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Received: 20 December 2019 Revised: 15 April 2020 Accepted: 20 April 2020
DOI: 10.1002/ett.3992
RESEARCH ARTICLE
Two hop verification for avoiding void hole in underwater
wireless sensor network using SM-AHH-VBF and
AVH-AHH-VBF routing protocols
Tariq Hussain1Zia Ur Rehman1Arshad Iqbal1Khalid Saeed2Iqtidar Ali1
1Institute of Computer Science and IT,
The University of Agriculture, Peshawar,
Pakistan
2Department of Computer Science,
Shaheed Benazir Bhutto University,
Sheringal, Pakistan
Correspondence
Tariq Hussain, Department of Computer
Science and IT, University of Agriculture,
Peshawar 25130, Pakistan.
Email: uom.tariq@gmail.com
Abstract
Underwater Wireless Sensor Networks (UWSNs) consist of several sensor nodes
deployed underwater and gathering information from the underwater situation.
Sometimes during a communication void regions occur when a forwarder node
is unable to find the next forwarder node closer to the sink within the trans-
mission ranges which results from its took extra energy consumption. In this
research work, we intend schemes for void hole avoidance. First one is, Avoid-
ing Void Hole Adaptive Hop by Hop Vector-Based Forwarding (AVH-AHH-VBF)
in an UWSN, and the second, scheme for increasing lifetime and minimizing
consumption of energy of the network, Sink Mobility (SM-AHH-VBF). Simula-
tion results show that our schemes outperform compared with baseline solution
in terms of average Packet Delivery Ratio (PDR), Average Propagation Distance
(APD), energy consumption. The simulation results verify the AVH-AHH-VBF
scheme results is equals to 14% and SM/AHH-VBF equal to 32% in terms of PDR,
AVH-AHH-VBF equals to 57% and SM equals to 39% for energy consumption,
AVH-AHH-VBF had a tradeoff of 63% because of considering two hops and SM
equals 20% tradeoff for the average delay and AVH-AHH-VBF equals 35% and
SM equals 61% improvement for average APD.
1INTRODUCTION
Roundabout 70% of the earth is covered by water whereas the remaining 30% of the earth is dry. Wireless Sensor Net-
works (WSNs) can be utilized for the activities of an underwater environment that is a type of WSNs. Underwater WSNs
(UWSNs) include sensor nodes of various types. The nodes in UWSNs are deployed under the water and their use is to
collect information from the resources deployed under the water. In the acoustic-based wireless communications, band-
width and propagation delay are the basic challenges. The acoustic WSNs utilize power which cannot be recharged or
interchanged. The conservation of energy of the acoustic WSN includes suitable schemes for communication in UWSNs.
The characteristics such as small communicating bandwidth, high propagation delay, node mobility, the harsh atmo-
sphere of water, the high bit error rate and prevailing the solutions of WSNs are not appropriate for UWSNs1architecture
asshowninFigure1.
The environment of UWSNs is critical for human beings because of the effects of humans on the earth's atmosphere
and the productions of carbon dioxide absorption. Due to the decline in the terrestrial assets, UWSNs become the focus
of researchers for observing the underwater environment. The routing schemes developed for Terrestrial WSNs (TWSNs)
Trans Emerging Tel Tech. 2020;e3992. wileyonlinelibrary.com/journal/ett © 2020 John Wiley & Sons, Ltd. 1of16
https://doi.org/10.1002/ett.3992
2of16 HUSSAIN .
FIGURE 1 An environment of underwater WSNs1
are not suitable for the acoustic environments because acoustic communication utilizes acoustic connections and radio
connections are not suitable for UWSNs. UWSNs topologies are also more dynamic as compared to the topologies of TWSN
since, the sensor nodes move naturally with the streams of water and their position changes more frequently. In UWSNs
the confinement of the sensor node is difficult as compared to the TWSNs. The organization of sensor nodes is sparse.
Furthermore, the nodes have limited energy. After the deployment of nodes, it is difficult to recharge or interchange
the batteries. Moreover, there are different challenges associated with UWSNs like low bandwidth, high propagation
delay, high bit error rate, and high cost of correspondence. In acoustic signals, the propagation delay is five times less as
compared to the radio signals (ie, 1500 m/s). However, there is a wide variety of uses of UWSNs such as disaster prevention,
oil/gas extraction, observing the aquatic environment, military defense, offshore exploration, commercial and scientific
purposes, and so on.2
We propose schemes for the avoidance of void hole in UWSN. The first one is, Avoiding Void Hole Adaptive Hop by
Hop Vector-Based Forwarding (AVH-AHH-VBF) scheme used acoustic signal in WSNs and the second scheme, reducing
the consumption of energy and improving the network lifetime in Sink Mobility secheme. In AVH-AHH-VBF scheme a
source nodes get information of sensor nodes up to two hops, so that in case of void nodes it then selects an alternate
non-void node, hence reduces the energy consumption as well as packet drop. Moreover, the forwarder node is selected
which is within the transmission range of source node and heading to the destination node instead of selecting a sensor
node which is in the region towards the destination and having minimum depth. The Sink Mobility (SM)-AHH-VBF
scheme a mobile sink is positioned in the sparse region which collects information from the sparse nodes and therefore
reduce the consumption of energy and maximizes the lifetime of the network.
1.1 Our contribution
The main contribution of the research includes:
•First of all, for avoiding the void hold, the node's information of up to two hops is used, so that the data information is
not forwarded to the void region. If the sensor nodes is void then shere information will be forwarded to the sensor nodes
which is not avoided. If there is a single sensor node in forwarding region then, in this case, data will be dropped. By
utilizing this method of transmission packet drop is reduced due to which consumption of energy is reduced. Moreover,
for the selection of efficient forwarder despite considering the only region to the destination (RTD), any sensor node
which is within the range of transmission of the source node and has completed routes towards the sink node is selected.
•The idea of sink mobility is utilized in the sparse region for enhancing the assembling of information, to the reduction
in packet drop and energy consumption.
•The performance of the proposed scheme is assessed with (AHH-VBF) protocol in terms of end-to-end delay, packet
delivery ratio (PDR), and energy consumption.
•Implementation is done to ratify the efficiency of the proposed schemes.
HUSSAIN . 3of16
2RELATED LITERATURE
Reference 3 has been proposed a routing protocol to achieve the energy conservation in a network with the coopera-
tive routing called energy efficiency in UWSNs. For amplification of signals, the relay nodes used amplify and forward
scheme and to combine the received signals as a supportive combine scheme, fixed ratio combining is incorporated at the
receiving side. For the relay node selection, it uses transmission distance and link quality information. In this scheme,
the path loss is minimized and the lifetime of the network is improved by the utilization of single and multi-hop commu-
nication. Furthermore, the adjustment of relay node and the transmission power of the source depend on the distance of
transmission with the destination nodes. The Protocols summary as shwon in table 1.
Reference 4 proposed a term of delay and PDR for UWSNs is known as stochastic performance analysis with reliability
and corporation (SPARCO). In spare network conditions, to improve the PDR with reduced energy consumptions and to
increase the lifetime of the network uses the SNR-based cooperative nodes and introduces cooperation. To ensure the data
reliability in SPARCO distance among neighbors node and channel quality is considered as a relay selection criteria. Also,
improvement in network stability period and reduction in path loss is achieved in the network balancing the transmission
load in single-hop and multi-hop communications.
In Reference 5 an adaptive transmission (ATM) scheme was proposed to achieve energy conservation and improving
the network lifespan for amplifying and forward relaying network. In this scheme, cooperative communication nodes
are selected based on instantaneous channel conditions. ATMs achieve high throughput and minimum energy in the
network by considering the transmit power allocation and relay position. To achieve the energy efficiency for USWNS, the
author suggested a mechanism of cooperative nodes selection on the basis of propagation delay. In addition, by sharing
the transmit power between relay and source in per transmitted block energy normalization is achieved. Based on the
signal-to-noise ratio (SNR) of the link the optimal number of relays are selected to improve the partner node selection
algorithm and to achieve better network performance.
In Reference 6, author proposed dynamic addressing in the technique that is called “Hop-by Hop dynamic addressing
based protocol for pipeline monitoring” (H2-DARP-PM) is selected for the suitable next-hop neighbors for providing
supports. For assigning the dynamic hop address to all nodes, this technique contributes to information forwarding and
improving the PDR but the energy consumption is also caused by this process.
In Reference 7 free space optical and electromagnetic communication protocols are presented along with Free Space
Optical and Electro Magnetic (FSO and EM) energy dissipation. Optimum analytical framework number of clusters have
been examined by the authors for Gaussian distributed UWSN. By changing the sinking location of the detecting zone.
Authors have suggested logical results at three different points that are sinking at the centers, adjacent centers, and
corners. This scheme caused a high E-to-E delay and resulted in less energy consumptions.
Reference 8 proposed four schemes for IoT enable UWSNs ATM range in B-DBR, DBR, CA-DBR and C-DBR. A-DBR
forwarded data packet towards the sink and by adaptively adjusting its communication range avoids void hole. C-DBR
uses the clustering technique for the collection of data and to reduce the consumption of energy and delay. To reduce the
collision on the acoustic channel, CA-DBR uses a node with less numbers of neighbor and for minimizing the consump-
tion of energy. B-DBR finds alternate routes to transfer the information toward the destination nodes. Moreover, these
methods have a high accumulated propagation distance.
In Reference 9 MGEDAR protocol was proposed in UWSNs to reduce the void hole problems. For void hole avoidance
protocols adjustment of transmission, the range is performed. To select the forward node, the cost function is used.
In Reference 10, author suggested two protocol EP-VIR-Three, BF-SPR-Three for the effective, noise-free and consis-
tent routing protocols. The main purpose of the study was to reduce the interference, utilization of extra energy, increasing
packet delivery, and void hole avoidance. But on the cost of delay still, they had to compromise on the consumption of
energy.
In Reference 11, the proposed adaptive energy efficient routing protocol was first to solve problems with reliable
data delivery using the shortest routing method. This strategy is designed by combining key features of the FLMPC One
Protocol, which uses two hop node information, and the FLMPC Two Protocol that uses three hop neighbor information
and for this, they used the “digkitra” algorithm to select the shortest route. Different sizes of packets and data rates are
also considered. However, energy consumption has been a problem.
In Reference 12, the authors proposed sigmoid soft limiter detector scheme. The sigmoid parameter is optimized to
achieve the best performance using the chernoff bound for probability of detection. In Reference 13, a cross-layer schemes
that use a shared link for the two-hop multilayer system on the data link layer and the orthogonal frequency division
multiplexing adaptive modeling are investigated. China has proposed a limited state Markov for this scenario. We use the
4of16 HUSSAIN .
TABLE 1 Protocols summary
Paper Feature Advantages Limitations
SACRP Self-adaptive cooperative routing protocol Significant improvement in terms of delay
and PDR
Energy consumption
(ACH)2 Adaptive-Clustering-Habit Minimum packet drop ratio, enhanced
lifetime
Communication delay occurs in this
technique.
EM and FSO Electro Magnetic and Free Space Optical along with energy dissipation of
EM and FSO
Less energy consumptions Causes high end-to-end delay
(CDSEEC), (SEEC) and (CSEEC) Sparsity Circular Depth Based Sparsity Aware Energy-Efficient Clustering
Sparsity, Aware Energy-Efficient and Clustering Circular Sparsity Aware
Energy-Efficient clustering
Reduced energy consumption Reduced PDR
CoDBR A cooperative depth-based routing Increased network efficiency and throughput And energy expenditure in the network
Energy efficiency in UWSNs with
cooperative routing
Utilizes single-hop and multi-hop communication Minimized path-loss and improve network
lifetime
Compromises high delay
SPARCO Stochastic performance analysis with reliability and cooperation and uses
SNR-based cooperative nodes selection
Improved network lifetime and improved
PDR
Energy consumption
ATMS Adaptive transmission scheme for amplify-and-forward relaying network.
Optimal number of relays is selected based on SNR of link
Achieve better network performance and
improve partner node selection algorithm.
High energy consumption
H2-DARP-PM author used in data
forwarding
Hop-by-Hop Dynamic Addressing based Routing Protocol for Pipeline
Monitoring. Dynamic addressing technique is used to select suitable next
hop neighbor for providing support
This technique improved the PDR High energy consumption
MGEDAR Modified Geographic and Opportunistic Depth Adjustment based Routing Improved energy consumption High delays
Adaptive energy efficient routing
protocols
These strategies are made by combining the noticeable features of “Forward
Layered Multi-path Power Control One (FLMPC-One) protocol,” that
make use of 2-hop node information, “Forward Layered Multi-path
Power Control Two (FLMPC-Two) protocol,” which uses 3-hop neighbor
information and for this they used “Dijkstra” algorithm for selecting
shortest path.
Reliable data delivery using the mechanism
of shortest path first.
There is been the problem of energy
consumption.
Adaptive forwarding layer multi-
path power control routing
protocol
To minimize the energy consumption, void hole and to increase reliability Energy consumption is extensively
minimized
Problem of transmitting multiple
copies in order to achieve reliability
and also pays the cost for delay.
Mobility based geo-opportunistic
routing strategy to avoid inter-
ference.
The overall network is subdivided in to smaller region to avoid interference
and to minimize the extra utilization of energy. Moreover, an optimum
number of nodes are chosen from the subdivided regions depending on
its nearness to the sink node to avoid void hole.
The loss of data packets is lessening by using
mobile sinks.
They faced high PDR due the usage
mechanism for avoiding interference.
Bellman–Ford Shortest Path-based
Routing (BF-SPR-Three) and
Energy-efficient Path-based Void
hole and Interference-free
Routing (EP-VIR-Three)
Effective, consistent and noise free routing protocol Reduce the utilization of extra energy,
reducing interference, avoiding void hole
and increasing packet delivery
Still they compromised energy
consumption on the cost of delay
AHH-VBF Location aware routing protocol, Concept of adaptive virtual pipe line Reducing duplicate packet, unnecessary
energy consumption is avoided
Void hole problem exists
HUSSAIN . 5of16
Relay Selection Scheme, which improves diversity in the relay system. In addition, the loss rate of the packet is calculated.
To optimize the packet loss rate, the optimized target packet error rate was obtained for the adaptive model. The digit
results confirm the accuracy of the proposed cross-layer scheme.
In Reference 14, the author investigated a cross-layer design uses adaptive modulation and joint queuing for two-hop
relay system. An amplify and forward Cooperative system with finite-length queue at the data link layer is assumed. The
packet loss rate and the average spectral efficiency is calculated. Reference 15 in the suggested work is the combined effect
of thermal noise and the average level crossing rate of minor interruptions in the relay fading channels, the probability
of closure, and the closed form feedback for the average durations of the two-hop amplifier and forward relay channels.
Provides Fifth Generation Networks that can be used for upper layer applications.
3PROPOSED SYSTEM
Architecture of single sink have been proposed in which the nodes are sensors placed under the ocean and the other nodes
named sink are placed at sea's surface as shown in Figure 2. The UWSN's architecture contains of anchored and relay
nodes. Nodes named relay are discretely and randomly placed on diverse places in the ocean and the other nodes named
anchor are placed in the ocean's bottom.11 For information of transmission the relay nodes generate acoustic signals with
water for the communication medium of channel. These nodes sense some desired data and transmit to receive nodes.
At some point, when the nodes named anchor sense some information, they forward this information to the sink node.
The radio signal is used by sink node for exchange of information via unguided medium. At the time when the data
are acknowledged to the sink node, it originates further communication with satellite via radio waves. After that, the
connection is established with the base station by satellite for communication.8
When there is void hole issue it disrupts data transfer and consumes more energy by taking into account the sink
and source node because it breaks the paths and at the result the unavailability of the node occurs. Therefore, for getting
rid of void hole problem, it has been introduced an approach via which the creation of the void hole will be got rid
before. Our first scheme named AVH-AHH-VBF source node gets of the two nodes so far that if there exist the void node,
then this scheme will utilize an alternative path in which there will be no void node issue. Thus, by minimizing the
consumption of energy and fewer packets drop will improve the network efficiency. Secondary, it chooses a node leading
forwarding procedure which has to be in the transmission range of the sender node and going towards the target also
known as destination instead of choosing a node which is area going towards the target that is, destination RTD and with
minimal depth as portrayed in Figure 2. In SM-AHH-VBF, the movable sink is placed in the region of sparse that collects
information from the nodes in sparse region and thus maximizes lifetime of network and minimizes consumption of
energy. Sink mobility is the outmost functional procedure for keeping balance the traffic in the network. The mobile sink
gathers data from the nodes called relay and results in reducing the energy that is obligatory for a relay node to transmit
FIGURE 2 Proposed schemes for
illustrating void hole avoidance
6of16 HUSSAIN .
FIGURE 3 Flow chart of
the proposed solution
the data packet to the destination node called sink node. It also delivers us the stable energy consumption and reduces
the traffic on the nodes called relay. The nodes designated as relay generates a ping message which contains the ID of
the nodes and the resourceful data of the mobile sink for recognition the place or location of that mobile sink, and then
the mobile sink directs an acknowledge to its nearby relay nodes. The relay nodes only generates and transmit the data
packet when that is in the communication range so that for saving the energy of the nodes that are utilized in mobile sink
detecting. The AVH-AHH-VBF and SM-AHH-VBF scheme as shown in Figure 3 scenario is given below.
Algorithm 1 The proposed solution AVH-AHH-VBF
Step 1∶Initialization
Step 2∶Deploy Nodes
Step 3∶Check type of packet
Step 4∶If type of packet is neighborrequest Then
Send 𝐴𝑐𝑘
𝐸𝑛𝑑 if
wait for the next pacets
if type of packet is 𝑎𝑐𝑘
update Neighbor table
𝑒𝑛𝑑 if
HUSSAIN . 7of16
if type of packet is data packet
get packet and soirce 𝐼𝐷
Step 5∶Find preferred forwarder nodes
Step 6∶For Scheme 1
Fetch 𝑡𝑤𝑜 ℎ𝑜𝑝 information
if node is void to 𝑡𝑤𝑜 hops
Drop packet
update neighbor table
else
transmite packet
Step 7∶for scheme 2
Send data to mobile sink node
if mobile sink node not available
drop packet
uudate neighbor table
eles
transmit packet
store 𝑎𝑙𝑙 measured data
𝑒𝑛𝑑
4PERFORMANCE METRICS
To evaluate the performance of the proposed schemes to achieve desired result such as End-to-End delay, energy
consumption, and PDR.
4.1 Packet delivery ratio
The PDR is a performance parameter of the ad-hoc networks. The total sum up the number of overall generated packets
within all the sources and the number of overalls received packets within all the receivers. That can be calculated using
Equation (1). This equation has been also used by8.
PDR =Packets Recieved
Packets Transmitted (1)
4.2 Energy consumption
When data packets are communicated successfully from source to sink node, the per node consumes energy during a
communication. Energy consumption measures in joule (J). Equation (2) used for calculating energy consumption.
𝐸𝑛𝑒𝑟𝑔𝑦 consumption =𝐸𝑐𝑜𝑛𝑠∕𝜂×Dp.(2)
where Econs is the consumption of energy the whole networks; 𝜂denotes the total range of the node in the networks; and
Dpdenotes the total range of data packet received at the sink. This equation has been also used by16.
8of16 HUSSAIN .
4.3 End-to-end delay
E-to-E delay can be stated as the average time taken by data packets for successful delivery source to destination. It
measures in seconds. Equation (3) is used for calculating E-to-E Delay as the following.8,9,17
End −to −End delay =
hmax
∑
h=1
D(ℎ𝑖, ℎ𝑗)
V(3)
where hrepresents hop counts of ith and jth nodes. Acoustic signal speed is represented by V, and distance between iand
jnodes is denoted by D(hi,hj).
5RESULTS AND ANALYSIS
This section includes results obtained using simulation and their detail analysis. In a simulation, we used the single-sink
architecture of measurement 12 ×12 ×12 km3, 50-500 nodes are arbitrarily carried. The transmission range is 2 km,
packet size is 72 bytes, and information rate is 6 kbps3. Every node's energy is 100 j. The utilization of energy are 100 W
for sending informations and 200 mw for gathering information. The previously mentioned parameters are taken from
References 18,19.
5.1 Throughput maximization
We take throughput the objective function of maximizing throughput20 and linear constraint accordingly to get the best
result as follows:
Max
rmax
∑
r=1
Th(r)∀r∈rmax.(4)
Constraints of the objective function are given as follows:
C1∶E𝑡𝑥,E𝑟𝑥 ≤Ei
C2∶E𝑡𝑥 ≤Erx
C3∶𝑇𝑋n≤𝑇𝑋max
C4∶D𝑖𝑗 ≤Dmax
𝑖𝑗
It is confirmed by the C1that the required energy for data transmission and receiving must be short of the node in
range as compared to the initial energy Ei. Likewise, the C2demonstrates some restriction that the energy of transmission
Etx must be short in range compared to the remaining residual energy Ere.ThetermC
3assures that for reception of signal
having better quality, the packet data must to be propagated within the maximum range of transmission TXmax of it. In
which TXnis the node's range of transmission TXmax is the node's maximum range of transmission. And similarly, C4
assures a predefined value which is known as threshold for successful communication of the distance between source i
and destination j.
5.1.1 Graphical analysis
The total bandwidth of the scenario is 2000 to 4000 kHz.8Where Bfrw shows forwarding node bandwidth with residual
energy. Bandwidth (B) is allocated to Bfrw and BNfrw in Equations (5)-(7):
200 ≤Bfrw ≤1000.(5)
HUSSAIN . 9of16
2000 ≤BNfrw ≤3000.(6)
2200 ≤Bfrw +BNfrw ≤4000.(7)
The feasible region is shown in Figure 4 via point extracted from equation and point on the boundary of this feasible
region8.
P1(200;2000)=2200KHz.
P2(1000;2000)=3000KHz.
P3(200;3000)=3200KHz.
Hence, selecting any feasible region value from these points results in the maximization of throughput.
5.2 Delay minimization
For minimizing delay, we take the objective function of delay that is always going to be minimized and take linear
constraints accordingly to become the best result as follows:
Max
rmax
∑
r=1
D(r)∀r∈rmax.(8)
Constraint function is given as follows:
C1 ∶Minimum ∑Dsink
i(r)
C2 ∶Minimum
j
∑
i=1
hopmin(r)
C3 ∶Maximum
n
∑
i=1
E𝑟𝑐 (r)
When node iand sink is minimum distance as shown in Equation (8). Through minimum hops data packets reach to
sink shown in Equation (9). Equation (11) shows that the selected forwarder node max residual energy. Where,
Dtotal(r)=Dtrans +Dproc +Dque ∀r∈rmax.(9)
FIGURE 4 Feasible region for throughput8
10 of 16 HUSSAIN .
FIGURE 5 Feasible region for delay
where Dtrans the transmission delay, Dproc the processing delay, and Dque is the queuing delay which is negligible, and we
take it as zero.
Dtotal =(Packetsize
Datarate ).(10)
Dproc =(Distnodes
Vsound ).(11)
Distnodes are the distance between nodes and Vsound is the sound wave speed.
5.2.1 Graphical analysis
To provide a clear visualization of a feasible region of end-to-end delay, graphical analysis is presented as shown in Figure 5
to compute all possible value within the feasible regions.
0.1387 ≤Dtrans ≤0.555
0.0082 ≤Dprop ≤0.033
0.1469 ≤Dtrans +Dprop ≤0.588.
Each vertex of the feasible regions value is shown as:
P1(0∶1387;0∶0082)=0∶1469s
P2(0∶1387;0∶033)=0∶1717s
P3(0∶555;0∶033)=0∶588s
P4(0∶555;0∶0082)=0∶5632s
HUSSAIN . 11 of 16
Algorithm 2 Data Transmission and Sink Mobility
Step1: Location of boundary node 𝛬
Step 2 Distance covered 𝜉
Step 3 Sojourn time 𝜏
Step 4 Trans threshold time 𝜎
Step 5 Each sensor calculates the distance with all sinks
Step 6 Search forwarder
for i=1:Nodes do
ifasink is within R then
Set sink id as a forwarder
else
Step 8 Set sensor node id as a forwarder
Step 9 procedure: Data Transmission()
while 𝜎not expired do
Transmit data to first forwarder node in the list
if 𝜎expired then
Step 9: Set the second highest NPV node as a forwarder
Repeat step 17 and transmit data to the third-highest NPV node
Continue till 𝜎
if single forwarder exists then
Transmit data to that node till MS is in R
if 𝜏expired then
Step 11 L1 sinks displace upwards and cover distance 𝜉
Step 12 L2 and L3 sinks displace downwards and cover distance 𝜉
Step 13 Repeat steps 26-29 till sinks reach opposite boundaries of layers
if sink arrived at opposite boundaries then
Update their x-axis position accordingly
if senor node position == 𝛬then
Perform multi-path routing
5.3 Performance comparison
We evaluate proposed strategies against AHH-VBF scheme for average Average Propagation Distance (APD), energy con-
sumption, E-to-E delay, and PDR. The result after correlation with AHH-VBF has appeared in Figures 6, 7, 8, and 9
individually.
FIGURE 6 PDR comparisons
12 of 16 HUSSAIN .
FIGURE 7 Energy consumption comparisons
FIGURE 8 Comparison for accumulated
propagation distance
FIGURE 9 End-to-end delay comparisons
5.3.1 Packet delivery ratio
As illustrated in the given Figure 6, the increasing number of nodes effects on SM-AHH-VBF's PDR tends it to the highest
level in contrast with the other existing schemes. The key factor of the increase in PDR is that amount of neighbor nodes
also increases along with the current scenario which results in efficient PDR value. As shown in figure in which the
PDR of the proposed scheme has started from the high value as compared to the other AHH-VBF and AVH-AHH-VBF
schemes. Which also indicates that PDR increases in the proposed scheme ultimately affects in adverse of RTD value in
which the occurrence of void hole decreases. For the specific targeted value of the threshold the PDR increases and then
HUSSAIN . 13 of 16
ultimately decreases because of the collision at the destination. Illustrated in the given Figure 6 there exist 450 number
of nodes for AHH-VBF, AVH-AHH-VBF, and SM-AHH-VBF in which the value of PDR increases drastically and then
decreases dramatically because of the collision at the destination side as the number of nodes increases that directly starts
collision so the result of PDR becomes poor as compared to the initial stage that is high. Though, in the proposed scheme
the PDR is performing better in all scenarios of the increasing number of nodes for both in dense and sparse areas of the
network. The proposed scheme named SM-AHH-VBF has outperformed in all scenarios in increasing number of nodes
compared to AHH-VBF and AVH-AHH-VBF due to the utilization of the approach of sink mobility which is the most
efficient approach for keeping load and balancing. Therefore, the proposed scheme performed improved in both dense
and sparse regions. AVH-AHH-VBF too performed better in contrast with AHH-VBF because it uses the information up to
two numbers of hops so for to avoid the void holes and to minimize the PDR. Hence, the better performing configuration
of all schemes is so far alike for the increasing the number of nodes the current scenario. Table 2 illustrates the comparison
of these schemes for various number of nodes with respect to PDR.
5.3.2 Energy consumption
In Figure 7 all the scenarios show gradient decrease as the numbers of node increases in all scenarios. The reason behind
this is that in the dense deployment of nodes or by increasing the number of nodes increase the chances of the availability
of the neighbor nodes and thus because of less distance less between nodes less energy is consumed as more packets are
sent. While in the scenarios where there are fewer nodes so there is a chance that nodes may be far apart from each other
and so because of the fewer chances of availability of the neighbor's nodes more energy is consumed as nodes may be far
apart from each other.
Figure 4 is clearly showing that AHH-VBF consumes more energy as compared to the AVH-VBF, SM-AHH-VBF, and
AVH-AHH-VBF, the fact is that AHH-VBF before data packet forwarding is not taking up to two hops information, it is
only considering the area toward the target destination, due to this packets are sometimes forwarded to the void hole and
so energy is consumed various times. AVH-AHH-VBF scheme compared to the AHH-VBF consider two-hop information
and so by avoiding the void hole the packet transmission is avoided in this scheme and so give good results as compared
with AHH-VBF. In the entire schemes, SM-AHH-VBF gives the best results because of improved PDR and the results are
showing that for successful transmission to the receiver sink more energy is consumed. Table 3 illustrates the comparison
of these schemes for various number of nodes with respect to energy consumption.
5.3.3 Comparison for accumulated propagation distance
In Figure 8 APD drops with the rise in several nodes. The drop of APD will result in the reduction of the propagation
delay, which in turn drops the E-to-E delay similarly because according to the definition of E-to-E delay the propagation
delay is a part of E-to-E delay. Table 4 shows the detail comparison of the APD for different number of nodes.
TABLE 2 PDR comparisons scheme Nodes AHH −VBF SM −AHH −VBF AVH −AHH −VBF
100 0.06 0.17 0.12
150 0.07 0.2 0.15
200 0.08 0.2 0.15
250 0.085 0.23 0.16
300 0.09 0.24 0.17
350 0.09 0.24 0.17
400 0.095 0.25 0.19
450 0.098 0.26 0.2
500 0.15 0.4 0.3
Average 0.09 0.24 0.17
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Nodes AHH −VBF SM −AHH −VBF AVH −AHH −VBF
100 53 8 40
150 54 840
200 48 7.5 36
250 30 622
300 30 6 22
350 30 622
400 18 4 14
450 18 414
500 14 3.5 11
Average 32 (Joule) 5 (Joule) 24 (Joule)
TABLE 3 Comparisons schemes of energy
consumption
Nodes AVH −AHH −VBF SM −AHH −VBF
100 50% 60%
150 55% 72%
200 42% 64%
250 30% 69%
300 37% 50%
350 33% 55%
400 25% 62%
450 28% 57%
500 16% 66%
Averag e 35% 61%
TABLE 4 APD comparisons schemes
5.3.4 End-to-end delay
Figure 9 shows that all the schemes are showing a decreased E-to-E delay. As we have fewer neighboring nodes
in the sparse region, therefore, E-to-E delay is high in the sparse region and as the number of nodes increases in
dense regions, data communication becomes faster so accordingly E-to-E delay improves. E-to-E delay includes hold-
ing time, transmission delay, propagation delay, and calculation time. APD for AVH-AHH-VBF increases while fetching
Nodes AVH −AHH −VBF SM −AHH −VBF
100 −50% −30%
150 −40% −25%
200 −63% −20%
250 −63% −20%
300 −56% −3%
350 −56% −3%
400 −53% −10%
450 −63% −39%
500 −60% −33%
Averag e −63% −20%
TABLE 5 Comparisons of E2E delay
HUSSAIN . 15 of 16
two-hop information, for making routing decisions. Hence, AVH-AHH-VBF performs the lowest then AHH-VBF and
SM-AHH-VBF. In the SM-AHH-VBF scheme as we have a mobile sink, that causes low holding time which in turn results
in better performance than scheme AVH-AHH-VBF. In the AHH-VBF scheme, the eligible node is selected on the bases
of distance from the virtual vector, this does not consider two-hop information, which in turns causes less time in trans-
mission of a packet and as it also does not have any mechanism for avoiding void region, therefore, it has the minimum
delay. Table 5 shows the detail comparison of AVH-AHHVBF and SM-AHH-VBF in term of E-2-E.
6CONCLUSION
The underwater environments are gaining much attention of the researchers and a hot research area because it has an
impact on our globe. Water and seas are covering most of the earth-space and therefore researchers are focusing on the
underwater sensor network and environment. UWSNs have their limitation and great challenges that is, high bit error, low
bandwidth, high correspondence cost, and high propagation delay. Therefore the topology designed for UWSNs should
be more resilient and durable.
The main problem in UWSNs is the deployment of nodes, it cost too much because of the administration, and planning
cost. For deployment of nodes, one needs a ship to reach the location and also planning is required to deploy node in such
a way to cover and monitor more area and also to void holes for less energy consumption and more throughput. For this
reason, many protocols were tested to gain efficient energy utilization, more throughputs by having low packet drop and
minimize end-to-end delay.
The AHH-VBF is using the concept of adaptive virtual pipeline and is the location-aware routing protocol, in
this scheme data packet forwarding is not taking up to two hops information, since the void hole is not avoided.
In the AVH-AHH-VBF scheme is using two-hop information and so because of this avoiding void hole. That results
in the improvement of lifetime and reduction in consumption energy. Sink Mobility in SM-AHH-VBF scheme. In
AVH-AHH-VBF scheme has a strategy to get the information up to two nodes, it can select a forward node toward its
destination in the transmission range of the source node, to avoid void holes and thus reducing energy consumption. In
SM-AHH-VBF, the strategy of deploying node sparsely so gathering data from fewer nodes reduces the consumption of
less energy and thus increasing the network lifetime.
In this research work, the author considers the three schemes for the increasing network lifetime, avoidance of void
hole, and minimization of consumption of energy first one is AHH-VBF, second one is SM-AHH-VBF, and third one
is AVH -AHH-VBF. The results were obtained and compared based on Avg PDR, Avg APD, and energy consumption.
The simulation results verify the efficiency of the proposed approach AVH-AHH-VBF that is 0.17 and SM-AHH-VBF
that is 0.24 in terms of average PDR, AVH-AHH-VBF is 24j, SM-AHH-VBF is 5j for average energy consumption, the
AVH-AHH-VBF had a tradeoff of 63% because of considering two hops and SM-AHH-VBF 20% tradeoff for average
end-to-end.
CONFLICT OF INTEREST
The authors declare no potential conflict of interest.
AUTHORS CONTRIBUTIONS
All the authors contributed to this research. The order of authors in this manuscript is maintained depending on the level
of contributions they made in this research.
ORCID
Tariq Hussain https://orcid.org/0000-0002-4761-0346
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How to cite this article: Hussain T, Rehman ZU, Iqbal A, Saeed K, Ali I. Two hop verification for avoiding
void hole in underwater wireless sensor network using SM-AHH-VBF and AVH-AHH-VBF routing protocols.
Trans Emerging Tel Tech. 2020;e3992. https://doi.org/10.1002/ett.3992
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