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Improved HydroCast: A Technique for Reliable Pressure Based Routing for Underwater WSNs

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In this paper, performance of hydraulic pressure based routing protocol (HydroCast) is examined, and (Improved HydroCast) a technique for reliable HydroCast is proposed. Pressure level of sensor nodes is used to route data packet in greedy multi hop fashion to sinks deployed on the surface of water. The goal of this paper is to define reliable routing technique, that is applicable for both low and high density under water wireless sensor networks. In this paper, varying number of mobile sensor nodes are randomly deployed along with some fixed nodes at different strategic locations in the network. These few fixed nodes play important role in minimizing average number of transmissions for data packet delivery, and maximizing packet delivery ratio in sparse network. Simulation results validate that the proposed technique.
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Improved HydroCast: A technique for reliable
pressure based routing for underwater WSNs
Muhammad1, Nadeem Javaid1,, Babar Ali1, Aqeb Yahya1, Zahoor Ali Khan2,3, Umar Qasim4
1COMSATS Institute of Information Technology, Islamabad, 44000, Pakistan
2Internetworking Program, FE, Dalhousie University, Halifax, NS B3J 4R2, Canada
3CIS, Higher Colleges of Technology, Fujairah Campus, 4114, UAE
4University of Alberta, Edmonton, AB, T6G 2J8, Canada
nadeemjavaidqau@gmail.com; www.njavaid.com
Abstract—In this paper, performance of hydraulic pressure
based routing protocol (HydroCast) is examined, and (Improved
HydroCast) a technique for reliable HydroCast is proposed.
Pressure level of sensor nodes is used to route data packet
in greedy multi hop fashion to sinks deployed on the surface
of water. The goal of this paper is to define reliable routing
technique, that is applicable for both low and high density under
water wireless sensor networks. In this paper, varying number of
mobile sensor nodes are randomly deployed along with some fixed
nodes at different strategic locations in the network. These few
fixed nodes play important role in minimizing average number
of transmissions for data packet delivery, and maximizing packet
delivery ratio in sparse network. Simulation results validate that
the proposed technique.
I. BACK GROU ND A ND M OT IVATIO N
Recently, underwater wireless sensor networks (UWSNs)
have gained attention of researchers, due to high demand of
these networks in many applications. Some of these applica-
tions are: prediction of natural disasters, security, measurement
of water traits, exploration of underwater world and pollution
control. Acoustic signal is used for communication in UWSNs.
Sensor nodes, equipped with acoustic modems and sensors, are
dropped in the interested area. These sensor nodes easily de-
termine their pressure level via pressure gauges, and establish
links with each other. Nodes transmit sensed information in
multi hop fashion to the nearest sink. Multiple sinks, equipped
with acoustic modems as well as radio frequency, are deployed
on the surface of water. They collect information from sensor
nodes, and forward them via radio signal to off shore data
center.
Due to harsh environment of underwater, some data packets
drop before they reach to destination. For minimizing this issue
and improving packet delivery ratio; progress to destination
and packet delivery probability of neighboring nodes are
considered in forwarding set selection as discussed in section
III. Sensor nodes which fail to find neighbors of lower pressure
level also drop their data packets. In Hydraulic pressure based
routing protocol (HydroCast) [1], recovery schemes are used to
overcome this problem. recovery path establishes for a sensor
node that does not has lower pressure level neighbors. When
the network density is low, long recovery path establishes for
a node that is in void region (a node is considered to be in
void region if it does not has neighbor of lower pressure level).
This results in high end to end delay and energy consumption.
In this paper, the long recovery path establishment problem in
sparse network is reduced and HydroCast protocol is improved
by specifying few strategic locations in the network, and
deploying fixed sensor nodes at those locations. These fixed
nodes play important role in establishment of short path for
data packet delivery to sink, due to which end to end delay
and energy consumption are reduced.
Authors in depth based routing protocol (DBR) [2] use
depth only as a routing metric for data packet forwarding.
In this protocol, node of lowest depth among neighbors of a
sender node is selected as a next hop forwarder. Data packet
forwarding decisions are locally taken at each node. In [3],
energy efficient depth based routing protocol (EEDBR) is
proposed. In EEDBR, both residual energy and depth of a
node are considered as routing metrics for data forwarding.
Like DBR, EEDBR also forwards data packets towards the
sink using greedy algorithm approach. Authors in [4] propose
a distributed protocol called hop by hop dynamic addressing
based routing protocol (H2-DAB). In this protocol, nodes
maintain two types of IDs called node ID and hop ID. Each
node gets hop ID based on its distance from sink. A node that
has lowest distance with sink, gets smallest hop ID and vice
versa. A sender node selects the neighbor of smallest hop ID
as a next hop forwarder.
Yoon et al. [5] propose an AUV (Autonomous Underwater
Vehicle) aided underwater routing protocol (AURP). Multiple
AUVs are used in AURP as relay nodes for collecting data
packets from sensor nodes. In [6], authors introduce void
aware pressure routing protocol (VARP). Periodic beaconing
messages are propagated in the entire network, which are used
to set up next hop forwarders and establish paths to the nearest
sink. In [7], time of arrival ranging mechanism is introduced to
limit the redundant packet transmission issue in DBR protocol.
A scheme for maximizing network life time using mobile
nodes is introduced in [8]. This scheme is designed particularly
for applications in which delay can be tolerated. Authors in [9]
define balanced transmission mechanism (BTM), that balance
energy consumption in the network. Sink node is deployed
fixed underwater, while sensor nodes move around the sink
with low speed. Optimum energy level is defined in this paper
to balance energy consumption between all sensor nodes in
the network.
In HydroCast, authors introduce a recovery mechanism
called lower depth first method, for routing data packets
from nodes that have no neighbor of lower pressure level.
Packet drop issue is minimized via this recovery method
and the protocol shows good performance when the network
density is high. However, long recovery route is established
for data packet delivery when the network density is low. Fig.
1 demonstrates the long recovery route issue in Hydrocast
when the network density is low. As much network density
decreases, probability of longer recovery route establishment
increases, due to which many nodes participate in forwarding
data packet from void region, as shown in Fig. 1. This problem
results in high end to end delay and energy consumption.
The aforementioned problem is tackled in this paper by
pointing out some different strategic locations in the network
and deploying fixed nodes over there. Theses different fixed
deployed nodes play important role in reducing length of
recovery paths in sparse network condition, due to which end
to end delay and average energy consumption of the network
are reduced.
1
2
3
4
5
6
7
8
Sensor node - source
Sink
Sensor node used as a relay
Sensor node
Recovery path
Void region
Fig. 1: Long recovery route issue in HydroCast
II. NETWORK ARCHITECTURE
In this paper, two different types of nodes called sim-
ple mobile sensor nodes and AUVs, are used separately in
the network, and their performance are evaluated separately.
Each node senses the environment and sends the collected
information in multi hop fashion to the nearest sink. Sinks
collect information from underwater sensor nodes via acoustic
channels and transmit them to offshore data centers through
radio channels. Different number of nodes are deployed fixed
at different strategic locations in the network. Theses fixed
nodes play important role in establishment process of short
route for data packet delivery when the network density is
low. The details of these fixed nodes are discussed in section
III.
III. NETWORK MODEL
This section includes following three subsections: forward-
ing set selection, recovery mode and role of fixed deployed
nodes. Firstly, the forwarding set selection is discussed in
which optimum neighbor is selected as a next hop forwarder
based on progress to destination and delivery probability.
Secondly, recovery mode is explained in which recovery
routes are established for nodes that are in void regions of
the network. Finally the role of fixed deployed nodes are
explained. Different number of fixed nodes are used along
with mobile sensor nodes for reducing average end to end
delay and energy consumption in the network.
A. Forwarding set selection
Each node finds its neighbor nodes and sub neighbor nodes,
and calculate the euclidean distance with neighbors. Suppose
that a source node has a set of neighbors Γkin which k
neighbors are sorted based on their progress to destination and
packet delivery probability [10]. Priority is based on how close
a node is located to the sink. The highest priority neighbor
contributes dp
n1pn1. Where dp
n1is the n1neighbor progress
to destination (in meters), and pn1is the n1neighbor packet
delivery probability. The highest priority neighbor becomes
next hop forwarder and if it fails to transmit, next neighbor
contributes dp
n2pn2(1 pn1)and so on. The expected packet
advancement (EPA) [11] is given as:
EP Ak) =
k
X
i=1
dp
nipni
i1
Y
j=0
(1 pni)(1)
When a node transmits a data packet, all neighboring nodes
that receive the packet, set a holding timer such that neighbor
node of highest priority will have the shortest holding time.
The selected forwarder node forwards the received data packet,
and broadcasts an acknowledgment (ACK) packet. Other
neighbors drop the copy of that data packet after listening ACK
packet. This ACK packet prevents generation of redundant
packets in the network and saves the resources. It must be
mentioned that using short ACK packet is much reliable as
compared to dependency on overhearing the data packet.
B. Recovery mode
The lower depth first recovery method using two dimension
surface flooding (Recovery+SD-R) in HydroCast is used to
reroute data packets from sensor nodes that are on local
minimum. Nodes having no neighbor of lower pressure level,
are considered to be on the local minimum. According to this
recovery mechanism, when a node finds to be on the local
minimum, it selects a higher pressure level neighbor as a
forwarder that can route the data packet either to a position
where normal forwarding can be resumed, or to another local
minimum node of lower depth. The process continues till the
data packet gets out of void region. In denser networks, short
recovery routes are usually established, and data packet can be
shortly resumed to greedy forwarding. The main issue in this
method is the establishment of long recovery routes when the
network density is low, as shown in Fig. 1. Such long recovery
routes result in high end to end delay and energy consumption.
C. Role of fixed deployed nodes
As the network density decreases, frequency of void re-
gions increases. This results in establishment of long re-
covery routes for data delivery in HydroCast. To minimize
this problem, we deploy different number of fixed nodes.
Firstly, five fixed nodes are deployed within different sink
transmission/reception ranges at different strategic locations in
the network, while rest of sensor nodes are randomly deployed.
Then we deploy ten fixed nodes in the network. Five nodes
are deployed within sink range, while the remaining five are
deployed right beneath them and within their transmission
range. Finally fifteen fixed nodes are deployed such that
five nodes are within different sink ranges, five other nodes
beneath first five fixed nodes, and the remaining five are
deployed beneath the second five nodes. By this way, all fixed
nodes have successful route to sink. These fixed nodes play
important role in reducing number of transmissions for data
packet delivery. Fig. 2 shows how a fixed node reduces the
length of recovery route; established for a node on the local
minimum. As average number of transmissions for data packet
delivery decreases, the average end to end delay and energy
consumption decrease. The impact of these fixed nodes are
further discussed in section IV.
1
2
3
4
Void region
Void region
Sensor node - source
Sink
Sensor node used as a relay
Sensor node
Recovery path
Fixed sensor node
Fig. 2: Short recovery route due to fixed node
IV. PERFORMANCE EVALUATION
Results of the proposed technique are evaluated via simula-
tions in this section and compared with HydroCast protocol.
This section consists of two subsections. Firstly, performance
of the proposed technique is evaluated when different number
of AUVs are deployed in the network. We see that our
proposed technique has better performance, particularly when
the network density is low. Secondly, different large number
of simple mobile nodes are deployed in the network. We see
that in denser networks, the performance of both HydroCast
and the proposed technique is same.
A. AUVs
In this subsection, the performance of the proposed tech-
nique is discussed under random AUV mobility. Different
number of AUVs from 20 to 100 are deployed in the network.
The network region size is taken 5 km * 5 km * 5 km. The
transmission range of AUV is set 1000 m. AUVs move in
different directions (keeping their pressure level same) with
constant speed of 15 knots (7.716 m/s). After every 60 s, each
AUV sends its sensed information to the nearest sink.
Fig. 3a shows the packet delivery ratio when five AUVs are
placed fixed, while rest of AUVs are moving independently
with maximum speed of 15 knots. The packet delivery ratio
of HydroCast is low in sparse networks, because many AUVs
suffer from void regions and some established recovery routes
break down due to fast and independent mobility of AUVs.
The packet delivery ratio of the proposed scheme is better
than that of HydroCast. Fixed deployed AUVs help many
independent moving AUVs in establishment of short routes
to sink. As network density increases, the role of these fixed
positioned AUVs decreases, because of less number of voids.
In Fig. 3b, the packet delivery ratio performance of the
proposed technique is shown when ten AUVs are placed fixed
at different strategic locations in the network. The proposed
technique shows high packet delivery ratio in sparse networks.
Fixed AUVs at different locations in the network, minimize the
effect of void regions and help fast moving AUVs to establish
short routes to sink.
Fig. 3c shows packet delivery ratio when fifteen AUVs
are deployed fixed in the network. Proposed technique shows
better performance than HydroCast in successful delivery of
packets to sink. The main reason is that fast moving AUVs
can establish short routes to sink from almost any part of the
network due to presence of fixed AUVs in different strategic
locations in the network.
Fig. 4a shows average end to end delay when five AUVs
are placed fixed at multiple positions in the network, while
rest of AUVs are freely moving. The proposed scheme has
lower end to end delay than HydroCast when less number
of AUVs are deployed in the network. The reason is that in
the proposed technique, AUVs able to establish short paths to
sink due to availability of fixed AUVs at different locations in
the network. In HydroCast, some AUVs establish long routes
in order to bypass void regions, this results in increase in
delay. The average end to end delay of the proposed technique
increases as density of the network increases, because more
number of packets successfully reach to sinks.
In Fig. 4b, the average end to end delay is shown when
ten AUVs are deployed fixed, and rest of AUVs are moving
independently. Proposed technique shows slightly higher end
to end delay than that of HydroCast when network is sparse.
This is because fixed AUVs minimize to a large extent the
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Nodes
Delivery Ratio
Hydrocast: EPA+Recovery+SD R
Improved Hydrocast
(a) 5 nodes fixed
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Nodes
Delivery Ratio
Hydrocast: EPA+Recovery+SD R
Improved Hydrocast
(b) 10 nodes fixed
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1
Nodes
Delivery Ratio
Hydrocast: EPA+Recovery+SD R
Improved Hydrocast
(c) 15 nodes fixed
Fig. 3: Packet delivery ratio
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1
1.5
2
2.5
3
3.5
4
4.5
5
Nodes
Latency
Hydrocast: EPA+Recovery+SD R
Improved Hydrocast
(a) 5 nodes fixed
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1
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Nodes
Latency
Hydrocast: EPA+Recovery+SD R
Improved Hydrocast
(b) 10 nodes fixed
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4.5
5
Nodes
Latency
Hydrocast: EPA+Recovery+SD R
Improved Hydrocast
(c) 15 nodes fixed
Fig. 4: Average end to end delay
effect of void regions in sparse networks, thus more AUVs
from different part of the network successfully deliver data
packets to sink. So due to high packet delivery ratio, end to
end delay slightly increases. When number of AUVs increase
in the network, end to end delay in HydroCast increases due to
increase in its packet delivery ratio. In the proposed technique,
AUVs establish short paths to sink with the help of fixed
positioned AUVs, which result in lower average end to end
delay.
Fig. 4c shows end to end delay when fifteen fixed AUVs are
deployed. Average end to end delay of the proposed technique
is higher than that of HydroCast in sparse network due to
high packet delivery ratio of the proposed technique, as Fig
3c indicates. As network density increases, our proposed tech-
nique shows low average end to end delay from HydroCast.
The reason as mentioned before, is the establishment of short
recovery routes to sinks.
Fig. 5 shows the average number of transmissions in order
to deliver the data packet to sink. In Fig. 5a, number of
transmissions is plotted when five AUVs are placed at fixed
positions in the network. The average number of transmissions
in the proposed technique is less than that of HydroCast when
network is sparse. The improvement is due to the role of
fixed AUVs that help other moving AUVs to establish short
recovery paths and deliver their data packets in less number
of transmissions to sink. As network density increases, the
role of these fixed deployed AUVs decreases. The average
number of transmissions in the proposed technique is reduced
more as shown in Fig. 5b when ten AUVs are placed fixed
at different locations in the network. In case of fifteen fixed
AUVs placement, other fast moving AUVs in sparse network
easily establish recovery paths to sink, and probability of
more short path establishment increases. This yields to more
decrease in average number of data packet transmissions to
deliver a data packet to sink, as shown in Fig. 5c.
Fig. 6a shows average end to end energy consumption
when five AUVs are placed fixed at different locations in
the network. When less number of AUVs are deployed in
the network, HydroCast exhibits more energy to deliver a
data packet to sink, due to long recovery path for nodes
on local minimum. In the proposed technique, the average
end to end energy consumption is reduced due to decrease
in length of recovery paths via placement of fixed AUVs at
different critical locations in the network. A slightly increment
in average end to end energy consumption is seen in Fig.
6b and 6c when ten and fifteen fixed AUVs are respectively
deployed at different critical locations in the network. This
slight increment in average energy consumption is due to the
high improvement in packet delivery ratio as indicated in Fig.
3.
B. Simple sensor nodes
In this subsection, the performance of the proposed tech-
nique is evaluated under deployment of large varying number
of simple sensor nodes moving randomly in the network of
size 1000 m * 1000 m * 1000 m. The number of sensor
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10
Nodes
Transmissions
Hydrocast: EPA+Recovery+SD R
Improved Hydrocast
(a) 5 nodes fixed
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Nodes
Transmissions
Hydrocast: EPA+Recovery+SD R
Improved Hydrocast
(b) 10 nodes fixed
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2
3
4
5
6
7
8
9
10
Nodes
Transmissions
Hydrocast: EPA+Recovery+SD R
Improved Hydrocast
(c) 15 nodes fixed
Fig. 5: Number of transmissions for delivery
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1
1.5
2
2.5
3
Nodes
Energy Per Node Per Packet (J)
Hydrocast: EPA+Recovery+SD R
Improved Hydrocast
(a) 5 nodes fixed
20 30 40 50 60 70 80 90 100
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1
1.5
2
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3
Nodes
Energy Per Node Per Packet (J)
Hydrocast: EPA+Recovery+SD R
Improved Hydrocast
(b) 10 nodes fixed
20 30 40 50 60 70 80 90 100
0.5
1
1.5
2
2.5
3
Nodes
Energy Per Node Per Packet (J)
Hydrocast: EPA+Recovery+SD R
Improved Hydrocast
(c) 15 nodes fixed
Fig. 6: Average end to end energy consumption
nodes are varied from 150 to 450. The transmission range
of a sensor node is set 250 m. Nodes move randomly about
20 m in 60 s. After every 60 s, all sensor nodes transmit their
sensed attributes in multi hop fashion to the nearest sink. A
node transmits its sensed data directly to sink only if sink is
within its transmission range.
The proposed technique has little impact when the network
density is high. This is because the probability of void regions
occurrence decreases with the increase in the network density.
Also, nodes in denser network easily find neighbors and
establish paths to sink by selecting more optimal forwarders.
Therefore, we only show the simulation results when fifteen
nodes are fixed deployed at different locations in the denser
networks, and rest of mobile nodes are randomly deployed.
Fig. 7a shows packet delivery ratio. Most of the nodes in
the network successfully deliver their data packets to sinks
in both HydroCast and Improved Hydrocast. The reason is
that as the network density increases, nodes easily establish
short recovery paths whenever they face void regions. In case
of sparse network, the proposed technique performs better
than HydroCast as Fig. 3 indicates. But as network density
increases, both the proposed technique and HydroCast show
same performance as Fig. 7a shows.
Nodes select easily optimum forwarders from their neighbor
list to transmit their data packets when more number of nodes
are deployed in the network. This results in establishment of
short paths to sinks, due to which low average end to end
delay is achieved. The proposed technique shows low end
to end delay in the sparse networks, but the performance of
HydroCast and the proposed technique become same as we
shift from sparse to denser network, as Fig. 7b indicates.
Fig. 7c shows that HydroCast on average, transmits data
packets to sinks in less number of transmissions when network
is denser. As discussed earlier, fixed nodes have less role when
the network density is high. In sparse networks the proposed
technique performance is much better, but as the density of the
network increases, both HydroCast and the proposed technique
start perform same approximately.
In Fig. 7d, the average end to end energy consumption is
plotted. Both HydroCast and Improved HydroCast in denser
networks show low average end to end energy consumption
due to short route establishment for data packet delivery and
less void regions. As we mentioned before, our proposed
technique has better results when the network density is low,
and show approximately similar behaviour with HydroCast in
denser network condition.
V. CONCLUSION
In this paper, a technique for reliable pressure based routing
is proposed to minimize the aforementioned issues. We defined
different strategic locations in the network, and deployed fixed
nodes over there. These few fixed nodes play important role
in helping other mobile sensor nodes to establish successfully
short routes to sink. Long recovery paths become shorter
with these fixed nodes, particularly in sparse networks. The
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Nodes
Delivery Ratio
Hydrocast: EPA+Recovery+SD R
Improved Hydrocast
(a) Packet delivery ratio
150 200 250 300 350 400 450
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Latency
Hydrocast: EPA+Recovery+SD R
Improved Hydrocast
(b) Average end to end delay
150 200 250 300 350 400 450
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Transmissions
Hydrocast: EPA+Recovery+SD R
Improved Hydrocast
(c) Number of transmissions for delivery
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Nodes
Energy Per Node Per Packet (J)
Hydrocast: EPA+Recovery+SD R
Improved Hydrocast
(d) Average end to end energy consumption
Fig. 7: Simulation results in high density networks
proposed technique is reliable and applicable for time critical
applications in both low and high density networks.
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... In [26], an improved hydro cast was proposed, which deploys an AUV for data gathering. Routing takes place in a greedy multihop fashion to the sink by using the pressure level of sensor nodes. ...
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