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

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In dense underwater sensor networks (UWSN), the major confronts are high error probability, incessant variation in topology of sensor nodes, and much energy consumption for data transmission. However, there are some remarkable applications of UWSN such as management of seabed and oil reservoirs, exploration of deep sea situation and prevention of aqueous disasters. In order to accomplish these applications, ignorance of the limitations of acoustic communications such as high delay and low bandwidth is not feasible. In this paper, we propose Adaptive mobility of Courier nodes in Threshold-optimized Depth-based routing (AMCTD), exploring the proficient amendments in depth threshold and implementing the optimal weight function to achieve longer network lifetime. We segregate our scheme in 3 major phases of weight updating, depth threshold variation and adaptive mobility of courier nodes. During data forwarding, we provide the framework for alterations in threshold to cope with the sparse condition of network. We ultimately perform detailed simulations to scrutinize the performance of our proposed scheme and its comparison with other two notable routing protocols in term of network lifetime and other essential parameters. The simulations results verify that our scheme performs better than the other techniques and near to optimal in the field of UWSN.
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arXiv:1307.7009v1 [cs.NI] 26 Jul 2013
1
AMCTD: Adaptive Mobility of Courier nodes in
Threshold-optimized DBR Protocol for Underwater
Wireless Sensor Networks
M. R. Jafri
1
, S. Ahmed
1,2
, N. Javaid
1,4
, Z. Ahmad
2
, R. J. Qureshi
3
1
Dept of Electrical Engineering, COMSATS Institute of IT, Islamabad, Pakistan.
2
Abasyn University, Peshawar, Pakistan.
3
SZABIST, Dubai, UAE.
4
CAST, COMSATS Institute of IT, Islamabad, Pakistan.
Abstract—In dense underwater sensor networks (UWSN), the
major confronts are high error probability, incessant variation in
topology of sensor nodes, and much energy consumption for data
transmission. However, there are some remarkable applications
of UWSN such as management of seabed and oil reservoirs,
exploration of deep sea situation and prevention of aqueous
disasters. In order to accomplish these applications, ignorance of
the limitations of acoustic communications such as high delay and
low bandwidth is not feasible. In this paper, we propose Adaptive
mobility of Courier nodes in Threshold-optimized Depth-based
routing (AMCTD), exploring the proficient amendments in depth
threshold and implementing the optimal weight function to
achieve longer network lifetime. We segregate our scheme in 3
major phases of weight updating, depth threshold variation and
adaptive mobility of courier nodes. During data forwarding, we
provide the framework for alterations in threshold to cope with
the sparse condition of network. We ultimately perform detailed
simulations to scrutinize the performance of our proposed scheme
and its comparison with other two notable routing protocols in
term of network lifetime and other essential parameters. The
simulations results verify that our scheme performs better than
the other techniques and near to optimal in the field of UWSN.
Index Terms—Weight function, Courier nodes, Underwater
Wireless Sensor Networks, Depth-based routing.
I. INTRODUCTION
I
N the recent years, UWSNs have emerged brilliantly for
its appliances in management, control and surveillance in
selected portions of deep oceans. 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 UWSN. The acquisition
of real-time wireless data access in underwater environment
is also a key demand for the future proposals in the field of
UWSN routing protocols. Unlike in terrestrial networks, the
use of acoustic communication causes a larger propagation
delay and low bandwidths, which has to be overcome in
the proposed techniques. Along with the scientific explo-
ration in the deep sea water, oil reserves management and
coastline protection are the major demands from UWSN.
In the recent literature, distinguished techniques address the
problems of high end-to-end delay, multipath fading and other
mobility issues. In this paper, we strive to achieve global
load balancing utilizing the adaptive movement of courier
nodes and implementation of optimal weight computations
for the sensor nodes. We also addresses the problems of high
transmission and receiving power consumption in UWSN by
employing better coordination among the nodes and minimal
data forwarding. In UWSN, recent advances have shown the
importance of long-term environment monitoring; demonstrat-
ing longer lifetime of network as the key requirement in the
future suggestions. The maintenance of higher throughput is
also required during the entire network lifetime. DBR [1] and
EEDBR [2] propose the well-organized energy consumption
schemes in the stability period but there is a lack of optimality
in throughput during the instability period of network. In
these techniques, the main load is on the low-depth nodes,
causing their rapid energy consumption and coverage holes
in network. There is a lack of load balancing in the typical
routing protocols due to unequal load distribution among the
nodes. Self-management and self-optimization are the major
achievements of our proposed technique in changing topology
of UWSN. In aqueous environment, the routing protocols
are divided into two major categories: localization-free and
localization-based routing schemes. Localization-free routing
protocols do not assume the localization information of sensor
nodes. Our scheme is also categorized in the latter category
and its routing is based on the depth information of sensor
nodes.
II. RELATED WORK
Routing protocols play a key role to prolong network
lifetime. In this regard, authors in [3], [4], [5], [6]
and [7] proposed different schemes for terrestrial WSNs. In
recent researches, it has been proved that the delay-tolerant
applications are the major intention of UWSN. Therefore, the
notable proposals in underwater routing protocols investigate
lack of global load balancing in the network to obtain extended
lifetime of network. An eminent technique in localization-free
category is Depth-based routing protocol (DBR), based on data
forwarding through low-depth sensor nodes. Energy-Efficient
depth-based routing scheme (EEDBR) is a constructive frame-
work for maximizing the network lifetime by utilizing both
depth and residual energy of the sensor nodes. It minimizes
2
the end-to-end delay along with better energy consumption of
the low-depth nodes. Both of the afore-mentioned techniques
strive to deal with the challenge of minimizing the load on
medium-depth sensor nodes in dense conditions. H2-DAB [8]
tackle the challenges of UWSN by implementing the dynamic
addressing scheme among the sensor nodes without requiring
the localization information. Another efficient scheme, R-
ERP2R [9] employs the routing metric based on the physical
distances between the nodes and exercises it to accomplish
higher throughput in UWSN. It also provides the energy
efficient solution for data forwarding along with better link
quality. The sensor nodes compute the holding time based on
their depth during the optimal forwarder selection to eradicate
the needless flooding of data packets. QELAR [10] offers
an outline for evenly distributed residual energy among the
nodes to calculate the reward function. It strives to achieve
longer network lifetime by selecting adequate forwarders for
source nodes. In dense underwater conditions, PULRP [11]
provides layered architecture and detailed algorithm to achieve
higher throughput among the network. It is free of localization
information and do not require fixed routing table, mini-
mizing the overhead in the network. In notable localization
based protocols, HH-VBF [12] 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 in spite of no state information of sensor nodes.
Therefore, we propose Adaptive Mobility of Courier nodes in
Threshold-optimized Depth-based routing protocol (AMCTD)
to attain reduced end-to-end delay, longer stability period and
better network lifetime to cope with the varying conditions of
UWSNs.
III. MOTIVATION AND CONTRIBUTION
In the previous literature of routing protocols in UWSN,
there is lot of prominent depth-based routing protocols. How-
ever, the efficient energy consumption remains the main ac-
quisition due to necessity of longer network lifetime. DBR
and EEDBR struggle to obtain longer network lifetime but
the stability period ends quickly due to unnecessary data
forwarding and much load on low-depth nodes in UWSN. In
the later technique, the nodes with high residual energy expire
earlier due to increased load, causing less number of available
neighbors for the remaining nodes in sparse condition and
immense coverage holes in the core of network. The main
deficiency of the depth based routing protocols such as DBR
and EEDBR is disorganized instability period due to the quick
energy consumption of medium-depth nodes which has been
addressed in our proposed scheme. However, EEDBR removes
key deficiencies of DBR but it has a lot of imperfections which
have been overcome by our scheme as follows:
The adaptive changes in depth threshold removes the
problem of lack of availability of threshold based neigh-
bors during the entire network lifetime.
The proficient movement of courier nodes minimizes
the end-to-end delay as well as decrements the energy
consumption of low-depth nodes utilizing on-spot data
collection, making the scheme advantageous for data-
sensitive purposes.
The optimal weight computation techniques cause longer
stability period and also offer better throughput in the
sparse conditions of network.
In this paper, we investigate the problems of little stability
period, swift energy consumption of low-depth nodes and poor
throughput during the instability period caused due to unequal
load distribution among the nodes. We promote global load
balancing in our proposed scheme by utilizing the optimized
movement of courier nodes in sparse conditions. Based on
the all-above analysis, this paper presents Adaptive Mobility
of Courier nodes in Threshold-optimized Depth-based routing
scheme (AMCTD) to accomplish resourceful energy expendi-
ture of nodes in UWSN.
IV. PROPOSED ROUTING PROTOCOL: AMCTD
In this section, we introduces our proposed protocol AM-
CTD descriptively. in the first section of network initialization,
each node computers its weight on the basis of density
of network and the movement of courier nodes has been
designed. in the second phase of data forwarding, the optimal
forwarders are decided on the basis of prioritization of weights
of the neighbours of the source node. in the third section of
weight updating and depth threshold adaption, network assigns
the weights on the prioritization of depth and residual energy
is changed according to the sparsity of network. in the last
section, the movement of courier nodes and variations in depth
threshold of nodes have been devised to cope with the sparsity
of network. the afore mentioned steps have been discussed as
following.
A. Network Model
In this section, we introduce our proposed protocol AMCTD
descriptively. In the first section of network initialization,
each node figures out its weight on the basis of density
of network and the courier nodes initiate their schematic
movement towards the surface of network. In the second
phase of Data forwarding, the source nodes decide the best
possible forwarders on the basis of prioritization of weights
among their threshold-based neighbors. In the third section
of weight updating, network allocates the weights to sensor
nodes, based on the prioritization of their depths instead of
residual energy to cope with the sparse conditions of network.
In the last section of depth threshold adaption, our routing
scheme devises the precise course for motorized movement
of courier nodes along with variations in depth threshold of
nodes to deal with coverage holes created in the later rounds
of network lifetime. The afore-mentioned steps have been
discussed briefly as following.
B. Network Architecture
In our first phase, courier nodes devise their schematic
sojourn tour in the network as the sensor node broadcast
their depth information to the neighbors using hello packets.
Therefore, the joint communication between the sensor nodes,
3
courier nodes and the sinks initializes the network operations.
The key elements of network architecture design are as follows
Sink sends hello packet to all the nodes to get their vital
information.
The network sets down the depth threshold of sensor
nodes to 60m to eliminate flooding process.
Each node calculates its weights using the below-
mentioned formula and employing the value of Priority
value.
W
i
= (priorityvalue R
i
)/(Depthofwater D
i
) (1)
where R
i
is the residual energy of node i, D
i
is the depth of
node I and Priority value is a constant . The weight calculation
technique minimizes the load on the low-depth nodes, causing
the increase in stability period. Moreover, the computational
formula for the weight of sensor nodes changes with the
varying density of network.
C. Initialization Phase
During this phase, each sensor node shares its weight and
depth information with its neighbors. Employing hello packets
transmission, each node identifies its neighbors in transmission
range and maintains the separate queue of neighbors under
depth threshold to identify the finest forwarder for its data
transmission. As the network initializes, the courier nodes start
their sojourn movements towards the surface of water. Due to
unlimited supply of energy, they aggregate the data of the
sensor nodes continuously, transmit it to the sink and then go
downward again to start the preceding sojourn tour. At one
hand, the courier nodes collect the data and on the other hand
change its mobility pattern to adapt with the changing network
density. Their schematic mobility model helps out the network
to diminish coverage holes creation in the instability period.
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 neighbor nodes to stop further forwarding by any other
neighbor of the source node.
D. Network Adaption Specifications and data forwarding
After sensing data, node sends its data toward sink using
the technique of CSMA/CA. Source node finds the optimal
forwarder among its threshold-based neighbors by comparing
their weights. The neighbor having the highest weight is
elected as forwarder and after receiving the packet it waits
for holding time before upward data transmission. It discards
the packet on receiving of same packet from any other
neighbor node during the holding time duration. The node
having lesser weight have much holding time, therefore, the
optimal forwarder has smaller holding time duration then
the other neighbors of source node. After every 50 rounds,
the nodes broadcast hello packet in the network to find the
number of dead node by the sink. It is used to cope with
the changing conditions of the network and computations
of network parameters. Furthermore, if the two neighbors
have same depth, the optimal forwarder will be one having
more residual energy. If courier node receives the packet, it
transmits acknowledgment to other neighbors of source node
to eliminate needless forwarding by any other neighbor node.
It utilizes the packet ID and source ID to accomplish this
purpose causing resourceful energy consumption of the source
node. The nodes continue to forward the packet of the source
node until it reaches to the base station or courier node.
E. Weight Updating Phase
This phase specifies the revisions in weight calculation of
sensor nodes according to the altering node density of the
network. After the number of dead node increases by 2 %,
each node calculates its weight by the following formula
W
i
= (priorityvalue D
i
)/R
i
(2)
This alteration is used to prioritize the depth among neighbors
and to reduce the significance of residual energy in the
calculation of the weight. It decreases the load on nodes
with high residual energy and low depth to become optimal
forwarder for consecutive transmissions. It chiefly plays role
in incrementing the instability period of the network.
F. Variation in Depth Threshold and Movement Scheme of
Courier Nodes
To adapt with the coverage problems of network, our design
proposes the optimal variation in the depth threshold and the
movement pattern of courier nodes, causing augmentation in
the instability period. As the number of dead nodes increases
by 75 %, First and third courier node starts to move between
the depth of 355m and the bottom of water, collecting the
data from the high-depth sensor nodes. It is assumed that
these nodes have minimal number of neighbors left alive
mostly due to sparsity of the network. The speed of the
movement of the courier node is also varied to a higher speed
to facilitate the remaining alive nodes. Moreover, the second
and fourth courier nodes move vertically between 100m and
200m to accumulate the data from intermediate-depth nodes.
The changing movement pattern mainly increases the network
lifetime in the later rounds. In this section, we also discuss the
well-organized framework for alterations in depth threshold.
As the number of dead nodes increases by 2 %, the depth
threshold is decreased to 40m to increase the quantity of
threshold-based neighbors of the source nodes. It eases out
the forwarding of data in low network density moreover, it is
changed to 20 m as the dead nodes increase by 200 in extreme
less dense condition to boost the network lifetime. In UWSNs,
the network lifetime is of prime importance; hence our scheme
proposes the modification in weight calculation again as the
number of dead nodes passes 80 % to prioritize the value of
residual energy among the remaining alive neighbors of the
source sensor nodes
W
i
= R
i
/(priorityvalue D
i
) (3)
where R
i
is the residual energy of node i and D
i
is the depth
of node i.
4
Fig. 1. Mechanism of Data Transmission in AMCTD
V. PERFORMANCE EVALUATION AND ANALYSIS
In this section, we assess the performance of AMCTD
in underwater sensor networks using MATLAB simulator.
We compare the performance of our protocol with other
energy efficient depth-based routing protocols such as DBR
and EEEDBR. Both of the afore-mentioned protocols are
considered to be an imperative landmarks in depth based
routing schemes.
A. Simulation Scenarios
In order to analyze the results in practical scenario, we
deploy the network of 225 nodes using random topology in
500mx500m environment. The transmission range of sensor
node is 100 meters, following the physical characteristics of
underwater acoustic modem. We have adopted the specifica-
tions of commercial acoustic modem, LinkQuest UWM1000,
where the data generating rate is 1 packet per second. The
power consumptions for the transmitting, receiving and idle
mode are 2w, 0.1w and 10mw respectively. The initial energy
of the node has been set to 70 joules while the packet size is
50 bytes. At the surface of water, the mutual distance between
the sinks is 100 meters consecutively. The courier nodes reside
in the bottom of water during the network initialization. Then,
they start to move upwards, transmit the received data to the
sink and then again moves downward toward the bottom. Each
node broadcasts hello packets after 50 rounds to check the
number of dead nodes from the sink. 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.
To ensure efficient media access among the nodes, 802.11-
DYNAV [13] protocol have been suggested. Each node shares
the vital physical metrics, especially depth threshold and
weight with its neighbors to keep it informed with the varying
circumstances of the network. Our routing algorithm shapes
the movement course of courier nodes to compensate with the
sparse conditions of the network exploiting the transmission
of hello packets. However, there is a trade-off between the
overhead (because of Hello packets transmission) and the
availability of up-to-date information.
The simulation parameters are given in Table 1.
Table 1
Parameters used in Simulations
Parameter Value
Network size 500m x 500m
Node number 225
Initial energy of normal nodes 70J
Data aggregation factor 0.6
Packet size 50 bytes
Transmission Range 100 meters
Number of Courier nodes 4
Number of Simulations 3
B. Performance Metrics
We demonstrate the following experimental metrics to ex-
press the performance of our proposed technique.
Network Lifetime: It is the time duration between the
network initialization and the complete energy exhaustion
of all the nodes.
Average Energy Consumption: It is the energy consump-
tion of all the active nodes of network in 1 round.
Probability of Dropped packets: It shows the probability
of loss of packets in 1 round.
Number of Dead nodes: It shows the number of dead
nodes of the network.
Confidence interval: It is an interval in which a measure-
ment or trial falls corresponding to a given probability.
C. Simulation Results and Analysis
We compare the network lifetime of our proposed technique
AMCTD with DBR and EEDBR. In the simulation of 15000
rounds, nodes have been deployed randomly in every simu-
lated technique. Figure 2 represents the comparison between
5
the network lifetime of AMCTD, EEDBR and DBR. The
results of network lifetime comprises on the average of the 3
simulation runs of the afore-mentioned techniques and their
comparisons, causing the increase in outcome authenticity.
In every single round of simulation, each alive node of the
network send the packet, and the packet is forwarded until it
reaches to the sink or courier node. In AMCTD, first node
dies at about 4500 round meanwhile, it also provides the
instability period of about 12000 rounds. After the dying of
initial nodes, the adaptive variations in the depth threshold
provide longer lifetime to the network. The modifications in
weight calculation technique and the prioritization of weight
also encourages the efficient instability period. It causes the
exclusion of abrupt energy consumption of sensor nodes due
to better availability of threshold-based neighbors. In last
3000 rounds, the movement pattern of courier nodes and
their sojourn intervals enhance the probability of efficient
data transmission. In the previous techniques of DBR and
EEDBR, the stability period ends quickly due to prioritization
of residual energy or depth solely in the selection of optimal
neighbors, which causes inefficient instability period. In our
proposed technique, resourceful utilization of energy becomes
possible due to modification of depth threshold according to
shifting network concentration. In order to show the reliability
0 5000 10000 15000
0
50
100
150
200
Rounds
Confidence interval of Allive nodes
AMCTD
EEDBR
DBR
Fig. 2. Confidence Intervals of number of Alive nodes in AMCTD, EEDBR
and DBR
of the estimates of simulation results, Figure 2 also shows
the confidence intervals between network lifetimes of the
contrasting techniques. Confidence intervals consist of a series
of values that operate as good estimates of the unknown results
of network parameters. The first interval descriptively justifies
the larger stability period of our proposed techniques which
is better than the other depth-based routing protocols due to
efficient energy consumption of the sensor nodes. The intervals
throughout the network lifetime illustrate the consistency in
the performance of our recommended technique avoiding the
sudden collapse of network due to coverage holes creation
in the network. Figure 3 shows the evaluation of dead node
variation in AMCTD, EEDBR and DBR alongwith the average
results of 3 simulation runs. The implementation of adaptive
mobility of courier nodes improves the stability period of
AMCTD then that of DBR and EEDBR, which is also caused
due to removal of redundant data forwarding by neighbor
nodes. The key reasons behind the much capable instability
period of our technique then of afore-mentioned schemes are
changes in depth threshold and optimal forwarder assortment,
prioritizing the sensor node depth in later rounds. The global
0 5000 10000 15000
0
50
100
150
200
Rounds
Confidence interval of Dead nodes
AMCTD
EEDBR
DBR
Fig. 3. Comparison of Dead nodes in AMCTD, EEDBR and DBR
load balancing is achieved by the low-depth movement of
courier nodes, nevertheless in previous schemes low-depth
nodes expire earlier due to a large extent of data forwarding.
In DBR and EEDBR, high-depth nodes pass away rapidly in
later rounds due to minimal quantity of neighbors. It affects
the network harshly and the network throughput suddenly
reduces because of swift raise in number of dead nodes.
The optimized weight computation removes the same node
maximal selection as a top forwarder; hence curtailing the load
the on nominal nodes and enhancing the network lifetime.
The adaptive movement of courier nodes further diminishes
the delay in data delivery to the base station. The confidence
interval defines the accuracy of results along with removal of
sudden collapse of network. Figure 4 describes the comparison
between the average energy consumption of network. In our
scheme, energy utilization of sensor nodes is much proficient
at start due to effective weight implementation and slighter
data forwarding. However, in the previous proposals, uneven
energy consumption of nodes is cause due to elevated data
forwarding towards the base stations by medium-depth nodes.
It sources low stability period along with sharp reduction
in the throughput of instability period. Moreover, the higher
energy utilization in our scheme in mid rounds is because of
increase in the network throughput. It upholds the throughput
and encourages smooth energy consumption by all the source
and forwarding nodes. The proficient energy consumption
causes enhancement in network lifetime and adjusts with the
changing state of network concentration. The average energy
consumption also undergoes a sudden drop off in the previous
techniques due to the unequal distribution of load on specific
low-depth nodes, causing low performance of network. A
lot of empty spaces and coverage holes are created in the
6
network during instability period which enlarge the probability
for the loss of packets. The confidence intervals confirm that
higher energy utilization in the previous techniques during the
initial rounds origins the abrupt fall of network performance
afterwards along with the poor coverage of network, therefore
causing inefficient instability period. The confidence intervals
0 5000 10000 15000
0
1
2
3
4
5
6
7
8
9
10
Rounds
Confidence interval of Average energy consumption
AMCTD
EEDBR
DBR
Fig. 4. Confidence Intervals of Total energy consumption in AMCTD,
EEDBR and DBR
of average energy consumption in our scheme encourage
the equal energy utilization along the entire lifetime, which
minimizes the coverage holes creation and encourages stable
instability period.
Figure 5 estimates the throughput of network during the
network lifetime. In our proposed technique, the presence
of courier nodes and the changes in depth threshold largely
enhances the reach ability of packets at base station. Due
to less decrement in residual energy of low-depth nodes,
the network performance at the later rounds is maintained
along with the constant end-to-end delay for packets. The
0 5000 10000 15000
0
50
100
150
200
Rounds
Confidence interval of Throughput of Network
AMCTD
EEDBR
DBR
Fig. 5. Comparison of Network Throughput in AMCTD, EEDBR and DBR
validation improvement due to the computation of average
results of 3 simulations not only justifies the advantages of
our proposed scheme, but also discriminates its details from
the previous depth-based routing protocols. Figure 6 presents
the comparison of Probability of loss of packets in AMCTD,
EEDBR and DBR. We have used uniform random model to
calculate the packet loss probability in our proposed method.
The probability of loss of packets is increased in the previous
techniques due to extensive distances between the source and
destination nodes along with high depth thresholds. However,
in our proposed framework, the loss probability decreases due
to reduced distances involved in data forwarding and less
electronic energy cost exploiting the variations in thresholds
and mobile courier nodes. Simulation results show that the
Probability of loss of packets in AMCTD is better than
of EEDBR and DBR of nodes over rounds. The adaptive
movement algorithm of courier nodes and the execution of
weight function reduce the number of lost packets along with
the decrement in end-to-end delay of packets at sink node.
Figure 6 also illustrates the confidence intervals of probability
of lost packets of the network. It not only shows the high loss
probability in previous techniques due to longer forwarding
distances, but also ensures the optimal link judgment in our
proposed techniques which increase the network throughput
even in the later rounds.
0 5000 10000 15000
0
10
20
30
40
50
60
70
80
90
100
Rounds
Confidence interval for loss of packets
AMCTD
EEDBR
DBR
Fig. 6. Confidence Intervals of Probability of Loss of Packets in AMCTD,
EEDBR and DBR
VI. CONCLUSION
In this paper, we recommend an Adaptive Mobility of
Courier nodes in Threshold-optimized Depth-based routing
protocol to maximize the network lifetime of UWSN. Our
considerations are supportive in decrementing the energy con-
sumption of low-depth sensor nodes specifically in the stability
period. Amendments in depth threshold for the sensor nodes
enlarge the number of threshold-based neighbors in the later
rounds, hence enhancing the instability period. Optimal weight
computation not only provides the global load balancing in
the network, but also gives proficient holding-time calculation
for the neighbors of source nodes. The adaptive movement of
courier nodes upholds the network throughput in the sparse
condition of network.
7
VII. FUTURE WORK
As for future directions, we are striving to design much
better courier nodes mobility pattern specifically toward the
source nodes in the sparse conditions as well as dense con-
ditions of network for network to perform equally fine in the
complete lifetime of UWSNs. We are also planning to integrate
MAC protocols [14], [15] and [16] with our routing scheme
in order to facilitate the sensor nodes by the mobility of courier
nodes, specifically in the sparse condition to achieve longer
network lifetime. Moreover, to increase the network lifetime
we are interested to implement routing schemes like [17],
[18], [19] and [20] in UWSNs.
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... Offers communication security for sensitive networks such as military, while reducing energy consumption, improving throughput and end-to-end delay compared to AMCTD [119] Performance comparison was not made against other secure, energy-efficient routing protocols Bayesian probability for residual energy, energy consumption rate, and link quality for each node after segregating the entire network into layers. This delivers improved network lifetime, energy-efficiency, and lower end-to-end delay as compared to other depth-based routing protocols such as DBR [98] and EEDBR [117]. ...
... To address this issue, a secure energy efficient and cooperative routing protocol (SEECR) has been proposed in [118] that uses multi-hop routing in a secured manner. SEECR delivers 23% energy tax improvement compared to the wellknown AMCTD (Adaptive Mobility of Courier Nodes in Threshold-optimized DBR) [119] protocol. ...
Preprint
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This is a pre-print of the published article “A survey on energy efficiency in underwater wireless communication”. To access the full text of the latest version of this article, please visit: https://www.sciencedirect.com/science/article/pii/S1084804521002885
... To address this issue, a secure energy efficient and cooperative routing protocol (SEECR) has been proposed in Saeed et al. (2020) that uses multi-hop routing in a secured manner. SEECR delivers 23% energy tax improvement compared to the well-known AMCTD (Adaptive Mobility of Courier Nodes in Threshold-optimized DBR) (Jafri et al., 2013) protocol. ...
... SEECR 2020 Offers secured communication for sensitive networks such as military whilst reducing energy consumption and increasing throughput and end-to-end delay compared to AMCTD (Jafri et al., 2013). ...
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... In contrast, existing routing algorithms in the depth-based routing protocols in UWSNs such as DBR [36], EEDBR [37], RE-PBR [11] did not employ void detection algorithms as it taking into account selecting the next forwarding sensor based on either residual energy, depth, or link quality. Moreover, the void handling techniques provided by HydroCast, AMCTD [38], and VAPR have some drawbacks. The utilization of GPS information in HydroCast causes high energy consumption. ...
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