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;
email@example.com (A.S.); firstname.lastname@example.org (A.K.)
2Department of Computer Science, Georgia Southern University, Statesboro, GA 30460, USA;
3Farasan Networking Research Laboratory, Department of Computer Science & Information System,
Jazan University, Jazan 82822-6694, Saudi Arabia; email@example.com (M.Y.A.);
*Correspondence: firstname.lastname@example.org; Tel.: +92-3005792728
Received: 24 July 2018; Accepted: 13 September 2018; Published: 28 September 2018
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.
underwater wireless sensor networks; adaptive transmission range; residual energy;
clustering; void hole; collision
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. [
]. 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. [
]. 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 [
]. 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 [
]. The void hole is avoided
using the Adaptive Hop-by-Hop Vector-Based Forwarding (AHH-VBF) routing protocol [
]. 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 [
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 [
]. 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 [
]. The IoT-enabled WSN has been helpful in connecting anything, anywhere.
Anywhere means sensors, vehicles, cameras, watches, phones, etc. [
]. 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 [
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
• 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
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
]. 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 [
]. 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) [
] 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.
], 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
Delay-sensitive schemes: Advancement of localization-free routing protocols of DBR, EEDBRand
] 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 [
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 [
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.
], 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 [
]. 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.
], 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
Depth-Based Routing (DBR) [
] 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) [
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) [
] provides improved network
lifetime and reduced energy consumption by the reactive routing approach.
In Adaptive Relay Chain Routing (ARCR) [
], 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.
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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 Location-aware routing protocol,
concept of adaptive virtual pipeline
Reduced duplicate packets and
consumption is avoided
Void hole problem exists
GEographic and opportunistic
routing with Depth
Adjustment-based topology control
Void hole avoidance results in
increased performance of the
consumption and high
Pressure-based routing protocol and
efﬁcient anycast routing algorithm Improved packet delivery ratio
Low performance and
Addressing-based routing protocol
for Pipeline Monitoring
Improved packet delivery ratio High energy
Improved delay-sensitive versions,
adaptable to time-critical
Minimize end-to-end delay and
improve performance and
Duplication of packets
occurs, high energy
consumption and void
hole problem exists
ACH2Free association mechanism where
nodes associate with CHs
consumption and enhances
FSO and EM
Free Space Optical and
Reduced energy consumption High end-to-end delay
CBSST 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
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
causes alteration in the
Energy-efﬁcient CH Selection that is
based on particle swarm
Energy efﬁciency achieved Only for homogeneous
EDDEEC Enhanced Developed Distributed
Shows improved performance in
terms of stability period,
network lifetime and packet
and reelection increases
SEEC, CSEEC and CDSEEC for
UWSNs Reduced energy consumption Low packet delivery
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
Location-free routing protocol
specially designed for time-critical
Improved network lifetime,
minimized end-to-end delay
Void holes still exist &
overhead due to control
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
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
when the relay nodes are
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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,
] 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
W ter su f c
W ter de t
Figure 1. Illustration of the void hole problem in WDFAD-DBR.
When the source node
initiates communication and ﬁnds
2 in its communication range,
before transmitting the data packet to
2, it acquires information about its neighbor node. It locates
in its transmission range and delivers the data to
2. Thus, it acknowledges the
with non-void node
status and receives the data packet. However, when
2 looks for its neighbors, it ﬁnds
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
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.
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.
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3.1. System Model
In proposed schemes, the 3D multi-sink network architecture is assumed [
], 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 .
W t r sur a e
W te de th
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
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 :
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
is the spreading coefﬁcient, which states the geometry of propagation, i.e.,
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.
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In UWSNs, the acoustic signal is affected by different noises, such as turbulence
Ns(f), waves Nw(f)and thermal noise Nth (f)[17,29–31]. 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
SN R(f,l) = Tp(f)−A(l,f)−N(f) + Di, (3)
represents the transmission power with frequency
denotes the directivity index to
evade unnecessary noise. During the reception of an acoustic signal, if
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 :
ν=1448.96 +4.591τ−5.304 ×10−2τ2+2.374 ×10−2τ3
+1.340(δ−35) + 1.63 ×10−1d+1.675 ×10−7d2
−1.025 ×10−2τ(δ−35)−7.139 ×10−13τd3.
represents the propagation speed of the acoustic signal, which is measured in ms
represents the temperature,
shows the salinity and
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 ≤τ≤30, 30 ≤δ≤40 and 0 ≤d≤8000.
3.1.3. Packet Types
In these schemes, there are three types of packets: namely
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.
packet has three data ﬁelds: type ID, source node ID and depth. The type ID represents
packet ID; source ID represents the source node ID; and depth is the depth of the source node.
On the other hand,
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
. The destination ID represents the ID of the destination node, and PID (Packet ID) represents
the order of packets.
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
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
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
packet from node
. Then, it calculates the previous and current depths of the node. After calculating
the depths of both nodes, node
calculates the distance difference of node
and the previous node
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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)
if node iis the preferable forwarder node of node jthen
Hold for next data packet
if node jis the preferable forwarder node of node ithen
up-to-date entry neighbor_tablemaking use of item (prevno de_depth,distance,tcurrent)
Move to the next step
if selected node iis not the preferable forwarder node of data_packet then
Upgrade neighbor_table using item (prevnode_depth,tcurrent,distance)
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
if the node is within the forwarding area then
Move to the next step
Hold for the next data_packet
Find the next depthmin in neighbor_table
if neighbor_table is empty then
Upgrade the depth in data_packet with currentnode _depth
Add (source ID, packet ID) into the queue
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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
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:
. In this way, every node can
reap newer statistics about neighbor nodes dynamically. As shown in Figure 2, in the A-DBR scheme,
senses data within its vicinity, it gathers the data packet and forwards it to nodes
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
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.
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
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:
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CH with max residual energy
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
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.
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
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
3 instead of depth adjustment, which consumes high energy. The node
forwards the data packet instead of dropping to node
4, which looks for its neighbors in the direction
of the destination and ﬁnds nodes
6. Thus, greedy forwarding again is resumed till the time
the packet reaches its destination.
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W t r su f c
W t r d pt
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
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()
fi←− get_next_ho p_f orw arder(n)
Forward the data packet
Queue the data packet
Re-schedule f orw ard_data_packet()
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).
r=1Econsumption (r)∀r∈rma 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
of the node; while transmission and receiving energy are restricted through
Equation (5b) using initial energy
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
is the transmission range of the node and
is the maximum transmission range
of the node; whereas, Econsumption is the total energy consumed in data communication, i.e.,
r=1Econsumption (r) = Etr ans +Ercv ∀r∈rm ax . (6)
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
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Datarate = 16,000 bps
. . .
. . .
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.
P2(0.693; 0.0087) Etrans+Ercv = 2.7837
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).
r=1Thr(r)∀r∈rmax . (12)
Constraints of the objective function are given in (12a)–(13).
C2:Etx ≤Ere (12b)
C3:TXn≤T Xmax (12c)
C4:Dij ≤Dma x
Frw . (12e)
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Equation (12a) ensures that the energy required for transmission and reception should be less
than the initial energy
of the node. Equation (12b) shows the constraint that transmission energy
ought to be less than the residual energy
. Equation (12c) ensures that in order to receive a good
quality signal, the data packet ought to be transmitted within its maximum transmission range
is the transmission range of the node and
is the maximum transmission range of the
node. Equation (12d) maintains a threshold of distance between sender
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
the bandwidth allocated to the forwarding nodes with high residual energy and
is the bandwidth
assigned to non-forwarding nodes, the bandwidth
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+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 [
] and Reliable and Energy-efﬁcient
Pressure-Based Routing (RE-PBR) .
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6.1. Simulation Setup
In the simulations, we have used multi-sink architecture of dimensions 10
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 [
]. 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  and are listed in Table 2.
Table 2. Simulation parameters.
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
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:
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
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:
is the hop count of nodes
. 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:
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:
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 =N−D N
is the number of alive nodes,
denotes the quantity of dead nodes after complete battery depletion and
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 7–10.
100 150 200 250 300 350 400 450 500
Number of Nodes
Energy consumption (J)
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
End-to-end Delay (sec)
Figure 8. Comparison of end-to-end delay.
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100 150 200 250 300 350 400 450 500
Number of Nodes
Figure 9. Comparison of Packet Delivery Ratio (PDR).
100 150 200 250 300 350 400 450 500
Number of Nodes
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.
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.
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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
Number of Alive Nodes
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
Network Lifetime (sec)
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
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
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
Packet Drop Ratio
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%
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Table 4. Performance trade-offs.
Schemes Features Achieved Parameters Trade-Offs
WDFAD-DBR Routing based on depth and
Less packet drops and improved
network lifetime, decreased
High energy consumption and
high end-to-end delay
RE-PBR Routing based on link
quality, depth and energy
Improved network lifetime and
High packet drop ratio and
Routing based on depth and
energy along with
Void hole avoidance results in
increased performance of the
network and reduced energy
High energy consumption in
sparse regions and increased
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
Routing based on depth and
energy with collision
Reduced energy consumption
and low delay Increased APD
Routing based on depth and
energy with tracking
High PDR and reduced delay Increased APD
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.
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.
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