iAMCTD: Improved Adaptive Mobility of Courier Nodes in Threshold-Optimized DBR Protocol for Underwater Wireless Sensor Networks

Abstract

We propose forwarding-function (íµí°¹ íµí°¹) based routing protocol for underwater sensor networks (UWSNs): improved adaptive mobility of courier nodes in threshold-optimized depth-based-routing (iAMCTD). Unlike existing depth-based acoustic protocols, the proposed protocol exploits network density for time-critical applications. In order to tackle flooding, path loss, and propagation latency, we calculate optimal holding time (íµí°» íµí±‡) and use routing metrics: localization-free signal-to-noise ratio (LSNR), signal quality index (SQI), energy cost function (ECF), and depth-dependent function (DDF). Our proposal provides on-demand routing by formulating hard threshold (íµí°» th), soft threshold (íµí±† th), and prime energy limit (íµí±… prime). Simulation results verify effectiveness and efficiency of the proposed iAMCTD.

Full text

Research Article
iAMCTD: Improved Adaptive Mobility of Courier Nodes in
Threshold-Optimized DBR Protocol for Underwater Wireless
Sensor Networks
N. Javaid,1,2 M. R. Jafri,2Z. A. Khan,3U. Qasim,4T. A. Alghamdi,5and M. Ali6
1CAST, COMSATS Institute of Information Technology, Islamabad 44000, Pakistan
2EE Department, COMSATS Institute of Information Technology, Islamabad 44000, Pakistan
3InternetworkingProgram,FE,DalhousieUniversity,Halifax,NS,CanadaB3J4R2
4University of Alberta, AB, Canada T6G 2J8
5CS Department, College of CIS, Umm AlQura University, Makkah 21955, Saudi Arabia
6Institute of Management Sciences, Peshawar 25000, Pakistan
Correspondence should be addressed to N. Javaid; nadeemjavaid@comsats.edu.pk
Received  February ; Accepted  June ; Published  July 
Academic Editor: Aneel Rahim
Copyright ©  N. Javaid et al. is is an open access article distributed under the Creative Commons Attribution License, which
permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
We propose forwarding-function (𝐹) based routing protocol for underwater sensor networks (UWSNs): improved adaptive
mobility of courier nodes in threshold-optimized depth-based-routing (iAMCTD). Unlike existing depth-based acoustic protocols,
the proposed protocol exploits network density for time-critical applications. In order to tackle ooding, path loss, and propagation
latency, we calculate optimal holding time (𝑇) and use routing metrics: localization-free signal-to-noise ratio (LSNR), signal
quality index (SQI), energy cost function (ECF), and depth-dependent function (DDF). Our proposal provides on-demand routing
by formulating hard threshold (th), so threshold (th), and prime energy limit (prime). Simulation results verify eectiveness and
eciency of the proposed iAMCTD.
1. Background
Underwater acoustic sensor networks (UASNs), as a subclass
of wireless sensor networks (WSNs), are specically used for
monitoring purposes in aqueous environment. e acoustic
wireless sensors along with sink(s), distributed under water,
constitute the basic body of UASN, where acoustic sensors
gather the information of interest and then following a
routing strategy forward these data to the end station.
WHOI Micro-Modem []andCrossbowMica[]are
among the commercially available sensors for underwater
environments. Sink is generally supposed to have no power
constraint, whereas the acoustic sensors are equipped with
limited battery power. ese networks provide diversied
rangeofapplicationslikepollutionmonitoring,oceancur-
rent detection, submarine discovery, deep sea explorations,
and seabed management.
Due to the nature of aqueous environment, improving
energy eciency at routing layer is a challenging task.
Moreover, as there are major dierences between terrestrial
and underwater conditions, hence the basic concepts of
terrestrial routing can not be implemented in UWSNs. To
tackle these problems, researchers exercise the role of low
speed acoustic signals for aqueous communication and sea
navigation systems, leading to high propagation delay and
transmission losses. High shipping activity, thermal noise,
and turbulent noise also increase the error rate. Authors in
[] design the underwater acoustic channel to descriptively
analyze the total noise density and path loss. On the other
hand [], it investigates diverse routing architectures for -
dimensional and -dimensional UWSNs. Fundamental anal-
yses of aqueous conditions show that reactive routing is more
challenging than the proactive one. Battery replacement and
ecient routing are among the solutions to overcome the
Hindawi Publishing Corporation
International Journal of Distributed Sensor Networks
Volume 2014, Article ID 213012, 12 pages
http://dx.doi.org/10.1155/2014/213012

 International Journal of Distributed Sensor Networks
power constrained problem of acoustic sensors; however, the
leading solution is not practical; thereby, we focus on the
lagging one.
e two major categories of underwater routing protocols
are localization-based and localization-free. e fundamental
dierence between these two is the awareness of location by
the sensor nodes. Localization-free protocols are also termed
as ooding based schemes, where nodes are only aware about
their depth. On the other hand, location-based protocols
assume that the sensor nodes are location aware. In literature,
both localization-based and localization-free schemes are
available: QELAR [], R-ERPR [], EEDBR [], and MPT
[]. ese protocols attempt to tackle high transmission
delay, path loss, and receiving energy consumption. However,
deciencies do exist. Depth Based Routing (DBR) protocol
[] is based on depth of sensor nodes as the trivial parameter
in ooding-based forwarding schemes. EEDBR []improves
the deciencies of DBR by considering residual energy while
selecting data forwarder(s). R-ERPR []isanothermagni-
cent depth-based routing scheme, which implements “ETX”
routing metric for improving energy eciency. QELAR []
designs routing architecture and cross-layer approach on the
basis of Q-machine learning, thereby improving localization-
based routing. MPT [] employs power-controlled and
source-initiated transmission by examining the deciencies
at the physical layer in UASNs, specically to deal with on-
demand applications. H-DAB []achievestheminimum
delay and higher network lifetime by using hop count as
routing metric. It also employs courier nodes to provide
adaptability for time-critical and data-sensitive applications.
ere is always a need of less complex and highly reliable
communication scheme in WSNs and UWSNs. Liu et al. []
improve reliability in transmissions by proposing a network
coding based cooperative communications scheme (NCCC).
It provides a scalable implementation of NCCC by selecting
coding vectors from nite eld. Localization schemes for
sensor nodes are not so accurate in UWSN, as in WSN. In
WSN, mobile beacon assisted localization is used.
In [], the authors propose -curve, which is a path
planing mechanism for mobile beacons to localize the sensor
nodes. Zou et al. [] develop a generic algorithm to control
the movement of autonomous underwater vehicles (AUVs) in
UWSN by gathering network information from sensor nodes.
Limited residual energy is one of the main challenges for sen-
sor nodes in UWSN. High numbers of (re)transmissions and
collisions cause consumption of energy. In [], the authors
tackle the issue by proposing contention-free multichannel
MAC protocol for UWSNs.
Another technique has been proposed by []inwhich
authors design spatially fair MAC (SFMAC) protocol for
UWSNs. It avoids the collisions with postponing clear-to-
send(CTS)foraspecicdurationascalculatedbythereceiver
node. Mobility of the target object is always an issue for
WSN.Furthermore,itisverydiculttoupdatethelocation
information of the target object by sensor nodes. In []; the
authors suggest an accurate target tracking scheme for WSNs.
ey achieve energy eciency in the scheme by minimizing
the missing rate of an object.
MUAP in [] examines the acoustic channel as well as
its losses and then proposes an energy model for the compu-
tation of channel losses in aqueous environment. Stojanovic
[] carefully examines the association between transmission
distance and channel capacity in underwater communi-
cation. CEDC [] uses doppler compensation model to
improve the scrutinizing of acoustic signals and present a
high data-rate communication model. In dense underwater
conditions, PULRP [] provides a cross-layer model as well
as a descriptive algorithm to maximize network’s throughput.
is model is localization-free technique with ecient energy
consumption model. AMCTD [] exploits the adaptive
mobility of courier nodes to maximize the network lifetime.
It computes the holding time on the basis of weight function
to cope with the problems of high transmission loss and low
network lifetime. e work presented in this paper is the
extension of [].
2. Motivation and Contribution
By examining the ooding as well as depth-based rout-
ing protocols, we compensate for the deciencies in these
schemes. In DBR and EEDBR, stability period quickly ends
due to unnecessary data forwarding and high load on low-
depth nodes. Another main deciency of these protocols is a
sharpinstabilityperiodduetothequickenergyconsumption
of medium-depth nodes. In AMCTD, the high transmission
loss is due to distant transmissions of medium-depth nodes.
AMCTD is not suitable for data-sensitive applications, espe-
cially during instability period due to the mobility pattern
of courier nodes. e consideration of only two parameters,
depth and residual energy, is not sucient for global load
balancing throughout the network lifetime.
We investigate the problems of medium-depth nodes in
the earlier rounds like high end-to-end delay and transmis-
sion loss, low stability period, and fast energy consumption.
Based on these, we optimize the movement of courier nodes
in sparse conditions for load balancing. Although AMCTD’s
performance is better than DBR and EEDBR, respectively;
however, deciencies do exist. We try to overcome the
deciencies as follows.
(i) In iAMCTD, the ecient movement of courier nodes
minimizes end-to-end delay and decreases the energy
consumption of low-depth nodes in sparse condi-
tions.
(ii) e 𝐹𝑠 computation technique causes longer stability
period than the previous ones along with reduced
transmission loss. It also decreases average end-to-
end delay of packets which is crucial for time-critical
applications.
(iii) Implementation of threshold-optimized data for-
warding is resourceful process for on-demand routing
and removes forwarding of unnecessary data in the
network.
(iv) Optimized mobility pattern of sink, in later rounds,
maintains end-to-end delay of the network, especially

International Journal of Distributed Sensor Networks 
for delay-sensitive applications even in sparse condi-
tions.
(v) We examine nodes’ depth and control overhead at
physicallayeraswellasnetworklayerandproposea
model which caters for the major acoustic problems.
We consider arbitrary deployment of nodes in D aque-
ous environment. e 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. Courier
nodes are equipped with mechanical modules for movement
control; however, these can only relay the network data
towards sink. Aer collecting threshold-based data, sensor
nodes transfer the data to sink, as soon as possible, either
by direct transmission or through courier nodes; otherwise
they broadcast the data towards their neighbors. Nodes
communicate with each other using low-range commercial
acoustic modems. In ooding-based techniques, control
packets’ transmission is the requirement of network in order
to achieve better management with the varying network
density. e multiple-sink model is procient at balancing the
networkloadandithasbeenanalyzedin[–].
3. The Proposed iAMCTD
We divide operation of the proposed iAMCTD into the
following four phases:
(i) network initialization and underwater channel
model,
(ii) implementation of on-demand data routing,
(iii) data forwarding phase,
(iv) adaptive mobility patterns of courier nodes.
e rst phase deals with nodes’ deployment, network
initialization, and thorp’s model [] for path loss and noise
density calculation in aqueous environment. Second phase
discusses implementation of on-demand data routing to facil-
itate the time-sensitive and critical-data applications. ird
phase expresses the data forwarding along with the variations
in depth thresholds for sensor nodes to cope with varying
network density. We develop ecient forwarding functions
for optimal data forwarding in aqueous environment. We also
discuss the data packet structure and the tradeo between
control overhead and network lifetime. On the basis of
control packets, sink identies the network density and
manages the network specications 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.
3.1. Network Initialization and Underwater Channel Model.
We assume that sensor nodes broadcast the data using
ooding-based approach. As each neighbor of the node
receives data of the sender node, courier nodes devise their
sojourntouratthestartofthenetworkandthenstart
collecting data from the source nodes. ese also report
the network density information to sinks in order to devise
mobility patterns and depth-threshold variations. During
knowledge acquisition, nodes share the depth and residual
energy information among themselves. As the network initi-
ates, courier nodes remain at equal horizontal distances from
each other and these start vertical movement towards the
bottom.
Given below channel model is used to calculate spreading
loss and total noise loss in underwater acoustics. We calculate
the total attenuation of signal on the bases of spreading loss
[] and thorps model for signal absorption loss. At a given
frequency ,thechannelmodelcalculatesabsorptionloss
()[] as follows:
10log 
=















0.112
1+2+442
4100+ +2.75∗1042 ≥ 0.4
+0.003
0.002+0.11 
1++0.011, <0.4,
()
where ()is measured in dB/km and in kHz. Here, thorps
model denes formula for the calculation of absorption loss.
By using the value of absorption loss, we calculate the value
of as follows:
=10𝛼(𝑓)
10 .()
Accordingly, all tunable parameters are given in .
Combining absorption loss and spreading loss, the total
attenuation (,)canbecomputedasfollows:
10log , =∗10log ()+∗10log ,()
where the rst term denotes spreading loss and the second
term is the absorption loss. e spreading coecient 
denes the geometry of the signal propagation in underwater
acoustics (i.e., =1for cylindrical, =2for spherical,
and  = 1.5 for practical spreading []). For the calculation
of ambient noise in underwater networks, total noise is
comprisedoffourmainparts:turbulentnoise,shippingnoise,
wind noise, and thermal noise. e following formulae give
the power spectral density [] of the four noise components
in , as a function of in kHz:
10log 𝑡=17−30log ,
10log 𝑠 = 40+20(−0.5)+26log 
−60log +0.03,

 International Journal of Distributed Sensor Networks
Sense attribute
No
No
No
Ye s
Ye s
Ye s
V≥H
th
Sth ≤V<H
th
Ri≥R
prime
Send data in next
round
Send data
F : Routing of on-demand data.
10log 𝑤 = 50+7.51/2 +20log 
−40log +0.4,
10log th =−15+20log , ()
where 𝑡,𝑠,𝑤,andth represent the noise due to turbu-
lence, shipping, wind, and thermal activities respectively. e
overall noise power spectral density is calculated as
=
𝑡+𝑠+𝑤+th .()
e ooding-based approaches employ receiver-based
data forwarding due to un-awareness of location. Each node
shares depth information with other nodes and the eligible
neighbors for forwarding are decided on the basis of depth
threshold. e neighbor nodes compute the forwarding
functions by considering channel losses, residual energy, and
depth dierences.
3.2. Implementation of On-Demand Data Routing. e pro-
posed protocol performs on-demand data routing as the
nodes react immediately to sudden and drastic changes
considering the value of sensed attribute. Figure  depicts the
transmission algorithm for threshold-based data.
Where is the current sensed value, th is the so
threshold, th is the hard threshold, 𝑖is the residual energy
of the th node, and prime is the residual energy limit for data
transmission. Aer sensing the attribute value, each node
compares it with the hard threshold. If it nds the value above
than th , then immediately it sends the data. Else, if the
value is between th and th, it compares its residual energy
with prime; if the residual energy is greater than prime,it
transmits data towards sink; otherwise it waits for the next
Sender ID Packet sequence Depth Payload...
number
F : Data packet format.
round. For temperature sensing applications, we assume th
as ∘C, th as 20∘C, and prime as  Joules. We analyze that
traditional techniques consume surplus energy in two cases:
transmission of redundant data and retransmission of sensi-
tive data. We exploit receiver based forwarding and minimize
the transmission of normal data messages in number; the
transmission energy loss is minimized. We also apply power
control strategies at physical layer to avoid aqueous channel
losses preventing critical data loss.
3.3. Data Forwarding Phase. e sensor nodes send sensed
data to in-range courier node(s). Upon reception, the courier
node(s) acknowledge the sender node(s) to reduce further
ooding. In the absence of courier nodes, sensor node
sends data towards their neighbor nodes. Each sensor node
calculates holding time for received packet on the basis of
forwarding function. In order to avoid packet collisions, all
nodes maintain a timer to send threshold-based sensitive
data. Nodes sense packets buer and maintain priority queue
to avoid transmission of same data and to forward packets
on the basis of holding time. us, the end-to-end delay
of critical data decreases, increasing network’s feasibility for
time sensitive applications. e sensor nodes, aer every
 rounds, 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 depth-thresholds. e data packet format of our
technique is shown in Figure  which consists of sender ID,
packet sequence number, depth information, and payload.
e trade-o between control overhead and network
lifetime is a serious consideration in the design of ooding-
based routing techniques, so we attempt to control this trade-
o by employing minimal overhead.
3.3.1. Variation in Depth reshold. In preceding depth-based
schemes, nonvarying depth-threshold is another key reason
of ooding in dense situations. It is also due to the absence
of eligible neighbors in sparse conditions specically during
instability period, which causes the loss of critical data.
erefore, we implement variations in depth-threshold of
nodes according to network density. e three optimal values
for depth thresholds are th1,th2,andth3,whichare
calculated by the base station in the network according to the
number of dead nodes in the network. In Figure ,1and 2
are the limits for the implementation of thresholds.
With a network of  nodes and each nodes’ range
100m, we assume the values of th1,th2,andth3as  m,
 m, and  m, and 1and 2are set as  and , respectively.
3.3.2. Forwarding Function Computation. In this section,
we analytically model the optimal value selection for 𝐹.
ere are three types of main 𝐹: signal quality index (SQI),

International Journal of Distributed Sensor Networks 
Check number of
dead nodes
If number of
nodes <𝜀
1
Ye s
Ye s
No
No
dead
If number of dead
nodes ≤𝜀
1
and number
of dead nodes ≥𝜀
2
Dth = Dth3
Dth = Dth2
Dth = Dth1
Implement depth
threshold
Transmit packet
F : Variations in depth threshold.
energy cost function (ECF), and depth dependent function
(DDF). As sensor node sends data, the neighbor node
having highest 𝐹has least holding time; hence, the optimal
forwarder will be selected. Sensor nodes select the 𝐹on
the basis of depth information. For a specic region (low-
depth, medium-depth, high-depth), appropriate type of 𝐹is
selected on the bases of residual energy, SNR ratio, and depth
dierences between sender and receiver. e mechanism for
the implementation of 𝐹is given in Figure .
Where, 1and 2are depth-distributions, and are
assumed  m and  m, respectively, in the region of
depth  m. 𝐹formulation considers the overall conditions
of aqueous environment. e 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 on nodes is high due to
frequent data forwarding, so the ECF grants the signicance
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 ooding scenarios. e
formulae for these functions and holding time 𝑇calculation
are given below:
𝑇=1−𝐹𝑡max,()
where 𝑡max is the maximum holding time and can be
selected according to environmental conditions. In depth-
based techniques, sensor nodes are not mindful of their loca-
tion but only of their depth. erefore, we dene localization-
free signal-to-noise ratio LSNR based on the notion of SNR
[] 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 (,)which considers depth
dierence instead of euclidean distance ,asthereceivercan
only compute the depth dierence with the sender node:
LSNR =𝑡
,,()
where 𝑡is the constant transmitted power. Attenuation noise
(AN factor) is the product of path loss and ambient noise.
indicates the frequency of the signal in kHz. By applying
LSNR, SQI is calculated as
SQI =LSNR 𝑖
𝑖,()
where 𝑖denotes the residual energy of th receiver node and
𝑖is the depth dierence between sender and the receiver
nodes. Figure  demonstrates the overall data forwarding
mechanism of iAMCTD. In medium-depth region, the nodes
are burdened due to maximum packet forwarding. eir
energy depletes earlier than the others, causing network
instability and loss of critical data (received from high-
depth nodes). erefore, it is a major requirement to con-
sider the residual energy of nodes while selecting optimal
forwarder(s). We compute ECF in such a manner that it
prioritizes both residual energy and depth of receiver nodes:
ECF =priorityvalue 𝑖
𝑖,()
where 𝑖is the depth of the sensor node and priorityvalue
isaconstantwhichcanbeadjustedaccordingtostability
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 ×𝑖
𝑖.()
In receiver-based forwarding, multiple nodes choose the
optimal forwarder between themselves. It is also important to
decrease the ooding 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 𝐹is given in Algorithm .

 International Journal of Distributed Sensor Networks
Network
If depth of node Ye s
Ye s
Calculate SQI as
is less than D1
is less than D1
function
function
function
No
If depth of node
Calculate ECF as
more than D2
No
Calculate DDF as
forwarding
forwarding
forwarding
forwarding functions on
received packet
Forward packet
initialization
Implement
and
F : Analytical model for data forwarding.
3.4. 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. Aer network initialization, courier nodes start
their movement towards the bottom of network along with
collecting data from the nodes in their vicinity. us, reduce
the load on medium-depth nodes by acting as a relay(s)
and then receive data from high-depth nodes, an ecient
approach for time-critical applications. Furthermore, courier
nodes change the mobility pattern as network density (con-
trolled by BS) varies. ese nodes maintain the depth and
vertical movement by using mechanical module [].
3.4.1. Aggregate Trac Model for Mobile Courier Nodes. We
propose Aggregate-Trac-Model (ATM) and formulate the
objective functions to achieve higher throughput by utilizing
adaptive mobility patterns of courier nodes.
Intheproposedmobilitypattern,weareinterestedin
maximizing the total information ow towards the sink by
all courier nodes, that is,
Maximize ()
subject to :
𝑦𝑦≥ ∀ ()

𝑗
𝑘

𝑖=1𝑦
𝑖,𝑗 ≥
𝑦∀, ()
𝑘

𝑖=1𝑦
𝑖,𝑗 ≥𝑦
𝑗∀, ()
𝑦,𝑜 −𝑜,𝑦 ≥0 ()
𝑖,𝑗 ={0,1}∀, ()
𝑗,𝑜 ={0,1}∀ ()
𝑠=
𝑢𝑢∀ ()
0≤
𝑢≤
max ∀ ()
≤2. ()
Equation () denes the objective function: maximize
the total information ow towards the sink. Equation ()
ensures that the total information ow towards sink from
courier node will not surpass the sum of information ows
of all couriers nodes towards sink. Equation () describes
that 𝑦is less than or equal to the sum of information ows

International Journal of Distributed Sensor Networks 
Onshore data center
Neighbor of A
having highest
Neighbor of B
having highest
SQI
ECF
Neighbor of E
having highest
DDF
A
B
C
E
D
Radio link
Accoustic link
Courier node
Sensor node Sink
Depth-based boundaries for FF computations
F:MechanismofdatatransmissioniniAMCTD.
1←Upper limit for depth
2←Lower limit for depth
𝑖←Depth of th node
SQI𝑖←Signal Quality Index of th node
ECF𝑖←Energy Cost Function of th node
DDF𝑖←Depth Dependent Function of th node
𝐹𝑖 ←Forwarding Function of node 
if 𝑖≤1then
𝐹𝑖 =SQI𝑖
else {1<𝑖≤2}
𝐹𝑖 =ECF𝑖
else
𝐹𝑖 =DDF𝑖
end if
A : Data forwarding mechanism.
from all descendent nodes in its one sojourn tour at dierent
sojourn locations, where shows the sojourn location of the
th courier node. Equation () deals with ow conservation
at sojourn location of the courier node. Equation ()shows
ow conservation between any courier node and sink, where
is the location of sink. Equations ()and()ensurethe
presence of courier nodes in the vicinity of sensor nodes
and sink, respectively, where “” shows the presence and “”
shows the absence of courier node(s). By providing the upper
bound max ,()and() limit the total sojourn time of any
courier node in one sojourn tour and the sojourn time at
any particular sojourn location, where denotes the duration
at any particular sojourn location. Furthermore, ()implies
distance-constrained and delay-bounded mobility model by
employing 2to limit the total distance covered by a courier
node in one sojourn tour. Under these constraints, we suggest
two mobility patterns for courier nodes (refer to Figure ).
Figure  illustrates the two specic mobility patterns of
courier nodes. ey also provide support to nodes with no
neighbors in the later rounds, which eciently deals with
instability period. Aer receiving packets in each round, they
broadcast the received packet sequence number(s) to reduce
ooding.
Figure  shows that if the number of alive nodes is greater
than 1, then courier nodes follow the second pattern in
which they are at equal horizontal distances from each other.

 International Journal of Distributed Sensor Networks
Onshore date center
Mobility pattern 1
(dense condition)
Mobility pattern
Mobility pattern
2
(sparse condition)
Radio link
Courier nodes Sink
F : Adaptive mobility pattern in dense and sparse conditions.
If the number of alive nodes is less than or equal to 1then
these follow the rst pattern in which the courier nodes
areinthesamehorizontaldistance.Insparseconditions,it
causes minimum end-to-end delay for remaining nodes. In
pollution monitoring application, we assume 1as , 2as
, and the horizontal distance between the courier nodes as
 m to idealize the real scenario of underwater acoustics.
4. Performance Evaluation
To evaluate the performance of iAMCTD, we compare
it with the selected existing schemes with the extensive
simulations. Following multiple-sink model of conventional
methods, we randomly deploy  nodes in the network
eld along with  courier nodes. We assume 100m as nodes’
transmission range and LinkQuest UWM []asthe
acoustic modem(s) having  kbps bit rate. Moreover, the
power consumptions in sending, receiving, and idle mode
areW,.W,andmW,respectively.esizeofdata
packets is  bytes and the size of control packet is  bytes.
We implement .-DYNAV [] protocol as a core media
access control protocol. Nodes are initially equipped with
70J and hello packets are exchanged aer every th round.
In each round, all alive nodes transmit threshold-based
data towards sink. Aer every th round, courier nodes
compute and then report the network density to sink which
supervises the depth thresholds and the adaptive mobility
of courier nodes. Moreover, variations in depth threshold
make our proposed scheme a feasible contender for time-
critical applications such as submarine detection and tsunami
detection. Aer network initialization, the courier nodes start
moving downward from the surface of water with a mutual
distance of  m between them.
e proposed and the existing protocols are evaluated in
terms of the following performance metrics.
(i) Network lifetime: duration between network initial-
ization and complete energy exhaustion of all the
nodes.
(ii) Average energy consumption: it is the average energy
consumption of all active nodes in one round.
(iii) End-to-end delay: it is the duration for a packet to be
transmitted from source to destination.
(iv) Number of dead nodes: it shows the number of nodes
having no energy.
(v) Transmission loss: it shows the average transmission
loss (dB) between source node and sink in one round.
Figure  illustrates that our scheme improves the stability
period of network by avoiding the forwarding of unnecessary
data along with maintaining lower transmission loss. It
reduces end-to-end delay by utilizing LSNR and DDF in
extreme low-depth and high-depth regions, thus providing
exibility for delay-sensitive applications in UWSN. In iAM-
CTD, the instability period starts from almost th round,
aer which the transmission loss (Figure ) remains even;
however, end-to-end delay (Figure )decreasesveryslowly.
Due to prioritization of SQI in the upper region, load
balancing is achieved. Moreover, the load on medium-depth
nodes is shared because of the adaptive movement of courier
nodes. Aer the expiry of initial nodes, the network destabi-
lizes due to diminution of eligible neighbors. In EEDBR, the
stability period is greater than DBR; however, the network
energy consumption is greater. In DBR, the stability period
is lesser than the other techniques as it considers only depth
of forwarding nodes as the ultimate determining factor;
however, the instability period is much better than EEDBR
as there is gradual increase in network energy consumption.
When the network becomes sparse, number of neighbors
decreases quickly causing network instability. In AMCTD,

International Journal of Distributed Sensor Networks 
Number of alive nodes
0
50
100
150
200
0 0.5 1 1.5 2 2.5
Rounds (r)
iAMCTD
AMCTD
EEDBR
DBR
×104
F : Number of alive nodes in iAMCTD, AMCTD, EEDBR,
and DBR.
there are only two forwarding selection attributes; depth
and residual energy. is consideration causes a trade-o
between the network lifetime and transmission loss which
isnotsuitableforreactiveapplications.eloadonhigh-
depth nodes becomes low due to the existence of courier
nodes in stability period causing load balancing in AMCTD.
During the instability period of AMCTD, network gradually
becomes sparse causing load on high residual-energy nodes,
whereasthenumberofneighborsismanagedbyvariations
in depth threshold. Lifetime of iAMCTD is increased due
to lower throughput by responsive network. Moreover, it
provides minimum transmission loss and delay which is
specically suitable for time-decisive applications such as
pollution monitoring.
Inoursuggestedscheme,employmentoforp’senergy
model species the detailed channel losses, which are useful
for selective data forwarding in responsive networks. Increase
in stability period also conrms reduction in redundant
transmissions.
Figure  shows the comparison of dead nodes in iAM-
CTD, AMCTD, EEDBR, and DBR. e condence interval of
results veries that iAMCTD has almost  more rounds of
stabilityperiodthanAMCTDduetobetter𝐹𝑠 and mobility
of courier nodes, which discourages multihop forwarding.
Number of dead nodes sharply increases in EEDBR as there
is high load on the nodes. In DBR, low-depth nodes die at an
earlier stage due to high data forwarding rate and constant
depth threshold. It neglects residual energy of nodes as well
as SNR. us, badly aecting network’s throughput.
To balance load on nodes, iAMCTD selects forwarders
on the bases of SNR, depth, and depth dierences. us,
preventing the creation of coverage holes which is to converse
the previous techniques. Main reason behind better stability
period is the implementation of ecient 𝐹𝑠 and removal
of unnecessary data forwarding in network. We conclude
Number of dead nodes
0
50
100
150
200
0 0.5 1 1.5 2 2.5
Rounds (r)
iAMCTD
AMCTD
EEDBR
DBR
×104
F : Number of dead nodes in iAMCTD, AMCTD, EEDBR,
and DBR.
0 0.5 11.5 2 2.5
0
0.5
1
1.5
2
2.5
3
3.5
Rounds (r)
Average energy consumption (J)
iAMCTD
AMCTD
EEDBR
DBR
×104
F : Total energy consumption in iAMCTD, AMCTD,
EEDBR, and DBR.
that none of the proactive routing protocols are accept-
able in delay-sensitive underwater applications. Furthermore,
proactive routing is more expensive and less competent than
on-demand routing in rapidly varying underwater environ-
ments.
Figure  showsthatiAMCTDconsumesalmostconstant
energy throughout the network lifetime as compared to the
selected protocols. is is due to the implementation of well
organized 𝐹𝑆. Our scheme mainly concerns reactive net-
works due to the requirement of time-sensitive applications
and addresses the problem of higher energy consumption
by utilizing depth dierence between data forwarders. In
AMCTD, nodes consume high energy as these transmit from
far distance; however, in DBR, energy consumption increases

 International Journal of Distributed Sensor Networks
0
50
100
150
200
roughput of network
0 0.5 11.5 2 2.5
Rounds (r)
iAMCTD
AMCTD
EEDBR
DBR
×104
F : Comparison of network throughput in iAMCTD,
AMCTD, EEDBR, and DBR.
0
5
10
15
20
25
30
35
40
45
50
Transmission loss (dB)
0 0.5 1 1.5 2
Rounds (r)
iAMCTD
AMCTD
EEDBR
DBR
×104
F : Comparison of transmission loss (dB) in iAMCTD,
AMCTD, EEDBR, and DBR.
steadily as number of eligible neighbor drops o with the net-
work density. In EEDBR, energy consumption is higher than
other techniques due to frequent selection of high energy
nodes, whereas, iAMCTD balances the energy consumption
by proper forwarder selection and mobility patterns of
courier nodes. In AMCTD, movements of courier nodes
provide stability up to some extent to the remaining nodes;
however, it is not sucient for the whole network. In our sys-
tem, there is higher energy consumption than the other pro-
tocols in the later rounds because of uniform network density.
DBR shows better energy consumption management than
EEDBR; however, it compromises on stability period. e
adaptive mobility of courier nodes reduces distant forwarding
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
End-to-end delay of network (s)
0 0.5 1 1.5 2 2.5
Rounds
iAMCTD
AMCTD
EEDBR
DBR
×104
F : End-to-end delay in iAMCTD, AMCTD, EEDBR, and
DBR.
inthelaterroundsandmaintainstheloadonmedium-depth
nodes.
Figure  portrays throughput comparison of iAMCTD,
AMCTD, EEDBR, and DBR. e throughput of iAMCTD
demonstrates the amount of threshold-optimized data for
responsive networks, as the proposed protocol avoids unnec-
essary data transmission, thereby leading to lower through-
put. However, it ensures minimal transmission loss and end-
to-end delay while delivering data. Previous schemes send
unnecessary data along with high propagation 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 consump-
tion.
e throughput of DBR and EEDBR declines very
quickly, which is unsuitable for both delay-tolerant and
delay-sensitive applications. AMCTD performs better than
the previous schemes in instability period, but our scheme
outperforms AMCTD due to less time lag in data delivery,
thus validating its better performance in ooding-based
protocols.
Figure  shows that transmission loss of the network in
iAMCTD is much less than the previous techniques due to
prioritization of SNR in calculations of 𝐹𝑠.Higherthrough-
put in AMCTD is achieved in compromise of transmission
loss as number of redundant transmissions between sender
nodes and sink is increased. In DBR, there are preferences
of distant transmissions; however, in EEDBR multiple trans-
missions increase transmission loss between sender node and
the sink. We utilize thorp’s attenuation model for underwater
acoustics to calculate the transmission loss in packet for-
warding between a source node and sink. It considers trans-
mission frequency, bandwidth eciency, and noise density
which scrutinize the signal quality during data transmission.

International Journal of Distributed Sensor Networks 
EEDBR has higher loss than other techniques as it employs
distantpropagationsaswellasmultipleforwarding.InDBR,
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 packet loss.
In AMCTD, channel loss conditions are better than DBR and
EEDBR, as the weight function computations consider both
depth and residual energy of forwarding nodes; therefore,
the propagations remain stable. In later rounds, the perfor-
mance of AMCTD gradually decreases with the decrement in
qualied forwarders; therefore, both the packet loss and delay
increase.
In our proposed scheme, transmission loss is one of
the key factors concerning the signicance of data-sensitive
responsive networks. e 𝐹𝑠 computations and adaptive
mobility of courier nodes cause almost constant path loss
throughout the network lifetime. Variations in depth thresh-
olds assist in selecting optimal data forwarder which increase
the overall network throughput.
Figure  shows that end-to-end delay of network in
iAMCTD is less than the previous techniques due to mini-
mum forwarding distances between the nodes in both dense
and sparse conditions. e delay in DBR suddenly increases
because of sharp energy depletion of medium-depth nodes.
However, in EEDBR higher delay is caused due to prioritiza-
tion of residual energy. In DBR, delay is much higher in initial
rounds due to distant data forwarding. It decreases gradually
with the sparseness of the network, aer about  rounds.
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 threshold variations and weight functions perform
global load balancing. In our technique, there is a minimum
possible time lag due to consideration of signal quality and
depth dierences between sender and receiver nodes. It is
more suitable for delay-sensitive applications specically in
on-demand routing.
5. Conclusion and Future Work
In this paper, we have proposed iAMCTD routing protocol
to maximize the lifetime of reactive UWSNs. Amendments
in 𝐹𝑠 calculation technique enhance the network lifetime
and reduce the transmission loss. Optimized mobility pat-
tern of sink, in later rounds, minimizes end-to-end packet
delay, especially for delay-sensitive applications and even in
sparse conditions. Furthermore, variations in depth thresh-
old increase the number of eligible neighbors thus minimize
critical data loss in delay-sensitive applications.
As for future directions, we intend to design mathemati-
cal analysis of courier nodes’ mobility. We are also interested
in performing detailed analysis of the acoustic channel model
to devise further routing metrics for optimal data forwarding.
Conflict of Interests
e authors declare that there is no conict of interests
regarding the publication of this paper.
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