DYN-NbC: Routing Scheme to Maximize
Lifetime and Throughput of Wireless Sensor
Ayesha Hussain, Nadeem Javaid, Member,IEEE
Abstract—In this paper, we present need-based clustering (NbC) with dynamic sink mobility (DYN-NbC) scheme for wireless sensor
networks (WSNs). Our proposed scheme increases the stability period, network lifetime, and throughput of the WSN. The scheme
incorporates dynamic sink mobility in a way that mobile sink (MS) moves from dense (in terms of number of nodes) regions towards
sparse regions. Intelligently moving the sink to high density regions ensure maximum collection of data. As, more number of nodes
(sensors) are able to send data directly to MS, therefore, signiﬁcant amount of energy is saved in each particular round. However, there
is a certain limitation to this approach. Nodes which are far from sink have to wait much for their turn. So, there are chances of buffer
(node storage) overﬂow that is not desirable. To overcome this issue our scheme includes NbC. Clustering (communication via CHs)
becomes the part for those regions which are away from MS. Simulation results show that DYN-NbC outperforms the other two
protocols D-LEACH and LEACH in terms of stability period, network lifetime, and network throughput.
Index Terms—Wireless sensor networks, dynamic/adaptiverouting, sink mobility, NbC, node density
The development of wireless sensor network (WSN) is mo-
tivated by military applications such as battleﬁeld surveil-
lance . Today such networks are used in many applica-
tions, such as industrial process monitoring and control,
machine health monitoring, etc. WSN consists of nodes
(sensors) deployed in a ﬁeld to monitor (sense) different
parameters like light, heat, and pressure. Nodes are very
small in size and have limited energy. So, there is always
a need of efﬁcient energy utilization in order to prolong
the network lifetime. Efﬁcient organization of nodes into
clusters is useful in reducing energy consumption. Many
energy-efﬁcient routing protocols are designed based on the
dynamic clustering structure [2,3]. The clustering technique
is used to perform data fusion, i.e., combines the data from
source nodes into a small set of meaningful information.
Moreover, it reduces the number of messages transmitted
and thus saves energy.
Different communication schemes (like direct and multi-
hop) are used for data transmission in small and large scale
networks. When network size is large, nodes (near to the
sink) act as a relay for data transmission of the other nodes
(far from the sink). Therefore, these nodes die quickly due to
more energy consumption. This creates the bottleneck which
is removed by introducing mobile sink (MS) in the network.
MS is responsible for effective load balancing, reducing
hotspot problem (energy depletion of near-sink nodes) and
hence, improving network lifetime. It moves in the network
and collects data directly from the nodes saving their energy
i.e. nodes send data to MS when it comes in the predeﬁned
sensing range. In this way, multi-hop transmission between
nodes is avoided and energy is saved. Trajectories and stop
times at different locations of MS is an active area of research
In this paper, we propose a routing protocol DYN-NbC
based on the techniques of sink mobility and need-based
clustering (NbC). It aims to maximize the lifetime and
throughput of the network. After nodes deployment, node
density is calculated in different regions of the ﬁeld. MS
moves from denser area towards areas of less node density.
This is because demand for data collection is high in dense
regions. Now, apart from the region in which MS lies, nodes
from other regions communicate with MS via cluster heads
(CHs). As a consequence, nodes do not have to wait much
for MS to come in their region, thus, assuring less data
loss and efﬁcient communication. Sink mobility along with
clustering helps to improve stability period (time period
till the ﬁrst node die) and network lifetime. This ultimately
leads to increase in network throughput.
Rest of the paper is organized as: section II provides
related work, section III deals with motivation, section IV
contains brief description of the proposed DYN-NbC, sec-
tion V takes into consideration the discussions of simulation
results, and section VI ends the research work with conclu-
sion and future work.
2 RELATED WORK
This section provides comprehensive literature review in-
cluding papers on clustering and sink mobility in WSN.
2.1 Clustering Protocols
LEACH  is the clustering based hierarchical protocol for
homogeneous WSNs. The main objective is to reduce global
communication by the formation of local clusters of nodes
based on minimum distance or received signal strength. In
each cluster, CH is selected (according to certain criteria set
by LEACH), which is responsible for data aggregation and
fusion. CH then further transmits data to base station (BS),
thus saving energy (number of transmissions and distance
is reduced). BS and sink are interchangeably used in this
work. In LEACH, CHs are randomly rotated over time to
balance the energy consumption of nodes. However, with
the passage of time, CH selection process becomes unstable
because of the death of signiﬁcant number of nodes.
Authors in  proposed a routing layer protocol, TEEN,
with the capability to react immediately after the detec-
tion of change in the sensed attribute of interest (reactive
approach). The CHs selection and nodes association tech-
niques are similar to those of LEACH. The difference lies
in data transmission only. In LEACH there is no check on
transmission of data. However, in TEEN there is hard and
soft threshold based transmission. This ultimately results in
decreased network throughput.
Before SEP , clustered routing protocols assumed that
nodes are initially equipped with same energy. However,
considering nodes heterogeneity, SEP deﬁnes two energy
levels. Based on these energy levels, nodes are categorized
into two types, i.e., normal and advanced. Advanced nodes
have αtimes more energy as compared to normal nodes.
Therefore, advanced nodes are more preferred for the selec-
tion of CHs due to their assigned probability weights. Rest
of the protocols operation is similar to that of LEACH.
Li Qing et al. in  proposed DEEC routing protocol for
heterogeneous WSNs. In this protocol, nodes are equipped
with different energy levels as the network operation begins.
The CH selection is based on the ratio of the residual energy
of a node to average energy of the network. The nodes with
higher residual energy have more chances to be CHs for a
particular round. This results in even energy distribution
among the nodes. DEEC prolongs stability period as the
nodes with increased residual energy become CHs more
frequently. The CH formation in DEEC is similar as in
LEACH; however, the probability for nodes to become CHs
2.2 Sink Mobility in WSN
Adaptive mobility solution for WSN is proposed in .
According to this solution MS moves inside the network
according to the current events. This signiﬁcantly reduces
energy consumption acquired by the multi-hop transmis-
sion (of event-driven data).
In , authors have explored sink mobility in WSN
to extend the network lifetime. Distance constrained MS
problem is being formulated to ﬁnd an optimal sojourn tour
(complete trip by considering all stop locations) by using
Mixed Integer Linear Programming (MILP) model.
In , authors have proposed a method based on the Set
Packing Algorithm (SPA) and Travelling Salesman Problem
(TSP). The goal is to achieve high efﬁciency in terms of
gathering data from the sensor nodes by using MS.
M. Gatzianas et al.  present distributed algorithm for
calculating the maximum lifetime of a WSN which routes
data to MS. The problem is further reduced into a simpler
equivalent form and solved via dual decomposition.
Framework for improving the network lifetime by utiliz-
ing sink mobility in delay-tolerant applications (applications
which tolerate delayed information delivery to the sink) is
proposed in . Authors of this paper have formulated
optimization problem to maximize the network lifetime,
subject to constraints of delay bound, energy, and ﬂow
In , authors have considered joint sink mobility in
a two-level network consisting of normal and advanced
nodes. Two sinks are jointly moving in a pre-deﬁned pattern
for gathering data. Nodes directly send data to MS when it
comes in the transmission range in a delay-tolerant fashion.
Idea of sleep and awake mode is also introduced to save
Constraining the sink movement to ﬁnite number of
locations, authors in  have constructed a framework for
the joint sink mobility and routing problem. Due to the NP-
hardness of the problem, it is further reduced into sub prob-
lems. Efﬁcient primal-dual algorithm is developed to solve
the sub problem involving single sink, then generalizing
the algorithm to approximate original problem involving
Waleed Alsalih et al.  proposed a mobile data col-
lector placement scheme for extending the lifetime of the
network. Placement problem is formulated as MILPs and
its solver is used to ﬁnd near-optimal placement (of data
collector) and routing paths to deliver data.
Authors in  have proposed a REDD scheme for
WSNs with multiple mobile sinks. The strategy works in
the manner that MS directly communicates with the source
by ﬁnding its location using geographical forwarding.
Authors in  have proposed a biased adaptive sink
mobility scheme. Based upon local network conditions such
as surrounding density and residual energy, adaptive mobil-
ity is deﬁned. According to which the sink moves probabilis-
tically in the areas less visited (to cover the entire network
ﬁeld in less time) and adaptively stopping in the regions of
high density (to collect more amount of data). Both random-
ized mobility and optimized deterministic traversals are
being proposed in this work. This method achieves signif-
icantly reduced latency without compromising the energy
efﬁciency and delivery success.
Jin Wang et al.  considered mobile sink based un-
even clustering algorithm to improve network performance
for WSNs. The behavior of uneven clustering algorithm is
studied with ﬁxed sink node and a mobile sink node respec-
tively. In clustering algorithm, CHs selection is mainly based
on competition range and residual energy to guarantee data
collection and transmission. The sink movement is deﬁned
along a pre-determined path with sojourn at some special
locations to communicate with nodes. Proposed algorithm
largely improves energy efﬁciency and extends network
Most of the earlier proposed schemes create execution over-
head of the nodes in the dense network and this overhead
is directly proportional to the number of nodes in the ﬁeld
(network density). More number of nodes will require the
mobile sink(s) to take large number of pauses increasing
the waiting time of the farther nodes to transmit (critical)
data. In this paper, we propose and implement DYN-NbC
protocol, which targets to reduce the waiting time using
Fig. 1. DYN-NbC Protcol Schematic
In this section, detailed description of proposed routing
protocol is provided.
4.1 Network Model
We consider a WSN with nodes deployed randomly in
100m×100mﬁeld. The area under observation is divided
in four quadrants Q1,Q2,Q3and Q4, with each quadrant
further subdivided into four regions (i.e. total 16 regions).
Initially, sink is placed outside the ﬁeld at (120,120).
4.1.1 Dynamic Sink Mobility based on Node Density
Sink moves towards the region of highest density in each
round. Therefore, maximum coverage of data gathering
is ensured. In this way our protocol incorporates dy-
namic/adaptive sink mobility.
At any point (regarding sink position), the nodes which
are not in the close range (sink’s quadrant) of sink become
the part of clusters. The basic mechanism of clustering
is involved (i.e. nodes send data to CHs and then CHs
communicate with sink).
The whole scenario is shown in Fig. 1.
4.2 Protocol Operations:
DYN-NbC operates in number of steps (phases). These steps
are discussed in detail in the following sub-sections.
4.2.1 Phase 1: Node Deployment
Initially, nodes are randomly deployed in the network ﬁeld.
It means sensor network is formed with non-uniform node
4.2.2 Phase 2: Regions Formation
After the deployment of nodes, ﬁeld is divided into four
quadrants named as Q1,Q2,Q3and Q4. Each quadrant
is further sub-divided in four regions (Q1−R1,Q1−R2,
4.2.3 Phase 3: Calculating Node Density
Third step is to calculate node density (nodes per unit
area) of each region. By ﬁnding the region with maximum
number of nodes and then moving the sink to that region
(at a particular round) is of interest. As, maximum data can
be retrieved i.e. demand for data collection in the respective
region is high.
4.2.4 Phase 4: Adaptive Sink Mobility
MS provides energy-efﬁcient direct data collection in WSNs.
This allows the nodes to reduce their transmission range to
the lowest value required to reach the mobile device, thus
saving energy. In our case, we propose biased sink mobility
with adaptive approach for efﬁcient (with respect to both
energy and latency) data collection in WSNs. Sink moves in
the direction of dense region in each separate round (shown
by the spiral motion in Fig. 1 that point towards most dense
region). This movement of sink is beneﬁcial as it allows
more number of nodes to transmit data directly to the sink
when it lies in the dense region over the shortest route (i.e.
the sink lies closest to the nodes in such particular region).
4.2.5 Phase 5: NbC
Sink mobility alone is not desirable in most of the cases. This
is because the nodes which are far from the sink have to wait
much for their turn (considering direct or even multi-hop
communication). So, to overcome this problem DYN-NbC
involves clustering in those regions which are far from sink.
As, sink moves to next location in each round, therefore,
apart from this quadrant (including four regions), in all of
the other quadrants our technique involves clustering.
As shown in Fig. 1, C1,C2, and C3are the clusters
formed in the quadrants in which the sink is absent. In each
cluster, CHs, responsible for data aggregation and fusion,
are selected according to the criteria set by LEACH . CHs
in LEACH are randomly rotated over time to balance the
energy consumption of nodes.
A given node igenerates a random number, and com-
pares it with a threshold value; T h(i), given as:
Where, p is the probability of CHs which is deﬁned
initially and mod(r, 1/p)returns the modulus after division
of r by 1/p. When the value of threshold is greater than
the random number, node is selected as CH. In eq. 1, r
represents the round in progress. Optimal number of CHs is
suggested to be 10% of the total nodes in the network.
We termed it as NbC, as clustering is done according to
the need. It also ensures energy-efﬁcient communication.
4.2.6 Phase 6: Communication
Once the scheme is devised according to which a technique
works, then comes the part of data exchange (i.e. how to
receive data from nodes at sink). In DYN-NbC, nodes com-
municate directly with sink when it comes in the respective
quadrant. However, nodes from all other quadrants transmit
data to CHs which then transmit to sink. The main objective
behind direct communication is reduced distance, as the
sink lies in the same quadrant as the nodes are.
Till nodes alive
calculation in each
No. of nodes
max in sub-
Place the sink in that
No change in sink
Transmit data to
Transmit data to Ms
Yes . No .
Fig. 2. Functionality of Proposed Scheme
4.2.7 Phase 7: Repetition of Phases(1-7) till last round
The entire functionality of protocol is repeated in each round
till the network ends.
5 RESULTS AND DISCUSSIONS
Transmitter/Receiver Electronics 50 nJ/bit
Data aggregation 50 nJ/bit/signal
Transmit ampliﬁer (if d to BS≤do) 10pJ/bit/m2
Transmit ampliﬁer (if d to BS>do) 0.0013pJ/bit/m4
Message size 4000 bits
Number of nodes 100
In this section, we evaluate the performance of DYN-
NbC. We consider a WSN with 100 nodes randomly
deployed in a 100m×100mﬁeld. Initially, we assume
the sink outside the ﬁeld at (120,120). In comparing
the performance of DYN-NbC with dynamic LEACH
(D-LEACH); LEACH with density-aware sink mobility and
LEACH, we ignore the effects of channel interference on the
propagation of radio waves. The parameters of ﬁrst radio
model  used in our simulation are shown in Table 1.
In subject to system performance, the following metrics
are used for evaluation purpose:
1) Stability period: Stability period is deﬁned as time inter-
val from the start of the network lifetime till the death of
ﬁrst node. It is measured in units of time (sec) or number
of rounds (time period in which network completes its one
operation). In our scenario, stability period is taken in terms
of rounds. During this period network remains stable, as, all
nodes are alive and operational. Hence, maximum efﬁciency
2) Network lifetime: Time duration from the start of
ﬁrst round till the death of last node is known as network
lifetime. It depends upon the number of nodes and the
initial energy assigned to nodes. Moreover, balancing en-
ergy consumption of nodes in a better way results in longer
3) Number of packets sent to BS: Number of packets sent
directly to sink is referred as packets sent to BS. Total of all
packets (dropped or successfully received) are counted for
4) Number of packets dropped: Sum of all the packets
dropped due to bad status of link.
5) Network throughput: Throughput of the network is
the number of data packets successfully received at BS.
We can say;
T hroughpu t =N umber of packets sent to BS−
Number of packets dropped
Generally, throughput has a direct relation with network
lifetime i.e. increased lifetime means more throughput
and vice versa. Moreover, if number of transmissions in-
crease(with the increase of number of nodes), more will be
the network throughput.
6) Propagation Delay (per packet): Delay encountered
during the transfer of packet from source to destination. It
is measured in seconds and computed by the given formula;
Where: ‘s’ is the distance between source (node) and des-
tination (CH/sink) and ‘v’ is the radio propagation speed
which is approximately 3×108m/s. It depends upon the
distance and channel conditions.
5.1 Alive Nodes
Fig. 3 shows a comparison of system lifetime using DYN-
NbC versus D-LEACH and LEACH, with each node initially
given 0.5J of energy. DYN-NbC shows improved stability
period as compared to D-LEACH and LEACH, since, the
ﬁrst node dies at later period. It also depicts enhanced
network lifetime because large number of nodes continue
their transmission session till more number of rounds. Also,
D-LEACH extends the stability period and network lifetime
of LEACH. This is due to the provision of sink mobility
that facilitate the nodes to communicate over short distance.
Thereby, allowing them to save their energy to a signiﬁcant
level. It is immediate to see that moving the sink always
improves the lifetime compared to ﬁxing it. We also see
sharpness (slop) in the graph line as the sink moves from
denser towards less dense area. This indicates much faster
decay of nodes from 900 rounds onwards. DYN-NbC fur-
ther prolongs the stability period as it allows more number
of nodes to transmit their data directly to the sink when it
lies in the denser region over the shortest route (i.e. the sink
lies closest to the nodes in such particular quadrant). While,
for other regions the nodes transmit data to CHs which
are responsible for further communication towards sink (i.e.
according to the need, clusters are formed in the regions
which are far from the current position of sink). Clustering
ensures energy-efﬁcient communication across the network.
0 500 1000 1500 2000 2500
Fig. 3. Number of Alive Nodes
0 500 1000 1500 2000 2500
Fig. 4. Dead Nodes
Based on these phenomena, network remains operational
till about 2300 rounds. Also, we can see that after 1500
rounds and till about 1800 rounds the line of graph remains
constant. It depicts that sink has arrived to a central point
of ﬁeld as the result of which the node density has been
equalized in all regions. So, the nodes transmit data to CHs
which then transmit to sink. Hence, for such duration, the
network appears to be more stable (no node decay).
5.2 Dead Nodes
From Fig. 4, it can be observed that our protocol depicts
least unstable period as compared to the other techniques.
This is because energy consumption is high (due to distant
communication) in case of LEACH and to some extent in
D-LEACH. As a result of which the nodes will start dying
at earlier rounds.
0 500 1000 1500 2000 2500
2.5 x 104
Packets sent to BS
Fig. 5. Number of Packets sent to Sink
5.3 Number of Packets sent to BS
Fig. 5 is showing the number of packets sent to BS in each
round. In D-LEACH nodes send data packets directly to
sink while it moves from dense to sparse region. As, initially
more number of nodes send data packets, therefore, graph
is showing a linear increase. However, reduced network
lifetime (1400 rounds) of D-LEACH consequently results
in lower throughput. Comparing the performance of DYN-
NbC with D-LEACH and LEACH, we can see that DYN-
NbC sends more packets. This is because there are less
chances of packet loss, as, the nodes which are far from
sink (based on the current position of sink) send packets via
CHs. Clustering along with sink mobility further enhances
network lifetime and thus improving throughput.
Fig. 6 shows the network throughput (packets sent to BS).
Taking the beneﬁts of sink mobility (allows nodes to com-
municate at shorter distances and also ensures full coverage
of data gathering) and NbC (nodes which are far, commu-
nicate with sink via CH), DYN-NbC sends more packets to
sink as compared to D-LEACH and LEACH.
5.5 Packets Dropped
When data packets travel from source to destination across
a wireless channel, some of them fail to reach destination
point. It is referred as packets dropped. To compensate it,
we use Random Uniformed Model  with the assumption
that packet drop is related to the status of that link through
which it is propagating. If a given link is in bad status,
packet is dropped, otherwise it is successfully received. For
simulation purpose, we set the probability of a link to be in
bad status as 0.3 and that of a link in good status as 0.7.
Fig. 7 depicts that in DYN-NbC, the rate at which the
packets are dropped is more as compared to D-LEACH
and LEACH. The reason is straight forward i.e. the nature
of the adopted packet drop model assumes that greater
0 500 1000 1500 2000 2500
Fig. 6. Network Throughput
0 500 1000 1500 2000 2500
Fig. 7. Number of Packets Dropped
packet sending rate is directly related to the rate at which
the packets are dropped. As, packet sending rate of the
proposed scheme is high, therefore, packet drop rate is also
5.6 Propagation Delay
Fig. 8 shows increased propagation delay for the proposed
protocol as compared to other protocols. More packet send-
ing rate of DYN-NbC causes the average per packet prop-
agation delay to increase as compared to D-LEACH and
LEACH. Hence, for achieving increased throughput and
enhanced network lifetime, the proposed scheme pays the
cost of propagation delay. Technically it is referred as a
tradeoff. Increased propagation delay of DYN-NbC makes it
less efﬁcient than D-LEACH and LEACH in the underlying
0 500 1000 1500 2000 2500
Fig. 8. Propagation Delay
Our scheme jointly considers adaptive sink mobility and
NbC for lifetime maximization. The idea is to move the
sink in the sensor network based on a strategy (moving
from dense to sparse region) that minimizes the total energy
usage. Further, clustering is done to minimize the time for
collecting data or the consumed energy of the nodes which
are far from sink. From simulation results, we conclude that
DYN-NbC prolongs the network lifetime and maximizes the
throughput in comparison to D-LEACH and LEACH.
Our future work will include the monitoring of the network
under the scenario of deploying mobile sensors along with
the provision of multiple mobile sinks (MMS) in the same
network dimensions. Thus, we will attempt to make it as
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