the closest location to it and
then sends its aggregated data when the sink reaches that
closest location.
The WSN is modeled through a directed graph {G=
γ∪γ0,£∪£0},where|γ|= Nand γ0is the set of sink
locations. Set of nodes is represented by N.£={γ∪γ}
is the set of edges/links between nodes, and £0is the set
of edges/links between sensor nodes and sink locations.
Sets of wireless links between sink locations and nodes
are given by £0={γ∪γ0}.σiis the data generation rate
and it is the same for all sensor nodes. ij is the wireless
link between iand jsensor nodes and ij =1, if iand
jare within each other’s communication range τi;other-
wise, ij =0, where ∀i,j∈N. The wireless link between a
sensor node and a CH is shown by ic ,whereccan be any
CH. The link between a sensor node and a sink location is
given by ik.
We consider the bounded distance for mobile sink
because it is either driven by petrol or by electricity. It
gets recharged or refueled after covering a certain dis-
tance. Moreover, a starting and ending point of the mobile
sink is considered the same and that location is denoted
by “ρ”. In this case, this location is outside the network
because mobile sink gets recharged/refueled there. The
stop time of the sink at each sink location is given as χr
k.
This is the time in which the mobile sink gathers data from
nodes/CH when it is at the sink location k∈γ0,dur-
ing round r. The speed of the mobile sink is assumed to
be infinity as the speed between two stops is considered
negligible as compared to its stay on sink location. δij is
the data amount from node ito node j,δic represents the
amount of data from node ito CH j and δik shows the data
amount from node ito sink location k. The energy dissi-
pated in transmission of unit data from node ito node jis
given as et
ij,whereaset
ic is the energy consumed for trans-
mitting one unit of data from node ito CH c. And et
ik is the
energy consumed for transmitting one unit of data from
node ito a sink location k. The initial energy for normal
nodes is given by
Ei=E0(21)
Initial energy of advanced nodes is shown by
Ei=E0(1+a)(22)
Super nodes have initial energy given by
Ei=E0(1+b)(23)
Ultra-super nodes have initial energy:
Ei=E0(1+u)(24)
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Akbar et al. EURASIP Journal on Wireless Communications and Networking (2016) 2016:66 Page 11 of 19
Maximize X=
r
k
χr
k(25)
Subject to
λck ∈{0, 1},∀c,k(26a)
k
λck =1ifδic =0
0Otherwise (26b)
r⎛
⎝eT
ij
ij
δr
ij +eT
ic
ic;i=c
δr
ic +eT
ik
ik
δr
ik +eR
ji
δr
ji⎞
⎠≤Ei,∀i
(26c)
c
r
cρ=1, for any r (26d)
c
r
ρc=1, for any r (26e)
χk>0, δij ≥0, ∀k,i,j(26f)
∀i,j∈γ,∀c∈γ,∀k∈γ0(26g)
This model is a mixed integer linear programming
model. We explain each equation below:
•Initial energy (22)-(25): As MBEENISH and
iMBEENISH protocols are heterogeneous, therefore,
these protocols utilize four levels of energy. So, the
initial energy of normal nodes is given by Eq. (22)
which is E0. Similarly, the initial energy of advanced
nodes is given by Eq. (23). Equation (24) represents
the energy of super nodes at the start of the network.
Equation (25) depicts the initial energy of ultra-super
nodes which is “u” times greater than the initial
energy of normal nodes.
•Objective function (26): The objective of MSM is to
maximize the network lifetime which is shown by Eq.
(26). This objective is achieved by maximizing the
sum of all stop times of the sink throughout the
network lifetime. The reason behind this is simple, i.e.,
the sink collects data from nodes or CHs whenever it
is not moving (the sink is at any sink location
k
).
•Flow constraints (26a)-(26b): In constraint (26b),
λck is an indicator function which shows that sink
location
k
is co-located with the location of a CH
c
.
λck is 1 only when the amount of data from node
i
to
CH
c
is 0 which is written in (26a) as δic =0. Equation
(26a) shows that the amount of data received by a CH
c
is zero when sink location
k
is co-located with that
CH, because all the nodes present in that cluster send
their data to
k
whenever the sink arrives there.
•Energy constraint (26c): This constraint shows that
the total energy spent by node
i
throughout the
network lifetime should be less than its initial energy.
Node
i
spends its energy while transmitting data to
other nodes, to a CH, or to a sink at its respective
location. In addition, node
i
consumes its energy in
receiving data from other nodes whenever it acts as a
CH. This constraint is valid for all nodes
i
∈γ.Inour
protocol, nodes do not use multi-hop transmissions;
therefore, a node only sends data to a CH or to the
mobile sink. So, δr
ij and δr
ji will be zero in our
proposed schemes.
•Sink movement (26d)-(26e): Constraints (26d) and
(26e) elaborate that the sink starts from ρand goes
through different sink locations in the network and
then returns back to ρfor recharging. cρis an
indicator function which shows that the sink goes to
an external location after every round. cρ∈{0,1},
where cρ=1 only when the sink moves from the CH
“
c
”toρ.
•Constraint (26f)-(26g): Constraint (26f) shows that
the stop times of the sink should be greater than zero
because the sink has to collect data when it is
stopped. And constraint (26g) depicts the
corresponding sets of different variables.
5.4 Packet retransmission model
Wireless communication faces many impairments like
interference, attenuation, and noise. Radio waves in free
space travel over a single well-defined radio path; how-
ever, in the air medium, they get scattered. This scattering
occurs due to the reflection from obstacles present near
mobile antennas. Reflection of waves causes attenuation
or packet drop. As a result, a user receives rapidly vary-
ing signals. This effect is called “fading”. Therefore, in
real scenarios, there is always a probability of packet
loss in wireless transmissions. So, whenever a packet is
dropped on a link ij,nodeiretransmits that packet and
waits for the acknowledgement again. So, it means that
the number of dropped packets is directly proportional
to the number of packets’ retransmissions. We present a
mathematical model with the objective of minimizing the
number of retransmissions. The objective function and its
constraints are given below:
Minimize =
r
r
Subject to
(27)
dic ≤dmax,∀i,c∈γ(27a)
dik ≤dmax,∀i∈γ;∀k∈γ0(27b)
This mathematical model is intended to minimize the
number of packets retransmitted in the network. Each
equation of this linear programming model is explained
below:
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Akbar et al. EURASIP Journal on Wireless Communications and Networking (2016) 2016:66 Page 12 of 19
•Objective function (27): The objective function in
Eq. (27) aims to minimize the total number of packet
retransmissions throughout the network lifetime; .
This objective is achieved by minimizing the number
of retransmissions in every round. ris the number
of retransmissions in a particular round
r
. As a result
of these packets dropped, the number of packets
successfully received by the BS will be less than the
number of packets transmitted.
•Distance constraints (27a)-(27b): These constraints
show the distance bound between a sender and a
receiver. The reason behind this distance bound is
that wireless communication at a long distance has
the greater probability of packet loss than the short
distance wireless communication. So, to minimize the
number of retransmissions, we define the maximum
distance between a sender and a receiver to be dmax.
In constraint (27a), dic shows the distance between a
node “
i
”andaCH“
c
” should be less than this
maximum distance. And constraint (27b) shows that
the distance between a node “
i
” and the sink location
“
k
” of mobile sink should be less than or equal to dmax,
otherwise, the probability of packet drop is greater.
5.5 Mechanism of sink mobility in MBEENISH and
iMBEENISH
This subsection clarifies the mechanism of sink mobility
which is used in our proposed scheme. Figure 3 shows the
sink mobility mechanism of MBEENISH and iMBEENISH
in a round. In every round, the mobile sink has to start its
journey from ρ, and step by step sojourn at five minimum
energy CHs in the network and then the mobile sink has
to return back to ρ. This movement of the sink from ρto a
CH inside the network area and from CH to ρis depicted
in Eqs. (26d) and (26e) of the MSM, respectively. This is
the tour of mobile sink in a single round. The mobile sink
checks for the minimum distant sink location to its cur-
rent location and then goes to that sink location which is
at a shortest distance. The mobile sink moves from ρto
that minimum energy CH which is at the shortest distance
from ρ. As the sink locations are predefined in a particu-
lar round, after gathering data from that CH and nodes of
that cluster, the mobile sink moves to the next sink loca-
tion which is at the shortest distance. MS is staying on the
minimum energy CH for gathering data and minimizes its
load. Being part of the same network, if a CH dies, the sys-
tem can become unstable. Nodes in blue color represent
the cluster where the energy of CH in minimum, they send
their data to the mobile sink directly instead of sending it
to their CH to save their energy as depicted in Eq. (26a) of
the MSM. Orange colored nodes are sensing the parame-
ters but they are not sending the data to anyone because
the sink has to pass through their cluster. So, these nodes
keep queuing the data in their buffers until the mobile
sink reaches their cluster. In Fig. 3, nodes in the green
color sense the parameters; however, instead of sending
Fig. 3 Sink mobility mechanism
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Akbar et al. EURASIP Journal on Wireless Communications and Networking (2016) 2016:66 Page 13 of 19
data directly to the mobile sink, they send the data to their
respective CH for minimizing energy consumption. The
CH, after receiving data from these nodes, aggregates and
sends the data to the shortest distant sink location.
To elaborate the working of iMBEENISH, we present
the whole scheme into three modules. The first mod-
ule in Fig. 4 shows finding of normal, advanced, super,
and ultra-super nodes. In the second module, shown in
Fig. 5, clusters are formed. The CHs’ selection technique
in our protocol is totally based upon probabilities which
are assigned to each node on the basis of their residual
energies. After clustering, the association of nodes with
CHs takes place and clusters are formed. In the third mod-
ule, Fig. 6 represents the data transmission from nodes
and CHs, where every node checks whether to transmit
its data to its corresponding CH or directly to the mobile
sink. After that, CHs aggregate and send the data to the
mobile sink, when the mobile sink comes to the most
feasible closest location. In this procedure, sink mobility
enables the nodes and CHs to transmit their data with
minimum energy consumption.
6 Simulation results
In this section, we assess the performance of BEENISH,
iBEENISH, MBEENISH, and iMBEENISH protocols using
MATLAB. The assessment is done by considering the sta-
bility period, network lifetime and packets sent to the BS,
and packets received at the BS as performance parame-
ters. The stability period is defined as the time interval
from the start of the network till the death of the first
node, whereas the instability period is the period from
Fig. 4 Module 1: finding normal, advanced, super, and ultra-super
nodes
Fig. 5 Module 2: cluster formation and selection of CHs
the death of the first node till the death of the last node.
Network lifetime is the time period until the last node
dies. Data packets sent to the BS is the measure of the
number of packets that are sent by nodes to the BS
throughout the network lifetime.
We consider a WSN where 100 nodes are randomly
deployed in the 10 m ×100 m network field. We are
not considering energy loss due to signal collisions and
Fig. 6 Module 3: transmission on the basis of minimum distance
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Akbar et al. EURASIP Journal on Wireless Communications and Networking (2016) 2016:66 Page 14 of 19
interference between signals of different nodes that are
due to dynamic random channel conditions. Table 2 rep-
resents the radio parameters we used in the proposed
scheme simulations. We compare the proposed schemes
(variants of BEENISH) with DEEC, DDEEC, and EDEEC.
For simulations, we consider the network that contains
40 normal nodes having E0initial energy, whereas 30
advanced nodes (m=0.6 fraction of normal nodes) with
2 times more energy (a=2.0) than normal nodes. The 21
super nodes (m0=0.5 fraction of normal nodes) contain-
ing b=2.5 times more energy than normal nodes. Finally,
9 ultra-super nodes (m1=0.3 fraction of normal nodes)
containing u=3 times more energy than normal nodes.
All nodes remain alive until their energy is consumed.
Figure 7 shows alive nodes against the number of rounds.
First node of DEEC, DDEEC, EDEEC, BEENISH, iBEEN-
ISH, MBEENISH, and iMBEENISH dies at 1287, 1523,
1595, 1754, 2046, 2237, and 2421 rounds, respectively, and
all nodes die at 6520, 5144, 8046, 8109, 8521, 8630, and
9102 rounds, respectively. Figure 7 shows that alive nodes
in BEENISH and iBEENISH gradually die which means
that these two protocols are more efficient protocols than
DEEC, EDEEC, and DDEEC. Nodes die in the follow-
ing sequence: normal, advanced, super, and ultra-super.
When a,b,u,m,m0,andm1are changed, the result-
ing network lifetime, stability period, and behavior of the
network also change. BEENISH and iBEENISH are per-
forming much better than the other protocols because the
threshold we set for the probability of nodes extend the
network lifetime and stability period as shown in Fig. 7.
From this figure, the stable period of MBEENISH is 92 %
of the stable period of iMBEENISH. Similarly, the stable
period of iBEENISH is 85 % of that of iMBEENISH. In
the same way, BEENISH is 74% of iMBEENISH in terms
of stability period. DEEC, DDEEC, and EDEEC are 54,
63, and 66 % of iMBEENISH protocol in terms of sta-
bility period. Figure 7b, c shows that with the variation
in the dimensions of network from 100 m ×100 m to
250 m×250 m and 500 m×500 m, with varying number of
nodes from 100 to 250 and 500, respectively; the network
becomes sparse and nodes consume more energy during
network establishment and transmissions.
Sink mobility prolongs the network lifetime and stabil-
ity period to a greater extent as shown in Fig. 7. The sink
moves from one location to the other and sojourns for a
certain time making virtual sojourn regions. The mobile
sink collects data from CHs and nodes which are lying
in its current virtual sojourn region, and then moves to
a next location and collects data from nodes and CHs of
that sojourn region. The stability period of iMBEENISH
is approximately 2350 rounds greater than iBEENISH and
the stability period of MBEENISH is almost 2000 rounds
more as compared to BEENISH. If we do not apply clus-
tering in the network then these protocols will improve
02000 4000 6000 8000 10000
0
10
20
30
40
50
60
70
80
90
100
No. of rounds
No. of Alive Nodes
Nodes alive during rounds
DEEC
DDEEC
EDEEC
BEENISH
iBEENISH
MBEENISH
iMBEENISH
(a) Alive nodes for network dimensions 100m×100m with 100 nodes
0 2000 4000 6000 8000 10000
0
50
100
150
200
250
No. of rounds
No. of Alive Nodes
Nodes alive during rounds
DEEC
DDEEC
EDEEC
BEENISH
iBEENISH
MBEENISH
iMBEENISH
(b) Alive nodes for network dimensions 250m×250m with 250 nodes
02000 4000 6000 8000 10000
0
50
100
150
200
250
300
350
400
450
500
No. of rounds
No. of Alive Nodes
Nodes alive during rounds
DEEC
DDEEC
EDEEC
BEENISH
iBEENISH
MBEENISH
iMBEENISH
(c) Alive nodes for network dimensions 500m×500m with 500 nodes
Fig. 7 a–cAlive nodes during network lifetime
effectively. This is because the sink mobility along with
clustering is a difficult task to handle. MBEENISH and
iMBEENISH are more energy efficient as compared to
DEEC, DDEEC, and EDEEC as shown in Fig. 7. This is
because the mobile sink goes to five sink locations to
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Akbar et al. EURASIP Journal on Wireless Communications and Networking (2016) 2016:66 Page 15 of 19
collect data from CHs and nodes. This results in effi-
cient consumption of energy. It is clearly seen from the
results that MBEENISH and iMBEENISH are more effi-
cient than the other selected protocols in terms of stability
period, network lifetime, and packets sent to the BS even
in the case of when the network contains more super and
advanced nodes as compared to normal nodes.
Figure 8 shows that MBEENISH and iMBEENISH send
more number of packets to the BS as compared to the
other selected protocols because every node checks the
distance between its corresponding CH and the BS, and
send packets to the BS if it is at a shorter distance. Pack-
ets sent to the BS by nodes and CHs collectively make
BEENISH, iBEENISH, MBEENISH, and iMBEENISH bet-
ter than DEEC, DDEEC, and EDEEC. This is because
DEEC, EDEEC, and DDEEC use clustering in such a way
that the BS does not receive that much packets from non-
CH nodes. But in iMBEENISH and MBEENISH, the sink
goes near to the CHs and collects data. It also collects data
from nodes which find it closer than their correspond-
ing CHs. According to the sink mobility model (MSM),
mobile sink sojourns at five sink locations in the net-
work and gathers the data from those five CHs. Also,
the sink collects data from every node which has any
sink location in its communication range. Thus, this can
lead to an increased number of packets sent to the BS.
Throughput of the expanded network fields with extended
number of nodes are shown in Fig. 8b, c. In Fig. 8b, the
throughput of iMBEENISH and MBEENISH is almost the
same. Figure 8c depicts that in initial rounds, iMBEENISH
has higher throughput than MBEENISH; however, after
that, MBEENISH has higher throughput because with 500
nodes in 500 m×500 m network dimensions it has a longer
stability period.
Wireless links have slightly higher probability of bad
link status, and there are chances that some of the pack-
ets may drop on their way. So, Figs. 8 and 9 show that
packets received are not the same as packets sent in every
round using (random uniformed model for dropped pack-
ets [30]). When nodes start to die, packets received at
the BS also start to decrease; when all nodes are dead,
throughput curve saturates (not increasing). In DEEC, the
selected CHs vary with time. As a result, the number of
receivedpacketsattheBSalsovary.AsshowninFig.9,
the packets received are 30 % less than the packets sent
to the BS which is shown in Fig. 8. Packets received for
the networks with increased area (and more number of
nodes as compared to the previous scenarios) are shown
in Fig. 9b, c. The experimental results show that with an
increase both in field dimensions and in number of nodes,
throughput of the network increases. Also, the number of
packets received by BS increases.
Figure 10 shows the rate at which CHs are selected
in DEEC, EDEED, DDEEC, BEENISH, iBEENISH,
0 2000 4000 6000 8000 10000
0
1
2
3
4
5
6x 105
No. of rounds
Packets Sent to BS
Pckets sent to the base station
DEEC
DDEEC
EDEEC
BEENISH
iBEENISH
MBEENISH
iMBEENISH
(a) Throughput for network dimensions 100m×100m with 100 nodes
0 2000 4000 6000 8000 10000
0
1
2
3
4
5
6
7x 105
No. of rounds
Packets Sent to BS
Pckets sent to the base station
DEEC
DDEEC
EDEEC
BEENISH
iBEENISH
MBEENISH
iMBEENISH
(b) Throughput for network dimensions 250m×250m with 250 nodes
0 2000 4000 6000 8000 10000
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5 x 105
No. of rounds
Packets Sent to BS
Pckets sent to the base station
DEEC
DDEEC
EDEEC
BEENISH
iBEENISH
MBEENISH
iMBEENISH
(c) Throughput for network dimensions 500m×500m with 500 nodes
Fig. 8 a–cThroughput of the network
MBEENISH, and iMBEENISH. From this figure, we
observe that among the selected routing protocols,
DEEC has the highest rate of CH selection, since the CH
selection in DEEC, DDEEC, and EDEEC is totally based
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Akbar et al. EURASIP Journal on Wireless Communications and Networking (2016) 2016:66 Page 16 of 19
0 2000 4000 6000 8000 10000
0
1
2
3
4
5
6x 10 5
No. of rounds
Packets Sent to BS
Pckets sent to the base station
DEEC
DDEEC
EDEEC
BEENISH
iBEENISH
MBEENISH
iMBEENISH
(a)Packets received at BS for network dimensions 100m×100m with
100 nodes
0 2000 4000 6000 8000 10000
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5 x 10 5
No. of rounds
Packets Received By BS
DEEC
DDEEC
EDEEC
BEENISH
iBEENISH
MBEENISH
iMBEENISH
(b)Packets received at BS for network dimensions 250m×250m with
250 nodes
02000 4000 6000 8000 10000
0
0.5
1
1.5
2
2.5
3
3.5 x 10 5
No. of rounds
Packets Received By BS
DEEC
DDEEC
EDEEC
BEENISH
iBEENISH
MBEENISH
iMBEENISH
(c)Packets received at BS for network dimensions 500m×500m with
500 nodes
Fig. 9 a–cPackets received at BS
on random number and threshold value and this criteria
does not guarantee optimum number of CHs. Due to this
reason, surplus CHs are selected, which cause an early
stage death of nodes in the respective protocols. Our
proposed protocols also depend on a random number;
however, we compensate this deficiency by adjusting the
probability of CHs’ selection. In this way, the chances of
CH selection tend toward its optimal value (as per our
proposed protocols). Rate of CH selection decreases with
new field dimensions and increased number of nodes, as
obvious from Fig. 10b, c due to sparsity.
0 2000 4000 6000 8000 10000
0
10
20
30
40
50
60
70
No. of rounds
No. of CHs
DEEC
DDEEC
EDEEC
BEENISH
iBEENISH
MBEENISH
iMBEENISH
(a) Rate of CH selection for network dimensions 100m×100m with
100 nodes
0 2000 4000 6000 8000 10000
0
50
100
150
No. of rounds
No. of Cluster Heads
DEEC
DDEEC
EDEEC
BEENISH
iBEENISH
MBEENISH
iMBEENISH
(b) Rate of CH selection for network dimensions 250m×250m with
250 nodes
0 2000 4000 6000 8000 10000
0
50
100
150
200
250
300
No. of rounds
No. of Cluster Heads
DEEC
DDEEC
EDEEC
BEENISH
iBEENISH
MBEENISH
iMBEENISH
(c) Rate of CH selection for network dimensions 500m×500m with
500 nodes
Fig. 10 a–c Rate of CH selection
6.1 Performance trade-offs: which parameters routing
schemes achieve on the cost of which ones
DEEC and DDEEC start from two energy levels, whereas
EDEEC starts from three energy levels. BEENISH pro-
tocol utilizes four energy levels of nodes. Normal nodes
have the least initial energy level, and ultra-super nodes
have the highest initial energy level. In BEENISH, CHs
are selected based upon the ratio between residual
energy of each node and average energy of the net-
work. Nodes with higher energy are more often selected
as CHs as compared to the lower energy ones. Lest
more energy three nodes are more punished than the
normal ones in BEENISH. iBEENISH solves this prob-
lem by dynamically adjusting the CH selection probabil-
ity. Results show that BEENISH and iBEENISH achieve
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Akbar et al. EURASIP Journal on Wireless Communications and Networking (2016) 2016:66 Page 17 of 19
longer stability periods, enhanced network lifetime, and
increased number of messages sent to the BS as com-
pared to DEEC, DDEEC, and EDEEC, respectively. The
sink mobility versions of the proposed BEENISH and
iBEENISH perform better than the non-sink mobility ver-
sions in terms of the selected performance evaluation
parameters.
Sink mobility extends the network lifetime and stabil-
ity period to a greater extent. The sink moves from one
location to the other and sojourns for a certain time mak-
ing virtual sojourn regions. The mobile sink collects data
from CHs and nodes which are lying in its current vir-
tual sojourn region, and then moves to a next location and
collects data from nodes and CHs of that sojourn region.
Table 3 Performance trade-offs made by the routing protocols being analyzed
Protocol Achievement Reference Price to pay Reference
iMBEENISH More alive nodes Fig. 7 Rate of CH selection Figs. 5 and 10
Packets sent to BS Fig. 8 Distance computation between
CH and BS
Fig. 4
Packets received at BS Fig. 9 Sink movement to the CHs Fig. 6
Rate of CH selection Fig. 10 Energy consumption
MBEENISH More alive nodes Fig. 7 Sharp energy depletion
Packets sent to BS Fig. 8 Distance computation between
CH and BS
Fig. 4
Packets received at BS Fig. 9 Sink movement to the CHs Fig. 3
Rate of CH selection Fig. 10 Lower stability period Fig. 1
iBEENISH More alive nodes Fig. 7 Sharp energy depletion
Packets sent to BS Fig. 8 Changes CH selection probability
dynamically
Fig. 10
Packets received at BS Fig. 9 CH selection of high-energy
nodes
Fig. 10
Rate of CH selection Fig. 10 Redundant transmission
BEENISH More alive nodes Fig. 7 Sharp energy depletion due to
four types of nodes
Fig. 1
Packets sent to BS Fig. 8 Rate of CH selection Fig. 10
Packets received at BS Fig. 9 Rate of CH selection Fig. 10
Rate of CH selection Fig. 10 Sudden energy depletion
EDEEC More alive nodes Fig. 7 Energy consumption due to three
nodes forwarding
Packets sent to BS Fig. 8 Throughput decreases due to
packetslossinthemidway
Fig. 9
Packets received at BS Fig. 9 Distant propagations Fig. 5
Rate of CH selection Fig. 10 More complexity involved
DDEEC More alive nodes Fig. 7 Energy consumption due to two
nodes forwarding
Packets sent to BS Fig. 8 As rounds pass, advanced nodes
will have the same CH selection
probability like that of the normal
ones
Fig. 10
Packets received at BS Fig. 9 CH selection Fig. 10
Rate of CH selection Fig. 10 Redundant transmission and
lower stability period
Fig. 7
DEEC More alive nodes Fig. 7 Overhead and complexity of
forming clusters
Fig. 10
Packets sent to BS Fig. 8 Less alive nodes Fig. 7
Packets received at BS Fig. 9 Less alive nodes Fig. 7
Rate of CH selection Fig. 10 Packets sent and received at BS Figs. 8 and 9
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Akbar et al. EURASIP Journal on Wireless Communications and Networking (2016) 2016:66 Page 18 of 19
The stability period of iMBEENISH is approximately 2350
rounds greater than iBEENISH and the stability period of
MBEENISH is almost 2000 rounds more as compared to
BEENISH. If clustering is not applied to the network, then
these protocols will improve effectively. This is because
the sink mobility along with clustering is a difficult task
to handle. MBEENISH and iMBEENISH are more energy
efficient as compared to DEEC, DDEEC, and EDEEC as
shown in Fig. 5 (Table 3). This is because the mobile sink
goes to five sink locations to collect data from CHs and
nodes. This results in more consumption of energy.
7Conclusions
BEENISH considers the network with four different
energy levels of nodes and selects CHs on the bases of
residual energy of nodes and average energy of the net-
work. So, in the BEENISH protocol, nodes with high
energy are frequently selected as CHs as compared to
low energy nodes. iBEENISH dynamically changes the CH
selection probabilities of high energy nodes when their
energy decreases. BEENISH and iBEENISH show better
performance as compared to DEEC, DDEEC, and EDEEC,
respectively, whereas iBEENISH shows better results than
BEENISH in terms of network lifetime and through-
put. Moreover, the implementation of the proposed sink
mobility model facilitates the desired objectives. That is
why MBEENISH and iMBEENISH perform better than
BEENISH and iBEENISH in terms of the selected perfor-
mance parameters.
Competing interests
The authors declare that they have no competing interests.
Acknowledgments
The authors would like to extend their sincere appreciation to the Deanship of
Scientific Research at King Saud University for funding this research through
Research Group Project No. RG#1435-051.
Author details
1COMSATS Institute of Information Technology, Islamabad, Pakistan. 2College
of CIS, King Saud University, Almuzahmiah, Saudi Arabia. 3Department of
Electrical and Computer Engineering, University of Idaho, Moscow, USA.
Received: 15 November 2015 Accepted: 4 February 2016
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