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DYN-NbC: A New Routing Scheme to Maximize Lifetime and Throughput of WSNs

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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 [1]. 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.
This section provides comprehensive literature review in-
cluding papers on clustering and sink mobility in WSN.
2.1 Clustering Protocols
LEACH [4] 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 [5] 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 [6], 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 [7] 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
is different.
2.2 Sink Mobility in WSN
Adaptive mobility solution for WSN is proposed in [8].
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 [9], 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 [10], 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. [11] 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 [12]. Authors of this paper have formulated
optimization problem to maximize the network lifetime,
subject to constraints of delay bound, energy, and flow
In [13], 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 [14] 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
multiple sinks.
Waleed Alsalih et al. [15] 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 [16] 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 [17] 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. [18] 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.
4.1.2 NbC
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 (Q1R1,Q1R2,
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 [4]. 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:
Th(i) =p
1p(mod(r, 1
p)) (1)
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.
Node deployment
Till nodes alive
Node density
calculation in each
No. of nodes
max in sub-
Place the sink in that
No change in sink
Nodes from
respective sub
region )
Transmit data to
CH s
Transmit data to Ms
Yes . No .
No .
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.
Radio Parameters
Parameter Value
Transmitter/Receiver Electronics 50 nJ/bit
Data aggregation 50 nJ/bit/signal
Transmit amplifier (if d to BSdo) 10pJ/bit/m2
Transmit amplifier (if d to BS>do) 0.0013pJ/bit/m4
Message size 4000 bits
Number of nodes 100
speed 3108m/sec
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 [19] 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
is achieved.
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
network lifetime.
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
this number.
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
Alive nodes
Fig. 3. Number of Alive Nodes
0 500 1000 1500 2000 2500
Dead nodes
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.
5.4 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 [20] 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
Packets received
Fig. 6. Network Throughput
0 500 1000 1500 2000 2500
Packets dropped
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
7x 10−3
Delay (s)
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
much adaptive as possible.
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... However, energy is still consumed in periodic selection of CH. DYN-NbC [10] uses both clustering and MS. In this protocol, sink moves to the highest node density region, whereas, in the other regions of the network field, clusters are formed and the CH selection is based on LEACH criteria. ...
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Mobile Sink (MS) based routing strategies have been widely investigated to prolongs the lifetime of Wireless Sensor Networks (WSNs). In this paper, we propose two schemes for data gathering in WSNs: (i) MS moves on Random paths in the network (RMS), and (ii) the trajectory of MS is Defined (DMS). In both the schemes, the network field is logically divided into small squares. The center point of each partitioned area is the sojourn location of the MS. We present three linear programming based models: (i) to maximize network lifetime , (ii) to minimize path loss and (iii) to minimize end-to-end delay. Moreover, a geometric model is proposed to avoid redundancy while collecting information from the network nodes. Simulation results show that our proposed schemes perform better than the selected existing schemes in terms of the selected performance metrics.
... Other researches, such as [8], [9], [10] and [11] , considerably maximize the network lifetime but they focused on heterogeneous networks, where the deployment of more powerful CHs should have been done in a deterministic fashion at some pre-calculated positions. [12] put forward the DYN- NbC need-based clustering with dynamic sink mobility which increased the throughput, the lifetime and the stability period by intelligently moving a mobile sink to high densly region, leading to a maximum data collection. [13] proposed the Enhanced Developed Distributed Energy-Efficient Clustering (EDDEEC) scheme which was a clustering-based routing mechanism for an adaptive and energy-aware routing protocol, which dynamically changed the probabilities of the nodes to become CHs in a balanced and efficient manner. ...
... As each node is equipped with limited energy source; usually a battery. Therefore, proper route selection for data transmission is of extreme significance [7], [8], [9]. In [10], authors discussed the relation between hop count and energy consumption on theoretical as well as practical point of view. ...
In this paper, we propose two new routing protocols for Wireless Sensor Networks (WSNs). First one is Angular Multi-hop Distance based Clustering Network Transmission (AM-DisCNT) protocol which uses circular deployment of sensors (nodes) for uniform energy consumption in the network. The protocol operates in such a way that nodes with maximum residual energy are selected as Cluster Heads (CHs) for each round. Second one is improved AM-DisCNT (iAM-DisCNT) protocol which exploits both mobile and static Base Stations (BSs) for throughput maximization. Besides the proposition of routing protocols, iAM-DisCNT is provided with three mathematical models; two linear programming based models for information flow maximization and packet drop rate minimization, and one model for calculating energy consumption of nodes. Graphical analysis for linear programming based mathematical formulation is also part of this work. Simulation results show that AM-DisCNT has 32%, and iAM- DisCNT has 48% improved stability period as compared to LEACH and DEEC routing protocols. Similarly, throughput of AM-DisCNT and iAM-DisCNT are improved 16% and 80%, respectively, in comparison to the counterpart schemes.
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Wireless Sensor Networks (WSNs) extend human capability to monitor and con- trol the physical world, especially, in catastrophic/emergency situations where hu- man engagement is too dangerous. There is a diverse range of WSN applications in terrestrial, underwater and health care domains. In this regard, the wireless sensors have significantly evolved over the last few decades in terms of circuitry miniaturization. However, small sized wireless sensors face the problem of limited battery/power capacity. Thus, energy efficient strategies are needed to prolong the lifetime of these networks. This dissertation, limited in scope to routing only, aims at energy efficient solutions to prolong the lifetime of terrestrial sensor networks (i.e., WSNs) and Underwater WSNs (UWSNs). In WSNs, we identify that uneven cluster size, random number of selected Clus- ter Heads (CHs), communication distance, and number of transmissions/recep- tions are mainly involved in energy consumption which lead to shortened net- work lifetime. As a solution, we present two proactive routing protocols for cir- cular WSNs; Angular Multi-hop Distance based Clustering Network Transmission (AM-DisCNT) and improved AM-DisCNT (iAM-DisCNT). These two protocols are supported by linear programming models for information flow maximization and packet drop minimization. For reactive applications, we present four routing protocols; Hybrid Energy Efficient Reactive (HEER), Multi-hop Hybrid Energy Ef- ficient Reactive (MHEER), HEER with Sink Mobility (HEER-SM) and MHEER with Sink Mobility (MHEER-SM). The multi hop characteristic of the reactive protocols make them scalable. We also exploit node heterogeneity by presenting four routing protocols (i.e., Balanced Energy Efficient Network Integrated Super Heterogeneous (BEENISH), Mobile BEENISH (MBEENISH), improved BEEN- ISH (iBEENISH) and improved Mobile BEENISH (iMBEENISH)) to prolong the network lifetime. Since the problems of delay tolerance and mobile sink trajecto- ries need investigation, this dissertation factors in four propositions that explore defined and random mobile sink trajectories. On the other hand, designing an energy efficient routing protocol for UWSNs demands more accuracy and extra computations due to harsh underwater environment. Subject to nodes’ energy consumption minimization, we present Autonomous Underwater Vehicle (AUV) and Courier Nodes (CNs) based routing protocol for UWSNs. We validate our propositions for both WSNs and UWSNs via simulations. Results show that the proposed protocols where we incorporated sink mobility perform better than the existing ones in terms of selected performance metrics.
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We study the impact of heterogeneity of nodes, in terms of their energy, in wireless sensor networks that are hierarchically clustered. In these networks some of the nodes become cluster heads, aggregate the data of their cluster members and transmit it to the sink. We assume that a percentage of the population of sensor nodes is equipped with additional energy resources—this is a source of heterogeneity which may result from the initial setting or as the operation of the network evolves. We also assume that the sensors are randomly (uniformly) distributed and are not mobile, the coordinates of the sink and the dimensions of the sensor field are known. We show that the behavior of such sensor networks becomes very unstable once the first node dies, especially in the presence of node heterogeneity. Classical clustering protocols assume that all the nodes are equipped with the same amount of energy and as a result, they can not take full advantage of the presence of node heterogeneity. We propose SEP, a heterogeneous-aware protocol to prolong the time interval before the death of the first node (we refer to as stability period), which is crucial for many applications where the feedback from the sensor network must be reliable. SEP is based on weighted election probabilities of each node to become cluster head according to the remaining energy in each node. We show by simulation that SEP always prolongs the stability period compared to (and that the average throughput is greater than) the one obtained using current clustering protocols. We conclude by studying the sensitivity of our SEP protocol to heterogeneity parameters capturing energy imbalance in the network. We found that SEP yields longer stability region for higher values of extra energy brought by more powerful nodes.
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The longevity of wireless sensor networks (WSNs) is a major issue that impacts the application of such networks. While communication protocols are striving to save energy by acting on sensor nodes, recent results show that network lifetime can be prolonged by further involving sink mobility. As most proposals give their evidence of lifetime improvement through either (small-scale) field tests or numerical simulations on rather arbitrary cases, a theoretical understanding of the reason for this improvement and the tractability of the joint optimization problem is still missing. In this paper, we build a framework for investigating the joint sink mobility and routing problem by constraining the sink to a finite number of locations. We formally prove the NP-hardness of the problem. We also investigate the induced subproblems. In particular, we develop an efficient primal-dual algorithm to solve the subproblem involving a single sink, then we generalize this algorithm to approximate the original problem involving multiple sinks. Finally, we apply the algorithm to a set of typical topological graphs; the results demonstrate the benefit of involving sink mobility, and they also suggest the desirable moving traces of a sink.
In order to prolong the network lifetime, energy-efficient protocols should be designed to adapt the characteristic of wireless sensor networks. Clustering Algorithm is a kind of key technique used to reduce energy consumption, which can increase network scalability and lifetime. This paper studies the performance of clustering algorithm in saving energy for heterogeneous wireless sensor networks. A new distributed energy-efficient clustering scheme for heterogeneous wireless sensor networks is proposed and evaluated. In the new clustering scheme, cluster-heads are elected by a probability based on the ratio between residual energy of node and the average energy of network. The high initial and residual energy nodes will have more chances to be the cluster-heads than the low energy nodes. Simulational results show that the clustering scheme provides longer lifetime and higher throughput than the current important clustering protocols in heterogeneous environments.
Improving energy efficiency and prolonging network lifetime is a challenging research issue for wireless sensor networks (WSNs). Nowadays, adding mobility technology into WSNs has drawn increasing attention. In this paper, we combine the uneven clustering algorithm with mobile sink strategy and propose our mobile sink based uneven clustering algorithm. First, we study the uneven clustering algorithm with a fixed sink node located at the center of a rectangle network. We analyze the performance of energy consumption and network lifetime, and compare our algorithm with LEACH. Then we use mobile sink node instead of fixed sink node to collect fused data under similar environment. Simulation results show that mobile sink node can efficiently mitigate hot spots near sink node as sink node moves either randomly or along a predetermined fixed path.
Regarding energy efficiency in Wireless Sensor Net-works (WSNs), routing protocols are engaged in a playful manner suggesting a consciousness of high value. In this research work, we present Away Cluster Heads with Adaptive Clustering Habit ((ACH) 2) scheme for WSNs. Our proposed scheme increases the stability period, network lifetime and throughput of the WSN. The beauty of our proposed scheme is its away Cluster Heads (CHs) formation, and free association mechanisms. The (ACH) 2 controls the CHs' election and selection in such a way that uniform load on CHs is ensured. On the other hand, free association mechanism removes back transmissions. Thus, the scheme operations minimize the over all energy consumption of the network. In subject to throughput maximization, a linear programming based mathematical formulation is carried out in which the induced subproblem of bandwidth allocation is solved by mixed-bias resource allocation scheme. We implement (ACH) 2 scheme, by varying node density and initial energy of nodes in homogeneous, heterogeneous, reactive and proactive simulation environments. Results justify its applicability.
Wireless Local Area Network (WLAN) has progressed quickly and found its wide applications recently because of mobility, flexibility, extensible and low cost. WLAN has become very important part of future communication. Quality of Service is a hot-point of research about wireless network. Two wireless network loss models, namely random uniformed model and Gilbert-Elliott model, are analyzed. The theoretic results were verified by simulation empirical results. The applications of both models are therefore proved.
Advances in technologies such as micro electro mechanical systems (MEMS) have empowered more efficient and smaller digital devices, which can be deployed in WSNs (wireless sensor networks) to gather useful information pertaining to a particular environment. In order to control effectively the physical system in a WSN, actuators may be employed to integrate such environmental information into the automation control system. Indeed, sophisticated entities deployed in wireless sensor and actuator networks (WSANs) act as functional robots. The approach of using the mobile sink, as an example of the actuator to control the movement of a sink, has been adopted by researchers in the past to achieve high efficiency in terms of gathering data from the sensors. This is due to the fact that in general, the sensors alone are unable to control the sink and need to send or relay a smaller amount of packet data. Although a number of methods exist in literature to utilize mobile sinks as actuators, most of these techniques are unable to guarantee data gathering from all of the sensors. As a consequence, more research effort is needed to improve the efficiency as well as fairness of data gathering. In WSANs, sinks and sensor entities should be actively controllable by the administrator. Therefore, we must consider an efficient way to access all nodes in the target networks. In this paper, we propose a novel method, based on the set packing algorithm and traveling salesman problem, to accomplish this goal. The effectiveness of our envisioned method is demonstrated through extensive computer-simulations.
Conference Paper
Wireless distributed microsensor systems will enable the reliable monitoring of a variety of environments for both civil and military applications. In this paper, we look at communication protocols, which can have significant impact on the overall energy dissipation of these networks. Based on our findings that the conventional protocols of direct transmission, minimum-transmission-energy, multi-hop routing, and static clustering may not be optimal for sensor networks, we propose LEACH (Low-Energy Adaptive Clustering Hierarchy), a clustering-based protocol that utilizes randomized rotation of local cluster based station (cluster-heads) to evenly distribute the energy load among the sensors in the network. LEACH uses localized coordination to enable scalability and robustness for dynamic networks, and incorporates data fusion into the routing protocol to reduce the amount of information that must be transmitted to the base station. Simulations show the LEACH can achieve as much as a factor of 8 reduction in energy dissipation compared with conventional outing protocols. In addition, LEACH is able to distribute energy dissipation evenly throughout the sensors, doubling the useful system lifetime for the networks we simulated.