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Improved Adaptive Mobility of Courier Nodes in Underwater Sensor Networks

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In Underwater Wireless Sensor Networks (UWSNs), the major challenges are high propagation latency in data transmission, dynamic topology of nodes due to wave movements and high power consumption of acoustic modem. Depth based routing as a subcategory of localization-free routing protocols, is designed to cope with the above mentioned challenges. In this subcategory, sensor nodes use their depth information to forward data towards sinks, which are stationed at the surface of aqueous environment. The most widely used depth-based routing protocols are Depth Based Routing (DBR) and Energy Efficient Depth Based Routing (EEDBR). DBR has low stability period due to increased data flooding in medium-depth nodes. Whereas in EEDBR, network lifetime expires quickly due to unnecessary data forwarding and overloaded low-depth nodes where network lifetime is the duration between network initialization and complete energy exhaustion of all the nodes. In this thesis, we overcome above mentioned deficiencies of DBR and EEDBR using the mobility of courier nodes in our proposed routing scheme; Adaptive Mobility of Courier nodes in Threshold-optimized Depth-based-routing (AMCTD). We also propose Forwarding-Function (FF) based routing protocol; improved Adaptive Mobility of Courier nodes in Threshold-optimized Depth-based-routing (iAMCTD). In order to tackle high path loss and increased propagation latency, we calculate optimal Holding Time (HT) by using devised parameters; Signal Quality Index (SQI), Energy Cost Function (ECF) and Depth Dependent Function (DDF).
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Improved Adaptive Mobility of Courier Nodes in
Underwater Sensor Networks
By
Muhammad Mohsin Raza Jafri
CIIT/FA12-REE-024/ISB
MS Thesis
In
Electrical Engineering
COMSATS Institute of Information Technology
Islamabad Pakistan
Fall, 2014
Improved Adaptive Mobility of Courier Nodes in
Underwater Sensor Networks
A Thesis Presented to
COMSATS Institute of Information Technology, Islamabad
In partial fulfillment
of the requirement for the degree of
MS (Electrical Engineering)
By
Muhammad Mohsin Raza Jafri
CIIT/FA12-REE-024/ISB
Fall, 2014
ii
Improved Adaptive Mobility of Courier Nodes in
Underwater Sensor Networks
A Post Graduate Thesis submitted to the Department of Electrical Engineering as partial
fulfillment of the requirement for the award of Degree of M.S (Electrical Engineering).
Name
Registration Number
Muhammad Mohsin
Raza Jafri
CIIT/FA12-REE-024/ISB
Supervisor
Dr. Nadeem Javaid,
Assistant Professor,
Center for Advanced Studies in Telecommunications (CAST),
COMSATS Institute of Information Technology (CIIT),
Islamabad Campus,
October, 2014.
iii
Final Approval
This thesis titled
Improved Adaptive Mobility of Courier Nodes in
Underwater Sensor Networks
By
Muhammad Mohsin Raza Jafri
CIIT/FA12-REE-024/ISB
has been approved
For the COMSATS Institute of Information Technology, Islamabad
External Examiner: ___________________________________
Dr. Ayyaz Hussain
Assistant Professor, Department of Computer Science and Software Engineering
IIU, Islamabad
Supervisor: ________________________________________________
Dr. Nadeem Javaid
Assistant Professor, Center for Advanced Studies in Telecommunications (CAST)
CIIT, Islamabad
HoD: ___________________________________________
Dr. Shahid A. Khan
Professor, Department of Electrical Engineering
CIIT, Islamabad
iv
Declaration
I Muhammad Mohsin Raza Jafri, CIIT/FA12-REE-024/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.
Signature of the student:
Date: ____________________________
____________________________
Muhammad Mohsin Raza Jafri
CIIT/FA12-REE-024/ISB
v
Certificate
It is certified that Muhammad Mohsin Raza Jafri, CIIT/FA12-REE-024/ISB has
carried out all the work related to this thesis under my supervision at the
Department of Electrical Engineering, COMSATS Institute of Information
Technology, Islamabad and the work fulfills the requirements for the award of the
MS degree.
Date: _________________________
Supervisor:
____________________________
Dr. Nadeem Javaid
Assistant Professor
Head of Department:
____________________________
Dr. Shahid A. Khan
Professor, Department of Electrical Engineering
vi
DEDICATION
This thesis is dedicated to my parents and brothers.
vii
ACKNOWLEDGMENT
I am heartily grateful to my supervisor, Dr. Nadeem Javaid, whose patience, encouragement,
guidance and insightful criticism from the beginning to the final level enabled me to have a deep
understanding of the thesis. I would like to thank for his support throughout my degree keeping
me going when times were tough, asking insightful questions, and offering invaluable advice.
Lastly, I offer my profound regard and blessing to everyone who supported me in any respect
during the completion of my thesis, especially my friends in every way offered much assistance
before, during, and at the completion stage of this thesis work.
Muhammad Mohsin Raza Jafri
CIIT/FA12-REE-024/ISB
viii
ABSTRACT
Improved Adaptive Mobility of Courier Nodes in Underwater Sensor
Networks
In Underwater Wireless Sensor Networks (UWSNs), the major challenges are high propagation
latency in data transmission, dynamic topology of nodes due to wave movements and high power
consumption of acoustic modem. Depth based routing as a subcategory of localization-free
routing protocols, is designed to cope with the above mentioned challenges. In this subcategory,
sensor nodes use their depth information to forward data towards sinks, which are stationed at
the surface of aqueous environment. The most widely used depth-based routing protocols are
Depth Based Routing (DBR) and Energy Efficient Depth Based Routing (EEDBR). DBR has
low stability period due to increased data flooding in medium-depth nodes. Whereas in EEDBR,
network lifetime expires quickly due to unnecessary data forwarding and overloaded low-depth
nodes where network lifetime is the duration between network initialization and complete energy
exhaustion of all the nodes. In this thesis, we overcome above mentioned deficiencies of DBR
and EEDBR using the mobility of courier nodes in our proposed routing scheme; Adaptive
Mobility of Courier nodes in Threshold-optimized Depth-based-routing (AMCTD). We also
propose Forwarding-Function (FF) based routing protocol; improved Adaptive Mobility of
Courier nodes in Threshold-optimized Depth-based-routing (iAMCTD). In order to tackle high
path loss and increased propagation latency, we calculate optimal Holding Time (HT) by using
devised parameters; Signal Quality Index (SQI), Energy Cost Function (ECF) and Depth-
Dependent Function (DDF).
ix
List of Publications
1.: N. Javaid, Mohsin Raza Jafri, Z. Khan, U. Qasim, T. A. Alghamdi, M. Ali "iAMCTD:
improved Adaptive Mobility of CourierNodes in Threshold-Optimized DBR Protocol for
Underwater Wireless Sensor Networks," published in International Journal of Distributed Sensor
Networks, Volume 2014 (2014). (ISI-Indexed, IF=0.9)
2.: N. Javaid, Mohsin Raza Jafri, S. Ahmed, Z. Khan, U. Qasim, S. S. Al-Saleh "Delay-
Sensitive Routing Schemes for Underwater Acoustic Sensor Networks," accepted in
International Journal of Distributed Sensor Networks, Volume 2014 (2014). (ISI-Indexed,
IF=0.9)
3.: Mohsin Raza, N. Javaid, A. Javaid, Z. A. Khan, "Maximizing the Lifetime of Multi-Chain
PEGASIS Using Sink Mobility," published in World Applied Sciences Journal, 21 (9): 1283-
1289, 2013 (ISI-Indexed)
4.: Mohsin Raza, S.Ahmed, N. Javaid, Z.Ahmed, R. J. Qureshi, "AMCTD:Adaptive Mobility of
Courier nodes in Threshold-optimized DBR Protocol for Underwater Wireless Sensor
Networks," published in BWCCA, 2013 Eighth International Conference on Broadband,Wireless
Computing, Communication and Applications, Compiegne, France, 2013.
5.: Mohsin Raza,N. Javaid, N. Amjad, M. Akbar, Z. Khan, U. Qasim, "Impact of Acoustic
Propagation Models on Depth-based Routing Techniques in Underwater Wireless Sensor
Networks," published by IANA, 28th IEEE International Conference on Advanced Information
Networking and Applications, Victoria, Canada, 2014.
6.: Mohsin Raza Jafri, Moid Sandhu, K. Latif, Z.A.Khan, A.H.Yasar, N.Javaid, "Towards
Delay-Sensitive Routing in Underwater Wireless Sensor Networks," accepted in 5th
International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN-
2014),Halifax, Nova Scotia, Canada.
7.: Mohsin Raza Jafri, N. Javaid, Z. Khan, U. Qasim "Scalability analysis of Routing protocols
in Underwater WSNs," submitted to International Journal of Distributed Sensor Networks,
Volume 2015 (2015). (ISI-Indexed, IF=0.9)
x
TABLE OF CONTENTS
1 Introduction 1
2 Related Work and Motivation 5
2.1 Courier nodes and AUVs . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.2 Motivation................................... 10
3 AMCTD: Adaptive Mobility of Courier nodes in Threshold-optimized
Dbr 12
3.1 Acousticmodels................................ 13
3.1.1 Energy consumption model . . . . . . . . . . . . . . . . . . . . . 13
3.1.2 Acoustic propagation models . . . . . . . . . . . . . . . . . . . . . 14
3.1.2.1 Thorps Formula . . . . . . . . . . . . . . . . . . . . . . 15
3.1.2.2 Monterey-Miami Parabolic Equation . . . . . . . . . . . 15
3.1.3 Delay computation model . . . . . . . . . . . . . . . . . . . . . . 17
3.2 AMCTD: The proposed protocol . . . . . . . . . . . . . . . . . . . . . . 19
3.2.1 Network initialization . . . . . . . . . . . . . . . . . . . . . . . . . 19
3.2.1.1 Movement scheme of Courier nodes . . . . . . . . . . . . 21
3.2.2 Data forwarding phase . . . . . . . . . . . . . . . . . . . . . . . . 21
3.2.3 Network adaption . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.2.4 Variation in Dth ............................ 23
3.3 Performance evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
3.3.1 Performance metrics . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.3.2 Simulation results and analysis . . . . . . . . . . . . . . . . . . . 24
4 iAMCTD: improved Adaptive Mobility of Courier nodes in Threshold-
optimized Dbr 30
4.1 Motivation................................... 31
4.2 iAMCTD: The proposed protocol . . . . . . . . . . . . . . . . . . . . . . 31
4.2.1 Network initialization . . . . . . . . . . . . . . . . . . . . . . . . . 32
4.2.2 Implementation of on-demand data routing . . . . . . . . . . . . . 32
4.2.3 Data forwarding phase . . . . . . . . . . . . . . . . . . . . . . . . 33
4.2.3.1 Variation in Dth ...................... 33
xi
4.2.3.2 FFcomputation ...................... 34
4.2.4 Adaptive mobility patterns of courier nodes . . . . . . . . . . . . 36
4.2.4.1 Aggregate traffic model for mobile courier nodes . . . . . 38
4.3 Performance evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
5 Conclusion 47
6 References 49
xii
LIST OF FIGURES
1.1 Mechanism of data forwarding in Depth-Based Routing . . . . . . . . . . 3
2.1 Architecture of UASN . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2 UnderwaterVehicles ............................. 8
2.3 Mechanism of HTcomputation and Forwarder selection in DBR . . . . . 10
2.4 Deficiencies in DBR and EEDBR . . . . . . . . . . . . . . . . . . . . . . 11
3.1 Propagation delay in multi-hop communication . . . . . . . . . . . . . . 18
3.2 Mechanism of data transmission in AMCTD . . . . . . . . . . . . . . . . 20
3.3 Network lifetime in AMCTD, EEDBR and DBR . . . . . . . . . . . . . . 25
3.4 End-to-end delay in AMCTD, EEDBR and DBR . . . . . . . . . . . . . 26
3.5 Transmission loss in AMCTD, EEDBR and DBR . . . . . . . . . . . . . 27
3.6 Packet delivery ratio of AMCTD, EEDBR and DBR . . . . . . . . . . . 27
3.7 Average energy consumption in AMCTD, EEDBR and DBR . . . . . . . 28
4.1 Routing of on-demand data in iAMCTD . . . . . . . . . . . . . . . . . . 33
4.2 Variations in Dth iniAMCTD ........................ 34
4.3 Analytical model for data forwarding in iAMCTD . . . . . . . . . . . . . 35
4.4 Mechanism of data transmission in iAMCTD . . . . . . . . . . . . . . . . 37
4.5 Adaptive mobility pattern of courier nodes in dense and sparse conditions 40
4.6 Comparison of energy consumption in iAMCTD, AMCTD, EEDBR and
DBR ..................................... 42
4.7 Network throughput in iAMCTD, AMCTD, EEDBR and DBR . . . . . 43
4.8 Transmission loss in iAMCTD, AMCTD, EEDBR and DBR . . . . . . . 44
4.9 End-to-end delay in iAMCTD, AMCTD, EEDBR and DBR . . . . . . . 44
4.10 Number of alive nodes of iAMCTD, AMCTD, EEDBR and DBR . . . . 45
xiii
LIST OF TABLES
3.1 Simulation Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
3.2 Comparison of number of alive nodes . . . . . . . . . . . . . . . . . . . . 25
3.3 Comparison of end-to-end delay (sec) . . . . . . . . . . . . . . . . . . . . 26
3.4 Comparison of average energy consumption (joule) . . . . . . . . . . . . 28
4.1 ListofNotations ............................... 38
4.2 Comparison of average energy consumption (joule) . . . . . . . . . . . . 43
4.3 Comparison of number of alive nodes . . . . . . . . . . . . . . . . . . . . 45
xiv
Chapter 1
Introduction
1
From the very beginning, oceans are essential way of transportation, military actions and
distributed tactical surveillance. Therefore, it is important to monitor the regions of the
oceans and to assist aqueous applications using Underwater Wireless Sensor Networks
(UWSNs). UWSNs employ sensor nodes to detect physical attributes of water such as
temperature, pressure and wave movement etc. In the recent years, UWSNs have been
emerged significantly for their applications in management, control and surveillance in
deep oceans. There are some remarkable applications of UWSN such as management
of the seabed and oil reservoirs, exploration of the deep sea situation and prevention of
aqueous disasters.
Underwater Acoustic Sensor Networks (UASNs) is a subclass of Underwater Wireless
Sensor Networks (UWSNs) which is used specifically for monitoring purposes in aqueous
environment using acoustic signals. The acoustic wireless sensors along with sink(s), are
distributed under water, constitute the basic body of UASN. Acoustic sensors gather
information of interest, and then by following a routing strategy, forward this data to the
end station. It is a promising and significantly capable routing architecture which estab-
lishes its applications in both commercial and defense areas by using acoustic signals for
communication. The frequency range of acoustic signal used for this purpose is between
10 kHz and 1 MHz.
WHOI Micro-Modem [1] and Crossbow Mica2 [2] are among the commercially avail-
able sensors for underwater environments. Generally, the sink node is supposed to have
high battery power as there batteries can also be replaced by using ships and surface sta-
tions, whereas the acoustic sensors are equipped with limited battery power. However,
by using these sensors, UASNs provide diversified range of applications like pollution
monitoring, ocean current detection and submarine discovery.
Due to harsh conditions of aqueous environment, it is hard to devise energy-efficient
routing techniques for UASN and its applications. As there are major differences between
terrestrial and underwater conditions so it is difficult to employ the basic concepts of ter-
restrial routing in UWSN. In order to cope with the challenges of underwater environment,
researchers exercise the role of low speed acoustic signals for aqueous communication and
sea navigation system which inhibits high propagation delay and transmission losses.
Further, high shipping activity, thermal noise and turbulence noise also causes increased
error rate, which may only be handled by optimal routing protocol that deals specifically
with the physical layer problems.
Authors in [3] design an underwater acoustic channel to descriptively analyze the
total noise density and path loss. On the other hand, Akyildiz et.al [4] investigate di-
verse routing architectures for 2-dimensional and 3-dimensional UWSNs. Fundamental
analyses of aqueous conditions show that reactive routing is more challenging than the
proactive one. Battery replacement and efficient routing are among the solutions to over-
come the power constrained problem of acoustic sensors. However, the leading solution
2
Figure 1.1: Mechanism of data forwarding in Depth-Based Routing
is not practical, thereby we focus mainly on the other one.
The dynamic conditions, variations in topologies, energy constraints and high error
probability during data forwarding are prominent challenges in the design of routing
protocols in UWSNs. Further, the acquisition of real time wireless data in underwater
environment is also a major requirement for the future proposals in the field of UWSN
routing protocols. We can achieve many important applications like coastline surveillance
and underwater mine detection by implementing real-time communication.
In an aqueous environment, the routing protocols are divided into two major cate-
gories: localization-free and localization-based routing schemes. Localization-free schemes
are also termed as flooding-based schemes as in these schemes we assume that nodes do
not know about their location and neighbors and therefore flood data packets in all di-
rections. Depth-based routing is one of the main subcategory in localization-free routing.
Fig. 1.1 shows the mechanism of data forwarding in depth-based routing. In depth-based
routing, Depth-Based Routing protocol (DBR) [5] is the premier routing scheme. It pro-
poses its routing model, in which sensor nodes utilize their depth information to route
data towards the sink. Nodes do not require the information of their location which can
be changed with the movement of water. However, they can find their depth by using
attached depth sensor with them.
In DBR, sensor nodes transmit their signal in omnidirectional range and their neigh-
bors with a lower depth forward the data towards base station (end station). In this
scenario, data flooding increases due to large number of neighbors. The concept of Depth
threshold (Dth) and Holding time (HT) are used to tackle this problem as Dth limits the
number of eligible neighbors for data forwarding by picking neighbors in specific range.
Furthermore, HTis the duration for which any node holds the received packet before
transmitting it. In this duration, node discards the packet after overhearing it from any
other node. Energy Efficient Depth Based Routing (EEDBR) [6] minimizes the deficien-
cies of DBR by considering the residual energy of sensor nodes in the selection of better
3
data forwarder. Nodes with high residual energy will have less holding time than the
other nodes, and will be selected as data forwarder.
Hop-by-Hop Dynamic addressing based routing (H2-DAB) [7] tackles the challenges of
UWSN by implementing the dynamic addressing scheme among the sensor nodes without
requiring the localization information. It also employs courier nodes to enhance network
throughput. Courier nodes are nodes with mechanical module, which can control their
vertical movement and collect data from the other nodes. These nodes can recharge their
batteries through on-surface sinks, when they are on the surface of water. These can
recharge the batteries of courier nodes. The presence of courier nodes increases the cost
of network therefore, these are only two percent of the total nodes of the network. The
main purpose of using courier nodes is to increase the network throughput. Another
efficient scheme, Reliable and Energy efficient Routing Protocol for underwater wireless
sensor networks (RERP2R) [8] employs the routing metric based on the physical distances
between the sensor nodes and exercises it to accomplish higher throughput in UWSNs. It
also provides an energy efficient solution for data forwarding along with better link qual-
ity. In this research work, we aim to focus on routing protocols in UWSNs. We intend
to investigate the problems of the low stability period, swift energy consumption of low
depth nodes and poor throughput during the instability period caused due to unequal
load distribution among the sensor nodes.
The rest of the thesis is organized as follows: discussion and concise overview of re-
lated work and motivation is presented in chapter 2. Chapter 3 contains first proposed
scheme for depth-based routing i.e. Adaptive Mobility of Courier nodes in Threshold-
optimized Dbr (AMCTD). Improved version of proposed routing protocol i.e. improved
Adaptive Mobility of Courier nodes in Threshold-optimized Dbr (iAMCTD) is described
in chapter 4. Finally, the conclusion of thesis is provided in chapter 5.
4
Chapter 2
Related Work and Motivation
5
Recent research [9] shows that UWSN is a major requirement for the defense purposes,
pollution monitoring and disaster management. Moreover, there are also a large vari-
ety of delay-tolerant applications of UWSNs such as deep sea exploration and various
navigation systems. For all of these applications, there are energy-efficient data routing
approaches which are categorized as localization-based and localization-free protocol such
as R-ERP2R [8], QELAR [10], EEDBR and MPT [11]. These protocols attempt to tackle
the challenges of high transmission delay, high path loss and large transmission energy
consumption. Machine learning-based adaptive routing protocol for energy-efficient and
lifetime-extended underwater sensor networks (QELAR) offers an outline for evenly dis-
tributed residual energy among the sensor nodes by calculating reward function. Reward
function is mainly used to find an optimal forwarder in transmission range of nodes. QE-
LAR attempts to achieve long lifetime by using Reinforcement Learning (RL) technique.
In addition, there are also other multiple routing schemes which are used to conserve the
energy of sensor nodes.
Authors in [12] propose a way to control the topology of nodes in order to stabilize
the network. For this purpose, the transmission power of nodes is minimized. Further-
more, the authors achieve network connectivity during the instability period by devising
virtual backbone-forming algorithm. Hanjiang Luo et.al [13] propose energy balancing
strategies in an underwater moored monitoring system in order to deal with sparse con-
ditions. They provide a mathematical model to investigate the power consumption of
sensor nodes. However, these schemes provide long stability period at the cost of large
delay or increased path loss. In dense underwater conditions, Path Unaware Layered
Routing Protocol for underwater sensor networks (PULRP) [14] provides the layered ar-
chitecture and detailed algorithm to achieve high throughput among the network. It is
free of localization information and do not require a fixed routing table, therefore, min-
imizing the overhead in underwater communication. In [15], authors suggest mobility
patterns for courier nodes in UWSN along with proposing optimal weight functions in
order to achieve efficient data forwarding towards the sink.
MUAP [16] examines the acoustic channel and its losses to deal with the tribulations
at the physical layer and also provides an energy model to compute the path losses in
aqueous transmissions. In [17], the authors propose Z-curve, which is a path planning
mechanism for mobile beacons to localize the sensor nodes. Jianmin et.al [18] suggest a
generic algorithm to control the movement of Autonomous Underwater Vehicle (AUV) in
UWSN by gathering network information from sensor nodes. Generally, limited residual
energy is also a big challenge for sensor nodes in UWSN. Furthermore, the high number of
transmission collisions also causes wastage of energy. In [19], the authors tackle the issue
by proposing Contention-free Multi-channel MAC protocol for UWSNs. Another tech-
nique has been proposed by [20], in which authors suggest Spatially Fair MAC (SFMAC)
protocol for UWSNs. It solves the problem of collisions with postponing Clear-To-Send
6
Figure 2.1: Architecture of UASN
(CTS) for a specific duration as calculated by the receiver node. Mobility of the target
object is always an issue for UWSN. Because, it is very difficult to update the location
information of the target object by sensor nodes. Figure 2.1 shows the architecture of
UASN.
CEDC [21] gives Doppler compensation model to improve the acoustic signal quality
and presents a splendid model for high data-rate communication. It employs a Doppler
estimation technique to eliminate Doppler shift stimulated by platform velocity and ac-
celeration. Sherif et.al [22] propose Delay Tolerant network (DTN) routing protocol
to tackle continuous node movements and utilize the single-hop and multi-hop routing
schemes. They also attempt to minimize collision overheads at the Medium Access Con-
trol (MAC) layer. In [23], authors minimize the delay in depth-based routing schemes
by using efficient delay-sensitive routing metrics. In notable localization-based protocols,
Hop-by-Hop Vector-Based Forwarding for underwater sensor networks (HH-VBF) [24]
uses the vectors assumptions between the source and the destination nodes, effective for
both dense and sparse conditions in the network. It gives a vector-based algorithm to
achieve low end-to-end delay in the network with out requiring state information of sensor
nodes. Multipath communication for Time critical applications in underwater acoustic
sensor networks (MPT) employs power-controlled and source-initiated transmission by
examining the deficiencies at the physical layer in underwater acoustics, specifically to
deal with on-demand routing applications. In addition to the above-mentioned schemes,
there are also some delay-sensitive protocols proposed by different research groups for
UWSN.
Mobicast Routing Protocol (MRP) [25] suggests adaptive mobility of Autonomous
Underwater Vehicle (AUV) to collect data with a minimum end-to-end delay. It ap-
plies Apple slice technique to solve the coverage hole problem with varying node density.
AUVs constitute part of a large group of undersea vehicles systems known as Unmanned
7
Figure 2.2: Underwater Vehicles
Underwater Vehicles (UUVs), a classification that includes non-autonomous Remotely
Operated underwater Vehicles (ROVs) which are controlled and powered from the sur-
face by an operator/pilot using remote control. A simple AUV collect data while following
a preplanned route at speeds between 1 and 4 knots. AUVs can navigate using an under-
water acoustic positioning system. When it is operating completely autonomously, the
AUV will surface and take its own GPS fix. In H2DAB, the authors use courier nodes to
improve their routing scheme. These Courier nodes collect the data packets from lower
layer nodes, especially from the nodes anchored at the bottom and after collecting data,
will deliver these packets directly to the surface sinks. These special nodes can sense as
well as can receive the data packets from other ordinary nodes and will deliver it to the
surface sinks directly. These nodes can move vertically, with the help of a mechanical
module, which helps to push the node in the water till the node reaches the bottom
where it will stop for a specified amount of time and then pull back to the ocean sur-
face. Equipped piston can do this by creating the positive and negative buoyancy. These
nodes can recharge their batteries through on-surface sinks, when they are on the surface
of water.
2.1 Courier nodes and AUVs
There are also some similarities and differences between courier nodes and AUVs. Lit-
erature shows that both courier nodes and AUVs can sense as well as receive the data
from normal sensor nodes and forward them to a surface base station. Furthermore, both
courier nodes and AUVs can buffer the sensed data and received data from other nodes
for a long duration. Both are used to minimize energy consumption and to increase net-
work throughput. Figure 2.2 shows the functionality of AUVs.
Moreover, an AUV is a robot which travels underwater without requiring input from
8
an operator. However, Courier nodes need input from an operator. AUV is a large sized
complex vehicle while courier nodes are of small size. The courier nodes dispose of a
module that allows them to push the node down into the water at different depths and
pull back the node to the surface. Where as, AUVs can move both vertically and hori-
zontally by themselves. The invention of AUVs dates back to 50 years, however, courier
nodes have been invented recently by a patent H2DAB. AUVs are more costly than
courier nodes due to their advanced and well-equipped design. AUVs can also be used as
a Mobile sink, however courier node can not act as MS due to their limited functionality.
The AUV can have a more powerful acoustic modem with high transmission range, how-
ever courier node has low range. Furthermore, AUVs consume more energy than courier
nodes for its movement.
Stefano et.al [26] try to minimize the packet latency and energy consumption of the
sensor nodes by optimized packet size selection along with examining its effects on MAC
layer protocols. Revised ERP2R (R-ERP2R) is also an energy-efficient routing scheme.
However, it is not desirable for data sensitive applications, especially during instability
period due to inefficient computation of Expected Transmission Count (Etx) in distant
transmissions. Milica Stojanovic et.al [24] scrutinize the association between transmission
distance and channel capacity in underwater communication. There are several acoustic
propagation models to analyze the effect of higher layer protocols at the physical layer.
MMPE [27] considers noise functions, depth information and node movement to de-
termine transmission loss. Thorps model [28] considers bandwidth efficiency as a primary
factor to establish signal propagation. Acoustic models are also used to detect energy
holes in the network. Dario Pompili et. al suggest the paradigms for both delay-sensitive
and delay-insensitive techniques in UWSN by formulating Integer Linear Programming
models.
Ayaz et.al [29] categorize and describe the routing schemes in UWSNs on the basis of
network architecture, mechanism of data forwarding and protocols operations. These are
further subcategorized into source initiated, multipath, flat and location-based routing.
In [30], the authors discuss the energy analysis of sensor nodes in UASNs on the basis
of depth of water. In [31], the authors devise multi-subpath routing to minimize propa-
gation delays and to improve packet delivery ratio in UWSN. A. F. Harris et.al designs
the underwater acoustic channel to descriptively analyze the total noise density and path
loss. Acoustic models are also used to detect energy holes in the network. In flooding-
based techniques, control packet transmission is the requirement of the network in order
to achieve better management with the varying network density. The multiple-sink model
is proficient at balancing the network load and it has been analyzed in [32], [33], and [34].
9
Non-eligible neighbor
of source node A
Depth threshold (Dth)=60m
Passive link
Range
Source node A
D
Active link
C
B
Dth
Depth of A =100m
Depth of B= 35m
Depth of C= 20m
Depth of D =30m
Depth difference of
A and B =65m
Depth difference of
A and C =80m
Depth difference of
A and D=70 m
Holding time for
B=3sec
Holding time for
C=1sec
Holding time for
D=2sec
Hence, C will send packet earlier then B and D due to less holding
time for a packet
Depth difference of eligible neighbor > Dth
Depth of eligible neighbor < Depth of Source node
Transmission Range =100m
STEP 1
STEP 2
STEP 3
STEP 4
STEP 5
Eligible neighbor of
source node A
On-surface sink
Figure 2.3: Mechanism of HTcomputation and Forwarder selection in DBR
2.2 Motivation
In prominent depth-based routing protocols, the efficient energy consumption remains
the main requirement due to necessity of long stability period. In DBR, we observe
that the network with a low number of nodes has more stability period than the other
conditions. It is due to the fact that the network has less forwarding’s of data packets in
low node density. Moreover, medium-depth nodes have less number of packets to forward
towards the sink. As the number of deployed nodes increases, stability period decreases.
Figure 2.3 shows mechanism of forwarder’s selection in DBR.
End-to end delay is lower only in the sparsely deployed network as only single path
forwarding is mostly used to deliver the packet to the sink. It causes minimum nodal
delay and less collisions in the network. As the number of deployed nodes increases, delay
highly increase due to the high number of redundant forwarding’s of the same packet from
the source node to the sink. It causes high nodal delay. Multiple transmissions of the
same packet are required due to a large number of collisions. Since each node holds the
packet for a certain time, the overall HTof the packet also increases. Figure 2.4 shows
the major deficiencies in DBR and EEDBR.
Now, we also discuss some of the deficiencies in EEDBR. The energy consumption
of EEDBR in denser network is very high due to increased node involvement in data
forwarding and redundant packet transmissions. This is because more nodes become
eligible for forwarding the data packet with the increase in network density. Moreover,
in EEDBR, there is a lack of optimality in throughput during the instability period of
the network.
10
Figure 2.4: Deficiencies in DBR and EEDBR
Major inefficiencies of DBR and EEDBR are as follows:
DBR and EEDBR struggle to obtain long network lifetime as the nodes expire
quickly due to unnecessary data forwarding and high load on low-depth nodes.
In DBR, quick energy consumption of medium-depth nodes causes creation of large
coverage holes in the core of network.
DBR and EEDBR struggle to obtain high throughput in random deployment of
sensor nodes due to the presence of coverage holes in the network.
In EEDBR, the medium-depth nodes expire earlier due to the increased load of data
forwarding, causing a less number of available neighbors for the remaining nodes.
In DBR, constant dth causes the selection of the same nodes as data forwarders
again and again; causing the quick energy consumption of these nodes.
11
Chapter 3
AMCTD: Adaptive Mobility of Courier nodes in
Threshold-optimized Dbr
12
3.1 Acoustic models
The acoustic models are employed to calculate energy consumption, delay, transmis-
sion loss and other important performance parameters in the acoustic environment. In
UWSNs, an acoustic signal is used for the communication between the sensor nodes,
which tackle the challenges of the aqueous environment in a better way. In the following
subsections, we discuss computational models for energy consumption and channel loss.
We also suggest the analytical model for calculation of end-to-end delay.
3.1.1 Energy consumption model
In this section, we analyze the energy consumption model [13] for acoustic communication.
First of all, we use the passive sonar equation to calculate Signal-to-Noise Ratio (SNR)
in an acoustic channel.
SN R =SL T L NL +DI DT (3.1)
In the above equation, SL denotes Source Level and TL is Transmission Loss. Moreover,
NL is Noise Loss, DI is Directive Index and DT is the Detection Threshold of the sonar.
All the above quantities are in dBreµP a.
We know that the transmission loss can be computed by using Thorp model [15] as
follows:
T L = 10log(d) + αd ×103.(3.2)
Where, αis the absorption coefficient and dis the distance between the sender and re-
ceiver nodes.
Another important factor, NL [35] consists of four noise components which are calcu-
lated by using the equations below. It depends upon frequency of the signal:
10log(Nt(f)) = 17 30log(f).(3.3)
10log(Ns(f)) = 40 + 20(s0.5) + 26log(f)60log(f+ 0.03) (3.4)
sis a shipping constant and wis a wind constant.
10log(Nw(f)) = 50 + 7.5w1/2+ 20log(f)40log(f+ 0.4) (3.5)
10log(Nth(f)) = 15 + 20log(f),(3.6)
where, Nw(f) , Ns(f) , Nt(f) and Nth(f) show the noise produced due to wind, ship-
ping, turbulence and thermal activities, respectively. These all factors largely depend on
13
frequency (f) as noise increases with the increase in frequency of signal. The aggregated
NL is calculated as:
NL =Nt(f) + Ns(f) + Nw(f) + Nth(f).(3.7)
SL can also be calculated by using passive sonar equation.
SL =SN R +T L +NL DI (3.8)
which is used to calculate Transmitted signal Intensity (IT) as given below:
IT= 10SL/10 ×0.67 ×1018 (3.9)
Therefore, the source Transmitted Power (PT(d)) can be calculated by using the fol-
lowing equation:
PT(d) = 2π×1m×H×IT(3.10)
In the above equation, H shows the depth of the network. Now, PT(d) can also be written
as:
PT(d) = 2πH ×1m×H×10S L/10 ×0.67 ×1018 (3.11)
Energy dissipation ET X (k, d) in sending kbits over a distance of dis given as follows:
ET X (k, d) = PT(d)×TT X .(3.12)
Where, TT X is the transmission time in seconds.
3.1.2 Acoustic propagation models
Acoustic propagation models examine path loss and transmission loss in an aqueous
channel. Recently proposed models such as MMPE [35] and thorp model consider depth,
signal height and combined noises in aqueous environment. However, some models regard
frequency and bandwidth efficiency as the deciding factors for variations in path loss and
delay. In the following subsections, we analyze two main acoustic propagation models
that compute the combined losses in UWSN.
14
3.1.2.1 Thorps Formula
Thorps formula considers the acoustic signal propagation as a molecular movement of
signal towards its adjacent particles. It predicts the amount of gradual decrease in signal
intensity, as the signal propagates towards the destination node. However, its main
emphasis is on the bandwidth efficiency and it proposes absorption coefficient (α) which
is a function of frequency and distance of transmitted acoustic signal. It also suggests a
model for the calculation of combined acoustic absorption loss. Thus, at a given frequency
f, thorps model calculates the total absorption loss as follows:
10logα(f) =
0.11f2/(1 + f2) + 44f2/(4100 + f)
+2.75 104f2+ 0.003 f 0.4
0.002 + 0.11(f /(1 + f)) + 0.011f, f<0.4
(3.13)
where, α(f) is measured in dB/km and fin kHz. Using the above calculated loss, we
calculate the value of αas follows:
α= 10α(f)/10.(3.14)
Accordingly, all the tunable parameters are given in dBreµP a. The total attenuation
can be calculated by adding absorption loss and spreading loss:
10logA(l, f ) = k10log(l) + l10log(α(f)).(3.15)
Where, the first term shows the spreading loss and the second term is the absorption loss.
lis the transmission distance in meters. The spreading coefficient kshows the geometry
of the signal propagation. (i.e., k= 1 is cylindrical, k= 2 is spherical, and k= 1.5 is
practical spreading [36]).
3.1.2.2 Monterey-Miami Parabolic Equation
Monterey-Miami Parabolic Equation (MMPE) is also an accurate model that computes
channel losses in acoustic channel. It has been formulated by using the main principle
of wave equation. It is a highly complex model, however more accurate than the Thorps
model. It also shows the impacts of variations in the depth information of the sender
and receiver nodes on the signal quality. Moreover, it considers the Euclidean distance
between the communicating nodes and the frequency of the transmitted signal. The basic
15
formula of MMPE model is given below [37]:
P L(t) = m(f, s, dA, dB) + w(t) + e() (3.16)
where:
PL(t): propagation loss while transmitting from node A to node B.
m(): propagation loss with random and periodic components; obtained from regression
of MMPE data.
f: frequency of transmitted acoustic signal in kHz.
s: Euclidean distance between node A and node B in meters.
w(t): periodic function to approximate signal loss due to wave movement.
dA: sender’s depth in meters.
dB: receiver’s depth in meters.
e(): signal loss due to random noise error.
The first part of the equation calculates the propagation loss caused due to random
and periodic components. It also performs nonlinear regression on the data to obtain
A(n) coefficients. By using the resulting data, m(f, s, dA, dB) function computes the
propagation loss [38] as follows:
m(f, s, dA, dB) = log
(s
0.9)A0dAA9sA7((dAdB)2)A10
(sdB)10A5!
+f2A1
1 + f2+41
4100 + f2+ 0.002+ 0.003
s
914+A6dB+A8s(3.17)
In the second part of the eq. 3.16, MMPE model estimates the losses caused due to wave
motion. For this purpose, it considers the sinusoidal movement of the water particles
around the acoustic signal. The w(t) [37] function considers scale factor function, wave-
length and wave effects function to predict the signal loss caused by the wave movement.
It can be mathematically written as:
w(t) = h(lw, t, dB, hw, Tw)E(t, Tw) (3.18)
where:
16
h(s): scale factor function.
lw: ocean wavelength in meters.
hw: wave height in meters.
dB: receiver’s depth in meters.
Tw: wave period in seconds.
E(): function of wave effects in nodes.
As we assume the continuous node movement in aqueous environment, h(s) depicts the
effect on wavelength of transmitted signal due to receiver node movement. It also scales
the movement of signal with the distance. We compute scale factor by using the following
formula:
h(Tw, lw, t, hw, dB) =
hw1(2dB
lw)
0.5
sin 2π(mod(Tw))
Tw
(3.19)
In the last part of MMPE, e( ) describes the random background noise by its mathe-
matical expression. In order to estimate the noise in dense conditions, the random noise
function used by e( ) follows a Gaussian distribution. The random noise function is
based on the proportion of the distance between communicating nodes and the source
transmitter range.
e() = 20 s
smax RN(3.20)
where:
e( ): random noise function.
s: Euclidean distance between node A and node B in meters.
smax: transmission range in meters.
RN: random number from a gaussian distribution centered at 0 and with variance 1.
3.1.3 Delay computation model
In this section, we suggest an analytical model to calculate the end-to-end delay in data
transmissions. We study the effects of acoustic channel characteristics on the speed and
propagation delay of the signal. As we know that the propagation delay for acoustic signal
17
is five times greater [35] than the Radio Frequency (RF) signals due to multi-path and
fading effects and it depends on the attenuation coefficient due to high Bit-Error-Rate
(BER) in aqueous conditions. Fig. 3.1 shows the description of propagation delay. The
Figure 3.1: Propagation delay in multi-hop communication
end-to-end delay between the sender and receiver is given by:
TEE= (n+ 1)(Ttx) + n(Trx) + Td
p,(3.21)
where, Ttx and Trx are the transmission and receiving time consumed by a sensor node
for a packet in seconds. nis the number of hops for a specific packet, whereas, Td
p[39] is
the overall propagation delay of packets between the source and base station expressed
as:
Td
p=Ts,b
s,i +X
i,jn
Ts,b
i,j +Ts,b
j,b n2i, j n(3.22)
Td
p=Ts,b
s,i +Ts,b
i,b n= 1 in(3.23)
Td
p=Ts,b
s,b n= 0 (3.24)
Equations (3.22), (3.23) and (3.24) show the computations of propagation delay for multi-
hop, single-hop and direct communication, respectively.
Propagation delay between two nodes has been calculated in [40] and is given as:
Tp=d/q, (3.25)
where, dis the distance between the sender and receiver in mand qis the speed of signal
in m/s which is calculated as follows [16] :
q= 1449.05 + 45.7t5.21t2+ 0.23t3
+(1.333 0.126t+ 0.009t2)(S35) + 16.3z+ 0.18z2,(3.26)
t=T/10 (3.27)
In the above equations, Tis the temperature in C,Sis salinity in ppt and zis the
depth in km. Above discussed equations compute the overall delay of packets between
the source nodes and BS, by considering signal speed with the depth of water.
18
3.2 AMCTD: The proposed protocol
In this work, we design Weight Functions (WF s) and variations in Dth to improve stability
period of the network. Variations in Dth provide load balancing by controlling the number
of eligible neighbors. Moreover, we employ courier node’s mobility in our scheme to
improve network throughput. Now, we introduce our proposed protocol, i.e. AMCTD
descriptively.
In the first phase of network initialization, each node computes its WFand the courier
nodes start their schematic movement. In the second phase, data forwarders are decided
on the basis of their WFvalue. These data forwarders have less HTof received packet
than the other neighbors of the source nodes. Thus, they transmit the data firstly. In
the third phase of network adaption, WFcomputation technique is changed to tackle the
sparsity of network. Fourth phase discusses the variations in Dth of nodes in order to
control the number of eligible neighbors.
3.2.1 Network initialization
AMCTD, just like other depth-based schemes, not only performs routing on the basis of
depth information, but also employs HTand Dth. Each sensor node transmits the sensed
data within its transmission range. The neighbor node, at a depth lower than the source
node and is located outside its Dth limit, computes HTfor received data packet. Dth
limit is given as:
dth < dpdc(3.28)
dcand dpdenote the depths of the current and previous node respectively during transfer
of a packet. During initialization, sink sends hello packet to all the nodes to get their
vital information. The network sets down the Dth of sensor nodes to D1to eliminate
the flooding process. D1is assumed as 225m out of the transmission range of 250m.
Each node figures out its WFon the basis of residual energy and its depth information.
Courier nodes devise their schematic sojourn tour in the network and the sensor nodes
broadcast their depth information to the neighbors using hello packets. Therefore, the
joint communication between the sensor nodes, courier nodes and the sinks initializes the
network operations.
19
Figure 3.2: Mechanism of data transmission in AMCTD
20
3.2.1.1 Movement scheme of Courier nodes
Courier nodes [7] initiate their schematic movement towards the bottom of the network.
These Courier nodes collect the data packets from lower layer nodes, especially from the
nodes anchored at the bottom and after collecting data, deliver these packets directly to
the surface sinks. These special nodes sense as well as receive the data packets from other
ordinary nodes and deliver it to the surface sinks directly. These nodes move vertically,
with the help of a mechanical module, which helps to push the node in the water till the
node reaches the bottom where it stop for a specified amount of time and then pull back
to the ocean surface. Equipped piston can do this by creating the positive and negative
buoyancy. These nodes recharge their batteries through on-surface sinks, when they are
on the surface of water.
In our scheme, we assume their deployment as two percent of the total nodes. Initial
deployment of these nodes is in specific central points as shown in fig. 3.2. However, it is
not necessary to keep their mutual distances constant. The up and down movement speed
of these nodes is between 3-5 m/s. To adapt to the coverage problems of the network,
the speed of the movement of the courier node is varied to a higher speed to facilitate
the remaining alive nodes. Their schematic mobility model helps out the network to
diminish coverage hole creation in the instability period. Coverage holes are the regions
of the network, which are not present in transmission range of any sensor node. Each node
also finds for the courier node among its neighbors to encourage on-spot data collection.
If courier nodes receive the packet of source node, it transmits acknowledgment to other
neighbors to stop further forwarding of certain packet.
3.2.2 Data forwarding phase
In the phase of Data forwarding, source node transmits the sensed data packet. After
receiving it, eligible neighbor nodes compute HTfor the received packet by using WF,
which is the time duration to hold certain data packet. Neighbor node having higher WF
value have lesser HTthan the other neighbors of the source node. Therefore, it transmits
packet earlier than the other nodes. The other nodes discard the certain packet after
overhearing it. Fig. 3.2 also shows that nodes having higher WFthan the other nodes
forward the received packets as they have lesser HTthan the other neighbors of the source
node.
The formulae for computation of HTand WF s are given below:
HT= (1 WF)Htmax ,(3.29)
21
Htmax is the maximum holding time and can be selected according to environmental
conditions. Weight function of node i(Wi) is computed as follows.
Wi= (priorityvalue Ri)/Di(3.30)
where Riis the residual energy of node i,Diis the depth of node iand Priority value is
a constant.
The number of transmissions and data-forwarding load on medium-depth nodes is
minimized as data forwarders are selected on the basis of the ratio between residual
energy and depth of the nodes. Nodes having higher residual energy and less depth
will be selected as data forwarder as they have less HTthan the other nodes. WFalso
minimizes the delay as it is used to select data forwarder with intermediate depth in
the range of the source node. It should not have a maximum depth difference, nor the
minimum depth difference with the source node. This mechanism also minimizes the
load on the low-depth nodes, causing the increase in stability period. Moreover, the
computational formula for the WFof sensor nodes changes with the varying density of
network. The data packet of our technique consists of sender ID, packet sequence number,
depth information and payload.
3.2.3 Network adaption
In the phase of network adaption, the criteria for the computation of WFis changed.
Network allocates the updated WF s criteria to sensor nodes, based on the prioritization
of their depths instead of residual energy to cope with the sparse conditions of network.
After every 300 seconds, the nodes broadcast hello packet in the network to find the
number of dead nodes by the sink. As the number of dead node increases by two percent,
each node calculates its WFby the following formula
Wi= (priorityvalue Di)/Ri(3.31)
The threshold of two percent dead nodes is used to slow down quick energy exhaustion of
low-depth nodes which are continuously selected as forwarders in network initialization
phase. This alteration is used to prioritize the depth among neighbors and to reduce
the significance of residual energy in the calculation of the WF s. It also decreases the
load on nodes with high residual energy to become optimal forwarder for consecutive
transmissions. It increases the instability period of the network.
22
3.2.4 Variation in Dth
In the last section, our scheme devises the variations in Dth of nodes to deal with coverage
holes created at the end of network lifetime. Algorithm 1 shows the mechanism for the
variations in Dth. In our assumed scenario, the transmission range of nodes is 250m.
Algorithm 1 Variations in Dth
N11st limit f or number of dead nodes
N22nd limit f or number of dead nodes
Dth Depth threshold f or any node i
Dth1Depth threshold f or dense condition(225)
Dth2Depth threshold f or sparse condition(150)
Dth3Depth threshold f or extreme sparse condition(25)
if N= 0 then
Dth =Dth1
end if
if N1< N then
Dth =Dth2
end if
if N2< N then
Dth =Dth3
end if
Dth1is 225m, when all the nodes are alive. As the number of dead nodes increases by
two percent, the Dth is decreased to Dth2which is 150m in order to increase the quantity
of neighbors. It eases out the forwarding of data in low network density. Moreover, it is
changed to 50m as the dead nodes increase by 200 in extreme sparse condition to boost
the network lifetime. In UWSNs, the network lifetime is of prime importance; hence we
proposes the modification in WFcalculation again as the number of dead nodes passes
80 percent to again prioritize residual energy among the remaining alive nodes.
Wi=Ri/(priorityvalue Di) (3.32)
where Riis the residual energy of node iand Diis the depth of node i.
Now, this alteration is again used to prioritize the residual energy among neighbors in
the calculation of the WF s . It decreases the load on nodes with low and medium depth to
become optimal forwarder for consecutive transmissions. It also plays its role in extreme
sparse situations.
3.3 Performance evaluation
We compare the performance of our protocol with other depth-based routing protocols
such as DBR and EEEDBR using MATLAB. We deploy the network of 225 nodes using
23
Table 3.1: Simulation Parameters
Parameter Value
Network size 500x500x500
Initial energy of normal nodes 40J
Data packet size 64 bytes
Control packet size 8 bytes
Transmission Range 250 meters
Number of Courier nodes 4
Number of nodes 225
random topology in 500mx500mx500m environment. We have adopted the specifications
of commercial acoustic modem, LinkQuest UWM1000 [41], where node generates packet
after every 16 seconds. The power consumptions for the transmitting, receiving and idle
mode are 2w, 0.1w and 10mw respectively. At the surface of water, the mutual distance
between the sinks is 250 meters consecutively. In every single simulation run, all the
nodes of the network sense data and transmit upwards, until it reaches to base station or
courier nodes.
The simulation parameters are given in Table 3.1.
3.3.1 Performance metrics
Network lifetime: It is the time duration when the energy of the all nodes of the
network exhaust completely.
End-to-end delay: It is the time duration required by a packet to reach from source
node to sink.
Packet delivery ratio: It the ratio between the number of packets transmitted by
the nodes to the number of packets received by the sink.
Transmission loss: It is the average signal loss between intermediate nodes during
data forwarding.
Average energy consumption: It is the total energy consumed by the network in
one cycle when all the alive nodes transmit the packet.
3.3.2 Simulation results and analysis
Fig. 3.3 represents the comparison between the network lifetime of AMCTD, EEDBR
and DBR. The stability period of AMCTD is higher than the DBR and EEDBR. It is
because of the presence of WFand courier nodes in AMCTD. The data forwarders having
24
Table 3.2: Comparison of number of alive nodes
Time (sec) DBR EEDBR AMCTD
500 225 225 225
1000 200 220 225
1500 172 189 222
2000 141 150 205
2500 122 131 191
3000 120 119 115
3500 116 105 61
4000 114 101 50
4500 114 81 37
5000 114 77 21
5400 1 24 1
higher WFhave lesser HTthan the other nodes. WFcauses the selection of the neighbors
having high residual energy and low depth as data forwarder.
0 1000 2000 3000 4000 5000 6000 7000 8000 9000
0
50
100
150
200
Network lifetime (sec)
Number of allive nodes
AMCTD
EEDBR
DBR
Figure 3.3: Network lifetime in AMCTD, EEDBR and DBR
Table 3.2 shows the tabular data for the comparison of number of alive nodes.
Presence of courier nodes minimizes the load on medium-depth node, whereas in DBR
and EEDBR, medium-depth nodes die quickly. In EEDBR, stability period is lower than
DBR and AMCTD however, network lifetime is lesser than AMCTD. EEDBR selects the
nodes having high residual energy as data forwarding nodes. However, still medium-depth
nodes forward large amount of data and die quickly.
25
Table 3.3: Comparison of end-to-end delay (sec)
Time (sec) DBR EEDBR AMCTD
500 65 42 30
1000 59 42 19
2000 21 18 28
3000 5 8 6
4000 4 3 0.9
5000 3 2.8 0.8
5400 0.9 0.8 0.2
0 1000 2000 3000 4000 5000 6000 7000 8000 9000
0
10
20
30
40
50
60
70
Network lifetime
End−to−end delay (sec)
AMCTD
EEDBR
DBR
Figure 3.4: End-to-end delay in AMCTD, EEDBR and DBR
Table 3.3 shows the tabular data for the comparison of end-to-end delay.
In DBR, low-depth die earlier than all the other nodes as the nodes with low depth are
selected as data forwarder and have less HT. Fig. 3.4 shows the evaluation of end-to-end
delay in AMCTD, EEDBR and DBR. In AMCTD, there is lower delay than the other
two schemes along with high network throughput and increased data forwarding. Each
packet requires less number of forwarding due to low Dth and courier nodes in our scheme.
26
0 1000 2000 3000 4000 5000 6000 7000
0
20
40
60
80
100
120
Time(sec)
TL of network(dB)
AMCTD
EEDBR
DBR
Figure 3.5: Transmission loss in AMCTD, EEDBR and DBR
In EEDBR, delay is even lesser than DBR as data transmission consumes low nodal delay.
Only two or three forwarding are required for packet to reach to sink. In EEDBR, less
number of transmissions are required as only high energy nodes are the better forwarders.
0 1000 2000 3000 4000 5000 6000
0
10
20
30
40
50
60
70
80
90
100
110
Time (sec)
Packet delivery ratio
AMCTD
EEDBR
DBR
Figure 3.6: Packet delivery ratio of AMCTD, EEDBR and DBR
In DBR and EEDBR, packet delivery ratio decreases with the death of medium-depth
nodes. Coverage holes are created as the forwarders of the source nodes die out and
data is lost. In AMCTD, the packet delivery ratio remains high due to the presence
of courier nodes during the stability period. However, throughput decreases with the
creation of coverage holes during the instability period. We have modified the formula
for the computation of weight function with the varying network density. It is due to the
factor that weight function is used to compute holding time, which in turn decides the
optimal data forwarder. If the process of forwarder selection remains same, same nodes
27
0 1000 2000 3000 4000 5000 6000 7000 8000 9000
0
10
20
30
40
50
60
70
80
90
100
Network lifetime (sec)
Total energy cosumption (J)
AMCTD
EEDBR
DBR
Figure 3.7: Average energy consumption in AMCTD, EEDBR and DBR
Table 3.4: Comparison of average energy consumption (joule)
Time (sec) DBR EEDBR AMCTD
500 97 49 52
1000 85 48 42
2000 55 29 39
3000 44 21 22
4000 41 19 8
5000 41 18 2
5400 1 3 0.8
are selected throughout the network lifetime, which is unsuitable for improved network
performance. Nodes will die out, if they are selected as data forwarder again and again.
So we vary the process for neighbor selection, hence different nodes are selected as data
forwarders and load is balanced.
Table 3.4 shows the tabular data for the average energy consumption of network.
Fig. 3.5 estimates the TL of network by using MMPE model. In our proposed technique,
the presence of courier nodes and the changes in Dth enhances throughput along with
decrease in TL. Signal loss depends upon the number of transmissions in one round, which
is proportional to number of successfully received packets. In the previous techniques,
uneven energy consumption of nodes is caused due to high data forwarding towards the
base stations by medium-depth nodes. It causes low stability period along with sharp
reduction in the network throughput. AMCTD upholds the throughput and encourages
smooth energy consumption of all the source and forwarding nodes. Fig. 3.6 shows the
packet delivery ratio of routing schemes. AMCTD has better ratio then previous schemes
due to courier nodes. The packet delivery ratio is the ratio of the number of packets
28
received by the sink to the number of packets transmitted by the nodes in the unit time.
Fig. 3.7 shows the average energy consumption of routing schemes. In DBR and EEDBR,
energy consumption is high as all data is sent through low depth nodes however, courier
nodes collect data from high depth nodes in AMCTD. As nodes start dying in DBR and
EEDBR, energy consumption also increases. Distant neighbors of source nodes die and
number of transmissions are increased.
29
Chapter 4
iAMCTD: improved Adaptive Mobility of Courier
nodes in Threshold-optimized Dbr
30
4.1 Motivation
Major deficiencies of AMCTD are as follows:
Lack of on-demand data routing in AMCTD
Consideration of only two parameters (residual energy and depth) in computation
of HT
Room for improvement in delay, stability period and transmission loss
4.2 iAMCTD: The proposed protocol
In iAMCTD, we overcome the above-mentioned deficiencies as follows:
In iAMCTD, the efficient movement of courier nodes minimizes end-to-end delay
and decreases the energy consumption of low-depth nodes in sparse conditions.
The Forwarding Functions (FF s ) computation technique causes longer stability pe-
riod and reduced TL than the previous schemes. It also decreases average end-to-
end delay of packets.
Implementation of Threshold-based data forwarding is resourceful process for on-
demand routing and removes forwarding of unnecessary data in the network.
We consider arbitrary deployment of nodes in 3D aqueous environment. The network
consists of sensor nodes, courier nodes, multiple sinks and onshore data centers. Sensor
nodes and courier nodes communicate with low-range acoustic modems, whereas sinks
(equipped with both RF and acoustic modems) communicate with onshore data centers
and sensor nodes. Upon the detection of physical environmental attributes, the sensor
nodes transmit the sensed data to either of the in-range node or sink.
After collecting threshold-based data, sensor nodes transfer the data to sink, as soon
as possible, either by direct transmission or through courier nodes; otherwise broadcast
the data towards their neighbors. Nodes communicate with each other using low-range
commercial acoustic modems. We divide operation of the proposed iAMCTD into the
following four phases:
Network initialization
Implementation of on-demand data routing
Data forwarding phase
Adaptive mobility patterns of courier nodes
31
The first phase deals with nodes’ deployment and network initialization. Second phase
discusses implementation of on-demand data routing to facilitate the time-sensitive and
critical-data applications. Third phase expresses the data forwarding along with the
variations in Dth for sensor nodes to cope with varying network density. We develop
efficient FFfor optimal data forwarding in aqueous environment. We also discuss the
data packet structure and the tradeoff between control overhead and network lifetime.
On the basis of control packets, sink identifies the network density and manages the
network specifications and mobility of courier nodes. Last phase uses Mixed Integer
Linear Programming (MILP) to analyze adaptive mobility patterns of courier nodes and
their multiple cases.
4.2.1 Network initialization
We assume that sensor nodes broadcast the data using flooding-based approach. As each
neighbor of the node receives data of the sender node, courier nodes devise their sojourn
tour at the start of the network and then start collecting data from the source nodes.
These also report the network density information to sinks in order to devise mobility
patterns and Dth variations. During knowledge acquisition, nodes share the depth and
residual energy information among themselves. As the network initiates, courier nodes
remain at equal horizontal distances from each other and these start vertical movement
towards the bottom. Each node shares depth information with other nodes and the
eligible neighbors for forwarding are decided on the basis of Dth . The neighbor nodes
compute the FFby considering channel losses, residual energy and depth differences.
4.2.2 Implementation of on-demand data routing
The proposed protocol performs on-demand data routing as the nodes react immediately
to sudden and drastic changes considering the value of sensed attribute. Fig. 4.1 depicts
the transmission algorithm for threshold-based data, where Vis the current sensed value,
Sth is the soft threshold, Hth is the hard threshold, Riis the residual energy of the ith node
and Rprime is the residual energy limit for data transmission. After sensing the attribute
value, each node compares it with the Hth. If, it finds the value above than Hth, then
immediately sends the data. Else, if the value is between Hth and Sth, it compares its
residual energy with Rprime; if the residual energy is greater than Rprime, it transmits data
towards sink, otherwise waits for the next round. For temperature sensing applications,
we assume Hth as 20C,Sth as 10Cand Rprime as 40 Joules. We analyze that traditional
techniques consume surplus energy in two cases; transmission of redundant data and
re-transmission of sensitive data.
32
Sense Attribute
Ri>=Rprime
No
Send Data
No
No
Send Data in next
Round
Yes
V>=Hth
Sth<=V<Hth
Yes
Yes
Figure 4.1: Routing of on-demand data in iAMCTD
4.2.3 Data forwarding phase
The sensor nodes send sensed data data to in-range courier node(s). Upon reception, the
courier node(s) acknowledge the sender node(s) to reduce further flooding. In the absence
of courier nodes, sensor node sends data towards their neighbor nodes. Each sensor node
calculates HTfor received packet on the basis of FF. In order to avoid packet collisions,
all nodes maintain a timer to send threshold-based sensitive data. Nodes sense packets
buffer and maintain priority queue to avoid transmission of same data and to forward
packets on the basis of HT.
Thus, the end-to-end delay of critical data decreases; increasing network’s feasibility
for time sensitive applications. The sensor nodes, after every 300 seconds, transmit control
packets for the computation of network density at the sink, which is further utilized to
manage the variation in the mobility patterns of courier nodes and Dths. The data packet
of our technique consists of sender ID, packet sequence number, depth information and
payload.
4.2.3.1 Variation in Dth
In preceding depth-based schemes, non-varying Dth is another key reason of flooding in
dense situations. It is also due to the absence of eligible neighbors in sparse conditions
specifically during instability period, which causes the loss of critical data. Therefore, we
implement variations in Dth of nodes according to network density. The three optimal
values for Dths are Dth1,Dth2and Dth3, which are calculated by the base station in the
network according to the number of dead nodes in the network. In fig. 4.2, ε1and ε2are
33
the limits for the implementation of Dths. With a network of 225 nodes, and each nodes’
Dth=Dth1
No
Yes
Dth=Dth2
Dth=Dth3
No
Yes
If No. of Dead
Nodes<Ɛ1
If No. of Dead
Nodes>=Ɛ1& No. of
Dead Nodes<=Ɛ2
Implement Depth
threshold
Check No. of
Dead Nodes
Transmit packet
Figure 4.2: Variations in Dth in iAMCTD
range 250m. We assume the values of Dth1,Dth2and Dth3as 200m, 150m and 50m, and
ε1and ε2are set as 5 and 150, respectively.
4.2.3.2 FFcomputation
In this section, we analytically model the optimal value selection for FF. There are three
types of main FF: Signal Quality Index (SQI), Energy Cost Function (ECF) and Depth
Dependent Function (DDF). As sensor node sends data, the neighbor node having highest
FFhas least HT; hence, the optimal forwarder will be selected. Sensor nodes select the FF
on the basis of depth information. For a specific region (low-depth, medium-depth, high-
depth), appropriate type of FFis selected on the bases of residual energy, SNR ratio and
depth differences between sender and receiver. The mechanism for the implementation
of FFis given in fig. 4.3. Where, D1and D2are depth-distributions, which are assumed
150m and 350m, respectively, in the region having a total depth of 500m.
FFformulation considers the overall conditions of aqueous environment. The upper region
of water has maximum interference, noise and shipping activity, hence the SQI prioritizes
SNR to achieve minimal transmission loss of the signal. In medium-depth region, load
34
Calculate SQI as
forwarding
function
No
Yes Calculate ECF as
forwarding
function
Calculate DDF as
forwarding
function
No
Yes
If Depth of Node
Is less than D1
If Depth of Node
is more than D1and
less than D2
Implement
Forwarding
Functions on
received packet
Network
Initialization
Forward packet
Figure 4.3: Analytical model for data forwarding in iAMCTD
on nodes is high due to frequent data forwarding, so the ECF grants the significance of
residual energy of nodes to attain global load balancing in the network. In last region, we
formulate DDF to accomplish distant forwarding and minimum flooding scenarios. The
formulae for these functions and HTcalculation are given below:
HT= (1 FF)Htmax ,(4.1)
Htmax is the maximum holding time and can be selected according to environmental
conditions. In depth-based techniques, sensor nodes are not mindful of their location but
only of their depth. Therefore, we define Localization-free Signal-to-Noise Ratio LSNR
based on the notion of SNR [3] to assess transmission loss of the signal between two
nodes. In addition, we introduce SQI as a routing metric on the basis of LSNR in low-
depth acoustic region. LSNR is computed on the basis of A(l, f ) which considers depth
difference linstead of euclidian distance d; as the receiver can only compute the depth
difference with the sender node.
LSN R =Pt/(A(l, f )N(f)),(4.2)
where, Ptis the constant transmitted power. Attenuation noise (AN factor) is the product
of path loss and ambient noise. findicates the frequency of the signal in kHz. By applying
35
LSNR,SQI is calculated as:
SQI = (LSN R(Ri))/li,(4.3)
where Ridenotes the residual energy of ith receiver node and liis the depth difference
between sender and the receiver nodes. Fig. 4.3 demonstrates the overall data forwarding
mechanism of iAMCTD. In medium-depth region, the nodes are burdened due to maxi-
mum packet forwarding. Their energy depletes earlier than the others, causing network
instability and loss of critical data (received from high-depth nodes). Therefore, it is a
major requirement to consider the residual energy of nodes while selecting optimal for-
warder(s). We compute ECF in such a manner that it prioritizes both residual energy
and depth of receiver nodes.
EC F =priorityvalue(Ri)/Di.(4.4)
Where, Diis the depth of the sensor node and priorityvalue is a constant which can be
adjusted according to stability requirement. In the last region, the channel losses are
not so high, however, forwarding load is higher than the other regions therefore, courier
nodes help to reduce load on these nodes to acquire network stability. High-depth region
of aqueous environment has high interference due to aqueous noises, sea life and mineral
density. In order to deal with these problems, we formulate DDF prioritizing both residual
energy and SQI of received signal; expressed as:
DDF = (SQI ×li)/Di.(4.5)
In receiver-based forwarding, multiple nodes choose the optimal forwarder between them-
selves. It is also important to decrease the flooding mechanism during forwarding. Multi-
ple transmissions of same data cause higher average energy consumption by the network
than by other interferences. So, it is necessary to cope with it. A simple algorithm for
data forwarding using FFis given in algorithm 2.
4.2.4 Adaptive mobility patterns of courier nodes
Courier nodes are highly resourceful in stabilizing network condition as these collect data
from sensor nodes by their adaptive mobility. After network initialization, courier nodes
start their movement towards the bottom of network along with collecting data from
the nodes in their vicinity. Thus, reduce the load on medium-depth nodes by acting as
a relay(s) and then receive data from high-depth nodes; an efficient approach for time-
critical applications. Furthermore, courier nodes change the mobility pattern as network
density (controlled by BS) varies.
36
Figure 4.4: Mechanism of data transmission in iAMCTD
37
Table 4.1: List of Notations
Notation Def inition
ay
i,j Information flow from descendent at position i to courier node y at position j
IyTotal information flow received by courier node y in 1 sojourn tour
Iy
jTotal information flow received by courier node y at location j
ITotal information flow received by sink from all courier nodes in 1 sojourn tour
Iy,o Information flow sent by courier node y towards sink
xi,j It shows presence of courier node at position j in vicinity of node at i in routing tree
TsTotal sojourn time of any courier node in 1 sojourn tour
τuIt is sojourn time of any courier node at position u during tour
lTotal distance covered by courier node in 1 sojourn tour
dTotal depth of network
Algorithm 2 Data forwarding mechanism
1: D1Upper limit f or depth
2: D2Lower limit f or depth
3: DiDepth of ith node
4: SQIiSignal Quality I ndex of ith node
5: ECFiEner gy C ost F unction of ith node
6: DDFiDepth D ependent F unction of ith node
7: FF i F orwarding F unction of node i
8: if DiD1then
9: FF i =SQIi
10: end if
11: if D1< DiD2then
12: FF i =ECFi
13: end if
14: if Di> D2then
15: FF i =DDFi
16: end if
4.2.4.1 Aggregate traffic model for mobile courier nodes
We propose Aggregate-Traffic-Model (ATM) and formulate the problem to achieve higher
throughput by utilizing adaptive mobility patterns of courier nodes. In the problem
formulation, we are interested in maximizing the total information flow towards the sink
by all courier nodes i.e.,
Maximize I (4.6)
38
Subject to:
X
y
IyIy(4.7)
X
j
k
X
i=1
ay
i,j Iyj, y (4.8)
k
X
i=1
ay
i,j Iy
jj, y (4.9)
Iy,o Io,y 0 (4.10)
xi,j ={0,1} ∀i, j (4.11)
xj,o ={0,1} ∀j(4.12)
Ts=X
u
τuu(4.13)
0τuτmax u(4.14)
l2d(4.15)
Equation (4.6) defines the objective function; maximize the total information flow
towards the sink. Equation (4.7) ensures that the total information flow towards sink
from courier node ywill not surpass the sum of information flows of all couriers nodes
towards sink. Equation (4.8) describes that Iyis less than or equal to the sum of informa-
tion flows from all descendent nodes in its one sojourn tour at different sojourn locations,
where jshows the sojourn location of the yth courier node. Equation (4.9) deals with
flow conservation at sojourn location of the courier node. Equation (4.10) shows flow
conservation between any courier node and sink. Where, ois the location of sink.
Equation (4.11) and (4.12) ensure the presence of courier nodes in the vicinity of
sensor nodes and sink, respectively. Where, ‘1’ shows the presence and ‘0’ shows the
absence of courier node. By providing the upper bound τmax, equation (4.13) and (4.14)
limit the total sojourn time of any courier node in one sojourn tour and the sojourn time
at any particular sojourn location, where udenotes the duration at any particular sojourn
location. Furthermore, equation (4.15) implies distance-constrained and delay-bounded
mobility model by employing 2dto limit the total distance covered by a courier node in
one sojourn tour.
Although, we suggest two mobility patterns for courier nodes however, we will im-
plement the proposed optimization model in optimizing tool in our future research work.
After achieving results of optimization, we can devise better mobility patterns for courier
nodes.
39
Figure 4.5: Adaptive mobility pattern of courier nodes in dense and sparse conditions
40
Fig. 4.5 illustrates the two suggested mobility patterns of courier nodes. They also
provide support to nodes with no neighbors in the later rounds, which efficiently deals
with instability period. After receiving packets in each round, they broadcast the received
packet sequence number(s) to reduce flooding. If the number of alive nodes is greater than
β1, then courier nodes follow the second pattern in which they are at equal horizontal
distances from each other. If the number of alive nodes is less than or equal to β1
then these follow the first pattern in which the courier nodes are in the same horizontal
distance. In sparse conditions, it causes minimum end-to-end delay for remaining nodes.
In pollution monitoring application, we assume β1as 200, β2as 100 and the horizontal
distance between the courier nodes as 100m to idealize the real scenario of underwater
acoustics.
4.3 Performance evaluation
In order to evaluate the overall functioning of iAMCTD, we compare the proposed
scheme with the selected existing ones; simulations are performed in MATLAB. Fol-
lowing multiple-sink model of conventional methods, we randomly deploy 225 nodes in
the network field along with 4 courier nodes. We assume 250mas nodes’ transmission
range and LinkQuest UWM1000 [41] as the acoustic modem. Moreover, the power con-
sumptions in sending, receiving, and idle mode are 2W, 0.1W, and 10mW, respectively.
The size of data packets is 64 bytes and the size of control packet is 8 bytes. We im-
plement 802.11-DYNAV [42] protocol as a core media access control protocol. Nodes
are initially equipped with 70Jand hello packets are exchanged after every 300 seconds.
Round is the time duration during which all alive nodes transmit threshold-based data
towards sink. After every 300 seconds, courier nodes compute and then report the net-
work density to sink which supervises the depth thresholds and the adaptive mobility
of courier nodes. The proposed and the existing protocols are evaluated in terms of the
following performance metrics.
Energy consumption: It is the total energy consumed by the network in one round
when all the alive nodes transmit the packet
Network lifetime: It is the time duration during which all nodes of network remain
alive.
End-to-end delay: It is the duration for a packet to be transmitted from source to
destination.
Network throughput: It shows the number of successfully received packets by sink
in one round.
41
Transmission loss: It shows the average transmission loss (dB) between source node
and sink in one round.
Fig. 4.6 shows that iAMCTD consumes almost constant energy throughout the network
lifetime as compared to the selected protocols. This is due to the implementation of well
organized FF S . Our scheme mainly concerns reactive networks due to the requirement
of time-sensitive applications and addresses the problem of higher energy consumption
by utilizing depth difference between data forwarders. In AMCTD, nodes consume high
energy as these transmit from far distance, however in DBR, energy consumption increases
steadily as number of eligible neighbor drops off with the network density. iAMCTD
balances the energy consumption by proper forwarder selection and mobility patterns of
courier nodes. In AMCTD, movements of courier nodes provides stability up to some
extent to the remaining nodes, however, it is not sufficient for the whole network. In
our system, there is higher energy consumption than the other protocols in the later
rounds because of uniform network density. EEDBR shows better energy consumption
management than DBR, however, it compromises on stability period.
0 5000 10000 15000
0
10
20
30
40
50
60
70
80
90
100
Time (sec)
Average energy cosumption (J)
iAMCTD
AMCTD
EEDBR
DBR
Figure 4.6: Comparison of energy consumption in iAMCTD, AMCTD, EEDBR and DBR
Table 4.2 shows the tabular data for the average energy consumption of network.
Lifetime of iAMCTD is increased due to lower throughput by responsive network. In our
suggested scheme, employment of Thorp’s energy model specifies the detailed channel
losses, which are useful for selective data forwarding in responsive networks. Increase in
stability period also confirms reduction in redundant transmissions.
Fig. 4.7 portrays throughput comparison of iAMCTD, AMCTD, EEDBR and DBR.
42
Table 4.2: Comparison of average energy consumption (joule)
Time (sec) DBR EEDBR AMCTD iAMCTD
500 94 52 41 31
1000 93 51 33 21
2000 83 49 29 19
3000 68 35 34 17
4000 56 24 49 17
5000 43 19 20 10
6000 42 18 19 11
7000 41 17 17 12
8000 41 15 30 14
9000 39 13 13 11
10000 0 2 0 10
The throughput of iAMCTD demonstrates the amount of threshold-based data for re-
sponsive networks. The proposed protocol ensures minimal TL and end-to-end delay
while delivering data. Previous schemes send unnecessary data along with high propaga-
tion losses; the throughput decreases sharply due to quick fall in network density. In our
protocol, throughput steadily decreases due to maintenance of network density and low
energy consumption. The throughput of DBR and EEDBR declines very quickly, which
0 5000 10000 15000
0
50
100
150
200
Time (sec)
Network throughput
iAMCTD
AMCTD
EEDBR
DBR
Figure 4.7: Network throughput in iAMCTD, AMCTD, EEDBR and DBR
is unsuitable for both delay-tolerant and delay-sensitive applications. AMCTD performs
better than the previous schemes in instability period, but our scheme outperforms AM-
CTD due to less time lag in data delivery, thus, validating its better performance in
flooding-based protocols.
Fig. 4.8 shows that transmission loss of the network in iAMCTD is less than the previ-
43
ous techniques due to prioritization of SNR in calculations of FF s . Higher throughput in
AMCTD is achieved in compromise of TL as number of redundant transmissions between
sender nodes and sink is increased. We utilize MMPE model to calculate the TL between
a source node and sink. It considers transmission frequency, bandwidth efficiency and
noise density which scrutinize the signal quality during data transmission. EEDBR has
higher loss than other techniques as it employs distant propagations as well as multiple
forwarding.
0 1000 2000 3000 4000 5000 6000 7000 8000 9000
0
20
40
60
80
100
120
Time (sec)
Transmission loss (dB)
iAMCTD
AMCTD
EEDBR
DBR
Figure 4.8: Transmission loss in iAMCTD, AMCTD, EEDBR and DBR
0 5000 10000 15000
0
10
20
30
40
50
60
70
Time (sec)
End−to−end delay
iAMCTD
AMCTD
EEDBR
DBR
Figure 4.9: End-to-end delay in iAMCTD, AMCTD, EEDBR and DBR
In DBR, the initial rounds show low losses due to high network density, however as
networks become sparse there is a sharp decrease in network performance causing high
44
Table 4.3: Comparison of number of alive nodes
Time (sec) DBR EEDBR AMCTD iAMCTD
500 225 225 225 225
1000 224 225 225 225
2000 201 220 225 225
3000 171 190 224 225
4000 144 127 201 222
5000 125 105 161 222
6000 124 99 146 221
7000 123 97 141 221
8000 123 91 121 201
9000 104 85 99 201
10000 0 24 0 200
15000 0 20 0 147
20000 0 8 0 64
25000 0 2 0 39
packet loss. In AMCTD, channel loss conditions are better than DBR and EEDBR,
as the WFcomputations consider both depth and residual energy of forwarding nodes,
therefore the propagations remain stable. Table 4.3 shows the comparison of number of
alive nodes of network.
0 0.5 1 1.5 2 2.5
x 104
0
50
100
150
200
Time (sec)
Number of allive nodes
iAMCTD
AMCTD
EEDBR
DBR
Figure 4.10: Number of alive nodes of iAMCTD, AMCTD, EEDBR and DBR
In later rounds, the performance of AMCTD gradually decreases with the decrement in
qualified forwarders, therefore both the packet loss and delay increase. In our proposed
scheme, TL is one of the key factors concerning the significance of data-sensitive respon-
sive networks. The FF s computations and adaptive mobility of courier nodes cause almost
45
constant path loss throughout the network lifetime. Variations in Dths assist in selecting
optimal data forwarder which increase the overall network throughput.
Fig. 4.9 shows that end-to-end delay of network in iAMCTD is less than the previous
techniques due to minimum forwarding distances between the nodes. In DBR, delay is
much higher in initial rounds due to distant data forwarding. It decreases gradually with
the sparseness of the network. After about 1200 seconds, in EEDBR, delay increases
whenever the network becomes scattered causing data forwarding at minimum distance.
End-to-end delay in AMCTD is better than previous techniques as both Dth variations
and WF s perform global load balancing. In our technique, there is a minimum possible
time lag due to consideration of signal quality and depth differences between sender and
receiver nodes. Fig. 4.10 shows the network lifetime of routing schemes which compares
the number of alive nodes. In order to increase network lifetime, iAMCTD selects for-
warders on the bases of SNR, depth, and depth differences. Thus, preventing the creation
of coverage holes which is converse the previous techniques. We also conclude that proac-
tive routing is more expensive and less competent than on-demand routing in rapidly
varying underwater environments.
46
Chapter 5
Conclusion
47
In this thesis, we conclude that there is a need of energy-efficient routing protocols for
UWSNs. In UWSN applications, efficient energy consumption of sensor nodes is a major
requirement. Therefore, we design two energy efficient routing protocols in localization-
free category; AMCTD and iAMCTD. In AMCTD, we incorporate mobility of courier
nodes and propose WF s. Moreover, AMCTD implements amendments in Dth and WFto
achieve enhanced network lifetime. It is divided into three major phases; WFupdating,
Dth variation and adaptive mobility of courier nodes. The considerations of AMCTD are
supportive in decrementing the energy consumption of low-depth sensor nodes specifically
in the stability period. In an improved version of AMCTD i.e. iAMCTD,FF s calculation
technique enhances the network lifetime and reduces the transmission loss. Variations in
Dth increase the number of eligible neighbors and minimize the loss of critical data. In
order to tackle flooding and path loss, it calculates optimal HTusing proposed routing
metrics; SQI,ECF and DDF.
48
Chapter 6
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49
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