ArticlePDF Available

Void Hole Avoidance for Reliable Data Delivery in IoT Enabled Underwater Wireless Sensor Networks

Authors:

Abstract and Figures

Due to the limited availability of battery power of the acoustic node, an efficient utilization is desired. Additionally, the aquatic environment is harsh; therefore, the battery cannot be replaced, which leaves the network prone to sudden failures. Thus, an efficient node battery dissipation is required to prolong the network lifespan and optimize the available resources. In this paper, we propose four schemes: Adaptive transmission range in WDFAD-Depth-Based Routing (DBR) (A-DBR), Cluster-based WDFAD-DBR (C-DBR), Backward transmission-based WDFAD-DBR (B-DBR) and Collision Avoidance-based WDFAD-DBR (CA-DBR) for Internet of Things-enabled Underwater Wireless Sensor Networks (IoT, UWSNs). A-DBR adaptively adjusts its transmission range to avoid the void node for forwarding data packets at the sink, while C-DBR minimizes end-to-end delay along with energy consumption by making small clusters of nodes gather data. In continuous transmission range adjustment, energy consumption increases exponentially; thus, in B-DBR, a fall back recovery mechanism is used to find an alternative route to deliver the data packet at the destination node with minimal energy dissipation; whereas, CA-DBR uses a fall back mechanism along with the selection of the potential node that has the minimum number of neighbors to minimize collision on the acoustic channel. Simulation results show that our schemes outperform the baseline solution in terms of average packet delivery ratio, energy tax, end-to-end delay and accumulated propagation distance.
Content may be subject to copyright.
sensors
Article
Void Hole Avoidance for Reliable Data Delivery in
IoT Enabled Underwater Wireless Sensor Networks
Arshad Sher 1, Aasma Khan 1, Nadeem Javaid 1,* , Syed Hassan Ahmed 2,
Mohammed Y Aalsalem 3and Wazir Zada Khan 3
1Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan;
arshadsher92@gmail.com (A.S.); aasmakhan749@gmail.com (A.K.)
2Department of Computer Science, Georgia Southern University, Statesboro, GA 30460, USA;
s.h.ahmed@ieee.org
3Farasan Networking Research Laboratory, Department of Computer Science & Information System,
Jazan University, Jazan 82822-6694, Saudi Arabia; aalsalem.m@jazanu.edu.sa (M.Y.A.);
wazirzadakhan@jazanu.edu.sa (W.Z.K.)
*Correspondence: nadeemjavaid@comsats.edu.pk; Tel.: +92-3005792728
Received: 24 July 2018; Accepted: 13 September 2018; Published: 28 September 2018


Abstract:
Due to the limited availability of battery power of the acoustic node, an efficient utilization
is desired. Additionally, the aquatic environment is harsh; therefore, the battery cannot be replaced,
which leaves the network prone to sudden failures. Thus, an efficient node battery dissipation
is required to prolong the network lifespan and optimize the available resources. In this paper,
we propose four schemes: Adaptive transmission range in WDFAD-Depth-Based Routing (DBR)
(A-DBR), Cluster-based WDFAD-DBR (C-DBR), Backward transmission-based WDFAD-DBR (B-DBR)
and Collision Avoidance-based WDFAD-DBR (CA-DBR) for Internet of Things-enabled Underwater
Wireless Sensor Networks (IoT, UWSNs). A-DBR adaptively adjusts its transmission range to avoid
the void node for forwarding data packets at the sink, while C-DBR minimizes end-to-end delay along
with energy consumption by making small clusters of nodes gather data. In continuous transmission
range adjustment, energy consumption increases exponentially; thus, in B-DBR, a fall back recovery
mechanism is used to find an alternative route to deliver the data packet at the destination node with
minimal energy dissipation; whereas, CA-DBR uses a fall back mechanism along with the selection of
the potential node that has the minimum number of neighbors to minimize collision on the acoustic
channel. Simulation results show that our schemes outperform the baseline solution in terms of
average packet delivery ratio, energy tax, end-to-end delay and accumulated propagation distance.
Keywords:
underwater wireless sensor networks; adaptive transmission range; residual energy;
clustering; void hole; collision
1. Introduction
Underwater Wireless Sensor Networks (UWSNs) have attracted both academia and industry to
explore the underwater resources by enabling a variety of aquatic applications. For instance, military
defense, monitoring the aquatic environment, disaster prevention, pollution monitoring, underwater
mineral extraction, etc. [
1
]. The sensor nodes are randomly deployed over a specified geographic
volume with the ability to sense, gather and transmit data towards the destined location (that may be
a single sink or multiple sinks) [2,3].
Data communication in the acoustic medium faces several challenges due to the peculiar features
of the aquatic environment like high propagation delays, high deployment cost, node movement due
to water currents, energy constraints, limited bandwidth, etc. [
4
,
5
]. Various routing protocols are
Sensors 2018,18, 3271; doi:10.3390/s18103271 www.mdpi.com/journal/sensors
Sensors 2018,18, 3271 2 of 25
proposed to enhance the network lifetime with optimal energy consumption and minimize delay from
the source to the destination using direct or multihop data transmission mechanisms [
6
8
]. To deliver
the data to the destination, geographic routing is widely used for both aforesaid data communication
methods depending on the nature of the environment. Geographic routing uses the greedy forwarding
strategy where each node finds the shortest path towards the destination to save its energy. However,
in the greedy forwarding strategy, immutable forwarder node selection is inevitable, which leads
to immature depletion of the node’s battery and creates a void hole [
9
]. The void hole is avoided
using the Adaptive Hop-by-Hop Vector-Based Forwarding (AHH-VBF) routing protocol [
1
]. It uses a
pipeline to restrict the transmission range, and it is adaptively adjusted to amend the forwarding area
to reduce duplicate packets’ transmission.
However, the void occurrence is not avoided in sparse deployment using this algorithm [
9
],
because it reacts once the data packet is trapped and the data communication process is paused.
This may occur because of the variation in the path quality, which is important for energy efficiency in
IoT-enabled WSN. The major factors to be considered in the path to avoid void occurrence are: shortest
distance and lesser number of links to enhance the network lifetime [
10
,
11
]. The IoT-enabled WSN
has the ability to sense, gather and transmit a huge amount of data over a long distance. However,
the limited batteries are the major hurdle in successful network operations. Thus, we need to schedule
the data transmissions in energy constraint networks. Similarly, the network virtualization is another
important aspect to find the path fault successfully in IoT-enabled WSN. The virtualization focuses on
the optimal utilization of sensing resources. Moreover, it also supports diversity in the network and
enables an efficient management of power resources. However, it has a reactive approach in handling
the link failure [
12
]. The IoT-enabled WSN has been helpful in connecting anything, anywhere.
Anywhere means sensors, vehicles, cameras, watches, phones, etc. [
13
]. The vehicles with customized
sensors can allow communication with nearby IoT-enabled WSN. In fact, vehicles are feasible for
communication because they do not have the energy limitation problem. However, our focus is on the
energy constraint in IoT networks in which the device battery needs to be efficiently utilized. Thus,
we need to have a routing algorithm that takes precautionary measures in advance to save data packet
loss and handle the occurrence of a void node efficiently while saving the node’s energy [
14
]. Therefore,
to achieve energy efficiency along with minimum delay in IoT-enabled UWSNs, the need for robust
routing algorithm emerges, which can be adapted according to the available resources.
In this paper, we propose four schemes: Adaptive transmission range-based
WDFAD-Depth-Based Routing (DBR) (A-DBR) and Backward transmission-based WDFAD-DBR
(B-DBR) are proposed to reduce the probability of void hole occurrence. While the A-DBR scheme
adjusts its transmission range to overcome the void hole problem to continue the greedy forwarding
of the data towards the sink, B-DBR exploits a fall back recovery mechanism to find out an
alternative route for delivering data at the destination. Additionally, we propose Cluster-based
WDFAD-DBR (C-DBR) to minimize end-to-end delay and reduce energy consumption. The Collision
Avoidance-based WDFAD-DBR (CA-DBR) handles the collision problem by selecting a potential
forwarder node with the minimum number of neighbors. The main scientific contributions of this
paper are:
Two techniques, A-DBR and B-DBR, are proposed to avoid void holes.
Two techniques, CA-DBR and C-DBR, are proposed to avoid collision and minimize the packet
drop ratio.
C-DBR selects the forwarder node with the maximum residual energy in order to enhance the
lifetime of the network.
The proposed schemes are compared with WDFAD-DBR in terms of average packet delivery
ratio, energy tax, end-to-end delay and accumulative propagation distance.
The rest of this paper is organized as follows: In Section 2, related work on existing schemes in
UWSNs and the problem statement are presented. In Section 3, the background is discussed including
Sensors 2018,18, 3271 3 of 25
the system model, energy consumption and propagation models. Section 4describes the proposed
schemes in detail. Simulation results are presented in Section 5. Performance trade-offs are given in
Section 6, followed by the conclusion in Section 7.
2. Related Work and Problem Statement
The proliferation of sensing devices has enabled real-time monitoring through WSNs.
These devices are low cost and can easily be deployed to gather data from the region of interest.
In this perspective, UWSN has emerged to provide a feasible surveillance system for the rich resource
of the acoustic environment. Therefore, the research community wants to explore underwater resources;
however, an efficient routing algorithm that can provide reliable communication is desired, such as
AHH-VBF, which is used to reduce energy consumption by adjusting the range of the forwarding
vector [
1
]. To reduce energy consumption and avoid void hole occurrence, it dynamically adjusts the
transmission power at each hop along with the vector. However, with the adjustment in transmission
power, the energy dissipation increases.
A GEographic and opportunistic routing with Depth Adjustment-based topology control for
communication Recovery over void regions (GEDAR) is proposed in [
15
]. It uses the greedy routing
strategy to forward packets towards the sink node. Moreover, a priority is assigned to each neighbor
node to avoid redundant transmissions by only allowing the highest priority node to transmit the data.
In case the transmission fails, the node with low priority in the table resumes transmission from an
alternate route. Additionally, this protocol uses a depth adjustment mechanism to provide continuous
communication among the network nodes. However, moving nodes to a new depth causes excessive
energy consumption and high end-to-end delay.
The Hydraulic-pressure-based anyCast (HydroCast) [
16
] algorithm was designed to deliver data
reliably to any sink positioned at the surface of the water. The forwarder node is chosen on the basis
of the packet status and the cost of the link. Through a gauge, the depth information is obtained for
successful data transmissions. This scheme has improved Packet Delivery Ratio (PDR) due to the low
ratio of void node occurrence at the cost of high communication overhead, which causes more energy
depletion of the network nodes.
In [
17
], the authors proposed the Hop-by-Hop Dynamic Addressing-based Routing Protocol
for Pipeline Monitoring (H2-DARP-PM). Dynamic hop addresses are assigned to each hop to enable
efficient forwarder node selection. This scheme assigns dynamic a hop address to every node that
contributes to data forwarding. This scheme improves the PDR; however, the energy consumption is
considerably high.
Delay-sensitive schemes: Advancement of localization-free routing protocols of DBR, EEDBRand
AMCTD [
18
] are presented for time-critical applications. The authors have made these routing
protocols scalable according to the application requirements to achieve minimum end-to-end delay
along with the minimal energy dissipation. However, duplicate packets are forwarded very often
because of the hidden terminal problem. In delay-sensitive EEDBR, the energy consumption is high,
whereas the packet drop ratio is considerably improved in AMCTD.
Free Space Optical (FSO) and Electro Magnetic (EM) wave-based communication schemes [
19
]
have been used to examine an analytical framework to find an optimum range of clusters. Moreover,
the logical results are computed to change the location of the sink to three different points: the center,
corners and midpoint of the network field. This scheme results in less energy consumption at the cost
of high end-to-end delay.
To save energy, sleep-awake scheduling is a widely-accepted mechanism. For instance, in [
20
],
the authors nominated an initiator node after the configuration of network nodes to gather data from
the desired nodes. The communication phase begins with the initialization of the transmission phase.
First of all, a head node is selected at each hop to lead the data packet towards the destination. Only the
head node transmits the data, and nodes in the neighborhood are switched to sleep mode to avoid
the unnecessary dissipation of the node’s battery. This scheme reduced the energy consumption
Sensors 2018,18, 3271 4 of 25
significantly with enhanced lifetime and increased PDR. However, the immutable selection of the head
node results in the sudden death of the node and degrades the network performance.
To collect data at distributed points, clustering is performed because it is scalable and flexible
in nature. The same features motivate the research community to explore this area in more detail.
In [
21
], the network was divided into irregular clusters for making local routing decisions to avoid
high data traffic at the sink node. Moreover, the algorithm forms irregular clusters based on a layered
architecture for event coverage and obtains the expected value of the clusters through theoretical
analysis. Additionally, this scheme uses a recovery strategy to balance the energy consumption among
the clusters to enhance the performance of the network.
A Particle Swarm Optimization-based Energy-efficient Cluster Head Selection (PSO-ECHS) was
proposed in [
22
]. To balance the energy consumption, various control parameters were taken into the
consideration like distance within the clusters and from the sink along with the residual energy of
each node in the cluster. With the help of the aforesaid metrics, a weighting function was formulated,
and probabilistic value was computed to nominate and rotate the head node for efficient energy
consumption. This scheme achieves high PDR at the cost of delay.
In [
23
], three schemes were proposed: Sparsity-Aware Energy-Efficient Clustering (SEEC), Circular
SEEC (CSEEC) and Circular Depth-based SEEC (CDSEEC). In SEEC, two mobile sinks are deployed in
sparse and dense regions to collect information and to reduce the probability of energy hole occurrence;
while a different geometry is considered for CSEEC to analyze the mobility of sinks, which improves
the PDR and maximizes the network lifetime. The same topology is considered for CDSEEC with a
different mobility pattern. The trade-off occurring against energy efficiency and PDR is the highest
end-to-end delay.
Depth-Based Routing (DBR) [
24
] uses a greedy approach to deliver packets towards the sink
based on the depth of a forwarder node. Each eligible source node transmits a packet based on depth
and also calculates the holding time to avoid duplicate packets’ transmission among the network
nodes. However, the consideration of only distance in the selection of the next hop node forces the
immutable nomination of the forwarder node. This leads to sudden death of the intermediate nodes.
Moreover, the holding time is not synchronized, resulting in transmission from the neighbor nodes
before even the acknowledgment arrives. However, DBR benefits from high network lifetime and PDR
at the cost of only end-to-end delay.
An improved Adaptive Mobility of Courier nodes in Threshold-optimized DBR (iAMCTD) [
25
]
is presented to handle flooding, latency and path loss. The routing is performed on demand to
maximize the network lifetime through an optimized mobility pattern of courier nodes, whereas,
the Energy-efficient Channel-Aware Routing Protocol (E-CARP) [
26
] provides improved network
lifetime and reduced energy consumption by the reactive routing approach.
In Adaptive Relay Chain Routing (ARCR) [
27
], the authors introduced mobile sensor nodes to
overcome the energy hole problem. Additionally, clusters were formed for collecting data via mobile
nodes to improve the network performance. This routing mechanism achieved energy efficiency and
maximum lifetime at the cost of low PDR.
The proposed work is different from the discussed related work based on the following
distinguishing features. In order to reduce the probability of void hole occurrence, A-DBR adjusts its
transmission range adaptively. However, the adjustment of transmission power causes extra energy
consumption when the distance increases between the source and destination. Thus, B-DBR looks for
a forwarder in all possible directions within its transmission range to find an alternate path to deliver
data at the destination; while C-DBR and CA-DBR minimize end-to-end delay along with collision by
making clusters in the network. The related work is summarized in Table 1.
Sensors 2018,18, 3271 5 of 25
Table 1.
Summary of UWSN routing schemes discussed in related work. AHH-VBF,
Adaptive Hop-by-Hop Vector-Based Forwarding; HydroCast, Hydraulic-pressure-based anyCast;
DBR, Depth-Based Routing; iAMCTD, improved Adaptive Mobility of Courier nodes in
Threshold-optimized DBR; E-CARP, Energy-efficient Channel-Aware Routing Protocol; ARCR,
Adaptive Relay Chain Routing.
Technique Features Achievements Limitations
AHH-VBF [1]Location-aware routing protocol,
concept of adaptive virtual pipeline
Reduced duplicate packets and
unnecessary energy
consumption is avoided
Void hole problem exists
GEDAR [15]
GEographic and opportunistic
routing with Depth
Adjustment-based topology control
for communication
Void hole avoidance results in
increased performance of the
network
High energy
consumption and high
end-to-end delay
HydroCast [16]
Pressure-based routing protocol and
efficient anycast routing algorithm Improved packet delivery ratio
Low performance and
increased energy
consumption
H2-DARP-PM [
17
]
Hop-by-Hop Dynamic
Addressing-based routing protocol
for Pipeline Monitoring
Improved packet delivery ratio High energy
consumption
Delay-sensitive
schemes [18]
Improved delay-sensitive versions,
adaptable to time-critical
applications
Minimize end-to-end delay and
improve performance and
network lifetime
Duplication of packets
occurs, high energy
consumption and void
hole problem exists
ACH2[28]Free association mechanism where
nodes associate with CHs
Minimizing energy
consumption and enhances
network lifetime
Transmission delay
FSO and EM
wave-based
communication
schemes [19]
Free Space Optical and
electromagnetic wave-based
communication schemes
Reduced energy consumption High end-to-end delay
CBSST [20]Cluster-Based Sleep/wakeup
Scheduling Technique for WSN
Reduced energy consumption,
enhanced network lifetime and
packet delivery ratio
Keeping the same CH
throughout the network
lifetime causes problems
for network lifetime
UCBNL [21]
A high efficiency Uneven Cluster
deployment algorithm Based on
Network Layered for event
coverage in UWSNs
Enhanced packet delivery ratio,
less energy consumption and
improved network lifetime
Irregular clustering
causes alteration in the
network
PSO-ECHS [22]
Energy-efficient CH Selection that is
based on particle swarm
optimization
Energy efficiency achieved Only for homogeneous
networks
EDDEEC [29]Enhanced Developed Distributed
Energy-Efficient Clustering
Shows improved performance in
terms of stability period,
network lifetime and packet
delivery ratio.
Imbalanced clustering
and reelection increases
overhead
Energy-efficient
routing
protocol [23]
SEEC, CSEEC and CDSEEC for
UWSNs Reduced energy consumption Low packet delivery
ratio
DBR [24]
Handles dynamic networks
efficiently, requires only local depth
information and greedy forwarding
Improved network lifetime and
packet delivery ratio
Void holes, increased
energy consumption and
high end-to-end delay
iAMCTD [25]
Location-free routing protocol
specially designed for time-critical
applications
Improved network lifetime,
minimized end-to-end delay
Void holes still exist &
overhead due to control
packets’ exchange
E-CARP [26]
Distributed cross-layer reactive
protocol, important for sensory data
collection and transmission
Improved network lifetime and
reduced energy consumption
Reduced throughput and
high path loss due to
mobility
ARCR [27]
Network is divided into clusters
and mobile nodes used to collect
data from other sensor nodes and
forward them to the sink
Achieves energy efficiency,
maximum network lifetime and
load balancing
Network disconnects
when the relay nodes are
disorganized
Sensors 2018,18, 3271 6 of 25
2.1. Problem Statement
To efficiently utilize the node battery and to reduce the end-to-end delay, the research community
has been devoted to bringing improvement in routing algorithms designed for UWSNs. For instance,
WDFAD-DBR [
4
] considers only two metrics: the depth of current and next expected forwarder node.
Although, the probability of void hole occurrence is reduced and inefficient energy consumption during
nodes communication is minimized, the probability of void hole occurrence still exists, as illustrated in
Figure 1.
W ter su f c
W ter de t
h
Figure 1. Illustration of the void hole problem in WDFAD-DBR.
When the source node
S
initiates communication and finds
S
2 in its communication range,
before transmitting the data packet to
S
2, it acquires information about its neighbor node. It locates
S
3
in its transmission range and delivers the data to
S
2. Thus, it acknowledges the
S
with non-void node
status and receives the data packet. However, when
S
2 looks for its neighbors, it finds
S
3, which has
no further nodes in its transmission range, resulting in loss of the data packet. Thus, this process
will continue until the death of
S
3. Additionally, this scheme is receiver based, where avoidance of
duplicate packets is very difficult. The reason is that neighbors in the hidden terminal region are unable
to receive the acknowledgment, leading to redundant transmissions at the destination. Moreover, it
also leads to channel interference in the case of simultaneous transmissions over the acoustic wireless
channel. Furthermore, it causes collision, leading to a high packet drop ratio and more end-to-end
delay. To overcome the aforementioned problems, we propose four schemes: A-DBR, C-DBR, B-DBR
and CA-DBR, to improve the network performance. The details are given in the following sections.
3. Background
In this section, we discuss the system, energy and propagation models along with the type of
packets used to configure the network. First of all, the system model is presented along with the
assumptions made in the proposed work. Then, the energy and propagation models are briefly
discussed followed by packet types.
Sensors 2018,18, 3271 7 of 25
3.1. System Model
In proposed schemes, the 3D multi-sink network architecture is assumed [
4
], which is composed
of the anchor, relay and sink nodes, as shown in Figure 2. The anchor nodes are fixed at the bottom
of the water. These nodes are used to sense and to gather data, while the relay nodes are placed at
different depths, which receive and forward the data towards the sink by collecting data from the
anchor nodes. The sink node is housed with acoustic and radio modems to communicate with nodes
deployed inside the water and in the terrestrial environment, respectively. Additionally, the burden of
overhead is reduced by forming a cluster. It helps in local data gathering where only the head node
of the cluster transmits data to the sink node. Additionally, it enables the network nodes to reduce
the probability of interference via restricting the number of nodes involved in data transmission over
the wireless acoustic channel. Furthermore, the selection of the Cluster Head (CH) is based on the
residual energy, which allows continuous rotation of the head node. The sink nodes are placed at the
surface of the water to direct data packets to the control center. An assumption is made that each sink
is connected with the others through radio links. Furthermore, if a packet is received at one sink, it is
assumed that data are successfully transmitted to the base station. The underwater settings affect
the consumption of energy and the delay propagation of sound waves. To explain the underwater
communication, Thorp’s propagation model is used [29].
`
W t r sur a e
W te de th
S
h
h1
`
R di l nk
R ut ng pa h CB-WDF D-DBR
Sate l te
Moni o i g c nte
Rela n d s
An hore nodes
Si k n d s
T a s iss on ran e ad us ment
Cl s er H a s
R u ing pa h A R-WDF D-DBR
Figure 2.
Proposed system model illustrating Adaptive transmission range in WDFAD-Depth-Based
Routing (DBR) and Cluster-based WDFAD-DBR (C-DBR).
3.1.1. Energy Consumption Model
Underwater channel attenuation over a distance lcan be demonstrated as [29]:
10logA(l,f) = c.10logl +l.10logα(f). (1)
The first term of this equation represents the spreading loss, and the second term shows the
absorption loss, where
c
is the spreading coefficient, which states the geometry of propagation, i.e.,
c= 1
is cylindrical spreading in shallow water, two is for spherical spreading in deep water and 1.5 depicts
the practical spreading, where, α(f)is the absorption coefficient.
Sensors 2018,18, 3271 8 of 25
In UWSNs, the acoustic signal is affected by different noises, such as turbulence
Nt(f)
, shipping
Ns(f), waves Nw(f)and thermal noise Nth (f)[17,2931]. These noises can be expressed as,
N(f) = Nt(f) + Ns(f) + Nw(f) + Nth (f). (2)
For the acoustic signal, the Signal to Noise Ratio (SNR) with frequency
f
and distance
l
can be
expressed as:
SN R(f,l) = Tp(f)A(l,f)N(f) + Di, (3)
where
Tp(f)
represents the transmission power with frequency
f
.
Di
denotes the directivity index to
evade unnecessary noise. During the reception of an acoustic signal, if
SN R(f
,
l)
becomes greater or
equal to detection threshold Dt, then the received signal is decoded correctly.
3.1.2. Delay Propagation Model
Underwater delay propagation considers temperature, the depth of water and salinity of water,
and it is given as follows [1]:
ν=1448.96 +4.591τ5.304 ×102τ2+2.374 ×102τ3
+1.340(δ35) + 1.63 ×101d+1.675 ×107d2
1.025 ×102τ(δ35)7.139 ×1013τd3.
(4)
Here,
ν
represents the propagation speed of the acoustic signal, which is measured in ms
1
,
τ
represents the temperature,
δ
shows the salinity and
d
denotes the depth of water. The acoustic
propagation speed is directly proportional to the temperature, salinity and depth of the water.
Equation (4) is effective when it fulfills the conditions as: 0 τ30, 30 δ40 and 0 d8000.
3.1.3. Packet Types
In these schemes, there are three types of packets: namely
neighborrequest
,
ack
and
datapacket
.
neighborrequest
consists of three fields: type ID, source node ID and depth. The type ID
denotes the packet type; source ID represents the address of the source node; and depth illustrates the
depth of the source node.
The
ack
packet has three data fields: type ID, source node ID and depth. The type ID represents
the
ack
packet ID; source ID represents the source node ID; and depth is the depth of the source node.
On the other hand,
datapacket
is composed of: type ID, source node ID, destination ID, depth and
PID. The type ID, source node ID and depth represent the same as for the packet types
neighborrequest
and
ack
. The destination ID represents the ID of the destination node, and PID (Packet ID) represents
the order of packets.
neighbortable
consists of the neighbor ID, depth, distance and time stamp. The neighbor ID
indicates the location of the neighboring node. The distance shows the distance from the neighboring
nodes. The time stamp represents the time for updating neighbors entry, and depth is the depth of the
source node.
packetqueue
is generated to keep the record of all the sent and received data packets in order to
restrain the replication of data packet transmission and for saving energy. The
Packetqueue
consists of
the fields of source ID, PID and flag. The source ID is the ID of the source node; PID is the Packet ID;
and flag indicates whether the data packet has been sent or not.
3.1.4. Explanation of Algorithm 1
Algorithm 1describes the packet forwarding mechanism in general. First, node
i
receives a
packet from node
j
. Then, it calculates the previous and current depths of the node. After calculating
the depths of both nodes, node
i
calculates the distance difference of node
i
and the previous node
Sensors 2018,18, 3271 9 of 25
corresponding to the Received Signal Strength Indicator (RSSI) of the Received Packet RSSI (RP-RSSI)
and the RSSI of Signals at Senders (SS-RSSI). In these schemes, SS-RSSI is known to all nodes in
the network. Therefore, every node in the network can calculate the distance between two nodes
corresponding to Thorp’s propagation model. Next, the Relative Coordinate (RC) is calculated,
corresponding to the depth difference between them.
Algorithm 1 Algorithm for forwarding data packets
Node ireceives data_packet from node j
Obtain prevnode _depth and currnode _de pth
Calculate distance relative to di f f (SS_RSSI,RP_RSSI )
Calculate RC(sender,receiver)corresponding to distance and di f f (prevnode _de pth,currnode_de pth)
SWITCH (packettype)
CASE 1:
NeighborRequest
if node iis the preferable forwarder node of node jthen
send ack
end if
Hold for next data packet
END CASE
CASE 2:
Ack
if node jis the preferable forwarder node of node ithen
up-to-date entry neighbor_tablemaking use of item (prevno de_depth,distance,tcurrent)
end if
END CASE
CASE 3:
DataPacket
Move to the next step
END CASE
END SWITCH
if selected node iis not the preferable forwarder node of data_packet then
Upgrade neighbor_table using item (prevnode_depth,tcurrent,distance)
Drop data_packet
end if
if node iis the preferable forwarder node of data_packet then
Obtain source ID, packet ID from data_packet
if (source ID, packet ID) within the queue then
Drop data_packet
end if
if the node is within the forwarding area then
Move to the next step
else
Hold for the next data_packet
end if
end if
Find the next depthmin in neighbor_table
if neighbor_table is empty then
Drop data_packet
end if
Upgrade the depth in data_packet with currentnode _depth
Add (source ID, packet ID) into the queue
Sensors 2018,18, 3271 10 of 25
The type of forwarded or received packets is checked accordingly. When a node receives the data
packet, it manages the data packet as follows:
If the node is not within the effective forwarding range of the preceding hop, it solely updates
its neighbor table. In the other case, it enqueues information if it finds no report regarding the
packet in the packet queue.
If the node is not within the forwarding area, it will wait for the subsequent packet; this means
that the packet is inside the suppression area. In any other case, the node searches for neighboring
nodes in the neighbor table.
If the neighbor table is empty, it will directly drop the data packet, as there are no nodes in the
forwarding area. Thus, void holes can be prevented earlier.
4. Proposed Schemes
In this section, we describe the proposed schemes in detail. Every scheme has the same perquisites
for configuring the network nodes. The forwarding mechanism of each scheme has been discussed
as follows:
4.1. A-DBR
In this section, the proposed scheme is discussed in detail. We have proposed the A-DBR scheme
to cater to the problems of the void hole discussed in Section 2.1. In this scheme, when a void hole
occurs, it adaptively adjusts its transmission range and forwards the data packet towards the sink
node, as shown in Figure 2. With a view to lessen neighborhood of requests, every node collects the
records on neighbor nodes upon receiving packets:
neighborrequests
or
ack
. In this way, every node can
reap newer statistics about neighbor nodes dynamically. As shown in Figure 2, in the A-DBR scheme,
when node
S
senses data within its vicinity, it gathers the data packet and forwards it to nodes
n
1 and
n
2. Moreover, when a void node occurs, this scheme adjusts its transmission range as illustrated in
Figure 2to find a forwarder node and continues forwarding of information without dropping the data
packet. In this scheme, when a node receives a packet, it handles the data packet as follows:
If the node is not within the effective forwarding range of the preceding hop, it solely updates its
neighbor table. In any other case, it enqueues information if it finds no report about the packets
in queue.
If the node is not within the forwarding area, it will wait for the subsequent packet, this means
that the packet is inside the suppression area. In any other case, the node searches for nodes in
the neighbor table.
If the table is empty, instead of dropping a packet, the source node adjusts its transmission range
and updates the neighbor table to avoid the void hole.
After updating the neighbor table, it is going to send the packet if no different transmission of the
data packet is heard towards the destination. It then updates the packet queue.
4.2. C-DBR
In C-DBR, we have formed clusters to restrict the access of the wireless channel by network nodes
to avoid collisions. To select the CH, a node with maximum residual energy is nominated to avoid the
immutable CH selection, which leads to high network lifetime. Additionally, it is assumed that the
sink node has the knowledge of all the sensor locations. Further, the CHs are found by the source node
based on the maximum residual energy. In C-DBR, node
S
forwards the sensed information towards
the CH, which aggregates the neighbor packets to transmit the packet towards the immediate cluster’s
CH near the destination. This process continues until it reaches the sink node, as shown in Figure 3.
Below are the steps for selecting the CHs of C-DBR:
Sensors 2018,18, 3271 11 of 25
Figure 3. Illustration of cluster formation in C-DBR.
In this scheme, when a node receives a packet, it manages the data packet as follows:
If the node is not within the effective forwarding range of a preceding hop, it solely updates its
neighbor table without forwarding the packet. In any other case, it enqueues information if there
is no report about the packet in the packet queue.
If the node is not within the forwarding area, it will wait for the subsequent data packet; this means
that the packet is inside the suppression area. Otherwise, the node searches for neighboring nodes
in the neighbor table.
Initially, there are no clusters in the network.
The network is then divided into clusters using the k-means clustering approach.
The source node broadcasts the message in the cluster.
The sensor node then compares its own energy with the source node energy.
If the sensor node energy is greater than the source node energy, then the sensor node sends a
reply message; else, the source node waits for another reply from the neighbor node that has the
maximum residual energy.
Once the CHs are selected, clusters are formed using the neighbor nodes within the
communication range.
CH then broadcasts the message to the member nodes along with its ID to receive the data packets.
CHs aggregates data to transmit a single data packet towards the sink directly or using the
multi-hop forwarding approach.
The neighbor table and packet queue are updated repeatedly till the death of all the network nodes.
4.3. B-DBR
In this section, we describe the B-DBR routing protocol, which finds the set of forwarders at each
hop using the greedy opportunistic forwarding mechanism. Additionally, it uses a fall back mechanism
to find an alternative route to deliver the data in the case of the void hole region. On the right side
of Figure 4of B-DBR, the fall back approach is illustrated. When node
S
looks up two-hop neighbor
information, there is the possibility of encountering a void node. In this case, B-DBR uses backward
transmission from node
n
3 instead of depth adjustment, which consumes high energy. The node
n
3
forwards the data packet instead of dropping to node
n
4, which looks for its neighbors in the direction
of the destination and finds nodes
n
5 and
n
6. Thus, greedy forwarding again is resumed till the time
the packet reaches its destination.
Sensors 2018,18, 3271 12 of 25
`
W t r su f c
W t r d pt
n6
7
n
h
h1 `
R d o l nk
Routi g p th
1 h p b ck a d t a s i si n
S t ll t
M n t r ng c nt r
R l y n d s
A c o ed no es
Si k no e
S1
R ut ng p th w t c l is on
Figure 4. Proposed system model illustrating B-DBR and Collision Avoidance-based (CA)-DBR.
4.3.1. Explanation of Algorithm 2
The main steps of the B-DBR protocol are represented in Algorithm 2. If a node is in the fall
back recovery mechanism, a new data packet will be queued until the selected node in the backward
direction has neighbors in the direction of the destination to resume the greedy forwarding mechanism.
If it finds the forwarder nodes greater than zero, the data packet is forwarded. However, when the
forwarder is not available, instead of dropping the data packet, it finds an alternate route to reschedule
the data transmission. This process repeats itself until the death of all the nodes in the network.
Algorithm 2 Main steps of the B-DBR scheme
if void node or known sinks = 0 then
Queue the data packets
Re-schedule forward data packet()
else
figet_next_ho p_f orw arder(n)
if |fi|>0then
Forward the data packet
else
Queue the data packet
Re-schedule f orw ard_data_packet()
Proposed_mechanism()
end if
end if
4.4. CA-DBR
In this section, we describe the CA-DBR routing protocol, which also finds the set of next hop
forwarders using the greedy opportunistic forwarding mechanism. CA-DBR selects those nodes that
have the minimum number of neighbor nodes to avoid the collision, as shown on the left side of
Figure 4. In this scheme, when a node receives a packet, it manages the data packet as follows:
Sensors 2018,18, 3271 13 of 25
If the node is not within the effective forwarding range of the preceding hop, it does not forward
the data packet, and it solely updates its neighbor table. Moreover, it enqueues information if the
packet has not been transmitted already.
Whereas, if the node is not within the transmission range of the forwarding node, then it will
wait for the subsequent data packet. This means that the packet is inside the suppression area.
This will reduce the collision and interference on the acoustic channel.
The fall back and nomination of forwarder node mechanisms are used together for minimal
energy consumption and a high packet delivery ratio.
After updating the neighbor table, if no different transmission of the packet is heard towards the
destination, it forwards the packet towards the sink node.
5. Linear Programming-Based Mathematical Formulation
Linear programming is a mathematical technique that is used to achieve optimal results.
To achieve optimal results, an objective function needs to satisfy the defined constraints. The feasible
regions are computed for minimal energy dissipation and high network throughput.
5.1. Feasible Region Energy Minimization
For minimizing the energy consumption, we have defined the objective function in Equation (5).
MinimumΣrmax
r=1Econsumption (r)rrma x. (5)
The linear constraints for energy minimization are given in Equations (5a)–(5c).
Etrans,Ercv Ere (5a)
Etrans,Ercv Einit (5b)
Trn Trmax. (5c)
Equation (5a) shows restriction on transmission and receiving energy, which must not exceed
the residual energy
Ere
of the node; while transmission and receiving energy are restricted through
Equation (5b) using initial energy
Einit
of the node. Equation (5c) shows the restriction that for receiving
a good quality signal, the sensed information should be transmitted within its given transmission
range; where
Trn
is the transmission range of the node and
Trmax
is the maximum transmission range
of the node; whereas, Econsumption is the total energy consumed in data communication, i.e.,
Σrmax
r=1Econsumption (r) = Etr ans +Ercv rrm ax . (6)
where,
Etrans =Ptrans Packet_size
Data_rate , (7)
Etrans is the transmission energy, and Ptrans is the transmission power.
Ercv =Prcv Packet_size
Data_rate . (8)
Ercv is the receiving energy, and Prcv is the receiving power.
5.1.1. Graphical Analysis
For clear visualization of the objective function of energy minimization, graphical analysis
is presented to compute all the values within the feasible region. Assuming
Packetsize =888 bits
,
Sensors 2018,18, 3271 14 of 25
Datarate = 16,000 bps
,
Ptrans ={
12.5, 25,
. . .
, 50
}
W and
Prcv ={
0.0395, 0.079,
. . .
, 0.158
}
W, the feasible
solution for energy minimization is computed as:
0.693 Etx 2.775 (9)
0.002 Erx 0.0087 (10)
0.695 Etx +Erx 2.7837 (11)
The feasible region of energy minimization is shown in Figure 5using points extracted from
Equations (9)–(11), and the points on the boundary of this feasible region are:
P1(0.693, 0.002)=0.695 J
P2(0.693, 0.0087)=0.7017 J
P3(2.775, 0.0087)=2.7837 J
P4(2.775, 0.002)=2.777 J
Hence, selecting any value from these points results in minimum energy consumption in the
network during communication.
0123456
Etrans (J)
0
0.2
0.4
0.6
0.8
1
1.2
Ercv (J)
P3(2.775,0.0087)
P1(0.693,0.002)
P2(0.693; 0.0087) Etrans+Ercv = 2.7837
P4(2.775,0.002)
Figure 5. Feasible region: energy minimization.
5.2. Throughput Maximization
To maximize the throughput, we take the objective function along with its linear constraints to
get the optimal results. The mathematical formulation is shown in Equation (12).
MaximumΣrma x
r=1Thr(r)rrmax . (12)
Constraints of the objective function are given in (12a)–(13).
C1:Etx,Ercv Ei(12a)
C2:Etx Ere (12b)
C3:TXnT Xmax (12c)
C4:Dij Dma x
ij (12d)
C5:MinimumΣrmax
r=1Br
Frw . (12e)
Sensors 2018,18, 3271 15 of 25
Equation (12a) ensures that the energy required for transmission and reception should be less
than the initial energy
Ei
of the node. Equation (12b) shows the constraint that transmission energy
Etx
ought to be less than the residual energy
Ere
. Equation (12c) ensures that in order to receive a good
quality signal, the data packet ought to be transmitted within its maximum transmission range
TXmax
;
where
TXn
is the transmission range of the node and
TXmax
is the maximum transmission range of the
node. Equation (12d) maintains a threshold of distance between sender
i
and receiver
j
for successful
communication. Equation (13) shows the restriction that the load on forwarder nodes and nodes that
have less residual energy ought to be minimum; where BFrw is the bandwidth of forwarder nodes.
5.2.1. Graphical Analysis
Assuming a scenario where total bandwidth is between 2000 kHz and 4000 kHz, where
BFrw
shows
the bandwidth allocated to the forwarding nodes with high residual energy and
BNFrw
is the bandwidth
assigned to non-forwarding nodes, the bandwidth
B
allocated to
BFrw
and
BNFrw
is computed as
follows using the aforementioned constraints in Equations (12a)–(12d):
200 BFrw 1000 (13)
2000 BNFrw 3000 (14)
2200 BFrw +EN Frw 4000 (15)
The feasible region is plotted in Figure 6, and points are extracted from Equations (13)–(15).
P1(200, 2000)=2200 kHz
P2(1000, 2000)=3000 kHz
P3(200, 3000)=3200 kHz
Thus, selecting any value from these points results in maximum network throughput.
0 500 1000 1500 2000 2500 3000 3500 4000
BFrw (KHz)
0
500
1000
1500
2000
2500
3000
3500
4000
BNFrw (KHz)
P1(200, 2000)
P2(1000, 2000)
P3(200, 3000)
BFrw+BNFrw = 4000
Figure 6. Feasible region: throughput maximization.
6. Simulation Results
In this section, we evaluate the performance of the proposed schemes A-DBR, C-DBR, CA-DBR
and B-DBR against the existing schemes: WDFAD-DBR [
2
] and Reliable and Energy-efficient
Pressure-Based Routing (RE-PBR) [10].
Sensors 2018,18, 3271 16 of 25
6.1. Simulation Setup
In the simulations, we have used multi-sink architecture of dimensions 10
×
10
×
10 km
3
.
The sensor nodes are randomly deployed in the given network field. The transmission range of a
node is 2 km; the packet size is kept at 72 bytes; and the data rate is 16 kbps [
4
]. Each node starts
with the initial energy of 100 J; where the consumption rate is 50 W during the transmission of data
and 158 mW in receiving the packet. The node number varies from 100–500 during the execution of
network operations. In order to handle the mobility of nodes, we have considered node speed in the
horizontal direction 2 m/s. Additionally, the speed of the acoustic signal is 1500 m/s along with the
bandwidth of 4 kHz. In a multi-sink architecture, we have deployed nine sinks at the surface of the
water, which are housed with both acoustic and radio modems. The header size of the data packet
is 11 bytes, and the ACKpacket is 50 bits. The simulations are conducted in Aqua-Sim (NS-2-based
underwater sensor network simulator) to evaluate the performance of the proposed schemes against
the baseline schemes. The aforementioned parameters are taken from [4] and are listed in Table 2.
Table 2. Simulation parameters.
Parameter Value
Nodes 100–500
Sinks 9
Network Dimensions (km3) 10 ×10 ×10
Movement Speed of Nodes (m/s) 2
Acoustic Propagation Speed (m/s) 1500
Initial Energy (J) 100
Transmission Range (km) 2
Transmission Power (dB reµPa) 90
Total Bandwidth (kHz) 4
Sending Energy (W) 50
Receiving or Idle Energy (mW) 158
Header Size (bytes) 11
Payload (bytes) 72
Data Rate (kbps) 16
Size of ACK (bits) 50
6.2. Metrics
The objective of performing simulations is to evaluate the performance of our proposed schemes
in terms of average Packet Delivery Ratio (PDR), energy tax, end-to-end delay and Accumulative
Propagation Distance (APD). These metrics are defined as:
Average PDR: It is defined as the total amount of data packets successfully received at the
sink node to the total number of packets generated by the network nodes. It is calculated as:
PDR =PacketsReceived
PacketsTransmitted .
Average energy tax: It is defined as the average energy consumption per node when a data
packet is sent successfully to the sink. It is measured in joules (J). It is computed according to
Equation (6).
Average end-to-end delay: It is defined as the average time to transmit data from the source to
the destination successfully. It is measured in seconds (s). The expression to find out complete
path delay is:
hmax
h=1
D(hi,hj)
V
.
h
is the hop count of nodes
i
and
j
. Vshows the speed of the acoustic
signal, and D(hi,hj)is the distance between node iand j.
Average APD: It is the average accumulated propagation distance of all the data packets that
are successfully sent to the sink nodes. It is measured in kilometers (km). The mathematical
formulation is as follows:
j
i
D(i,j).
Sensors 2018,18, 3271 17 of 25
Network lifetime: It is the time period for which the network remained operational. It is measured
in seconds (s). The mathematical expression is:
tmax
t=1
NL(t)
, where
NL
shows the network lifetime
in unit time (t).
Packet drop ratio: It is defined as the ratio of the number of packets transmitted, however
not delivered successfully at the destination node. It is formulated as 1 PDR.
Alive nodes: It is the total number of nodes still alive after the termination of network operations.
The mathematical expression is
AN =ND N
, where
AN
is the number of alive nodes,
DN
denotes the quantity of dead nodes after complete battery depletion and
N
depicts the
total number of nodes deployed in the network.
6.3. Performance Comparison
For performance evaluation, simulations are executed by comparing our proposed schemes with
the WDFAD-DBR protocol. We evaluate our proposed schemes against WDFAD-DBR in terms of
average PDR, energy tax, end-to-end delay and APD. The simulation results after comparison with
WDFAD-DBR are shown in Figures 710.
100 150 200 250 300 350 400 450 500
Number of Nodes
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Energy consumption (J)
RE-PBR
WDFAD-DBR
B-DBR
C-DBR
A-DBR
CA-DBR
Figure 7.
Comparison of energy tax. RE-PBR, Reliable and Energy-efficient Pressure-Based Routing;
B-DBR, Backward transmission-based WDFAD-DBR.
100 150 200 250 300 350 400 450 500
Number of Nodes
6.5
6.6
6.7
6.8
6.9
7
7.1
End-to-end Delay (sec)
RE-PBR
WDFAD-DBR
B-DBR
C-DBR
A-DBR
CA-DBR
Figure 8. Comparison of end-to-end delay.
Sensors 2018,18, 3271 18 of 25
100 150 200 250 300 350 400 450 500
Number of Nodes
0.6
0.65
0.7
0.75
0.8
0.85
0.9
0.95
1
PDR
RE-PBR
WDFAD-DBR
B-DBR
C-DBR
A-DBR
CA-DBR
Figure 9. Comparison of Packet Delivery Ratio (PDR).
100 150 200 250 300 350 400 450 500
Number of Nodes
4
4.5
5
5.5
6
6.5
7
7.5
8
8.5
APD (km)
RE-PBR
WDFAD-DBR
B-DBR
C-DBR
A-DBR
CA-DBR
Figure 10. Comparison of Accumulative Propagation Distance (APD).
6.3.1. Energy Tax
Figure 7shows the energy tax of the baseline and proposed schemes. It clearly shows that
as the density of nodes increases, it also increase the energy tax. Moreover, the high density of
nodes also leads to more probability of data packet collision. This collision causes an increase in the
packet drop ratio, which results in high energy consumption. The results in Figure 7show that our
schemes outperforms WDFAD-DBR in terms of energy tax. The proposed scheme A-DBR adjusts its
transmission range adaptively when it finds no node in its range and continues to forward the data
packet without any loss. In the proposed scheme C-DBR, the approach of clustering minimizes the
transmission distance, directly affecting the energy consumption. In CA-DBR, fall back along with
nomination of the forwarder node that has minimum number of neighbor nodes are selected. This also
reduces the probability of packet loss and energy consumption.
The existing scheme WDFAD-DBR has high energy consumption due to high packet loss.
Moreover, RE-PBR shows higher battery dissipation due to the longer routing path from the source to
the destination in search of a high quality link. Initially, it has a greater dissipation rate compared to
Sensors 2018,18, 3271 19 of 25
WDFAD-DBR when the node density is 100–150 because it is difficult to find a high link quality node.
Furthermore, the battery utilization is almost the same at a 200-node density with B-DBR, but the more
nodes get deployed in the network, the better the proposed scheme performs. This is only because
when the node density increases, the number of backward transmissions becomes very less, thus the
consumption rate decreases, as well.
The proposed scheme B-DBR uses one-hop backward transmission whenever it finds a void node.
This scheme forwards the data packet towards the node that is located at a higher depth, and it has
forwarders to forward the data packets towards the sink node. In a dense region, all protocols show
almost similar behavior in terms of energy tax.
6.3.2. End-to-End Delay
Figure 8shows the end-to-end delay of the proposed schemes and the existing WDFAD-DBR
scheme. A-DBR overcomes the void hole problem by adjusting its transmission range, which results in
minimum packet drop and reduced end-to-end delay. In the proposed scheme C-DBR, the network is
split into clusters to minimize the transmission distance, which helps with reducing the end-to-end
delay. The CHs aggregate the sensed information and forward it towards the sink. The proposed
scheme CA-DBR avoids collision, which reduces the end-to-end delay. The proposed scheme B-DBR
uses backward transmission when it finds a void hole to continue data forwarding.
WDFAD-DBR has high end-to-end delay because it uses holding time, which increases its
end-to-end delay. The proposed schemes outperform WDFAD-DBR, as void hole probability still exists
in this protocol. The packet drop is due to void hole occurrence, which results in increased end-to-end
delay in WDFAD-DBR.
End-to-end delay of of RE-PBR is moderate as compared to the proposed schemes B-DBR
and C-DBR, whereas almost the same with A-DBR. This is because the aforesaid techniques use
backtracking and cluster-based approaches, which take time to process the route from source to
destination, while the later proposed scheme has the mechanism of transmission power adjustment
instead of alternate route, which enables it to reduce delay.
6.3.3. PDR
Figure 9shows the PDR of the existing scheme WDFAD-DBR and our proposed schemes. In all
schemes, PDR is increasing with the increase in node density. The reason for the increase in PDR is
that void hole probability decreases with the increase in the density of nodes. The reason for less PDR
of WDFAD-DBR is that this scheme only considers data transmission up to two hops, which does
not eliminate the void hole occurrence. In WDFAD-DBR after two hops, a void hole may occur that
will result in packet drop, which decreases the PDR. The PDR of WDFAD-DBR is slightly better than
the RE-PBR because of better neighbor selection and holding time to avoid redundant transmissions,
which lead to a better network lifetime and improved PDR than RE-PBR; whereas, RE-PBR is effective
when the node density is 300–400; after that, its PDR again decreased because of high collision and
higher packet drop rate.
In the proposed scheme A-DBR, if a void hole occurs after a two-hop transmission, then it
adaptively adjusts its transmission range to find the forwarding neighbors and forward the packet
towards the sink. In the second proposed scheme C-DBR, the network is divided into to clusters to
further enhance the PDR and increase the network lifetime. Each cluster head is selected on the basis
of residual energy, and cluster heads then communicate with one another to transmit the packets and
adaptively adjust the transmission range, as well. In the proposed scheme CA-DBR, there are less
packet drops, which results in increased PDR. In the proposed scheme B-DBR, instead of dropping
the packet in the case of the void region, it uses backward transmission and forwards the data packet
towards the base station. In WDFAD-DBR, it considers the current depth of the node and its expected
next neighbor node depth; however, considering depth up to two hops is not the solution for void hole
avoidance; thus, the PDR of WDFAD-DBR decreases.
Sensors 2018,18, 3271 20 of 25
6.3.4. APD
Figure 10 shows that the APD of the existing scheme WDFAD-DBR is better than the proposed
schemes, as it selects forwarders on the basis of depth of nodes. WDFAD-DBR has more APD, as it
picks up the node with the minimum depth difference, and high priority is assigned through the
calculated difference. If the forwarder node has neighbors, only then does it select it to relay the data.
Therefore, a longer path is opted for, which is evident from Figure 10. On the other hand, the higher
APD of RE-PBR is because of link quality, which leads to a longer data path, although the reliability of
data delivery is high in RE-PBR as compared to the proposed and baseline schemes.
All the proposed schemes have increased APD as they avoid the shortest route to the sink
to avoid collision and void holes. C-DBR performs better than A-DBR due to clustering in the
network. Additionally, the transmission distance decreases, which ultimately increases the APD of
the proposed scheme C-DBR. A-DBR adjusts the transmission range to reduce the void hole problem.
Thus, the packet drop ratio decreases, which ultimately results in increased APD. The proposed
schemes B-DBR and CA-DBR perform better than other algorithms as they are also selecting forwarders
on the basis of depth along with backward transmission and collision avoidance.
6.3.5. Alive Nodes
Figure 11 depicts the number of nodes alive in the network after a certain time period. At 20 s,
the alive node number is different in each scheme, but remains almost within 470–500. However,
the decrease is gradual in each scheme after a certain time period, which is obvious because of more
and more data transmissions among the network nodes. WDFAD-DBR has a low number of alive
nodes as it sends data to the neighbor node, which has further at least one more neighbor node. Thus,
the immutable forwarder nomination leads to quick death of the node and creates a partition in the
network; while RE-PBR has very strict criteria for neighbor selection based on the link quality, which
also causes repeated selection and leads to void node creation. This void node creation is handled via
A-DBR. CA-DBR, C-DBR and B-DBR all have almost the same alive node number.
20 30 40 50 60 70 80 90 100
Time(s)
300
320
340
360
380
400
420
440
460
480
500
Number of Alive Nodes
RE-PBR
WDFAD-DBR
B-DBR
C-DBR
A-DBR
CA-DBR
Figure 11. Comparison of alive nodes.
6.3.6. Network Lifetime
Network lifetime is shown in Figure 12, which depends on the utilization of the node battery;
if the dissipation of the node’s available power is efficient, then it will perform the network operation
for a longer time. B-DBR leads in the lifetime plot compared to all the schemes. This scheme has a fall
back recovery mechanism to ensure the data reach the destination. However, it does not find the data
Sensors 2018,18, 3271 21 of 25
route all the time and drops the data packet; that is the reason it has a greater drop ratio than other
proposed schemes, as given in Figure 13.
The WDFAD-DBR and RE-PBR have a greater drop ratio, but still, the lifetime is shorter, because,
in-spite of dropping packets, the same node is elected again and again, until its death. The immutable
destination node selection is due to the assignment of weight to forwarder nodes based on the link
and depth difference in RE-PBR and WDFAD-DBR, respectively.
On the other hand, the other proposed schemes (A-DBR, CA-DBR and C-DBR) show almost the
same behavior throughout the network lifespan. A-DBR has lower performance due to the adjustment
of the transmission range, which consumes more battery and leads to a shorter network lifetime;
while CA-DBR picks a collision-free path, which means a route with good quality gets selected time
and again, leading to the sudden death of the intermediate nodes and creating a void hole. Thus,
it reduces the network lifetime. On the other hand, C-DBR creates clusters to gather data packets
locally and transmits a composite packet towards the destination. Thus, the repeated selection of a
high energy node leads to the quick depletion of the node’s battery.
100 150 200 250 300 350 400 450 500
Number of Nodes
700
720
740
760
780
800
820
840
860
880
900
Network Lifetime (sec)
RE-PBR
WDFAD-DBR
B-DBR
C-DBR
A-DBR
CA-DBR
Figure 12. Comparison of network lifetime.
6.3.7. Packet Drop Ratio
Figure 13 illustrates the number of packets dropped in the network. RE-PBR has a greater packet
drop ratio when the node number is 100 because it explicitly chooses three metrics (energy, depth
and link quality) to ensure a high quality signal reaches the destination. Although it ensures reliable
data delivery, if the link quality is not good, it drops the data packet. This is the reason it has a high
drop ratio.
WDFAD-DBR has a lower drop ratio as compared to RE-PBR because it looks up two hops
and assigns priority to each neighbor node. Although it has a lower drop ratio, it is higher than all
proposed schemes.
The proposed schemes show variation in the results with respect to each other and have better
performance than RE-PBR and WDFAD-DBR. The CA-DBR has the minimum drop rate compared to
the baseline and other proposed schemes. The reason is the consideration of collision on the wireless
channel. It chooses the link that has the minimum number of nodes surrounding the source node.
This parameter helps with delivering the data successfully at the destination. Whereas C-DBR and
A-DBR have a similar pattern of drop ratio, A-DBR adjusts the transmission range to bypass the hot
spot problem; although, sometimes, it depletes energy suddenly in transmission adjustment; while
C-DBR is a cluster based scheme and transmits the composite data packet, which means, if a composite
Sensors 2018,18, 3271 22 of 25
packet of 10 packets is dropped, it would be considered as one. The B-DBR uses a backtrack process to
find an alternate path instead of dropping the data packet; therefore, it has a lower drop ratio than
RE-PBR and WDFAD-DBR.
100 150 200 250 300 350 400 450 500
Number of Nodes
0
0.05
0.1
0.15
0.2
0.25
0.3
Packet Drop Ratio
RE-PBR
WDFAD-DBR
B-DBR
C-DBR
A-DBR
CA-DBR
Figure 13. Comparison of the packet drop ratio.
7. Performance Trade-Offs
In this section, we review the performance of our proposed schemes A-DBR, C-DBR, CA-DBR and
B-DBR with the existing base scheme WDFAD-DBR. A-DBR reduces void nodes by adaptively adjusting
the transmission range and achieves reduced energy consumption in dense regions; while WDFAD-DBR
makes a routing decision based on the weighting sum of depth difference up to two nodes. Therefore,
the possibility of confronting a void hole still exists, which results in packet loss and high end-to-end
delay. C-DBR has reduced end-to-end delay at the cost of high energy consumption as compared to
A-DBR due to clustering. CA-DBR experienced less consumption of energy and low end-to-end delay at
the cost of high APD. B-DBR is able to minimize the void node probability, which results in high PDR
and reduced end-to-end delay at the cost of high APD. Other proposed schemes have increased APD
as they avoid the shortest route to the sink in order to avoid collision and void holes. The summary of
simulation results is presented in Table 3and the trade-offs are shown in Table 4.
Table 3. Summarized simulation results.
Parameters RE-PBR WDFAD-DBR B-DBR A-DBR C-DBR CA-DBR
Network lifetime 88% 87% 89% 85% 88.4% 90%
End-to-end delay 15% 09% 10% 13.6% 14.6% 14.8%
APD 22% 30% 35.8% 21% 27% 26%
PDR 90% 91.5% 93% 94.2% 95% 96%
Alive nodes 60% 60% 62% 64% 63.3% 64%
Packet drop ratio 10% 8.5% 7% 5.8% 5% 4%
Energy consumption 38% 39% 24% 21% 26% 22%
Sensors 2018,18, 3271 23 of 25
Table 4. Performance trade-offs.
Schemes Features Achieved Parameters Trade-Offs
WDFAD-DBR [4]Routing based on depth and
energy
Less packet drops and improved
network lifetime, decreased
APD
High energy consumption and
high end-to-end delay
RE-PBR [10]Routing based on link
quality, depth and energy
Improved network lifetime and
low delay
High packet drop ratio and
more APD
A-DBR
Routing based on depth and
energy along with
transmission range
adjustment
Void hole avoidance results in
increased performance of the
network and reduced energy
consumption
High energy consumption in
sparse regions and increased
APD
C-DBR Routing based on depth and
energy along with clustering
Improved PDR, low end-to-end
delay and APD
Increased energy consumption
due to clustering compared to
A-DBR
CA-DBR
Routing based on depth and
energy with collision
avoidance
Reduced energy consumption
and low delay Increased APD
B-DBR
Routing based on depth and
energy with tracking
features
High PDR and reduced delay Increased APD
8. Conclusions
In this paper, we proposed routing protocols that are energy efficient, reliable and show improved
network lifetime. The first scheme A-DBR reduced the void nodes’ occurrence by adaptively adjusting
the transmission range. Additionally, it achieved minimum energy consumption by avoiding data loss
in the network. In the second scheme C-DBR, end-to-end delay is reduced at the cost of high energy
consumption. The third scheme B-DBR minimized the void node occurrence probability along with
a high packet delivery ratio. However, the cost of high accumulative propagation distance is paid.
The fourth scheme CA-DBR experienced less energy consumption along with low end-to-end delay.
Simulation results show the effectiveness of the proposed schemes in terms of average packet delivery
ratio, average energy tax and average end-to-end delay.
Author Contributions:
A.S., A.K. and N.J. proposed and implemented the novel schemes; S.H.A., M.Y.A. and
W.Z.K. completed the mathematical modelling; All authors together refined the manuscript; Finally, A.S. and N.J.
responded to the queries of the reviewers.
Conflicts of Interest: The authors declare no conflict of interest.
References
1.
Yu, H.; Yao, N.; Liu, J. An adaptive routing protocol in underwater sparse acoustic sensor networks.
Ad Hoc Netw. 2015,34, 121–143. [CrossRef]
2.
Coutinho, R.W.; Boukerche, A.; Vieira, L.F.; Loureiro, A.A. Underwater Wireless Sensor Networks: A New
Challenge for Topology Control-Based Systems. ACM Comput. Surv. 2018,51, 19. [CrossRef]
3.
Coutinho, R.W.L.; Boukerche, A.; Vieira, L.F.M.; Loureiro, A.A.F. A novel void node recovery paradigm for
longterm underwater sensor networks. Ad Hoc Netw. 2015,34, 144–156. [CrossRef]
4.
Yu, H.; Yao, N.; Wang, T.; Li, G.; Gao, Z.; Tan, G. WDFAD-DBR: Weighting depth and forwarding area
division DBR routing protocol for UASNs. Ad Hoc Netw. 2016,37, 256–282. [CrossRef]
5.
Jiang, S. On reliable data transfer in underwater acoustic networks: A survey from networking perspective.
IEEE Commun. Surv. Tutor. 2018,20, 1036–1055. [CrossRef]
6.
Kheirabadi, M.T.; Mohamad, M.M. Greedy routing in underwater acoustic sensor networks: A survey. Int. J.
Distrib. Sens. Netw. 2013,9, 701834. [CrossRef]
7.
Li, N.; Martínez, J.F.; Chaus, J.M.M.; Eckert, M. A survey on underwater acoustic sensor network routing
protocols. Sensors 2016,16, 414. [CrossRef] [PubMed]
8.
Han, G.; Jiang, J.; Bao, N.; Wan, L.; Guizani, M. Routing protocols for underwater wireless sensor networks.
IEEE Commun. Mag. 2015,53, 72–78. [CrossRef]
Sensors 2018,18, 3271 24 of 25
9.
Mitra, S.; Roy, A. Communication void free routing protocol in wireless sensor network. Wirel. Pers. Commun.
2015,82, 2567–2581. [CrossRef]
10.
Khasawneh, A.; Latiff, M.S.B.A.; Kaiwartya, O.; Chizari, H. A reliable energy-efficient pressure-based routing
protocol for underwater wireless sensor network. Wirel. Netw. 2018,24, 2061–2075. [CrossRef]
11.
Farhan, L.; Kharel, R.; Kaiwartya, O.; Hammoudeh, M.; Adebisi, B. Towards green computing for Internet
of things: Energy oriented path and message scheduling approach. Sustain. Cities Soc.
2018
,38, 195–204.
[CrossRef]
12.
Kaiwartya, O.; Abdullah, A.H.; Cao, Y.; Lloret, J.; Kumar, S.; Shah, R.R.; Prasad, M.; Prakash, S. Virtualization
in wireless sensor networks: Fault tolerant embedding for internet of things. IEEE Internet Things J.
2018
,5,
571–580. [CrossRef]
13.
Aliyu, A.; Abdullah, A.H.; Kaiwartya, O.; Cao, Y.; Lloret, J.; Aslam, N.; Joda, U.M. Towards video streaming
in IoT Environments: Vehicular communication perspective. Comput. Commun.
2018
,118, 93–119. [CrossRef]
14.
Faheem, M.; Tuna, G.; Gungor, V.C. LRP: Link quality aware queue based spectral clustering routing protocol
for underwater acoustic sensor networks. Int. J. Commun. Syst. 2017,30, e3257. [CrossRef]
15.
Coutinho, R.W.; Boukerche, A.; Vieira, L.F.; Loureiro, A.A. Geographic and opportunistic routing for
underwater sensor networks. IEEE Trans. Comput. 2016,65, 548–561. [CrossRef]
16.
Noh, Y.; Lee, U.; Lee, S.; Wang, P.; Vieira, L.F.; Cui, J.H.; Gerla, M.; Kim, K. Hydrocast: Pressure routing for
underwater sensor networks. IEEE Trans. Veh. Technol. 2016,65, 333–347. [CrossRef]
17.
Abbas, M.Z.; Bakar, K.A.; Ayaz, M.; Mohamed, M.H.; Tariq, M. Hop-by-Hop Dynamic Addressing Based
Routing Protocol for Monitoring of long range Underwater Pipeline. KSII Trans. Internet Inf. Syst.
2017
,11,
731–763.
18.
Javaid, N.; Jafri, M.R.; Ahmed, S.; Jamil, M.; Khan, Z.A.; Qasim, U.; Al-Saleh, S.S. Delay-sensitive routing
schemes for underwater acoustic sensor networks. Int. J. Distrib. Sens. Netw. 2015,11, 532676. [CrossRef]
19.
Yadav, S.; Kumar, V. Optimal Clustering in Underwater Wireless Sensor Networks: Acoustic, EM and FSO
Communication Compliant Technique. IEEE Access 2017,5, 12761–12776. [CrossRef]
20.
Sasikala, V.; Chandrasekar, C. Cluster based Sleep/Wakeup Scheduling Technique for WSN. Int. J.
Comput. Appl. 2013,72, 15–20.
21.
Yu, S.; Liu, S.; Jiang, P. A High-Efficiency Uneven Cluster Deployment Algorithm Based on Network Layered
for Event Coverage in UWSNs. Sensors 2016,16, 2103. [CrossRef] [PubMed]
22.
Rao, P.S.; Jana, P.K.; Banka, H. A particle swarm optimization based energy efficient cluster head selection
algorithm for wireless sensor networks. Wirel. Netw. 2017,23, 2005–2020. [CrossRef]
23.
Sher, A.; Javaid, N.; Azam, I.; Ahmad, H.; Abdul, W.; Ghouzali, S.; Niaz, I.A.; Khan, F.A. Monitoring square
and circular fields with sensors using energy-efficient cluster-based routing for underwater wireless sensor
networks. Int. J. Distrib. Sensor Netw. 2017,13, 1550147717717189. [CrossRef]
24.
Yan, H.; Shi, Z.J.; Cui, J.H. DBR: Depth-based routing for underwater sensor networks. In Networking 2008
Ad Hoc and Sensor Networks, Wireless Networks, Next Generation Internet; Springer: Berlin, Germany, 2008;
pp. 72–86.
25.
Javaid, N.; Jafri, M.R.; Khan, Z.A.; Qasim, U.; Alghamdi, T.A.; Ali, M. Iamctd: Improved adaptive mobility
of courier nodes in threshold-optimized dbr protocol for underwater wireless sensor networks. Int. J. Distrib.
Sens. Netw. 2014,10, 213012. [CrossRef]
26.
Zhou, Z.; Yao, B.; Xing, R.; Shu, L.; Bu, S. E-CARP: An energy efficient routing protocol for UWSNs in the
internet of underwater things. IEEE Sens. J. 2015,16, 4072–4082. [CrossRef]
27.
Kong, L.; Ma, K.; Qiao, B.; Guo, X. Adaptive relay chain routing with load balancing and high energy
efficiency. IEEE Sens. J. 2016,16, 5826–5836. [CrossRef]
28.
Ahmad, A.; Javaid, N.; Khan, Z.A.; Qasim, U.; Alghamdi, T.A.
(ACH)2
: Routing Scheme to Maximize
Lifetime and Throughput of Wireless Sensor Networks. IEEE Sens. J. 2014,14, 3516–3532. [CrossRef]
29.
Yildiz, H.U.; Gungor, V.C.; Tavli, B. Packet Size Optimization for Lifetime Maximization in Underwater
Acoustic Sensor Networks. IEEE Trans. Ind. Inform. 2018. [CrossRef]
Sensors 2018,18, 3271 25 of 25
30.
Bu, R.; Wang, S.; Wang, H. Fuzzy logic vector-based forwarding routing protocol for underwater acoustic
sensor networks. Trans. Emerg. Telecommun. Technol. 2018,29, e3252. [CrossRef]
31.
Khalid, M.; Cao, Y.; Ahmad, N.; Khalid, W.; Dhawankar, P. Radius-based multipath courier node routing
protocol for acoustic communications. IET Wirel. Sens. Syst. 2018,8, 183–189. [CrossRef]
©
2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
... In addition, the issue of void-holes occurrence is significantly reduced. Another protocol having the same motive of void-hole avoidance and efficient data transmission for IoUT is proposed in [38]. In which, authors have proposed four different schemes, which are the modified versions of the Weighting Depth and Forwarding Area Division Depth-Based Routing (WDFAD-BDR) routing protocol [39]. ...
... A-DBR [38] Routing path is established on the basis of depth and energy metrics along with adjustment of transmission range. ...
... Routing path became very large due to the backwardtransmission mechanism. C-DBR [38] Clustering technique is used and routing path is established on the basis of depth and energy metrics. ...
Article
Internet of Underwater Things (IoUTs) deals in a resource-constrained environment and has several open issues, challenges, and potential applications in both onshore and offshore fields. The distinctive features of acoustic medium and persistent node mobility have spurred the development of a routing protocol for IoUT that ensures the efficient data transfer at the surface station. The efficient data transfer metrics can be achieved either by the emergence of cluster-based or chain-based routing protocols. This paper provides a simulation-based quantitative analysis of the most recent and prominent cluster-based and chain-based routing protocols for IoUT that are intended to enable the efficient data transfer from source to destination. In this context, we use NS3 to conduct extensive simulations for the quantitative analysis of different routing protocols relating to packet delivery rate, packet drop rate, normalized energy consumption, normalized network lifetime, density of alive nodes, and density of dead nodes. In addition to quantitative analysis, we also provide the performance trade-offs, limitations, and applications for IoUT routing protocols for these two classifications. In last, we also perform quantitative analysis for these two classifications head-to-head. This work aims to provide helpful insights into selecting a suitable protocol for routing applications to meet the various specifications and requirements of IoUT for efficient data dissemination.
... In order to have an efficient opportunistic routing protocol, it is paramount to solve many challenges among which is Communication Void (CV) [16,17]. This challenge is faced by the sending nodes when no neighbor node is within its transmission range, which impedes the node from forwarding the packet to the next-hop or destination [25]. Given the dynamic, sparse, and unreliable network topology inherited in Underwater Wireless Sensor Networks (UWSNs), these topologies suffer from high packet loss and low throughput especially if inefficient algorithms are used to handle the void communication problem [25,26]. ...
... This challenge is faced by the sending nodes when no neighbor node is within its transmission range, which impedes the node from forwarding the packet to the next-hop or destination [25]. Given the dynamic, sparse, and unreliable network topology inherited in Underwater Wireless Sensor Networks (UWSNs), these topologies suffer from high packet loss and low throughput especially if inefficient algorithms are used to handle the void communication problem [25,26]. Needless to say that the adoption of an inefficient void handling algorithm may negatively affect the nodes' energy consumption and reduce the network lifetime. ...
Article
Full-text available
The Internet of Underwater Things (IoUT) is an emerging area in marine science and engineering. It has witnessed significant research and development attention from both academia and industries due to its growing underwater use cases in oceanographic data collection, pollution monitoring, seismic monitoring, tactical surveillance, and assisted navigation for waterway transport. Information dissemination in the underwater network environment is very critical considering network dynamism, unattainable nodes, and limited resources of the tiny IoUT devices. Existing techniques are majorly based on location-centric beacon messages, which results in higher energy consumption, and wastage of computing resources in tiny IoUT devices. Towards this end, this paper presents an efficient void aware (EVA) framework for information dissemination in IoUT environment. Network architecture is modeled considering potential void region identification in the underwater network environment. An efficient void aware (EVA) information dissemination framework is proposed focusing on detecting void network region, and intelligent void aware data forwarding. The comparative performance evaluation attests to the benefits of the proposed framework in terms of energy consumption, network lifetime, packet delivery ratio, and end-to-end delay for information dissemination in IoUT.
... Reduction of energy depletion of sensor nodes improves the network life of UWSNs, which in turn decreases the overall network cost [4,6], because several applications, such as coastline observation and assurance, sea calamity anticipation, observation of underwater contamination, military protection, and route assistance, and checking the marine oceanic environment and underwater asset investigation, are applied in this domain [1,4]. The probability of creating void holes is significantly reduced, whereas transmission reliability and end-to-end delay are improved effectively [4,8]. The success of all UWSN applications depends on the efficiency of the routing protocols that influence the entire services of UWSNs. ...
... By contrast, the relay nodes are placed at a different underwater positions that forward the received packets toward the sink [5]. The sink node has sound and radio modems [8]. The sound modems are used for acoustic communication, whereas the radio modem facilitates radio communication outside the aquatic environment. ...
Article
Full-text available
Underwater acoustic sensor network (UASN) refers to a procedure that promotes a broad spectrum of aquatic applications. UASNs can be practically applied in seismic checking, ocean mine identification, resource exploration, pollution checking, and disaster avoidance. UASN confronts many difficulties and issues, such as low bandwidth, node movements, propagation delay, 3D arrangement, energy limitation, and high-cost production and arrangement costs caused by antagonistic underwater situations. Underwater wireless sensor networks (UWSNs) are considered a major issue being encountered in energy management because of the limited battery power of their nodes. Moreover, the harsh underwater environment requires vendors to design and deploy energy-hungry devices to fulfil the communication requirements and maintain an acceptable quality of service. Moreover, increased transmission power levels result in higher channel interference, thereby increasing packet loss. Considering the facts mentioned above, this research presents a controlled transmission power-based sparsity-aware energy-efficient clustering in UWSNs. The contributions of this technique is threefold. First, it uses the adaptive power control mechanism to utilize the sensor nodes’ battery and reduce channel interference effectively. Second, thresholds are defined to ensure successful communication. Third, clustering can be implemented in dense areas to decrease the repetitive transmission that ultimately affects the energy consumption of nodes and interference significantly. Additionally, mobile sinks are deployed to gather information locally to achieve the previously mentioned benefits. The suggested protocol is meticulously examined through extensive simulations and is validated through comparison with other advanced UWSN strategies. Findings show that the suggested protocol outperforms other procedures in terms of network lifetime and packet delivery ratio.
... The major challenge of the IoUTs is sending data towards the sink stations; due to the continuous movement of the nodes, it becomes difficult to transfer data. In order to overcome this problem, we make the orbit-based routing path in our proposed scheme with supporting thories listed in [16,17] and Opportunistic Routing Protocols (ORPs) [18,19] are examined to expand performance with a dynamic selection of one best forwarding device from the other. Location-Based Opportunistic Routing Protocols (LBORPs) are recognized and identified to perform better using knowledge for dynamic selection of forwarder devices through their location and route message packets to the receiver [7]. ...
Article
Full-text available
The Internet of Underwater Things (IoUTs) enables various underwater objects be connected to accommodate a wide range of applications, such as oil and mineral exportations, disaster detection, and tracing tracking systems. As about 71% of our earth is covered by water and one-fourth of the population lives around this, the IoUT expects to play a vital role. It is imperative to pursue reliable communication in this vast domain, as human beings’ future depends on water activities and resources. Therefore, there is a urgent need for underwater communication to be reliable, end-to-end secure, and collision/void node-free, especially when the routing path is established between sender and sonobuoys. The foremost issue discussed in this area is its routing path, which has high security and bandwidth without simultaneous multiple reflections. Short communication range is also a problem (because of an absence of inter-node adjustment); the acoustic signals have short ranges and maximum-scaling factors that cause a delay in communication. Therefore, we proposed Rotational Orbit-Based Inter Node Adjustment (ROBINA) with variant Path-Adjustment (PA-ROBINA) and Path Loss (PL-ROBINA) for IoUTs to achive reliable communication between the sender and sonobuoys. Additionally, the mathematical-based path loss model was discussed to cover the PL-ROBINA strategy. Extensive simulations were conducted with various realistic parameters and the results were compared with state-of-the-art routing protocols. Extensive simulations proved that the proposed routing scheme outperformed different realistic parameters; for example, packet transmission 45% increased with an average end-to-end delay of only 0.3% respectively. Furthermore, the transmission loss and path loss (measured in dB) were 25 and 46 dB, respectively, compared with other algorithms, for example, EBER2 54%, WDFAD-BDR 54%, AEDG 49%, ASEGD 55%, AVH-AHH-VBF 54.5%, and TANVEER 39%, respectively. In addition, the individual parameters with ROBINA and TANVEER were also compared, in which ROBINA achieved a 98% packet transmission ratio compared with TANVEER, which was only 82%.
... The high packet drop occurs when the residual energy of each node becomes lower than the residual energy of the network. Sher et al. (2018) improve the data delivery by introducing a four-way mechanism to avoid void holes and reduce collision in the network. For efficient data delivery, the forwarder nodes are selected based on the residual energy of the nodes. ...
Article
Full-text available
Localization of sensors in Underwater Internet of Things (UIoTs) is difficult due to the mobility. This changing makes the routing decisions difficult, which results in unreliable communication. This paper proposes Adaptive Transmission based Geographic and Opportunistic Routing (ATGOR) protocol for reliable communication between nodes. ATGOR operates in two parts: election of a small cube to avoid redundant transmissions and selection of reliable nodes which forward data from the selected small cube for optimal transmissions. Furthermore, to guarantee the reliability of the data packets in a harsh acoustic environment, we propose Mobility Aware ATGOR (MA-ATGOR), which predicts the locations of neighboring sensor nodes for successful data delivery. In addition, prediction of tthe locations of the sensor nodes helps in avoiding the void holes along with high packet delivery. The performance of the proposed routing protocols is validated based on the PDR, number of void nodes and energy consumption per packet, through simulations.
... e main cause for routing voids is the higher energy consumption of the sensor nodes in the network, i.e., nodes which lose energy makes the hole. In this context, there are two major research problems arise [23][24][25]: ...
Article
Full-text available
Underwater wireless sensor networks (UWSNs) enable various oceanic applications which require effective packet transmission. In this case, sparse node distribution, imbalance in terms of overall energy consumption between the different sensor nodes, dynamic network topology, and inappropriate selection of relay nodes cause void holes. Addressing this problem, we present a relay-based void hole prevention and repair (ReVOHPR) protocol by multiple autonomous underwater vehicles (AUVs) for UWSN. ReVOHPR is a global solution that implements different phases of operations that act mutually in order to efficiently reduce and identify void holes and trap relay nodes to avoid it. ReVOHPR adopts the following operations as ocean depth (levels)-based equal cluster formation, dynamic sleep scheduling, virtual graph-based routing, and relay-assisted void hole repair. For energy-efficient cluster forming, entropy-based eligibility ranking (E2R) is presented, which elects stable cluster heads (CHs). Then, dynamic sleep scheduling is implemented by the dynamic kernel Kalman filter (DK2F) algorithm in which sleep and active modes are based on the node’s current status. Intercluster routing is performed by maximum matching nodes that are selected by dual criteria, and also the data are transmitted to AUV. Finally, void holes are detected and repaired by the bicriteria mayfly optimization (BiCMO) algorithm. The BiCMO focuses on reducing the number of holes and data packet loss and maximizes the quality of service (QoS) and energy efficiency of the network. This protocol is timely dealing with node failures in packet transmission via multihop routing. Simulation is implemented by the NS3 (AquaSim module) simulator that evaluates the performance in the network according to the following metrics: average energy consumption, delay, packet delivery rate, and throughput. The simulation results of the proposed REVOHPR protocol comparing to the previous protocols allowed to conclude that the REVOHPR has considerable advantages. Due to the development of a new protocol with a set of phases for data transmission, energy consumption minimization, and void hole avoidance and mitigation in UWSN, the number of active nodes rate increases with the improvement in overall QoS. 1. Introduction Underwater wireless sensor network (UWSN) has many applications over the ocean environment. In UWSN, energy efficiency is the major constraint since the nodes are resource constraint [1–3]. This represents one of the main reasons that leads to the appearance of void holes, reducing the performance of the network. To achieve energy efficiency, various approaches were presented in UWSN. Here, the data transmission is carried over multiple hops between a number of sensor nodes through a selected route to reach the autonomous unmanned vehicles (AUVs), and then the final surface sink node and further collision-free medium access (MAC) protocols were presented. However, routing is also the best way to improve energy efficiency [4]. A cluster-based mobile data gathering is used to improve energy efficiency in the large-scale network [5]. The basic cluster concept is considered in this work to form initial clusters [6, 7]. This cluster formation is performed in nonoptima manner which is inefficient [8]. However, cluster head (CH) is performed in a random manner which makes this work ineffectual [9]. In addition, processing the distributed clustering algorithm needs a large amount of control packet exchange which consumes lots of energy. Autonomous unmanned vehicles (AUVs) are specially designed for data gathering in the underwater environment [10–12]. An AUV-assisted energy-efficient clustering UWSN mechanism faces many serious issues as follows [13, 14]:(i)Energy consumption in existing research works is high, which leads to a large number of holes in the network.(ii)Network clusters with unequal size introduce energy imbalance in certain regions, leads to a large number of holes.(iii)Optimal sleep scheduling is necessary in order to reduce the energy consumption of the nodes and avoid holes.(iv)Route selection considers only limited metrics, which leads to large packet loss and energy consumption which induces trap nodes. In AUV-assisted UWSN, the predefined path determination is the critical issue which increases the distance to the nodes, the energy consumption, and delay in data transmission [15, 16]. On the other hand, the unnecessary sensing of the sensor nodes increases energy consumption. These are only limited factors since the forwarder selection mechanism must consider more criteria. Furthermore, route selection based on single metric is ineffective in underwater scenarios [17, 18]. Traditional routing algorithms follow ocean depth-based routing. This leads to high packet loss due to the void hole issue. Void hole avoidance and recovery is an emerging part of UWSN. Furthermore, it occurs frequently in the sparse node distribution with a limited amount of energy. In addition, various important issues remain untouched in UWSN for reducing energy consumption and avoiding energy hole creation [19, 20]. Table 1 describes the abbreviations that we have used throughout the paper:(i)There is no unified protocol for reliable and energy-efficient data transmission for a specific type of UWSN.(ii)Existing protocols focus on one aspect for energy consumption, i.e., clustering, routing, or void hole repair. Hence, energy consumption may occur by other aspects of the issue.(iii)Current protocols used a single AUV for data collection, which increases the end-to-end delay of each sensor, and thus, energy consumption rate is increased [21, 22]. Abbreviation Expansion UWSN Underwater wireless sensor network AUVs Autonomous unmanned vehicles ReVOHPR Relay-based void hole prevention and repair protocol DK2F Dynamic kernel Kalman filter BiCMO Bicriteria mayfly optimization EEDG Energy-efficient data gathering E2R Eligibility ranking CH Cluster head LECA Level-based equal clustering algorithm AEC Energy-efficient clustering MFO Moth flame optimization CMDG Cluster-based mobile data gathering
... The main cause for routing voids is the higher energy consumption of the sensor nodes in the network (i.e.) nodes which losses energy makes the hole. In this context, there are two major research problem arises [18], [19], [20], [35].  Most of the works have concentrated on energy efficient route selection without deploying AUV in the network. ...
Preprint
Underwater Wireless Sensor Networks (UWSN) enables various oceanic applications which require effective packet transmission. In this case, sparse node distribution, dynamic network topology and inappropriate selection of relay nodes cause void holes. Addressing this problem, we present a Relay based Void Hole Prevention and Repair protocol (ReVOHPR) by multiple Autonomous Underwater Vehicles (AUV) for UWSN. ReVOHPR efficiently identifies and avoids void holes and trap relay nodes to avoid it. ReVOHPR adopts the following operations as Ocean Depth (levels) based Equal Cluster Formation, Dynamic Sleep Scheduling, Virtual Graph based Routing, and Relay Assisted Void Hole Repair. For energy efficient cluster forming, Entropy based Eligibility Ranking (E2R) is presented which elects stable cluster heads (CHs). Then, dynamic sleep scheduling is implemented Dynamic Kernel Kalman Filter (DK2F) algorithm in which Sleep and Active modes based on the nodes current status. Inter Cluster Routing is performed by maximum matching nodes which selects by Dual criteria and also data transmitted to AUV. Finally, void holes are detected and repair by Bi-Criteria Mayfly Optimization (BiCMO) algorithm. The BiCMO focuses on reducing the number of holes, data packet loss and maximizes Quality of Service (QoS) and energy efficiency of the networks. This protocol is timely deal with node failures in packet transmission via multi-hop routing. Simulation is implemented by NS3 (AquaSim module) simulator that evaluates the performance in network simulation for following metrics as average energy consumption, delay, packet delivery rate and throughput.
... Internet of Things (IoT) based underwater sensor and actor networks have good potential to explore this water-covered area. Underwater wireless sensor networks (UWSNs) have attracted researchers from industry and academia for exploring underwater resources by enabling a variety of applications such as aquatic environment monitoring, disaster prevention, pollution monitoring, mineral extraction, flood and tsunami warning, and military defense [1]. A specified geographic volume is covered with randomly deployed sensor nodes with the ability to sense, gather, and forward data toward the destination (the sink) [2]. ...
Article
In the task of data routing in Internet of Things enabled volatile underwater environments, providing better transmission and maximizing network communication performance are always challenging. Many network issues such as void holes and network isolation occur because of long routing distances between nodes. Void holes usually occur around the sink because nodes die early due to the high energy consumed to forward packets sent and received from other nodes. These void holes are a major challenge for I‐UWSANs and cause high end‐to‐end delay, data packet loss, and energy consumption. They also affect the data delivery ratio. Hence, this paper presents an energy efficient watchman based flooding algorithm to address void holes. First, the proposed technique is formally verified by the Z‐Eves toolbox to ensure its validity and correctness. Second, simulation is used to evaluate the energy consumption, packet loss, packet delivery ratio, and throughput of the network. The results are compared with well‐known algorithms like energy‐aware scalable reliable and void‐hole mitigation routing and angle based flooding. The extensive results show that the proposed algorithm performs better than the benchmark techniques.
... Sparsity Circular Depth Based Sparsity Aware Energy-Efficient Clustering (CDSEEC), Aware Energy-Efficient Clustering (SEEC), and Circular Sparsity Aware Energy-Efficient grouping (CSEEC) was suggested for UWSNs in comparison Sher et al. [25]. Two flexible sinks are in the SEEC convention meager district to collect data to reduce the risk of energy opening formation. ...
Article
Full-text available
Underwater Wireless Sensor Network (UWSN) accomplishes the consideration of a few scientists and academicians towards itself. Because of the brutality of the climate lies submerged represents various difficulties, i.e., high transmission delay, outstanding piece mistake rate, more expense in usage, sinks development and energy imperatives, unequal surface highlights of an area and low data transfer capacity, and so forth Void opening evasion is compulsory for to motivation behind limiting the utilization of energy and amplifying throughput and region inclusion. In this exploration work, the creator planned plans for void opening shirking initial one is, Avoiding Void Hole Adaptive Hop by Hop Vector-Based Forwarding (AVH-AHH-VBF) in submerged remote sensor organization and a second plan for limiting utilization of energy and expanding the lifetime of the organization, Sink Mobility-Adaptive Hop by Hop Vector-Based Forwarding (SM-AHH-VBF). Reproduction results show that our plans beat contrasted and standard arrangement as far as normal Packet Delivery Ratio (PDR), energy charge. Our reproduction confirms the effectiveness of our proposed procedure AVH-AHH-VBF equivalents to 0.17 and SM-AHH-VBF equivalents to 0.24 regarding normal PDR, AVH-AHH-VBF equivalents to 24j and SM-AHH-VBF equivalents to 5j for the normal energy charge, AVH-AHH-VBF had a tradeoff of 63% in light of considering two jumps and SM-AHH-VBF approaches 20% tradeoff for normal start to finish.
Article
Full-text available
Owing to the hasty growth of communication technologies in the Underwater Internet of Things (UIoT), many researchers and industries focus on enhancing the existing technologies of UIoT systems for developing numerous applications such as oceanography, diver networks monitoring, deep-sea exploration and early warning systems. In a constrained UIoT environment, communication media such as acoustic, infrared (IR), visible light, radiofrequency (RF) and magnet induction (MI) are generally used to transmit information via digitally linked underwater devices. However, each medium has its technical limitations: for example, the acoustic medium has challenges such as narrow-channel bandwidth, low data rate, high cost, etc., and optical medium has challenges such as high absorption, scattering, long-distance data transmission, etc. Moreover, the malicious node can steal the underwater data by employing blackhole attacks, routing attacks, Sybil attacks, etc. Furthermore, due to heavyweight, the existing privacy and security mechanism of the terrestrial internet of things (IoT) cannot be applied directly to UIoT environment. Hence, this paper aims to provide a systematic review of recent trends, applications, communication technologies, challenges, security threats and privacy issues of UIoT system. Additionally, this paper highlights the methods of preventing the technical challenges and security attacks of the UIoT environment. Finally, this systematic review contributes much to the profit of researchers to analyze and improve the performance of services in UIoT applications.
Article
Full-text available
Underwater Wireless Sensor Networks (UWSNs) use acoustic waves to communicate in underwater environment. Acoustic channels have various limitations that can be low bandwidth, a higher end to end delay and path loss at certain nodes. Considering the limitations of UWSNs, energy efficient communication and reliability of network UWSNs has become an inevitable research area. The current research interests are to operate sensors for a longer time. Currently investigated research area towards efficient communication have various challenges, like flooding, multiple copies creation path loss and low network life time. Different from previous work which solve these challenges by measuring the depth, residual energy and assigning hop-ID's to node. This article has proposed a novel scheme called Radius-based Courier Node (RMCN) routing. RMCN uses radius-based architecture in combination with cost function, track-id, residual energy, and depth to forward data packets. The RMCN is specifically designed for long term monitoring with higher energy efficiency and packet delivery ratio. The purpose of RMCN is to facilitate network for longer periods in risky areas. The proposed routing scheme has been compared with DBR and EMGGR in respect of alive nodes left, end to end delay, delivery ratio and energy consumption.
Article
Full-text available
Underwater wireless sensor networks (UWSNs) will pave the way for a new era of underwater monitoring and actuation applications. The envisioned landscape of UWSN applications will help us learn more about our oceans, as well as about what lies beneath them. They are expected to change the current reality where no more than 5% of the volume of the oceans has been observed by humans. However, to enable large deployments of UWSNs, networking solutions toward efficient and reliable underwater data collection need to be investigated and proposed. In this context, the use of topology control algorithms for a suitable, autonomous, and on-the-fly organization of the UWSN topology might mitigate the undesired effects of underwater wireless communications and consequently improve the performance of networking services and protocols designed for UWSNs. This article presents and discusses the intrinsic properties, potentials, and current research challenges of topology control in underwater sensor networks. We propose to classify topology control algorithms based on the principal methodology used to change the network topology. They can be categorized in three major groups: power control, wireless interface mode management, and mobility assisted–based techniques. Using the proposed classification, we survey the current state of the art and present an in-depth discussion of topology control solutions designed for UWSNs.
Article
Full-text available
Multimedia oriented Internet of Things (IoT) enables pervasive and real-time communication of video, audio and image data among devices in immediate surroundings. Today’s vehicles have the capability of supporting real time multimedia acquisition. Vehicles with high illuminating infrared cameras and customized sensors can communicate with other on-road devices using dedicated short-range communication (DSRC) and 5G enabled communication technologies. Real time incidence of both urban and highway vehicular traffic environment can be captured and transmitted using vehicle-to-vehicle and vehicle-to-infrastructure communication modes. Video streaming in vehicular IoT (VSV-IoT) environments is in growing stage with several challenges that need to be addressed ranging from limited resources in IoT devices, intermittent connection in vehicular networks, heterogeneous devices, dynamism and scalability in video encoding, bandwidth underutilization in video delivery, and attaining application-precise quality of service in video streaming. In this context, this paper presents a comprehensive review on video streaming in IoT environments focusing on vehicular communication perspective. Specifically, the significance of video streaming in vehicular IoT environments is highlighted focusing on the integration of vehicular communication with 5G enabled IoT technologies, and smart city oriented application areas for VSV-IoT. A taxonomy is presented for the classification of related literature on video streaming in vehicular network environments. Following the taxonomy, critical review of literature is performed focusing on major functional model, strengths, and weaknesses. Metrics for video streaming in vehicular IoT environments are derived and comparatively analyzed in terms of their usage and evaluation capabilities. Open research challenges in VSV-IoT are identified as future directions of research in the area. The survey would benefit both IoT and vehicle industry practitioners and researchers, in terms of augmenting understanding of vehicular video streaming and its IoT related trends and issues.
Article
Full-text available
With a wide scope for exploration and research, underwater wireless sensor network (UWSN) is a fast growing research area in current scenario. UWSNs need energy efficient designing approach because underwater sensor nodes are battery driven. Also the deployed batteries can not be easily recharged by non-conventional energy resources like solar energies. Clustering is an effective technique to design an energy efficient UWSNs. Due to the sparse deployment of nodes and dynamic nature of the channel, the clustering characteristics of UWSNs are different from those of terrestrial wireless sensor networks (TWSNs). In this paper, we focused on optimal clustering for UWSNs which are compliant with any one of the acoustic, free space optical (FSO) and electromagnetic (EM) wave based communication techniques. Besides, we proposed an energy dissipation model of sensor node for FSO and EM wave based communication and compared with contemporary energy dissipation model for acoustic based communication. In particular, the suitability of the above three techniques for underwater communication is investigated and their performance is compared on the basis of energy consumption and optimal clustering.
Article
Full-text available
Recently, virtualization in wireless sensor networks (WSNs) has witnessed significant attention due to the service requirement for IoT. Related literature on virtualization in WSNs explored resource optimization without considering communication failure in WSNs environments. The failure of single communication link in WSNs impacts a number of virtual networks running IoT services. In this context, this paper proposes a framework for optimizing fault tolerance in virtualization in WSNs, focusing on heterogeneous networks for service-oriented IoT applications. The state of the art on virtualization in WSNs is critically reviewed. An optimization problem is formulated considering fault tolerance and communication delay. An adapted non-dominated sorting based genetic algorithm (A-NSGA) is developed to solve the optimization problem. The major components of A-NSGA include chromosome representation, fault tolerance and delay computation, crossover and mutation, and non-dominance based sorting. Analytical and simulation based comparative performance evaluation has been carried out. From the analysis of results, it is evident that the framework effectively optimizes fault tolerance for virtualization in WSNs.
Article
Reliable data transfer aims to guarantee that the destination node can successfully receive what have been sent to it, and the basic mechanisms extensively for this purpose in radio frequency (RF) networks include redundancy and retransmission. However, this issue becomes much more challenging in underwater acoustic (UWA) networks (UWANs) in comparison with RF networks due to the following peculiar features of UWA channels: poor quality and high dynamics of UWA channels, much smaller channel capacity and much larger propagation delay, as well as asymmetric connectivity of UWA links. These features either limit extensive application of redundancy mechanisms or influence the performance of retransmission mechanisms. Therefore, many research results have been reported in the literature, with several different design strategies and various proposals available. This paper conducts a survey on many schemes proposed from the data link layer to the transport layer, and discusses challenging issues necessary for further research.
Article
Recently, energy efficiency in sensor enabled wireless network domain has witnessed significant attention from both academia and industries. It is an enabling technological advancement towards green computing in Internet of things (IoT) eventually supporting sensor generated big data processing for smart cities. Related literature on energy efficiency in sensor enabled wireless network environments focuses on one aspect either energy oriented path selection or energy oriented message scheduling. The definition of path also varies in literature without considering links towards energy efficiency. In this context, this paper proposes an energy oriented path selection and message scheduling framework for sensor enabled wireless network environments. The technical novelty focuses on effective cooperation between path selection and message scheduling considering links on path, location of message sender, and number of processor in sensor towards energy efficiency. Specifically, a path selection strategy is developed based on shortest path and less number of links on path (SPLL). The location of message sender, and number of processor in specific sensor are utilized for developing a longer hops (LH) message scheduling approach. A system model is presented based on M/M/1 queuing analysis to showcase the effective cooperation of SPLL and LH towards energy efficiency. Simulation oriented comparative performance evaluation attest the energy efficiency of the proposed framework as compared to the state-of-the-art techniques considering number of energy oriented metrics.
Article
In this paper, to monitor the fields with square and circular geometries, three energy-efficient routing protocols are proposed for underwater wireless sensor networks (UWSNs). First one is, sparsity-aware energy efficient clustering (SEEC), second one is, circular SEEC (CSEEC), and the third one is, circular depth based SEEC (CDSEEC) routing protocol. All three protocols are proposed to minimize the energy consumption of sparse regions. Whereas, sparsity search algorithm (SSA) is proposed to find sparse regions and density search algorithm (DSA) is used to find dense regions of the network field. Moreover, clustering is performed in dense regions to minimize redundant transmissions of a data packet. While, sinks mobility is exploited to collect data from sensor nodes with an objective of minimum energy consumption. A depth threshold (d th) value is also used to minimize number of hops between source and destination for less energy consumption. Simulation results show that our schemes perform better than their counterpart schemes (DBR, EEDBR) in terms of energy efficiency.