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NADEEM: A Novel Reliable Data Delivery Routing Protocol for Underwater WSNs


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In this research work, we propose three schemes: neighbor node approaching distinct energy efficient mates (NADEEM), fallback approach NADEEM (FA-NADEEM) and transmission adjustment NADEEM (TA-NADEEM). In NADEEM, immutable forwarder node selection is avoided with the help of three distinct selection parameters. Also, void hole is avoided using fallback recovery mechanism to deliver data successfully at the destination. Moreover, transmission range is dynamically adjusted to resume greedy forwarding among the network nodes. The neighbor node is only eligible to become forwarder when it is not a void node. Additionally, linear programming based feasible regions are computed for an optimal energy dissipation and to improve network throughput. Extensive simulations are conducted for three parameters: energy, packet delivery ratio (PDR) and fraction of void nodes. Further, an analysis is performed by varying transmission range and data rate for energy consumption and fraction of void node. The results clearly depict that our proposed schemes outperform the baseline scheme (GEDAR) in terms of energy consumption and fraction of void nodes.
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NADEEM: A Novel Reliable Data
Delivery Routing Protocol
for Underwater WSNs
Nadeem Javaid(B
COMSATS University Islamabad, Islamabad 44000, Pakistan
Abstract. In this research work, we propose three schemes: neigh-
bor node approaching distinct energy efficient mates (NADEEM), fall-
back approach NADEEM (FA-NADEEM) and transmission adjustment
NADEEM (TA-NADEEM). In NADEEM, immutable forwarder node
selection is avoided with the help of three distinct selection parame-
ters. Also, void hole is avoided using fallback recovery mechanism to
deliver data successfully at the destination. Moreover, transmission range
is dynamically adjusted to resume greedy forwarding among the network
nodes. The neighbor node is only eligible to become forwarder when it is
not a void node. Additionally, linear programming based feasible regions
are computed for an optimal energy dissipation and to improve network
throughput. Extensive simulations are conducted for three parameters:
energy, packet delivery ratio (PDR) and fraction of void nodes. Further,
an analysis is performed by varying transmission range and data rate for
energy consumption and fraction of void node. The results clearly depict
that our proposed schemes outperform the baseline scheme (GEDAR) in
terms of energy consumption and fraction of void nodes.
1 Introduction
In the last decade, UWSNs have gained a lot of attention from researchers
because of their potential to monitor underwater environment. UWSNs have
extensive variety of applications such as military defense, monitoring aquatic
environment, disaster prevention, oil/gas extraction, offshore exploration, com-
mercial and scientific purposes, etc. [1,2].
In underwater atmosphere protocols of terrestrial wireless sensor networks
(TWSNs) cannot work perfectly. The TWSN and UWSN have differences in
many aspects such as use of acoustic links instead of radio links, UWSNs topology
is more dynamic than TWSNs because nodes move freely with water currents
and change their position frequently, localization of nodes is difficult as compared
to TWSNs. In addition, UWSNs face many challenges like low bandwidth, high
propagation delay and high communication cost [3].
Various routing protocols are proposed to minimize the energy consumption
and avoid void occurrence [4,5]. To recover data from void node, fallback recovery
Springer Nature Switzerland AG 2019
L. Barolli et al. (Eds.): WAINA 2019, AISC 927, pp. 103–115, 2019.
104 N. Javaid
mechanism and transmission adjustment approaches are widely used. Hence, an
efficient protocol is required which can minimize energy consumption and also
be able to avoid void node occurrence.
Opportunistic routing is exploited to transmit data reliably by selecting mul-
tiple neighbor nodes at each hop. However, geographic routing paradigm causes
void hole problems because of the selection of only one node for data transmis-
sion. On the other hand, the opportunistic routing results in redundant trans-
mission and consumes more energy [6]. Thus, to achieve multiple objectives at
the same time, both schemes are combined to improve energy efficiency and
avoid void node occurrence in the network.
Motivated by above consideration, we propose neighbor node approaching
distinct energy efficient mates (NADEEM) and its two variant with special fea-
tures of void recovery using fallback and transmission adjustment approaches.
The NADEEM uses geographic and opportunistic routing for energy effi-
cient data communication. While in transmission adjustment NADEEM (TA-
NADEEM), the transmission of the void node is adjusted to bypass the void
region and forwards the data to the immediate available node. While fallback
recovery NADEEM (FA-NADEEM) uses backward transmission and finds a dif-
ferent routing path for successful data delivery at the destination.
2 Related Work and Problem Statement
Depth based routing (DBR) [7] only requires depth information to forward data
packets towards the sink with greedy approach. Each node forwards the data
packet based on depth parameter and also suppresses communication to avoid
redundant transmission via calculating holding time. The priority is assigned to
forwarder node solely on the basis of depth, however, it depends that packet is
new and not delivered at the destination. The DBR provides improved network
lifetime and high data delivery ratio. However, this greedy mechanism with one
parameter forces immutable selection of forwarder node due to which network
nodes get partitioned and resources remain under utilized.
An improved adaptive mobility of courier nodes in threshold-optimized DBR
(iAMCTD) [8] is a location free routing protocol, which is designed for time
critical applications. It provides an improved network lifetime, minimized end-
to-end delay with the help of mobile courier nodes. However, this scheme results
in low throughput, because of avoidance of the unnecessary transmission.
In balanced load distribution (BLOAD) [9], authors tackle the problem
of energy holes using the mechanism of fragmentation of data packet. The
fragments of every data are delivered via direct and multi hop transmissions
towards the destination. Moreover, nodes deployed away from the destination are
equipped with higher battery resulting in improved network lifetime. Authors
in weighting depth and forwarding area division-DBR (WDFAD-DBR) [10]con-
sidered the depth of current and next forwarding node in the direction of sink
deployed at the water surface. The information helped in avoiding void node
selection with less packet drop ratio. However, knowledge up to hops never guar-
antee reliable forwarder selection especially when the deployment is sparse.
NADEEM: A Novel Reliable Data Delivery Routing Protocol 105
An adaptive clustering habit ((ACH )2) is presented in [11], which proposes
the mechanism of free association of nodes with cluster heads (CHs). The CHs
are first elected on the premise of threshold value and then, an optimal num-
ber of CHs is selected based on the distance between each head node. In this
way data load managed among the head nodes which significantly minimizes
the energy consumption. Moreover, it reduces propagation distance and evades
back transmissions to reduce energy dissipation however, high communication
overhead in associating normal nodes with head nodes.
A hop-by-hop dynamic addressing based routing protocol for pipeline moni-
toring (H2-DARP-PM) is proposed in [12] for efficient forwarder node selection.
It uses dynamic addressing to obtain reliable and most effective forwarder node
in terms of energy consumption. Also, assigns dynamic hop address to each node
which contributes in delivering data at the destination. Although, the PDR is
enhanced, however, at cost of high energy consumption.
Cluster based sleep wake scheduling is performed by assuming initiator nodes
in [13]. The initiator nodes initiate communication and select the CHs. The CH
with high energy is selected to active mode while other nodes sent to sleep mode.
The transmission is resumed once head node is nominated and this decreases
energy consumption along with high lifetime and throughput. However, the
immutable selection of CH degrades the network performance. While authors
in [14] proposed an energy efficient cluster head selection protocol named parti-
cle swarm optimization (PSO-ECHS). To improve the performance of this algo-
rithm, various metrics including distance between the clusters, distance between
sinks and residual energy of nodes are taken into account.
3 Proposed System Model
To enable communication among the sensor nodes, an acoustic architecture is
required to illustrate the working mechanism of the network. Our proposed
schemes are based on multi sink architecture where nodes are randomly deployed
in the ocean and sink nodes at the surface of water [2] as shown in Fig. 1.Itcon-
sists of two types of nodes: relay nodes and anchored nodes [10]. The nodes
that are anchored are static at the bottom of the ocean whereas relay nodes
are deployed randomly in the acoustic environment. The relay and anchor nodes
use acoustic signal to carry information towards the destination. The anchored
nodes obtain the sensed information and forward it towards the sink located
at the surface with the help of relay nodes. Whereas, at the surface, sink nodes
relay data through radio signals for transmitting information at the base station.
It is assumed that packets received at a single sink are considered to be received
at all sink nodes successfully.
In our work, we made some assumptions because our focus was on the energy
efficiency and void avoidance. (i) A consideration has been made that every node
(relay, anchored and sink nodes) can obtain its coordinates with the help of local-
ization services [6]. (ii) The communication is symmetric, where communication
between any two random nodes results in the consumption of the same amount
106 N. Javaid
of energy. For instance, node i transmits data to node j with energy E and when
j sends data to i, the battery dissipation remains the same. It only changes when
the distance between two nodes vary. (iii) All acoustic nodes has the power to
adjust communication range autonomously and sink node can receive multiple
packets at the same time without any data loss or collision. (iv) The effect
of water currents is in the direction of horizontal, while in vertical direction
the movement is almost negligible. In the following paragraph an overview of
Thorp’s propagation model [2] has been presented to model the energy con-
sumption according to the distance between the source and the destination.
Fig. 1. System model
3.1 UWSNs Propagation Model
The path loss due to unhindered propagation route for a signal having frequency
fover a distance lis given as [15]:
A(l, f )=lsα(f)l,(1)
where sis the spreading factor and α(f) is the absorption coefficient. The geom-
etry of propagation is described using the spreading factor kand its values
for spherical, cylindrical and practical spreading are: s=2,s=1,ands=1.5
respectively. The absorption coefficient α(f), in dB/km for fin KHz, is described
by the Thorp’s formula [2]as:
10logA(l, f)=s.10logl +l.10logα(f).(2)
The common signal-to-noise ratio (SNR) over lis given as:
SNR(l)=Energyb/A(l, f )
NADEEM: A Novel Reliable Data Delivery Routing Protocol 107
where Energyband Noise0are constants that reveal per bit energy transmis-
sion and noise power density on a non declining additive white gaussian noise
(AWGN) channel [3]. As in [16], rayleigh fading is used to model small scale
system where SNR has the following distribution probability:
The probability of error is given as:
ρe(Y)ρl(Y)lY. (5)
Here, ρe(Y) denotes the random modulation at a specific value of SNR and Y
is the probability of error. In this paper, we use the binary phase shift keying
(BPSK) modulation in which each bit is carried by a symbol [17]. The error
probability over lis taken from [18,20].
2(1 SNR(l)
The data packet delivery probability for nbits is given by:
ρ(l, n)=(1ρe(l))n.(7)
4 Proposed Work
In this section, we describe our proposed schemes in detail. Initially, we will dis-
cuss the beacon message dissemination in the network for configuration. Addi-
tionally, neighbor node selection mechanism will be presented for successful data
communication. Moreover, an efficient mate selection is discussed to show its
effectiveness for reliable data transmission.
4.1 Dissemination of Beacon Message
The beacon dissemination is vital to enable the configuration of the nodes for
successful data communication. Once all nodes and sinks are deployed in the
network volume, collaboration is important to relay data in multihop fashion
from a source to destination. Thus, a beacon is transmitted by a sink node from
the surface of water which is also equipped with acoustic modem. This message
includes unique information like identification coordinates (x, y, z). The sinks
are equipped with positioning system and have the area facts of each other [2].
Similar is the case for sensor nodes. The beacon transmitted by sensor nodes con-
sists of sequence number, ID and x, y, z coordinates. The positioning system is
ineffective in underwater due to excessive frequency signal absorption [2]. There-
fore acoustic localization services are used for acquiring nodes information [6].
108 N. Javaid
Moreover to reduce the energy consumption and minimize the communication
overhead, the size of the beacon is reduced by only keeping information of node
ID and coordinates. After receiving the information, a node updates its entry
in the neighbor table. Beacons are generated to inform about the location of
the sensor node. With this knowledge, neighbor nodes can also access an energy
optimal forwarder node within the communication range.
4.1.1 Distinct Mates Selection
An optimal utilization of resources is always desired. In acoustic environment,
node battery is one of the most crucial asset that needs to be efficiently con-
sumed during the process of data communication. The void occurrence causes
network partition, thus an approach which can avoid the occurrence or bypass it,
is required. Therefore, we opt to select various forwarding mates from a neigh-
borhood [19,21]. The multiple neighbor selection concept is to ensure backup
when node with highest priority fails to deliver the desired data packet at the
respective destination. The following mathematical expression is used to find out
mates with desired features.
M(i)= E(i)
(Trange ×N)+1.(8)
In aforesaid equation, M(i) shows the mate selected for data communication
in the network. While idepicts the number of mates selected (ivaries from 0
to N, where Ncould be any real positive integer). The right hand side of the
expression shows computation mechanism of priority factor. Here E(i) denotes
the energy of node iand it is multiplied with Nnumber of neighbors to increase
its suitability because higher the neighbor number is, more will be backup avail-
able incase of transmission failure and 1 is added to avoid when Nnumber is
0. The fundamental concept of this approach is to boost the data packet in the
direction of sinks on the surface. The selection of multiple neighbor nodes results
in less number of retransmissions of the data packets [18,20].
Moreover, one more factor is considered before nominating the forwarder
node, packet advancement (ADV) [18] to choose the forwarder node which leads
the packet towards the destination. The ADV is defined as the difference between
distance of a source node Siand destination node Diwith difference of distance
between neighbor Yand Ni[2]. Where njbe the node which has a packet
to transmit and its neighbors set is represented as Nj(t) and set of sinks is
represented as Sj(t), where tis representing the time. Therefore, the neighbor
node set in geographic and opportunistic routing is given as:
where S
iis from set of sinks Si(t) and it is closest sink of node njas:
NADEEM: A Novel Reliable Data Delivery Routing Protocol 109
4.1.2 Energy Efficient Mate Selection
The sender node set selection is based on normalized ADV (NADV) as proposed
in [2,3,20]. Using NADV, the most appropriate next hop forwarder mate from
the neighbor node set Niis selected. The NADV relates the foremost economical
trade-off between the proximity and link cost to choose the priorities of the eligi-
ble nodes. So greater the NADV of node is greater will be its priority for selection
as next forwarder. For every next-hop forwarder node nfNi, NADV is:
NADV(nf)=ADV (nf)×ρ(lj
here, ADV (nf)=D(nj,s
f)isthenfADV towards the closest sink,
fis the distance between source node njand forwarder node nfand ρ(lj
is the probability of nbits over distance lj
fand it is given in Eq. 7.
After the node set selection and next hop forwarder set selection assumes
fiNiis the set that is formed according to the priorities of NADV [2,20].
As the goal of geographic and opportunistic routing is to find the fiNito
maximize the expected packet advance (EPA). The EPA of fiis formulated
in Eq. 11.
d=1NADV (nd)Πd1
j=0 (1 ρ(lj
Ultimately, the set with maximum EPA is chosen as next forwarder node set.
The node with highest precedence is chosen from the forwarder set as a next hop
forwarder. It declares its location records to its neighbor node. If a node is not
decided as a next hop forwarder node for a longer period, its packet is discarded.
When the sparsity of the network increases, the void node occurrence probabil-
ity also rises. However, the lack of recovery mechanism make it vulnerable to
high packet drop ratio. Therefore, FA-NADEEM is used to ensure that packet
from void region must be recovered and delivered at the destination via an
alternate path. Figure 2illustrates the mechanism of FA-NADEEM. It is evi-
dent when source node Stransmits the data packet, it obtains the information
up to two hops, along with neighbor information to ensure the suitability of the
elected node to proceed with the data communication. There are four neighbor
nodes: n1, n2 n3 and n4. The first hop node is n2 because it is more effective to
relay packet in terms of energy and also helps in reducing the hop number. The
NADEEM further explores the neighbors of n2 and then n3 and so on. When
the n3 is reached, it encounter void region. Thus, fallback approach is adopted
in NADEEM and called FA-NADEEM. Figure 2shows n3 declares it void and
instead of dropping the packet, it looks in its list based on the priority to ensure
it has to traverse least number of hops in recovery procedure. Similarly, n3 falls
back and transmits the data to n4 in its communication range. Then from n4,
the process of data forwarding is continued in the direction of sink deployed
at the water surface. In communication period, then fall back recovery proce-
dure is adopted to discover different routes which leads towards the destination.
110 N. Javaid
As soon as a node is found, greedy forwarding is resumed to save node battery.
Let’s assume a scenario depicted in Fig. 2, where node Sforwards data packet
to the node n2 and when at node n3 it detects the void hole region then it uses
the fall back mechanism to forward the data packet towards node n4.
Fig. 2. Working mechanism of FA-NADEEM
The TA-NADEEM has the same steps of NADEEM except the adjustment of
transmission to avoid void hole problem which is illustrated in Fig. 3. During
communication TA-NADEEM computes the number of neighbors of the for-
warder node after adjusting the transmission range. Let us assume a scenario as
depicted in Fig. 3, where node Shas to send a data packet towards the destina-
tion, it has no neighbor in its transmission range. Instead of opting for fallback
procedure, it simply adjusts the transmission range and delivers the data packet
directly to the immediate node comes into its adjusted communication range.
Fig. 3. Adjustment of transmission range in TA-NADEEM
When Sfails to find a neighbor node, it adjusts its transmission range as we
mentioned earlier in system model that every node has the ability to adjust the
NADEEM: A Novel Reliable Data Delivery Routing Protocol 111
transmission range incase of void occurrence to avoid packet drop. The imme-
diate forwarder is n1, S acquires the information about the neighbors of n1
to ensure that selected node is not a void. Thus, it looks into the neighbor
table of n1 and finds that it has two neighbors at higher depth than n1. Thus,
n1 is selected as the effective forwarder node to continue with the process of
data routing. If there is no neighbor in the communication vicinity of n1, then
further adjustment in transmission range is done until a suitable forwarder is
located. Additionally, it updates the information in packet queue if there is no
report regarding the packet delivery. It helps in avoiding redundant data packets
transmission resulting in less energy consumption which prolongs the network
lifespan. Moreover, in future, nodes look into the neighbor table and avoids the
void occurrence. In this way extra energy consumed in transmission adjustment
is accommodated.
5 Simulation Results and Discussion
To evaluate the performance of proposed schemes (NADEEM, FA-NADEEM
and TA-NADEEM), we conducted extensive simulations against existing scheme
GEDAR [3]. The detail is given in the upcoming subsections.
5.1 Performance Control Parameters
To perform simulations, the calibration of input parameters which directly affect
the performance of the network system. The simulations are run for 10 times and
average is plotted using the line graphs. Moreover, number of nodes is randomly
deployed in a 3 dimensional (3D) network volume of 1500m ×1500 m ×1500 m
with an increment of 50 nodes to 150 until it reaches to 450 nodes. Additionally,
the transmission range is set to 250 m for comparison and it varies in transmission
analysis from 150 m to 350 m. Also, data rate is set to 16 kbps for performance
evaluation against existing scheme GEDAR [3], whereas to analyse the deviation
in energy and void occurrence is observed on 32 kbps, 64 kbps and 128 kbps. In
addition, payload of the data packet is set to 150 bytes. The values of energy
consumption are: Pt=2W,Pr=0.1W and Pi= 10 mW for transmission,
reception and idle energies, respectively [3].
5.2 Fraction of Local Maximum Nodes
Figure 4illustrates the ratio of void nodes of NADEEM and its variants against
the GEDAR. It is evident from the results that void ratio decreases with the
increase in node number. The highest transmission failure ratio can be observed
of FA-NADEEM which falls significantly up to 250 nodes. Then again, after the
node density of 300, the amount of void node occurrence decreases to a certain
level, which remains consistent till 450 nodes. The decrease in ratio is because
of the fall back mechanism of FA-NADEEM which looks for an alternative route
112 N. Javaid
from all nodes in the communication range to recover and deliver data packet
successfully at the destination.
NADEEM has less void appearances at the end of the node increment because
it does not have transmission adjustment, fall back and depth adjustment mech-
anisms (Fig. 4). This help in saving node battery ultimately leading to less nodes
and more number of alive nodes. Thus, when the sparsity of the nodes decreases,
the probability of void occurrence also decreases.
GEDAR and TA-NADEEM show almost identical trend in rise and fall of
void proportion during the nodes communication. Although, the values are dif-
ferent and at the end TA-NADEEM beats the baseline scheme because of direct
transmission adjustment, instead of exchanging beacon message to find out the
different route (Fig. 4). When different route is not available then depth of the
node is adjusted, these aspects consume a lot of energy and suddenly node bat-
tery depletes. Whereas, TA-NADEEM adaptively adjusts its transmission power
and bypass the void node successfully.
From 150–200 nodes GEDAR shows sudden decrease in fraction of void nodes
(Fig. 4). As GEDAR considers movement of void node towards its neighbor node
without checking whether the neighbor nodes have enough forwarder nodes or
not, therefore it has high fraction of void nodes compared to proposed schemes.
150 200 250 300 350 400 450
Number of nodes
Fraction of local maximum nodes
Fig. 4. Comparison of fraction of void nodes
5.3 Packet Delivery Ratio
Figure 5shows that the energy tax gradually decreases with the increase in
density of network. The highest PDR is achieved by FA-NADEEM due to its
ability of finding data route from an alternate node within the communication
vicinity. When a trap of void node occurs, instead of dropping the packet, the
source node proceed with fall back procedure and looks in 360to resume the
data transmission process.
NADEEM depicts moderate success ratio of packets transmission compared
to other schemes in Fig. 5. The performance is neither high nor low, it operates
NADEEM: A Novel Reliable Data Delivery Routing Protocol 113
on static parameters, however, the forwarder selection is not based on single
parameter of depth.
On the other hand, the performance regarding success ratio of GEDAR and
TA-NADEEM is identical in Fig. 5. The energy consumption is high in both of
the methodologies because in GEDAR, depth adjustment in vertical direction
which consumes more energy as compared to simple recovery methods. Similarly,
the TA-NADEEM opts towards the adjustment of transmission range, the energy
consumption depends on the distance between the source and destination. When
the transmission power is adjusted, it requires more energy to transmit the
signal to overcome the issues of attenuation and fading in dynamic acoustic
150 200 250 300 350 400 450
Number of nodes
Packet delivery ratio
Fig. 5. Comparison of PDR
5.4 Energy Consumption
The comparison of energy consumption in delivering single packet at the desti-
nation of all the schemes is depicted in Fig. 6. The minimum energy consumption
can be observed from the graphical representation of TA-NADEEM and slightly
high of GEDAR. The energy consumed for one packet is almost 0.02 J of TA-
NADEEM, whereas, the node battery dissipation in GEDAR is high than all the
proposed schemes which very small number of nearly 0.12 J.
The energy consumption of FA-NADEEM is comparatively more than the
other two proposed schemes and almost the same with GEDAR (Fig. 6). The
reason of more energy is of alternate route which increases the route length and
resulting in more energy requirement. Although, it achieves better PDR (Fig. 5),
however, still the utilization of limited resource battery is considerably higher.
On the contrary, TA-NADEEM shows minimum energy dissipation in deliv-
ering a single data packet. It happens due to only energy required in adjusting
power and factors like message exchange, depth adjustment are not involved.
Thus significant amount of energy is saved in TA-NADEEM. Whereas NADEEM
has initially low energy requirements, but, when node density increases, the col-
lision rate of data packets also maximizes resulting in high packet loss.
114 N. Javaid
150 200 250 300 350 400 450
Number of nodes
Energy per data packet per node (J)
Fig. 6. Comparison of energy consumption
6 Conclusion
In this paper, we have proposed NADEEM, FA-NADEEM and TA-NADEEM
routing protocols to enhance the energy efficiency and minimize the fraction
of void node occurrence. The NADEEM minimizes the energy consumption by
avoiding immutable forwarder selection. It also achieves higher packet delivery
ratio. Whereas, FA-NADEEM avoids successfully the void node through send-
ing data from the alternate route. However, the use of alternate path increases
the number of hops which resulted in more energy consumption. Moreover, it
has higher throughput as compared to NADEEM. On the other hand, when
FA-NADEEM fails to find an another forwarding path then transmission of
NADEEM is adjusted to ensure data delivery at the destination node. Addition-
ally, TA-NADEEM looked for an immediate forwarder node and its neighbors to
ensure that selected node is not void. It has low network throughput, however,
minimum energy consumption for one packet. The results are validated through
extensive simulations. The outcome clearly shows that proposed schemes outper-
formed the baseline existing scheme (GEDAR) in terms of energy consumption
and void avoidance.
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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.
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In Underwater Linear Sensor Networks (UW-LSN) routing process, nodes without proper address make it difficult to determine relative sensor details specially the position of the node. In addition, it effects to determine the exact leakage position with minimized delay for long range underwater pipeline monitoring. Several studies have been made to overcome the mentioned issues. However, little attention has been given to minimize communication delay using dynamic addressing schemes. This paper presents the novel solution called Hop-by-Hop Dynamic Addressing based Routing Protocol for Pipeline Monitoring (H2-DARP-PM) to deal with nodes addressing and communication delay. H2-DARP-PM assigns a dynamic hop address to every participating node in an efficient manner. Dynamic addressing mechanism employed by H2-DARP-PM differentiates the heterogeneous types of sensor nodes thereby helping to control the traffic flows between the nodes. The proposed dynamic addressing mechanism provides support in the selection of an appropriate next hop neighbour. Simulation results and analytical model illustrate that H2-DARP-PM addressing support distribution of topology into different ranges of heterogeneous sensors and sinks to mitigate the higher delay issue. One of the distinguishing characteristics of H2-DARP-PM has the capability to operate with a fewer number of sensor nodes deployed for long-range underwater pipeline monitoring.
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Due to limited energy resources, energy balancing becomes an appealing requirement/ challenge in Underwater Wireless Sensor Networks (UWSNs). In this paper, we present a Balanced Load Distribution (BLOAD) scheme to avoid energy holes created due to unbalanced energy consumption in UWSNs. Our proposed scheme prolongs the stability period and lifetime of the UWSNs. In BLOAD scheme, data (generated plus received) of underwater sensor nodes is divided into fractions. The transmission range of each sensor node is logically adjusted for evenly distributing the data fractions among the next hop neighbor nodes. Another distinct feature of BLOAD scheme is that each sensor node in the network sends a fraction of data directly to the sink by adjusting its transmission range and continuously reports data to the sink till its death even if an energy hole is created in its next hop region. We implement the BLOAD scheme, by varying the fractions of data using adjustable transmission ranges in homogeneous and heterogeneous simulation environments. Simulation results show that the BLOAD scheme outperforms the selected existing schemes in terms of stability period and network lifetime.
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Most existing deployment algorithms for event coverage in underwater wireless sensor networks (UWSNs) usually do not consider that network communication has non-uniform characteristics on three-dimensional underwater environments. Such deployment algorithms ignore that the nodes are distributed at different depths and have different probabilities for data acquisition, thereby leading to imbalances in the overall network energy consumption, decreasing the network performance, and resulting in poor and unreliable late network operation. Therefore, in this study, we proposed an uneven cluster deployment algorithm based network layered for event coverage. First, according to the energy consumption requirement of the communication load at different depths of the underwater network, we obtained the expected value of deployment nodes and the distribution density of each layer network after theoretical analysis and deduction. Afterward, the network is divided into multilayers based on uneven clusters, and the heterogeneous communication radius of nodes can improve the network connectivity rate. The recovery strategy is used to balance the energy consumption of nodes in the cluster and can efficiently reconstruct the network topology, which ensures that the network has a high network coverage and connectivity rate in a long period of data acquisition. Simulation results show that the proposed algorithm improves network reliability and prolongs network lifetime by significantly reducing the blind movement of overall network nodes while maintaining a high network coverage and connectivity rate.
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Underwater wireless sensor networks (UWSNs) have been showed as a promising technology to monitor and explore the oceans in lieu of traditional undersea wireline instruments. Nevertheless, the data gathering of UWSNs is still severely limited because of the acoustic channel communication characteristics. One way to improve the data collection in UWSNs is through the design of routing protocols considering the unique characteristics of the underwater acoustic communication and the highly dynamic network topology. In this paper, we propose the GEDAR routing protocol for UWSNs. GEDAR is an anycast, geographic and opportunistic routing protocol that routes data packets from sensor nodes to multiple sonobuoys (sinks) at the sea's surface. When the node is in a communication void region, GEDAR switches to the recovery mode procedure which is based on topology control through the depth adjustment of the void nodes, instead of the traditional approaches using control messages to discover and maintain routing paths along void regions. Simulation results show that GEDAR significantly improves the network performance when compared with the baseline solutions, even in hard and difficult mobile scenarios of very sparse and very dense networks and for high network traffic loads.
The design of routing protocols for Underwater Acoustic Sensor Networks (UASNs) poses many challenges due to long propagation, high mobility, limited bandwidth, multi-path and Doppler effect. Because of the void-hole caused by the uneven distribution of nodes and sparse deployment, the selection of next hop forwarding nodes only based on the state of current node may result in the failure of forwarding in the local sparse region. In order to reduce the probability of encountering void holes in the sparse networks, in this paper we present weighting depth and forwarding area division DBR routing protocol, called WDFAD-DBR. The novelties of WDFAD-DBR lie in: firstly, next forwarding nodes are selected according to the weighting sum of depth difference of two hops, which considers not only the current depth but also the depth of expected next hop. In this way, the probability of meeting void holes is effectively reduced. Secondly, the mechanisms for forwarding area division and neighbor node prediction are designed to reduce the energy consumption caused by duplicated packets and neighbors' requests, respectively. Thirdly, we make theoretical analyses on routing performance in case of considering channel contending with respect to delivery ratio, energy consumption and average end-to-end delay. Finally we conduct extensive simulations using NS-3 simulator to verify the effectiveness and validity of WDFAD-DBR.
Recently, underwater wireless sensor networks (UWSNs) have emerged as a promising networking technique for various underwater applications. An energy efficient routing protocol plays a vital role in data transmission and practical applications. However, due to the specific characteristics of UWSNs, such as dynamic structure, narrow bandwidth, rapid energy consumption, and high latency, it is difficult to build routing protocols for UWSNs. In this article we focus on surveying existing routing protocols in UWSNs. First, we classify existing routing protocols into two categories based on a route decision maker. Then the performance of existing routing protocols is compared in detail. Furthermore, future research issues of routing protocols in UWSNs are carefully analyzed.
Wireless Sensor Networks (WSNs) were envisaged to become the fabric of our environment and society. However, they are yet unable to surmount many operational challenges such as limited network lifetime, which strangle their widespread deployment. To prolong WSN lifetime, most of the existing clustering schemes are geared towards homogeneous WSN. This paper presents Enhanced Developed Distributed Energy Efficient Clustering (ED-DEEC) scheme for heterogeneous WSN. EDDEEC mainly consists of three constituents i.e., heterogeneous network model, energy consumption model and clustering based routing mechanism. Our heterogeneous network model is based on three energy levels of nodes. Unlike most works, our energy consumption model takes into account the impact of radio environment. Finally, the proposed clustering mechanism of EDDEEC changes the cluster head selection probability in an efficient and dynamic manner. Simulation results validate and confirm the performance supremacy of EDDEEC compared to existing schemes in terms of various metrics such as network life.