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Proceedings of the International Conference on Emerging Trends in Engineering and Management (ICETEM14)
30 – 31, December 2014, Ernakulam, India
INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING &
TECHNOLOGY (IJCET)
ISSN 0976 – 6367(Print)
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Volume 5, Issue 12, December (2014), pp. 42-50
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ARCHITECTURAL STUDY OF LITTORAL ZONE
SENSING USING UNDERWATER ACOUSTIC WIRELESS
SENSOR NETWORK
Sreeranjini Nandakumar
Final Year M.Tech, Dept. of Computer Science & Engineering, Sree Narayana Gurukulam College of Engineering
Kerala, India
Assoc. Prof. P.Krishna Kumaran Thampi
Dept. of Computer Science & Engineering, Sree Narayana Gurukulam College of Engineering, Kerala, India
ABSTRACT
The development in Wireless Sensor Network (WSN) technology and its widespread application pay way for
the development of Under Water Wireless Sensor Network (UWSN). In UWSN, sensor nodes are deployed under water
or its surface for sensing information. These sensor nodes which are inexpensive & portable in nature; normally with
limited memory, power and computational capabilities provides reliability and robustness. Also, the cost for deploying
such a network is less than using Autonomous Underwater Vehicles (AUV).As radio wave requires high transmission
power and large antennas, acoustic signals are preferred in UWSN. By using UWSN, we can perform oceanographic
monitoring which includes detecting natural calamities (like Tsunami, earth quakes etc.), monitoring coastline
(protecting coastline, identifying intruders etc.), monitoring water quality, detecting submarines etc. But, some factors
like node mobility, security, energy consumption, propagation delay, bandwidth, Doppler Effect, error rate etc. acts as a
barrier in the advancement of this area. The major problem is the node mobility due to ocean currents. It is difficult to
control the position of nodes and we can’t predict the location accurately. Also, currently existing security measures used
in terrestrial WSN can’t be efficiently used in UWSN. New secure protocols must be used. The energy consumed by the
sensor nodes must be controlled by sleep-wake up algorithm. Otherwise battery has to be recharged frequently and it
affects overall lifetime of the UWSN. As a solution to these problems, various architectures aiming efficient deployment
of sensor nodes under water were developed. This paper presents a study on various UWSN deployment architectures
(mainly static and mobile models) focusing their features and limitations.
Keywords: Energy Consumption, Node Mobility, Security, UWSN, UWSN Architecture, WSN.
1. INTRODUCTION
Costal security is a major challenge in the world. There are couple of methods to monitor and secure the littoral
zone. Underwater wireless sensor network offers wide opportunities to explore under water to capture information about
the oceanographic creatures, environment changes, details about submarines, identifying enemies and intruders etc.
[1][6]. There are lots of differences between terrestrial wireless sensor networks and under water wireless sensor
network. In case of terrestrial wireless network, we can use radio signals for communication. But in UWSN, it is not
preferred and advisable to use radio signals for long distance communication as it will get attenuated.
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Proceedings of the International Conference on Emerging Trends in Engineering and Management (ICETEM14)
30 – 31, December 2014, Ernakulam, India
Normally a set of sensor nodes are deployed into water including sea bed and surface. There will be a surface
station at the surface of water to which the information is passed by the sensor nodes. Later, this surface station send the
information accumulated there to base station situated at on-shore. The architectures for deploying sensor nodes for
monitoring water bodies follow this basic deployment strategy. But the way, in which the sensor nodes deployed, the
type of nodes used may vary.
The UWSN faces so many challenges [1][6] related to node mobility, energy consumption, security,
propagation delay, time synchronization etc. Developing a suitable deployment model for UWSN will help to overcome
these challenges for an extent. To mitigate energy consumption, proper energy efficient architecture is needed. Sensor
node mobility leads to propagation delay between the nodes. An efficient architecture was needed to manage the time
synchronization of nodes. The popular time synchronization algorithms are TSHL, MU-Sync, D-Sync and Da-Sync [6].
Each of them has its own merit and demerit.
Therefore architecture should focus on all these challenges and find a way to mitigate or minimize the same. We
can broadly classify UWSN architectures based on two factors as shown in Fig.1.
Figure 1: UWSN categorization
Sensor nodes are anchored on the sea bed in 2-D [3], whereas in 3-D [5] nodes are placed at different depths.
The static, mobile and hybrid type of UWSN architectures can follow either 2-D or 3-D deployment of nodes.
2. UWSNArchitectures
2.1 Pompili & Melodia Two Dimensional (2-D) Architecture
In this architecture [3, 5], a set of sensor nodes are anchored on the sea bed. Each set of sensor nodes are
represented as sensor clusters, one sensor in each cluster were marked as Underwater Sink (UW-gateway). All the sensor
nodes in a cluster sense the data and forward it towards UW-Sinks. These UW-sinks are liable for transmitting the data
towards the Surface Station (SS), placed on the sea surface. UW-Sinks consist of two acoustic transceivers- a horizontal
and a vertical. Horizontal transceiver is used for the communication with ordinary sensor nodes, where Vertical is used
for forwarding the collected data to surface station. Multiple transceivers where equipped with surface station and one
channel is reserved for each underwater-sink. Long range transmission capability should be incorporated in vertical
transceivers for sending data to the SS located on water surface. For communicating with on-shore surface sink, the
surface station must be equipped with RF or satellite transmitter. To improve network lifetime and reduce energy
consumption, multi-hop communication where used between sensor nodes and UW-Sink. Multi-hop communication also
reduces signaling overhead.
Figure 2: Pompili & Melodia 2-D Architecture
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Proceedings of the International Conference on Emerging Trends in Engineering and Management (ICETEM14)
30 – 31, December 2014, Ernakulam, India
Challenges & Limitations
1) Determine the way to minimize number of sensor nodes to be deployed without compromising coverage and
sensing.
2) Provide robustness in case of failure by providing redundant nodes for replacing failure ones.
3) Optimal sensor node deployment within coverage area.
4) Based on static topology
5) Performs only ocean bottom monitoring.
6) Large transmission power is required for UW-sink to send the data to surface station placed on water surface.
2.2 Pompili & Melodia Three Dimensional (3-D) Architecture
Pompili & Melodia proposed another model called three- dimensional architecture [5, 9] to detect and observe
aquatic life that can’t be obtained using 2-D architecture. In 2-D architecture, we can only achieve ocean bed monitoring.
But using 3-D architecture, it is possible to collect information of each sea layer. In this architecture, nodes are placed at
different depths rather sea-bed or on the sea-surface. This is done by attaching each sensor node to a surface buoy via
cables and then adjusts depth of node by changing the length of cable. Once sensor nodes sense the data, it forwards it to
surface buoy via intermediary nodes. It is easy to deploy and follows a quick implementation strategy. But there are some
challenges.
Figure 3: Pompili & Melodia 3-D architecture
Challenges & limitations
1)
2)
3)
4)
5)
Increased cost of cabling.
Difficult to maintain
Presence of more than one buoys may disturb the ship navigation
Enemies in military can easily identify the network
Affected by weather.
Solution to these challenges pay the way for a new deployment method, which is a slight variation of above
mentioned architecture. Here, each sensor nodes are anchored and have an engine with it. The wire connecting anchor
and sensor node is adjusted to different depth by this engine. (Fig.4).
Different deployment methodologies [9] - 3-D random, Bottom random, Bottom grid.
In 3-D random, each sensor node itself set their depth by adjusting the length of wire to the anchor and finally
informs the position to the surface station. But in the case of bottom-random, surface station calculate depth for each
node considering the target coverage and inform this information to the sensor node. In bottom-grid, AUV’s are used to
place the sensor node at desired depth to obtain a grid topology.
Figure 4: Modified 3-D Architecture
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Proceedings of the International Conference on Emerging Trends in Engineering and Management (ICETEM14)
30 – 31, December 2014, Ernakulam, India
Challenges & limitations
1) No UW-sink. Hence the node itself finds a path to relay data captured to the surface station.
2) Ensure connectivity to SS in spite of mobility due to depth adjustments.
3) Sensor nodes should adjust their depth collaboratively to cover the target area.
2.3 Multipath Virtual Sink Architecture
Communication is said to be successful only if the message is received at the right recipient at the right time at
the right format. This architecture [7] shown in Fig.5 focus on reliability and robustness. It aims to ensure that the data
send must be received by any of the sink and then send to on-shore station. For this, clustering approach is used in which
each cluster has one or more aggregation points called ‘local aggregation points’. Once a sensor node senses the data, it
forward the data to local aggregation points within a cluster. These local aggregation points (collectively known as
virtual sinks) which are connected to form a mesh network is connected to local sinks.
So once the local aggregation point gets the data, then it forwards it to local sinks via multiple paths. This
architecture uses a local data caching, through which we can avoid retransmission due to unsuccessful delivery of data.
That is, sensor nodes store the data until it feels that channel conditions and environment is good. Once it feels favorable
condition, it sends the data. The chances for retransmissions can be reduced. In case of time-critical application like
monitoring purposes retransmission is preferable because of time lag in above mentioned technique.
Ordinary sensor node
Local aggregation point
Local sink
Figure 5: Multipath Virtual Sink Architecture
Challenges & limitations
1)
2)
3)
4)
5)
6)
Static topology
How clustering is done is not specified
High traffic
Congestion can occur at aggregation points. So suitable congestion methods must be incorporated
Effective data management techniques should be equipped to handle large amount of redundant data.
Large numbers of nodes are active at any time which reduces the lifetime of network.
2.4 Energy -Efficient Adaptive Hierarchical & Robust Architecture (EDETA)
This architecture [2] is one of the efficient hierarchical routing protocols. Here, set of sensor nodes are grouped
into different clusters and a Cluster Head (CH) is selected for each cluster. These CHs are linked together to form a tree
structure. The sensor nodes sense data and forwards to corresponding CH. Then each CH relay the stored data to the sink
by choosing a path.
The operations in EDETA can be classified under two phases. Initialization phase and normal phase.
Initialization phase: - Performs clustering and selects CH for each cluster.
Normal phase: - Sensor nodes relay the sensed data to CH at regular intervals. CH then forwards that data to the sink by
finding its parent in the tree structure.
Clusters are collapsed periodically and initialization phase get executed for forming new clusters and to select
CHs. CH selection is based on a random number generated by each sensor node. This number is compared with a
threshold (using equation (1)) [2] and remaining energy in that node. A node is selected as CH if the random number is
less than the threshold calculated and remaining energy greater the energy calculated by the equation(2) [2].
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Proceedings of the International Conference on Emerging Trends in Engineering and Management (ICETEM14)
30 – 31, December 2014, Ernakulam, India
……. (1)
c- Number of clusters to be formed
N- Total number of nodes in the network
α- parameter which depend on the time on which this calculation taken place
…... (2)
ET – remaining energy in CH
Tconfig – value that limit duration of initialization phase
TSuperFrame- value that limit duration of one round of normal phase
MAXRounds- Maximum rounds in normal phase
Challenges & limitations
1) Does not mention about maintaining and managing the cluster with respect to node mobility.
2) Large number of message passing between CHs and nodes.
3) Silent about how cluster is formed.
2.5 Scalable Localization with Mobility Prediction Architecture
This architecture [4] is developed with the help of principles of hydrodynamics. Many environmental factors
that causes movement of underwater objects are closely related (like water current, temperature etc.). Research in
hydrodynamics finds that objects in under water moves according to a predictable pattern. These patterns can be find out
by analyzing the above mentioned environmental factors. The proposed architecture was [4] mainly tested in shallow
area. The network architecture can be described as follows. It contains three types of nodes namely surface buoys, anchor
nodes and ordinary nodes.
Surface buoys-They have GPS to estimate its location.
Anchor nodes- Powerful nodes with large memory, power capabilities. They can directly communicate with the surface
buoy. Have self-localization capability by communicating with surface buoys and by using existing UW GPS. They
require modems to communicate directly with surface buoy.
Ordinary nodes- Limited memory, processing capability and low battery power. They can’t directly communicate with
the surface buoy. These nodes are only able to communicate with near-by neighbours and local anchor nodes. In that
way, they can perform localization.
Fig.6. shows a deployment model for this architecture in which nodes are placed at different depths for wide
exploration of oceanographic details. Mainly two types of localization process: Anchor node localization and ordinary
node localization.
Anchor node
Ordinary sensor node
Figure 6: Scalable Localization with Mobility Prediction Architecture
Strong and complex prediction algorithms can be implemented on anchor nodes as they are powerful. They can
measure their current location by communicating with surface buoy. Also they estimate the current location based on
previous location estimates and then generates a mobility pattern. Then the estimated location value is compared with the
measured one. It will reject the mobility pattern and update the values, if the Euclidean distance between the measured
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Proceedings of the International Conference on Emerging Trends in Engineering and Management (ICETEM14)
30 – 31, December 2014, Ernakulam, India
location and estimated location is greater than threshold. Once the condition is satisfied, then it broadcasts localization
message containing current location and mobility pattern to the network. An ordinary node is always waiting for
receiving localization message. If it does not receive any localization message for a long time, then it considers that it is
not within the range. If it receives any localization message, then algorithm for localization and mobility pattern
prediction get executed. A reference list is maintained by each ordinary node. That list contains the information of
reference nodes that a particular node known. The reference nodes are those nodes which know their location. Initially all
anchor nodes are named as reference nodes and assign their confidence values to 1. It shows the accuracy of estimated
values. When a node receives localization message, it check whether it comes from known reference node. If not, then an
entry for that node is made in the list. That is reference list is updated. Then the four nodes having highest confidence
value from the list is taken and calculate location and mobility pattern.
Challenges & limitations
1)
2)
3)
4)
5)
6)
Complex prediction algorithm are using
More time is required
Use of several surface buoys causes security and management issues
Costly
Require modem for communicating with surface buoys by anchor nodes.
Modems consume more battery power
2.6 Tic-Tac-Toe Architecture
This architecture [8] is highly energy-efficient, dynamic and self-organizing. This architecture announces that it
can avoid connection disruption between sources to sink because of node movement due to ocean currents. It also has
self-organizing capability in case of topology changes. It forms a virtual topology within the deployed sensor node
architecture and send the sensed data to the sink, floating on the water surface via a path discovered. It uses only two
types of nodes: sink nodes and ordinary nodes.
Sink nodes are powerful with sufficient memory, battery power and computational capability. Ordinary nodes
are those with fewer amounts of memory, battery power and computational capability. The ordinary nodes are anchored
under water at different depths including sea bed whereas sink nodes are placed on the water surface. Acoustic signal is
used for the communication between ordinary nodes and sink nodes. The sink nodes use RF to communicate with the
base station at on-shore or with other sink nodes.
Figure 7: Tic-Tac-Toe Architecture
It supports 3-D deployment of sensor nodes. It is energy efficient by using sleep-wake-up scheduling algorithm.
This allows only a part of the nodes to be alive at any time and rest of them will be in sleep state. We can denote active
nodes as ‘O’ and sleeping nodes as ‘X’. Then the side view of this architecture forms pattern of ‘O’ and ‘X’, where each
single row appears as O-X-O.., which resembles Tic-Tac-Toe game. That is why this architecture is named as ‘Tic-TacToe’.
The formation of this architecture involves three steps.
1) Discovering neighbours
2) Choosing best neighbour
3) Setting duty cycle.
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Proceedings of the International Conference on Emerging Trends in Engineering and Management (ICETEM14)
30 – 31, December 2014, Ernakulam, India
Discovering neighbours- Nodes placed on seabed broadcasts ‘REQ’ message. Then those nodes which receives ‘REQ’
message will reply back with ‘RPLY’ message if depth of received node is less than the sender node. Sender node waits
for RPLY message only ‘RPLYtimeout’ period.
RPLYtimeout=2*tprop+REQsendingdelay+REQprocessingdelay+RPLYsendingdelay
Choosing best neighbour- It is required to choose a neighbour that maintains a long connectivity with the sender. For
this, a metric named ‘time-to-disconnect’ is calculated for each neighbour nodes discovered. Time-to-disconnect is the
time that will take by a node to become out of range. One with highest ToD() is taken as best neighbour.
Setting duty cycle- This step informs the scheduling process to other neighbours by sending ‘CMND’ packet. On
receiving this, all other neighbour nodes other than best neighbor will goes to sleep state for td (time-to-disconnect)
period. Then those selected nodes find its best neighbour and process repeats until a path discovered up to sink node.
The nodes passed through different state. Mainly dormant, selected, active and sleep state. Initially every node is in
dormant state. A node moves to ‘selected’ state once it is selected as best neighbour from the neighbour list. All other is
in ‘sleep’ state. After that, a ‘selected’ node moves to ‘active’ state once it finds its neighbour. Once td time elapses, it
returns to ‘dormant’ state.
Challenges & limitations
1) Does not provide a way to find other route if the discovered path get corrupted during information transaction.
2) Silent on depth calculation for discovering neighbours
3) No method or strategy is specified for information recovery
3. COMPARISON
Features and limitations of different UWSN architectures with its deployment strategies are illustrated below.(TABLE.1)
Table 1: Comparative Study
Architecture
Pompili&Melodia
2-D Arch.
Pompili&Melodia
3-D Arch.
MultiPath Virtual
Sink Arch.
Features
* Nodes deployed
only on sea bed
* ocean bottom
monitoring
*Nodes are deployed
at different levels.
*Cluster based
*Deployment
strategies are 3-D
random, bottom grid,
bottom random
*Cluster based
*Each cluster has
local aggregation
points collectively
called virtual sinks
*Takes multiple
paths
Challenges & limitations
*Minimize number of sensor nodes
to be deployed without
compromising coverage
*Provide robustness.
*Optimal sensor node deployment
* Static topology
*Performs only ocean bottom
monitoring
*Large transmission power
*Increased cost of cabling.
*Difficult to maintain
*Presence of more than one buoys
may disturb the ship navigation
*Enemies in military can easily
identify the network
*Affected by weather.
*Consider static topology
*Formation ofclustering is done is
not specified.
*High traffic
*Congestion can occur at
aggregation points.
*Effective data management
techniques should be equipped
*Large number of nodes are active
at any time which reduces the
lifetime of network
48
Static
√
Mobile
2-D
√
√
√
3-D
√
√
Proceedings of the International Conference on Emerging Trends in Engineering and Management (ICETEM14)
30 – 31, December 2014, Ernakulam, India
EDETA
Scalable
localization with
mobility prediction
for UWSN
Tic-Tac-Toe
*Hierarchical,
cluster based
protocol model.
*Energy-efficient
* Robust architecture
*Does not mention about
maintaining and managing the
cluster with respect to node
mobility.
*Large number of message passing
*Silent about how cluster is formed
*Based on principles
of hydrodynamics
*Generate mobility
pattern & estimate
location
*Complex prediction algorithm are
using
*More time is required
*Use of several surface buoys
causes security and management
issues
*Costly
*Require modems
*High batter power required
*Energy-efficient
*Scalable
*Self-organizing
*Forms virtual
topology
*Silent on depth calculation for
discovering neighbour
*No method or strategy is specified
for information recovery
*Does not provide a way to find
other route if the discovered path
get corrupted during information
transaction.
√
√
√
√
√
√
4. CONCLUSION
This paper discussed various architectures (static and mobile) in UWSN based on spatial coverage and node
mobility. We investigated their features, limitations and challenges. Most of the architectures developed in earlier period
are of static in nature and which cannot be used in real scenario. Mobile architectures supporting node movement reflects
actual scenario, but it involves complex routing and prediction algorithms. Pompili&Melodia 2-D architecture seems to
be easy, but it can only be used for ocean bed monitoring. Their 3-D architecture involves large number of cables and
buoys which is later replaced by anchoring sensor nodes. EDETA which proposes an energy efficient, robust architecture
does not mention about cluster formation and maintenance. Likewise Multipath Virtual Sink Architecture suffers high
energy consumption and large communication between nodes. Complex prediction algorithm makes Scalable localization
with mobility prediction Arch. more difficult to deploy and maintain even though it consider node mobility. In Tic-TacToe Arch. depth calculation and handling of node failure is not mentioned. Each architecture mentioned in this paper
solves some or more problems, but it itself introduces another complexity or new challenges. So there must be an
architecture that mitigate all these problems & implement suitable algorithms for supporting node mobility, energy
consumption, reliability, security and robustness.
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