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Void Hole and Collision Avoidance in Geographic
and Opportunistic Routing in UWSNs
By
Aasma Khan
CIIT/SP15-RSE-021/ISB
MS Thesis
In
Software Engineering
COMSATS Institute of Information Technology
Islamabad - Pakistan
Fall, 2017
COMSATS Institute of Information Technology
Void Hole and Collision Avoidance in Geographic
and Opportunistic Routing in UWSNs
A Thesis Presented to
COMSATS Institute of Information Technology, Islamabad
In partial fulllment
of the requirement for the degree of
MS (Software Engineering)
By
Aasma Khan
CIIT/SP15-RSE-021/ISB
Fall, 2017
ii
Void Hole and Collision Avoidance in Geographic
and Opportunistic Routing in UWSNs
A Post Graduate Thesis submitted to the Department of Computer Science as partial
fullment of the requirement for the award of Degree of MS (Software Engineering).
Name Registration Number
Aasma Khan CIIT/SP15-RSE-021/ISB
Supervisor:
Dr. Nadeem Javaid,
Associate Professor,
Department of Computer Science,
COMSATS Institute of Information Technology (CIIT),
Islamabad Campus.
Co-Supervisor:
Dr. Mariam Akbar,
Assistant Professor,
Department of Computer Science,
COMSATS Institute of Information Technology (CIIT),
Islamabad Campus.
iii
Final Approval
This thesis titled
Void Hole and Collision Avoidance in Geographic
and Opportunistic Routing in UWSNs
By
Aasma Khan
CIIT/SP15-RSE-021/ISB
has been approved
For the COMSATS Institute of Information Technology, Islamabad
External Examiner:
Dr. Muhammad Hassan Islam
Associate Professor, Center for Advanced Studies in Engg (CASE), Rawalpindi
Supervisor:
Dr. Nadeem Javaid
Associate Professor, Department of Computer Science, Islamabad
Co Supervisor:
Dr. Mariam Akbar
Assistant Professor, Department of Computer Science, Islamabad
HoD: Dr. Majid Iqbal Khan
Associate Professor, Dept. of Computer Science,
COMSATS Institute of Information Technology, Islamabad
iv
Declaration
I Aasma Khan (Registration No. CIIT/SP15-RSE-021/ISB) hereby declare that I
have produced the work presented in this thesis, during the scheduled period of study.
I also declare that I have not taken any material from any source except referred to
wherever due that amount of plagiarism is within acceptable range. If a violation
of HEC rules on research has occurred in this thesis, I shall be liable to punishable
action under the plagiarism rules of the HEC.
Date: January 1, 2018 Aasma Khan
CIIT/SP15-RSE-021/ISB
v
Certicate
It is certied that Aasma Khan (Registration No. CIIT/SP15-RSE-021/ISB) has
carried out all the work related to this thesis under my supervision at the Department
of Computer Science, COMSATS Institute of Information Technology, Islamabad and
the work fullls the requirement for award of MS degree.
Date: January 1, 2018
Supervisor:
Dr. Nadeem Javaid
Associate Professor, Department of Computer Science
Co-Supervisor:
Dr. Mariam Akbar
Assistant Professor, Department of Computer Science
Head of Department:
Dr. Majid Iqbal Khan
Department of Computer Science
vi
DEDICATION
𝒟
edicated
to my mentor Dr. Nadeem Javaid and my loving parents.
vii
ACKNOWLEDGEMENT
Alhamdulillah, all praises to Allah Almighty, the most Merciful and the most Gra-
cious, for giving me the strengths and His blessing in completing this thesis.
Afterwards, I express my sincere gratitude to my research Supervisor Dr. Nadeem
Javaid and my co-supervisor Dr. Mariam Akbar for their guidance,concerned atti-
tude, inspiration and suggestions during my research work. I would never have been
able to reach this stage without the prayers and great support from my family. I am
also thankful to my parents who always give me lots of encouragement and support.
Thanks and best wishes for all those who have made this learning experience so ideal
for me.
Furthermore, I am also very grateful to my family members; especially my parents
and brothers for their prayers, moral and nancial support. Without them, it was
not even possible for me to complete this degree.
viii
ABSTRACT
Void Hole and Collision Avoidance in Geographic and
Opportunistic Routing in UWSNs
Underwater Wireless Sensor Networks (UWSNs) have been considered as an emerging
and promising method for exploring and monitoring deep ocean. The UWSNs face
many challenges due to high transmission delays, high deployment cost, movement
of nodes, energy constraints, etc. In UWSNs nodes are sparsely and unevenly de-
ployed, that may results in void hole occurrence. Secondly low propagation speed in
UWSNs causes high end-to-end delay and energy consumption. In this paper, we pro-
pose four schemes: Adaptive Transmission Range in WDFAD-DBR (ATR-WDFAD-
DBR), Cluster Based WDFAD-DBR (CB-WDFAD-DBR), Backward Transmission
based WDFAD-DBR (BT-WDFAD-DBR) and Collision Avoidance based WDFAD-
DBR (CA-WDFAD-DBR). The rst scheme ATR-WDFAD-DBR scheme adjusts its
transmission range when it nds a void node and then continues to forward data to-
wards the sink. CB-WDFAD-DBR is used to minimize end-to-end delay and energy
consumption. In BT-WDFAD-DBR fall back recovery mechanism is used to nd an
alternative route to deliver the data when void hole occurs. In CA-WDFAD-DBR
fall along with nomination of forwarder node which has minimum number of neigh-
bor nodes is selected. Simulation results show that our schemes outperform compared
with baseline solution in terms of average Packet Delivery Ratio (PDR), energy tax,
end-to-end delay and Accumulated Propagation Distance (APD).
ix
Contents
List of Figures xii
List of Tables xiii
List of Symbols xiv
1 Introduction 1
1.1 Introduction................................ 2
1.2 Contributions............................... 3
1.3 ThesisStructure.............................. 4
2 Literature Review 5
2.1 State of the Art Work Related to UWSNs . . . . . . . . . . . . . . . 6
2.2 Problem Description . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3 System Methodology 15
3.1 SystemMethodology ........................... 16
3.1.1 UWSNs Propagation Model . . . . . . . . . . . . . . . . . . . 16
3.1.2 PacketTypes ........................... 17
3.2 Proposed System Model and Proposed Schemes . . . . . . . . . . . . 18
3.2.1 SystemModel........................... 20
3.2.2 Proposed Scheme ATR-WDFAD-DBR . . . . . . . . . . . . . 20
3.2.3 Proposed Scheme CB-WDFAD-DBR . . . . . . . . . . . . . . 22
3.2.4 Proposed Scheme BT-WDFAD-DBR . . . . . . . . . . . . . . 24
x
3.2.5 Proposed Scheme CA-WDFAD-DBR . . . . . . . . . . . . . . 25
4 Results and Discussions 27
4.1 Simulation Results for UWSNs . . . . . . . . . . . . . . . . . . . . . 28
4.1.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . . 28
4.1.2 Metrics .............................. 29
4.1.3 Performance Comparison . . . . . . . . . . . . . . . . . . . . . 29
4.2 Performance Trade-os . . . . . . . . . . . . . . . . . . . . . . . . . . 33
4.3 Linear Programming based Mathematical Formulation . . . . . . . . 35
4.3.1 Feasible Region Energy Minimization . . . . . . . . . . . . . . 35
4.3.2 Feasible Region for Throughput Maximization . . . . . . . . . 37
5 Conclusion 40
5.1 Conclusion................................. 41
xi
List of Figures
2-1 Illustration of void hole problem in WDFAD-DBR . . . . . . . . . . . 11
3-1 Proposed system model illustrating CB-WDFAD-DBR and ATR-WDFAD-
DBR.................................... 21
3-2 Proposed system model illustrating CA-WDFAD-DBR and BT-WDFAD-
DBR ................................... 21
3-3 Illustration of cluster formation . . . . . . . . . . . . . . . . . . . . . 24
4-1 Comparison of energy tax . . . . . . . . . . . . . . . . . . . . . . . . 30
4-2 Comparison of end-to-end delay . . . . . . . . . . . . . . . . . . . . . 31
4-3 ComparisonofPDR ........................... 32
4-4 ComparisonofAPD ........................... 33
4-5 Feasible region: Energy minimization . . . . . . . . . . . . . . . . . . 37
4-6 Feasible region: Throughput maximization . . . . . . . . . . . . . . . 39
xii
List of Tables
2.1 Summary of UWSNs Routing Schemes Discussed in Related Work . . 12
4.1 Simulation Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . 28
4.2 Performance Trade-os . . . . . . . . . . . . . . . . . . . . . . . . . . 34
xiii
List of Abbreviaions
Acronyms
UWSNs Underwater Wireless Sensor Networks
TWSNs Terrestrial Wireless Sensor Networks
WSNs Wireless Sensor Networks
GEDAR Geographic and Opportunistic Routing using Depth Adjustment Routing
OR Opportunistic Routing
DBR Depth Based Routing
iAMCTD Improved Adaptive Mobility of Courier nodes in Threshold-optimized
BLOAD Balanced Load Distribution
ARCR Adaptive Relay Chain Routing
E-CARP Energy ecient Channel Aware Routing Protocol
WDFAD-DBR Weighting Depth and Forwarding Area Division-DBR
ATR-WDFAD-DBR Adaptive Transmission Range in WDFAD-DBR
CB-WDFAD-DBR Cluster Based WDFAD-DBR
BT-WDFAD-DBR Backward Transmission based WDFAD-DBR
CA-WDFAD-DBR Collision Avoidance based WDFAD-DBR
AHH-VBF Adaptive Hop-by-Hop Vector-Based forwarding routing protocol
ORR Opportunistic Routing based on Residual Energy
ELBAR Energy Ecient and Load Balanced distributed Routing
GPS Global Positioning System
PDR Packet Delivery Ratio
ORIA Opportunistic Routing with in-network Aggregation
AUV Autonomous Underwater Vehicle
xiv
ASSORT Asynchronous Sleep-wake Schedules an Opportunistic Routing
MORR Modied Opportunistic Routing
AUV Autonomous Underwater Vehicle
Co-EAAR Cooperative Energy Aware Anycast Routing
xv
Chapter 1
Introduction
1
Chapter 1
1.1 Introduction
Underwater Wireless Sensor Networks (UWSNs) has attracted researchers to explore
the underwater resources and to monitor the underwater environment. UWSNs enable
many aquatic applications such as military defense, monitoring aquatic environment,
disaster prevention, pollution monitoring, underwater mineral extraction, etc [1]. The
sensor nodes are distributed at dierent location in underwater that sense information
and forwards it towards the destination, that may be single sink or multi sinks [2],
[3].
UWSNs communication has to face several challenges due to unfavorable underwa-
ter environment such as high propagation and transmission delays, high deployment
cost, nodes movement, energy constraints, expensive manufacture, limited battery,
etc [4]. In addition, because of its large size the set up of underwater nodes is expen-
sive as it require the services of ship [5]. UWSNs are mostly deployed sparsely in the
monitoring area as it has expensive development and designing cost. Furthermore, it
has larger area to monitor.
Various routing protocols are proposed to enhance the PDR and with minimum
consumption of energy and delay in UWSNs, wherein greedy routing schemes are
the most distinguished techniques because of the simplicity of use in UWSNs [6]-[8].
Geographic routing uses greedy forwarding strategy where every node nds the node
which is closest to the sink. However, in greedy forwarding strategy their is a chance
of void hole occurrence [9]. Adaptive Hop-by-Hop Vector-Based forwarding routing
protocol (AHH-VBF) [1] uses a scheme that changes radius of pipeline adaptively
for amending forwarding area for packets that help in reducing duplicate packets.
However this scheme still does not eliminate the void hole problem when the network
is sparse.
Due to aforementioned challenges, protocols used for terrestrial sensor networks
cannot be used for UWSNs. Terrestrial void handling schemes are impractical and
they can not be used in underwater setting. For that reason, protocols that are
energy ecient and that have minimum delay is required that handles the void nodes
2
1.2. Contributions Chapter 1
eciently.
For energy eciency and minimizing end to end delay while designing UWSNs
routing protocol, clustering is among the procient technique. Clustering in UWSNs
is dierent from clustering in terrestrial sensor networks due to the harsh environment
and sparse deployment of nodes in underwater [10].
In this research work, motivated by the above concerns, we propose four schemes,
rstly to reduce the probability of void hole we propose Adaptive Transmission Range
in WDFAD-DBR (ATR-WDFAD-DBR) and Backward Transmission based WDFAD-
DBR (BT-WDFAD-DBR). ATR-WDFAD-DBR scheme adjusts its transmission range
when it nds a void node and then continues to forward data towards the sink. In
BT-WDFAD-DBR fall back recovery mechanism is used to nd an alternative route
to deliver the data when void hole occurs. Secondly, we propose schemes for collision
avoidance. First one is Cluster Based WDFAD-DBR (CB-WDFAD-DBR) to minimize
end to end delay and consumption of energy and second one is Collision Avoidance
based WDFAD-DBR (CA-WDFAD-DBR) in which nodes with minimum neighbors
is selected to minimize the consumption od energy and delay eectively. Using these
schemes authenticity of network communications can be improved.
1.2 Contributions
The main scientic contributions of this research work are numbered herein; i) Two
techniques ATR-WDFAD-DBR and BT-WDFAD-DBR are proposed to avoid void
holes ii) Two techniques CA-WDFAD-DBR and CB-WDFAD-DBR are proposed to
avoid collision and packet drop iii) CHs in CB-WDFAD-DBR are chosen on the
premise of maximum residual energy in order to enhance the lifetime of the network
iv) proposed schemes are compared with WDFAD-DBR in terms of average PDR,
energy tax, end to end delay and APD v) simulations are performed to validate the
eectiveness of our proposed schemes.
3
Chapter 1
1.3 Thesis Structure
The rest of this thesis is organized as follows: in chapter 2 related work on existing
schemes in UWSNs and problem statement is presented. In chapter 3 some of the
preliminaries and system model along with proposed schemes is discussed. In chapter
4 mathematical formulation based on linear programming, simulation results and
performance trade-os are presented. Finally, chapter 6 concludes this research work.
4
Chapter 2
Literature Review
5
Chapter 2
2.1 State of the Art Work Related to UWSNs
In this chapter the related work on routing protocols in UWSNs is presented along
with their features, advantages and limitations.
An AHH-VBF is proposed in [1]. In this protocol radius of the virtual pipeline is
adaptively adjusted during packet transmission. To reduce energy consumption this
protocol dynamically adjusts transmission power. To reduce end to end delay holding
time is computed. However, void hole problem is not solved using the technique of
adaptively adjusting the radius of virtual pipeline.
A GEographic and opportunistic routing with Depth Adjustment based topology
control for communication Recovery over void regions (GEDAR) routing protocol
is proposed in [11]. This protocol uses greedy routing strategy to forward packets
towards the sink node. To avoid duplication in GEDAR, nodes that are on lower
priority suppress their packet transmission. The important feature of this protocol is
that it uses depth adjustment scheme when a void hole occurs i.e. it moves the node
to new depth in case of void node to resume the greedy forwarding. Using this scheme
performance of the network is enhanced as it avoids void hole successfully. However,
moving nodes to new depth causes excessive energy consumption and increased end-
to-end delay.
Hydraulic-pressure-based anyCast routing (HydroCast) is presented in [12]. It
is a routing protocol premise on pressure. In this paper, authors have designed an
eective routing algorithm for reliable broadcasting to any of the sink and also to
solve the problem of void hole. Forwarder node is chosen on the premise of status
of the packet and cost of the link. Conversely the gauge used has to perfectly guess
the depth of that node. The depth information obtained can be used for geographic
anycast routing. It has improved PDR and it have decreased the probability of void
nodes. Its limitations are increased energy consumption and overhead.
Hop-by-Hop Dynamic Addressing based Routing Protocol for Pipeline Monitoring
(H2-DARP-PM) is proposed in [13]. This scheme uses dynamic addressing to provide
support for the selection of a suitable next hop neighbor. This scheme then assigns
6
2.1. State of the Art Work Related to UWSNs Chapter 2
dynamic hop address to every node that contributes in data forwarding. This scheme
improves the PDR however, it causes high energy consumption.
Delay sensitive schemes are proposed in [14]. In this paper, advancement of lo-
calization free routing protocols of DBR, EEDBR and AMCTD is presented that are
improved versions of delay sensitivity. Authors have made these routing protocols
adjustable to time critical applications. Using this scheme, the authors have achieved
minimum end-to-end delay and minimum consumption of energy. However duplica-
tion of packets occurs in improved version of DBR, energy consumption increases in
improved version of EEDBR and high packet drop in improved version of AMCTD
because of distant transmission of nodes that are on medium depth.
Adaptive Clustering Habit
((𝐴𝐶𝐻)2)
is presented in [15] for terrestrial WSNs.
Main feature of this scheme is its free association mechanism where nodes associate
with cluster heads. This scheme reduces propagation distance and evades back trans-
mission. This results in minimizing consumption of energy and enhances network
lifetime. Cluster Heads (CHs) are rst elected on the premise of threshold. Then,
optimum number of cluster heads is selected on the premise of ideal distance be-
tween them. In this way load is balanced among cluster heads. In this paper, the
authors have achieved maximum packet delivery ratio and enhanced network lifetime
for WSNs. However, this scheme results in high communication delay.
Free Space Optical (FSO) and ElectroMagnetic (EM) wave based communication
schemes are presented in [16]. Energy dissipation model of FSO and EM is also
proposed in this paper. Authors have examined the analytical framework to nd out
the optimum range of clusters for Gaussian distributed UWSNs. The authors then
have computed logical results by changing the location of sink at three dierent points
i.e. when sink is at the center, at corners and at the adjacent midpoint of the sensing
area. This scheme results in less energy consumption. However this results in high
end-to-end delay.
Cluster based sleep wake scheduling is proposed in [17]. In this paper, the authors
have used a technique in which some of the nodes are assumed as initiator nodes.
These initiator nodes then selects the CHs. The CH node with high energy left is
7
Chapter 2
chosen as a head node. This head node is set to active mode while other nodes are sent
to sleep mode. The transmission is then continued by selected head node towards the
sink node. This scheme decreases energy consumption, enhances lifetime and packet
delivery ratio. However, keeping the same CH throughout the network lifetime then
it causes problem to network lifetime.
An irregular clustering algorithm based on network layered for event coverage
is proposed in [18]. Authors have rst performed theoretical analysis to obtain the
expected value of nodes in the network. Next the network is then divided into irregular
clusters. In this paper they have used recovery strategy to balance the consumption of
energy in the clusters. In this paper they have achieved enhanced PDR, less energy
consumption and improved network lifetime. However, irregular clustering causes
alteration in the network.
An Energy Ecient Cluster Head Selection that is based on Particle Swarm Op-
timization (PSO-ECHS) is proposed in [19]. Particle encoding and tness function is
used to develop this algorithm. For improving the energy eciency, the authors have
considered various metrics including distance between the clusters, distance between
sinks and residual energy of nodes in the clusters. Thus using these metrics they have
derived weighting function for clusters. In this paper, the authors have achieved high
PDR, improved lifetime and energy eciency.
Enhanced Developed Distributed Energy Ecient Clustering (EDDEEC) is pro-
posed for heterogeneous WSN in [20]. In this paper, the authors have divided this
scheme in to three parts. First one is heterogeneous network model, second one is
energy consumption model and third one is routing based on clusters. Their rst
part that is heterogeneous network model have taken in to account three levels of
energy for nodes and for energy consumption model authors have considered inu-
ence of radio environs. In third part of their scheme authors have tried to change
the probability of CH selection prociently. The scheme proposed in this scheme
shows improved performance in term of stability period, network lifetime, and PDR.
However, the clustering is imbalanced and reelection increases overhead.
Three schemes Sparsity Aware Energy-Ecient Clustering (SEEC), Circular Spar-
8
2.1. State of the Art Work Related to UWSNs Chapter 2
sity Aware Energy-Ecient clustering (CSEEC) and Circular Depth Based Sparsity
Aware Energy-Ecient Clustering (CDSEEC) for UWSNs are presented in [21]. In
SEEC two mobile sinks are deployed in sparse region to collect the information and
to reduce the probability of energy hole creation. In CSEEC also mobile sinks are
deployed in circular network to evade energy hole creation. Likewise, CDSEEC is pro-
posed in this paper to minimize the consumption of energy. However, these schemes
results in low packet delivery ratio.
Depth Based Routing (DBR) [22] is a depth based routing protocol that handles
dynamic networks eciently. It requires local depth information to forward packets
greedily towards the sink. In this protocol, each eligible node forwards the packet
on the basis of depth priority. The DBR provides improved network lifetime and
high PDR. However, due to greedy approach it causes void holes, increased energy
consumption and high end-to-end delay.
Improved Adaptive Mobility of Courier nodes in Threshold-optimized DBR (iAM-
CTD) [23] is a location free routing protocol specially designed for time critical appli-
cations. In this paper, to handle, ooding, latency and path loss it performs routing
on demand to maximizes the lifetime of the network by optimized mobility pattern
of courier nodes. It provides improved network lifetime, minimized end-to-end delay
due to ecient movement of courier nodes. However, void hole problem still exists.
Energy ecient Channel Aware Routing Protocol (E-CARP) [24] is distributed
cross layer reactive routing protocol. It provides improved network lifetime and
reduced energy consumption by avoiding control packets however, it costs reduced
throughput and high path loss due to mobility of nodes.
In Adaptive Relay Chain Routing (ARCR) [25], the authors introduced to use of
mobile sensor nodes to reduce the problem of energy hole and keeping sink xed in
its particular location. To achieve this, clusters are formed in the network and for
collection of data mobile nodes are used. This routing mechanism achieves energy
eciency, maximum network lifetime and load balancing. However, the network
disconnects when the sensor nodes are unsuitable to forward the data.
In our proposed schemes, rstly to reduce the probability of void hole we propose
9
Chapter 2
ATR-WDFAD-DBR is scheme which adjusts its transmission range when it nds a
void node and in case of BT-WDFAD-DBR it uses backward transmission then contin-
ues to forward data in direction of the sink. Secondly, we propose CB-WDFAD-DBR
and CA-WDFAD-DBR to minimize end-to-end delay, collision and energy consump-
tion. Related work is summarized in Table 2.1.
2.2 Problem Description
The energy eciency and end-to-end delay are greatly eected because of void holes
presence in UWSNs. Therefore, to preserve energy of the node and to reduce end-
to-end delay research community has been developed to bring improvement in the
design of routing algorithms. However, trade o always exists. For instance, in
WDFAD-DBR [4] two metrics are considered: the current hop depth and next ex-
pected forwarder node depth. In this way the probability of void hole and energy
consumption is reduced however, as WDFAD-DBR only considers sum of depth dif-
ference up to two hops therefore the probability of void hole still exists as shown
in Fig. 2-1. On the other hand, this scheme is the receiver based scheme where
its transmission is decided based on receiver node due to which duplication of data
packets occurs as every forwarder is not able to listen to the preferable forwarder
node. WDFAD-DBR also adds extra end-to-end delay due to its mechanism of hold-
ing packets for certain time. Moreover it does not consider the channel interference
that results in collision of packets and increases end-to-end delay. To overcome the
aforementioned problems we have proposed four schemes ATR-WDFAD-DBR, CB-
WDFAD-DBR, BT-WDFAD-DBR and CA-WDFAD-DBR to overcome the void hole
and collision problem to improve the performance of UWSNs.
10
2.2. Problem Description Chapter 2
W ter su f c
W ter de t
h
Fig. 2-1: Illustration of void hole problem in WDFAD-DBR
11
Chapter 2
Table 2.1: Summary of UWSNs Routing Schemes Discussed in Related Work
Technique Features Achievements Limitations
AHH-VBF [1] Location aware rout-
ing protocol, Con-
cept of adaptive vir-
tual pipeline
Reduced duplicate
packets and un-
necessary energy
consumption is
avoided
Void hole problem
exists
GEDAR [11] GEographic and op-
portunistic routing
with depth adjust-
ment based topology
control for communi-
cation Recovery over
void regions
Void hole avoidance
results in increased
performance of the
network
High energy con-
sumption and high
end-to-end delay
HydroCast [12] Pressure based rout-
ing protocol and e-
cient anycast routing
algorithm
Improved packet de-
livery ratio Low performance
and increased energy
consumption
H2-DARP-PM [13] Hop-by-hop dynamic
addressing based
routing protocol for
pipeline monitoring
Improved packet de-
livery ratio High energy con-
sumption
Delay sensitive
schemes [14] Improved delay
sensitive versions,
adaptable to time
critical applications
Minimize end-to-end
delay and improve
performance and
network lifetime
Duplication of pack-
ets occurs, high
energy consump-
tion and void hole
problem exists
ACH
2
[15] Free association
mechanism where
nodes associate with
CHs
Minimizing energy
consumption and
enhances network
lifetime
Transmission delay
FSO and EM wave
based communica-
tion schemes [16]
Free space optical
and electromagnetic
wave based commu-
nication schemes
Reduced energy con-
sumption High end-to-end de-
lay
CBSST [17] Cluster based
sleep/wakeup
scheduling tech-
nique for WSN
Reduced energy con-
sumption, enhanced
network lifetime and
packet delivery ratio
Keeping the same
CH throughout the
network lifetime
causes problem to
network lifetime
UCBNL [18] A high eciency
uneven cluster de-
ployment algorithm
Based on network
layered for event
coverage in UWSNs
Enhanced packet
delivery ratio, less
energy consump-
tion and improved
network lifetime
Irregular clustering
causes alteration in
the network
12
2.2. Problem Description Chapter 2
Continuation of Table 2.1
PSO-ECHS [19] Energy ecient
CH selection that
is based on particle
swarm optimiza-
tion
Energy eciency
achieved It works only for
homogeneous net-
works
EDDEEC [20] Enhanced devel-
oped distributed
energy ecient
clustering
Shows improved
performance in
term of stability
period, network
lifetime and packet
delivery ratio.
Imbalanced cluster-
ing and reelection
increases overhead
Energy ecient
routing protocol
[21]
Sparsity Aware
Energy-Ecient
Clustering (SEEC),
Circular Sparsity
Aware Energy-
Ecient clustering
(CSEEC) and
Circular Depth
Based Sparsity
Aware Energy-
Ecient Clustering
(CDSEEC) for
UWSNs
Reduced energy
consumption Low packet deliv-
ery ratio
DBR [22] Handles dynamic
networks eciently,
requires only local
depth informa-
tion and greedy
forwarding
Improved network
lifetime and packet
delivery ratio
Void holes, in-
creased energy
consumption and
high end-to-end
delay
iAMCTD [23] Location free rout-
ing protocol spe-
cially designed for
time critical appli-
cations
Improved network
lifetime, minimized
end-to-end delay
Void hole problem
still exists and over-
head due to control
packets exchange
13
Chapter 2
Continuation of Table 2.1
E-CARP [24] Distributed cross
layer reactive pro-
tocol, important
for sensory data
collection and
transmission in
UWSNs
Improved net-
work lifetime and
reduced energy
consumption
Reduced through-
put and high path
loss due to mobility
ARCR [25] Network is divided
into clusters and
mobile nodes are
used to collect data
from other sensor
nodes and onward
it to the sink
Achieves energy ef-
ciency, maximum
network lifetime
and load balancing
Network discon-
nects when the
relay nodes are
disorganized
14
Chapter 3
System Methodology
15
Chapter 3
3.1 System Methodology
3.1.1 UWSNs Propagation Model
The underwater settings aects the consumption of energy and the delay propagation
of sound waves. To explain the underwater communication Thorp propagation model
is used.
3.1.1.1 Energy Consumption Model
Underwater channel attenuation over a distance
𝑙
can be demonstrated as [20]
10𝑙𝑜𝑔𝐴(𝑙, 𝑓 ) = 𝑐.10𝑙𝑜𝑔𝑙 +𝑙.10𝑙𝑜𝑔𝛼(𝑓)
(3.1)
First term of this equation is representing the spreading loss and second term is
representing the absorption loss. Where
𝑐
is the spreading coecient which states
the geometry of propagation. (i.e.,
𝑐
= 1 is cylindrical spreading in shallow water,
𝑐
= 2 is spherical spreading in deep water, and
𝑐
= 1.5 is practical spreading.
𝛼(𝑓)
is
the absorption coecient.
In UWSNs, the acoustic signal is aected by dierent noises. Such as, turbulence
𝑁𝑡(𝑓)
, shipping
𝑁𝑠(𝑓)
, waves
𝑁𝑤(𝑓)
and thermal noise
𝑁𝑡ℎ(𝑓)
[13] [20]. These noises
can be expressed as,
𝑁(𝑓) = 𝑁𝑡(𝑓) + 𝑁𝑠(𝑓) + 𝑁𝑤(𝑓) + 𝑁𝑡ℎ(𝑓).
(3.2)
For acoustic signal, signal to noise ratio (SNR) with frequency
𝑓
and distance
𝑙
can
be expressed as:
𝑆𝑁 𝑅(𝑓, 𝑙) = 𝑇𝑝(𝑓)−𝐴(𝑙, 𝑓 )−𝑁(𝑓) + 𝐷𝑖
(3.3)
where
𝑇𝑝(𝑓)
represents the transmission power with frequency
𝑓
.
𝐷𝑖
is representing
the directivity index to evade unnecessary noise. While receiving a signal if the
𝑆𝑁 𝑅(𝑓, 𝑙)
becomes greater or equal to detection threshold
𝐷𝑡
, than received signal
is decoded correctly.
16
3.1.2. Packet Types Chapter 3
3.1.1.2 Delay Propagation Model
Underwater delay propagation considers temperature, depth of water and salinity of
water and it is given as follows [1]:
𝜈= 1448.96 + 4.591𝜏−5.304 ×10−2𝜏2+ 2.374 ×10−2𝜏3
+ 1.340(𝛿−35) + 1.63 ×10−1𝑑+ 1.675 ×10−7𝑑2
−1.025 ×10−2𝜏(𝛿−35) −7.139 ×10−13𝜏 𝑑3
(3.4)
Here
𝜈
represents the propagation speed of acoustic signal which is measured in
𝑚𝑠−1
,
𝜏
represents the temperature,
𝛿
represents the salinity and d represents the depth of
water. The acoustic propagation speed is directly proportional to temperature, water
salinity and water depth. The Eq. 4 is going to be eective when it fullls the
conditions as:
0≤𝜏≤30
,
30 ≤𝛿≤40
and
0≤𝑑≤8000
.
3.1.2 Packet Types
In these schemes there are three types of packets, namely
𝑛𝑒𝑖𝑔ℎ𝑏𝑜𝑟𝑟𝑒𝑞 𝑢𝑒𝑠𝑡
,
𝑎𝑐𝑘
and
𝑑𝑎𝑡𝑎𝑝𝑎𝑐𝑘𝑒𝑡
.
𝑛𝑒𝑖𝑔ℎ𝑏𝑜𝑟𝑟𝑒𝑞 𝑢𝑒𝑠𝑡
consists three elds: type ID, source node ID and depth. Type ID
denotes the packet type, source ID represents the address of source node and depth
represents the depth of source node.
𝑎𝑐𝑘
packet have three data elds: type ID, source node ID and depth. The type
ID represents the ack packet ID, source ID represents source node ID and depth is
the depth of source node.
On the other hand,
𝑑𝑎𝑡𝑎𝑝𝑎𝑐𝑘𝑒𝑡
composed of elds: type ID, source node ID, des-
tination ID, depth and PID. The type ID, Source node ID and depth represent the
same as of packet type
𝑛𝑒𝑖𝑔ℎ𝑏𝑜𝑟𝑟𝑒𝑞 𝑢𝑒𝑠𝑡
and
𝑎𝑐𝑘
. The destination ID represents the ID
of the destination node and PID represents the order of packets.
𝑁𝑒𝑖𝑔ℎ𝑏𝑜𝑟𝑡𝑎𝑏𝑙𝑒
consists of neighbor ID, depth, distance and time stamp. neighbor
ID indicates the location of neighboring node. the distance indicates the distance of
neighboring nodes. The time stamp represents the time for reforming neighbors entry
17
Chapter 3
and depth is the depth of source node.
𝑃 𝑎𝑐𝑘𝑒𝑡𝑞𝑢𝑒𝑢𝑒
is generated to keep the record of all the sent and received data packets
in order to restrain the replication of data packets transmission and for saving energy.
The
𝑃 𝑎𝑐𝑘𝑒𝑡𝑞𝑢𝑒𝑢𝑒
consists of elds of source ID, PID and ag. The source ID is the ID
of source node, PID is the packet ID and ag indicates that whether the data packet
has been sent or not.
Algorithm 1 describe the packet forwarding mechanism in general. First node
𝑖
receives a packet from node
𝑗
. It then calculates the previous and current depth of
the node. After calculating the depths of both nodes, node
𝑖
calculates the distance
dierence of node
𝑖
and previous node corresponding to Received Signal Strength In-
dicator (RSSI) of RSSI of received packet (RP-RSSI) and RSSI of signals at senders
(SS-RSSI). In theses schemes SS-RSSI is known to all nodes in the network. So
every node in the network can calculates the distance between two nodes correspond-
ing to Thorp propagation model. Next the Relative coordinate (RC) is calculated
corresponding to the depth dierence between them.
Type of forwarded or received packets is then checked accordingly. Whilst a node
receives a packet, it manages the data packet as follows:
∙
If the node is not with in eective forwarding range of preceding hop, it solely
up to date its neighbor table. In any other case, it enqueues information if it
nds no report regarding the packet in packet queue.
∙
If the node is not with in the forwarding area, it will await the subsequent
packet, this means that the packet is inside the suppression area. In any other
case, the node searches neighboring nodes in neighbor table.
∙
If neighbor table is empty, it will directly drop the data packet as there are no
nodes in the forwarding area. So, void holes may be prevented earlier.
3.2 Proposed System Model and Proposed Schemes
In this section, we describe the system model and proposed schemes in detail.
18
3.2. Proposed System Model and Proposed Schemes Chapter 3
Algorithm 1
Algorithm for forwarding data packets
Node
𝑖
receives
𝑑𝑎𝑡𝑎
_
𝑝𝑎𝑐𝑘𝑒𝑡
from node
𝑗
Obtain
𝑝𝑟𝑒𝑣𝑛𝑜𝑑𝑒
_
𝑑𝑒𝑝𝑡ℎ
and
𝑐𝑢𝑟𝑟𝑛𝑜𝑑𝑒
_
𝑑𝑒𝑝𝑡ℎ
Calculate distance relative to
𝑑𝑖𝑓𝑓 (𝑆𝑆
_
𝑅𝑆𝑆𝐼 , 𝑅𝑃
_
𝑅𝑆𝑆𝐼 )
Calculate
𝑅𝐶(𝑠𝑒𝑛𝑑𝑒𝑟, 𝑟𝑒𝑐𝑖𝑒𝑣𝑒𝑟)
corresponding to distance and
𝑑𝑖𝑓𝑓 (𝑝𝑟𝑒𝑣𝑛𝑜𝑑𝑒
_
𝑑𝑒𝑝𝑡ℎ, 𝑐𝑢𝑟𝑟𝑛𝑜𝑑𝑒
_
𝑑𝑒𝑝𝑡ℎ)
SWITCH
(𝑝𝑎𝑐𝑘𝑒𝑡𝑡𝑦𝑝𝑒)
CASE 1:
𝑁𝑒𝑖𝑔ℎ𝑏𝑜𝑟𝑅𝑒𝑞𝑢𝑒𝑠𝑡
if
node
𝑖
is the preferable forwarder node of node
𝑗
then
send ack
end if
Hold on for next data packet
END CASE
CASE 2:
𝐴𝑐𝑘
if
node
𝑗
is the preferable forwarder node of node
𝑖
then
up to date entry neighbor_table making use of item
(
𝑝𝑟𝑒𝑣𝑛𝑜𝑑𝑒
_
𝑑𝑒𝑝𝑡ℎ, 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒, 𝑡𝑐𝑢𝑟𝑟𝑒𝑛𝑡
)
end if
END CASE
CASE 3:
𝐷𝑎𝑡𝑎𝑃 𝑎𝑐𝑘𝑒𝑡
Move to next step
END CASE
END SWITCH
if
selected node
𝑖
is not the preferable forwarder node of
𝑑𝑎𝑡𝑎
_
𝑝𝑎𝑐𝑘𝑒𝑡
then
Upgrade
𝑛𝑒𝑖𝑔ℎ𝑏𝑜𝑟
_
𝑡𝑎𝑏𝑙𝑒
using item(
𝑝𝑟𝑒𝑣𝑛𝑜𝑑𝑒
_
𝑑𝑒𝑝𝑡ℎ, 𝑡𝑐𝑢𝑟𝑟𝑒𝑛𝑡 , 𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒
)
Drop
𝑑𝑎𝑡𝑎
_
𝑝𝑎𝑐𝑘𝑒𝑡
end if
if
node
𝑖
is the preferable forwarder node of
𝑑𝑎𝑡𝑎
_
𝑝𝑎𝑐𝑘𝑒𝑡
then
Obtain source-ID, Packet-ID from
𝑑𝑎𝑡𝑎
_
𝑝𝑎𝑐𝑘𝑒𝑡
if
(source-ID, Packet-ID) within Queue
then
Drop
𝑑𝑎𝑡𝑎
_
𝑝𝑎𝑐𝑘𝑒𝑡
end if
if
node is within the forwarding area
then
Move to next step
else
Hold on for next
𝑑𝑎𝑡𝑎
_
𝑝𝑎𝑐𝑘𝑒𝑡
end if
end if
Find next
𝑑𝑒𝑝𝑡ℎ𝑚𝑖𝑛
in
𝑛𝑒𝑖𝑔ℎ𝑏𝑜𝑟
_
𝑡𝑎𝑏𝑙𝑒
if
𝑛𝑒𝑖𝑔ℎ𝑏𝑜𝑟
_
𝑡𝑎𝑏𝑙𝑒
is empty
then
Drop
𝑑𝑎𝑡𝑎
_
𝑝𝑎𝑐𝑘𝑒𝑡
end if
Upgrade depth in
𝑑𝑎𝑡𝑎
_
𝑝𝑎𝑐𝑘𝑒𝑡
with
𝑐𝑢𝑟𝑟𝑒𝑛𝑡𝑛𝑜𝑑𝑒
_
𝑑𝑒𝑝𝑡ℎ
Add (source-id, packet-id) into Queue
19
Chapter 3
3.2.1 System Model
In proposed schemes multi sink network model is assumed. 3D multi-sink network
architecture [4] is composed of anchored, relay and sink nodes as shown in Fig. 3-1.
The anchored nodes are the ones that are xed at the bottom of water; these nodes are
used only to sense and gather data while the relay nodes are placed at dierent loca-
tion in underwater, which sends data and also forwards the received packets towards
the sink. Sink nodes are tted with both sound and radio modems. The former used
for acoustic communication, while latter one facilitates radio communication outside
the aquatic environment. The network is divided through clusters formation because
it helps in reducing interference by collecting data packets at local head node which
further sends a composite data packet towards the destination. CHs are chosen on
the premise of maximum residual energy. CHs communicate with neighbor CH and
forward the data packets towards the sink. The sink nodes are placed at the surface of
water, which direct the data packets to satellite, and the satellite communicates the
information to the control center. The sink nodes directly connect with each other
through radio links, so it is assumed that they are sent successfully if any of the base
station nodes receives the data.
3.2.2 Proposed Scheme ATR-WDFAD-DBR
In this section, proposed scheme is discussed in detail. In order to cater the above
mentioned problem of void hole avoidance we have proposed ATR-WDFAD-DBR
scheme. In this scheme when a void hole occurs then it adaptively adjusts its trans-
mission range and forwards the data packet towards the sink node as shown in Fig.
3-1.
3.2.2.1 Forwarding Mechanism
With a view to lessen neighbor of requests, every node collects the to be had records
on neighbor nodes upon receiving packets, both facts,
𝑛𝑒𝑖𝑔ℎ𝑏𝑜𝑟𝑟𝑒𝑞 𝑢𝑒𝑠𝑡𝑠
or
𝑎𝑐𝑘
. On this
way, every node can reap newer statistics about neighbor nodes dynamically. In this
20
3.2.2. Proposed Scheme ATR-WDFAD-DBR Chapter 3
`
W t r sur a e
W te de th
S
h
h1
`
R di l nk
R ut ng pa h CB-WDF D-DBR
Sate l te
Moni o i g c nte
Rela n d s
An hore nodes
Si k n d s
T a s iss on ran e ad us ment
Cl s er H a s
R u ing pa h A R-WDF D-DBR
Fig. 3-1: Proposed system model illustrating CB-WDFAD-DBR and ATR-WDFAD-
DBR
`
W t r su f c
W t r d pt
n6
7
n
h
h1 `
R d o l nk
Routi g p th
1 h p b ck a d t a s i si n
S t ll t
M n t r ng c nt r
R l y n d s
A c o ed no es
Si k no e
S1
R ut ng p th w t c l is on
Fig. 3-2: Proposed system model illustrating CA-WDFAD-DBR and BT-WDFAD-
DBR
21
Chapter 3
scheme, whilst a node receives a packet, it handles the data packet as follows:
∙
If the node is not with in eective forwarding range of preceding hop, it solely
up to date its neighbor table. In any other case, it enqueues information if it
nds no report about the packet in packet queue.
∙
If the node is not with in the forwarding area, it will await the subsequent
packet, this means that the packet is inside the suppression area. In any other
case, the node searches neighboring nodes in neighbor table.
∙
If neighbor table is empty, instead of dropping a packet source node adjusts
its transmission range and updates the neighbor table. So, void holes may be
evaded.
∙
After updating neighbor table, it is going to ahead the packet if no dierent
transmission of the data packet is heard towards the destination. It then updates
the packet Queue.
As shown in Fig. 3-1, in proposed scheme ATR-WDFAD-DBR when a node
𝑆
sense
data it forwards it towards the nodes
𝑛1
and
𝑛2
and when a void node occurs this
scheme adjusts its transmission range in this way node
𝑛3
comes with in the transmis-
sion range and continue to forward the information without the loss of data packet.
3.2.3 Proposed Scheme CB-WDFAD-DBR
In our second scheme, we have divided network in to clusters. To select the CH the
node with maximum residual energy is selected this also helps in increasing network
lifetime. It is assumed that sink node has the knowledge of all the sensor nodes
location. The CHs are found by the source node based on maximum residual energy
and keep changing based on residual energy to maximize the network lifetime. Below
are the steps for selecting CHs and illustrated in Fig. 3-3.
3.2.3.1 Forwarding Mechanism
In this scheme, whilst a node receives a packet, it manages the data packet as follows:
22
3.2.3. Proposed Scheme CB-WDFAD-DBR Chapter 3
∙
If the node is not with in eective forwarding range of preceding hop, it solely
up to date its neighbor table without forwarding the packet. In any other case,
it enqueues information if there is no report about the packet in packet queue.
∙
If the node is not with in the forwarding area, it will await the subsequent data
packet, this means that the packet is inside the suppression area. In any other
case, the node searches neighboring nodes in neighbor table.
∙
Initially there are no clusters in the network.
∙
Network is then divided in to clusters.
∙
Source node broadcasts message in the cluster.
∙
The sensor node then compares its own energy with the source nodes energy.
∙
If the sensor node energy is greater than source node energy then sensor nodes
send a reply message. Else Source node waits for another reply from sensor
node with max residual energy.
∙
Once the CHs are selected, clusters are formed in the UWSNs.
∙
CH than broadcast message to the other nodes in the clusters and also to the
sink node along with its ID.
∙
CHs aggregates data and communicate with one another to forward data packets
towards the sink node.
∙
Neighbor table and packet queue is then updated accordingly.
In CB-WDFAD-DBR node
𝑆
forwards the sensed information towards found CH.
This found CH than transmit the aggregated data towards the next cluster’s CH
until it reaches to the sink node as shown in Fig. 3-3.
23
Chapter 3
Cluster
CH with max residual energy
Fig. 3-3: Illustration of cluster formation
3.2.4 Proposed Scheme BT-WDFAD-DBR
In this section, we describe proposed BT-WDFAD-DBR routing protocols for mon-
itoring underwater sensors. BT-WDFAD-DBR nds the set of next hop forwarders
using the greedy opportunistic forwarding mechanism and fall back mechanism is
used to nd an alternative route to deliver the data in case of void hole region.
3.2.4.1 Forwarding Mechanism
The main steps of BT-WDFAD-DBR protocol are as follows, rst if a node is in fall
back recovery mechanism new data packets will be queued and the greedy forward-
ing mechanism is rescheduled to resend these data packets. If node is not in the
communication void region then it will continue to forward the data greedily towards
the sink. Otherwise, it will switch to fall back recovery mechanism. These steps are
represented in Algorithm 2.
In proposed scheme BT-WDFAD-DBR as shown on the right side of Fig. 3-2
the node
𝑆
looks up to two hop neighbor information. In case of void region BT-
WDFAD-DBR uses backward transmission at node
𝑛3
instead of depth adjustment
and forward it to node
𝑛4
that continues to forward packet to the sink node.
24
3.2.5. Proposed Scheme CA-WDFAD-DBR Chapter 3
Algorithm 2
Main steps of BT-WDFAD-DBR scheme
if
void node or known sinks = 0
then
Queue the data packets
Re-schedule forward data packet()
else
𝑓𝑖←− 𝑔𝑒𝑡
_
𝑛𝑒𝑥𝑡
_
ℎ𝑜𝑝
_
𝑓𝑜𝑟𝑤𝑎𝑟𝑑𝑒𝑟(𝑛)
if
|𝑓𝑖|>0
then
Forward the data packet
else
Queue the data packet
Re-schedule
𝑓𝑜𝑟𝑤𝑎𝑟𝑑
_
𝑑𝑎𝑡𝑎
_
𝑝𝑎𝑐𝑘𝑒𝑡()
Proposed_mechanism()
end if
end if
3.2.5 Proposed Scheme CA-WDFAD-DBR
In this section, we describe proposed CA-WDFAD-DBR routing protocols for mon-
itoring underwater sensors. CA-WDFAD-DBR also nds the set of next hop for-
warders using the greedy opportunistic forwarding mechanism. CA-WDFAD-DBR
selects those nodes which have minimum number of neighbor nodes to avoid the
collision as shown on the left side of Fig. 3-2 at node
𝑆1
.
3.2.5.1 Forwarding Mechanism
In this scheme, whilst a node receives a packet, it manages the data packet as follows:
∙
If the node is not with in eective forwarding range of preceding hop, it does
not forwards the data packet and it solely up to date its neighbor table. In any
other case, it enqueues information if there is no report about the packet in
packet queue.
∙
If the node is not with in the forwarding area, it will await the subsequent data
packet, this means that the packet is inside the suppression area. In any other
case, the node searches neighboring nodes in neighbor table.
∙
Where fall along with nomination of forwarder node which has minimum number
of neighbor nodes is selected as a forwarder node.
25
Chapter 3
∙
After updating neighbor table, it is going to ahead the packet if no dierent
transmission of the packet is heard towards the destination. It then updates
the packet Queue.
26
Chapter 4
Results and Discussions
27
Chapter 4
4.1 Simulation Results for UWSNs
In this section, we evaluate the performance of proposed schemes ATR-WDFAD-DBR,
CB-WDFAD-DBR, CA-WDFAD-DBR and BT-WDFAD-DBR against the existing
WDFAD-DBR [2] scheme.
4.1.1 Simulation Setup
In simulations, we have used multi-sink architecture of dimensions 10
×
10
×
10
𝑘𝑚3
,
in which sensor nodes are randomly deployed. The transmission range of sensor nodes
is 2 km, packet size is kept 72 bytes, data rate is 16 kbps [4]. Each node starts with
the initial energy of 100 J. The values of energy consumption are 50 W for sending
data and 158 mW for reception. The aforementioned parameters are taken from [4]
and are enlisted in Table 4.1.
Table 4.1: Simulation Parameters
Parameter Value
Nodes 100-500
Sinks 9
Network Dimensions
(𝑘𝑚3)
10
×
10
×
10
Movement Speed of Nodes (m/s) 2
Acoustic Propagation Speed (m/s) 1500
Initial Energy (J) 100
Transmission Range (km) 2
Transmission Power (dB re
𝜇
Pa) 90
Total Bandwidth (KHz) 4
Sending Energy (W) 50
Receiving or Idle Energy (mW) 158
Header Size (bytes) 11
Payload (bytes) 72
Data rate (kbps) 16
Size of ACK (bits) 50
Simulation time for one round (s) 1000
28
4.1.2. Metrics Chapter 4
4.1.2 Metrics
The objective of performing simulations is to evaluate the performance of our proposed
schemes, in terms of average PDR, energy tax, end-to-end delay and APD. These
metrics are dened as:
∙
Average PDR:
It is dened as the total range of packets successfully received
by the sink node to the total range of packets generated by source node.
∙
Average Energy Tax:
It is dened as the average energy consumption per
node when a data packet is sent successfully to the sink. It is measured in joule
(J). The equation used for calculating energy tax as in [4] is as follows:
𝐸𝑛𝑒𝑟 𝑔𝑦𝑇 𝑎𝑥 =𝐸𝑐𝑜𝑛𝑠
𝜂×𝐷𝑝
(4.1)
where
𝐸𝑐𝑜𝑛𝑠
is the energy consumption of the whole network;
𝜂
denotes the
total range of nodes in the network and
𝐷𝑝
denotes the total range of data
packets eectively received at the sink node.
∙
Average End-to-end Delay:
It is dened as the average time to transmit
data from source to destination successfully. It is measured in seconds (s).
∙
Average APD:
It is dened as the average accumulated propagation distance
of all the data packets which are successfully sent to the sink nodes. It is
measured in kilometers (km).
4.1.3 Performance Comparison
For performance evaluation, simulations are executed by comparing our proposed
schemes with the WDFAD-DBR protocol. We evaluate our proposed schemes against
WDFAD-DBR in terms of average PDR, energy tax, end-to-end delay and APD. The
simulation results after comparison with WDFAD-DBR are shown in Figs. 4-1, 4-2,
4-3, and 4-4 respectively.
29
Chapter 4
100 150 200 250 300 350 400 450 500
Node number
0
0.05
0.1
0.15
0.2
0.25
Energy tax (J)
WDFAD-DBR
BT-WDFAD-DBR
CA-WDFAD-DBR
ATR-WDFAD-DBR
CB-WDFAD-DBR
Fig. 4-1: Comparison of energy tax
4.1.3.1 Energy Tax
Fig. 4-1 shows the energy tax of baseline and proposed schemes. It clearly shows that
as the density of nodes is increasing energy tax is decreasing. As the density of node
increases collision of packets also increases. This collision causes increase in energy
consumption which ultimately increases energy tax. The results in Fig. 4-1 show
that our schemes outperform WDFAD-DBR in term of energy tax. The proposed
scheme ATR-WDFAD-DBR adjusts its transmission range adaptively when it nds
no node in its transmission range and continues to forward the data packet without
any packet loss. In proposed scheme CB-WDFAD-DBR dividing the network into
clusters minimizes the transmission distance and collision that also results in energy
minimization. In proposed scheme CA-WDFAD-DBR, fall along with nomination
of forwarder node which has minimum number of neighbor nodes is selected. This
also reduces the probability of packet loss and energy consumption. Existing scheme
WDFAD-DBR is causing high energy consumption due to high packet loss. The
Proposed scheme BT-WDFAD-DBR uses one hop backward transmission whenever
it nds a void node. This scheme forwards the data packet towards the node that is
located at higher depth and it has forwarders to forward the data packets towards
the sink node. In dense region all protocols show almost similar behavior in term of
energy tax.
30
4.1.3. Performance Comparison Chapter 4
100 150 200 250 300 350 400 450 500
Node number
2
3
4
5
6
7
8
9
10
End-to-end delay (s)
WDFAD-DBR
BT-WDFAD-DBR
CA-WDFAD-DBR
ATR-WDFAD-DBR
CB-WDFAD-DBR
Fig. 4-2: Comparison of end-to-end delay
4.1.3.2 End-to-end Delay
Fig. 4-2 shows the end-to-end delay of proposed schemes and existing WDFAD-DBR
scheme. The ATR-WDFAD-DBR overcomes the void hole problem by adjusting
its transmission range that results in minimum packet drop and minimum end-to-
end delay. In proposed scheme CB-WDFAD-DBR, network is split into clusters to
minimize the transmission distance which helps in reducing the end-to-end delay.
The CHs aggregate the sensed information and forward it towards the sink. The
proposed scheme CA-WDFAD-DBR is avoiding collision that reduces the end-to-end
delay. The proposed scheme BT-WDFAD-DBR uses backward transmission when
it nds void hole to continue data forwarding. WDFAD-DBR has high end-to-end
delay because it uses holding time that increases its end-to-end delay. The proposed
schemes are outperforming WDFAD-DBR as void hole probability still exists in this
protocol. The packet drop is due to void hole occurrence that results in increased
end-to-end delay in WDFAD-DBR.
4.1.3.3 PDR
Fig. 4-3 shows the PDR of existing scheme WDFAD-DBR and our proposed schemes.
In all schemes PDR is increasing with the increase in node density. The reason
for increase in PDR is that void hole probability decreases with the increase in the
31
Chapter 4
100 150 200 250 300 350 400 450 500
Node number
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
PDR
WDFAD-DBR
BT-WDFAD-DBR
CA-WDFAD-DBR
ATR-WDFAD-DBR
CB-WDFAD-DBR
Fig. 4-3: Comparison of PDR
density of nodes. The reason for less PDR of WDFAD-DBR is that this scheme
only considers data transmission up to two hops that does not eliminates the void
hole. In WDFAD-DBR after two hops void hole may occur that will result in packet
drop which decreases the PDR. In proposed scheme ATR-WDFAD-DBR if a void
hole occurs after two hop transmission than it adaptively adjusts its transmission
range to nd the forwarding neighbors and forward the packet towards the sink. In
the second proposed scheme CB-WDFAD-DBR network is divided in to clusters to
further enhance the PDR and increase the network lifetime. Each cluster head is
selected on the basis of residual energy and cluster heads then communicate with
one another to transmit the packets and adaptively adjust the transmission range as
well. In proposed scheme CA-WDFAD-DBR there is less packet drop which results
in increased PDR. In proposed scheme BT-WDFAD-DBR instead of dropping the
packet in case of void region it uses backward transmission and forwards the data
packet towards the base station. In WDFAD-DBR it considers current depth of node
and its expected next neighbor node depth however, considering depth up to two
hops is not the solution for void hole avoidance so for this reason PDR of WDFAD
decreases.
32
4.2. Performance Trade-os Chapter 4
100 150 200 250 300 350 400 450 500
Node number
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
APD (km)
WDFAD-DBR
BT-WDFAD-DBR
CA-WDFAD-DBR
ATR-WDFAD-DBR
CB-WDFAD-DBR
Fig. 4-4: Comparison of APD
4.1.3.4 APD
Fig. 4-4 shows the APD of existing scheme WDFAD-DBR is better than proposed
schemes as it selects forwarders on the basis of depth of nodes. Other proposed
schemes have increased APD as they avoid shortest route to sink because of avoiding
collision and void holes. CB-WDFAD-DBR performs better than ATR-WDFAD-DBR
due to clustering of the network, transmission distance decreases which ultimately in-
crease the APD of proposed scheme CB-WDFAD-DBR. In ATR-WDFAD-DBR it ad-
just the transmission range adaptively to reduces the void hole problem so the packet
drop ratio decreases that ultimately results in increased APD. Proposed schemes BT-
WDFAD-DBR and CA-WDFAD-DBR perform better than other schemes as they are
also selecting forwarders on the basis of their depth along with backward transmission
and collision avoidance.
4.2 Performance Trade-os
In this section, we review the performance of our proposed schemes A