IRJET- Underwater Acoustic Wsn:Routing Protocol

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 03 | Mar 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 7779
Abstract- An innovative and evolving Sparsity-aware
energy efficient clustering protocol (SEEC) is proposed
to provide an improved energy efficiency cluster system
which can also capable of handling cluster-head and
mobile sensor node connectivity failures. In addition, to
develop, implement and test modified SEEC, a new
simulator called USNeT (Underwater Sensor Network
simulation Tool) has been designed, developed and
implemented. This USNeT simulator follows the object-
oriented design style and all network entities are
implemented as classes in the C++, encapsulating
thread mechanisms. Initially significant adjustments
have been made in order for the algorithm to become
more energy efficient. Some of these alterations are:
transmission range management, recluster process
activation for each group separately, sensor node
sleeping mode and unwanted information rejection. All
the simulation results which were implemented against
Sparsity-aware energy efficient clustering protocol
(SEEC) protocol indicate a small but significant
improvement in the performance of the SEEC especially
in energy efficiency. This study also suggests that
system Cluster Head (CH) failures could be further
minimized when simultaneously a CH (primary CH) and
a backup CH are selected. Thus, when a primary CH fails
due to an irreparable fault, a backup CH will take its
place and it will operate as a head node. Therefore, the
modified- SEEC is redefined and optimized to be able to
handle this issue and also to diminish any
communication link establishment interruptions. The
analysis of the simulation results shows that the
redefined SEEC (modified-SEEC) is more energy efficient
and can effectively enhance the network survivability
capacity in the event of cluster-head failures, than the
scheme with the non-optimized algorithm SEEC and the
modified- SEEC protocol. Thereafter, the modified- SEEC
is used again, to address sensor node connectivity
failures. In case of a mobile sensor node that is close to
a cluster but not in the range of a CH, modified- SEEC
changes the status of the nearest sensor node to a CH
and then it establishes a communication link between
them. Simulation results show once more that the new
cluster based routing algorithm ensures the
connectivity of the network without sacrificing the
energy efficiency of the network.
Keywords : Cluster Head, Sparsity aware energy
efficient(SEEC), Threshold Bucket,
1.INTRODUCTION
Underwater sensor networks are envisioned to enable
applications for oceanographic data collection, pollution
monitoring, offshore exploration, disaster prevention,
seismic monitoring, equipment monitoring, assisted
navigation and tactical surveillance applications. Multiple
Unmanned or Autonomous Underwater Vehicles (UUVs,
AUVs), equipped with underwater sensors, will also find
application in exploration of natural undersea resources
and gathering of scientific data in collaborative monitoring
missions. To make these applications viable, there is a
need to enable underwater communications among
underwater devices. Underwater sensor nodes and
vehicles must possess self-configuration capabilities, i.e.,
they must be able to coordinate their operation by
exchanging configuration, location and movement
information, and to relay monitored data to an onshore
station.
Wireless underwater acoustic networking is the enabling
technology for these applications. Under Water Acoustic
Sensor Networks (UW-ASNs) consist of a variable number
of sensors and vehicles that are deployed to perform
collaborative monitoring tasks over a given volume of
matter. To achieve this objective, sensors and vehicles self-
organize in an autonomous network, which can adapt to
the characteristics of the ocean environment. The above
described features enable a broad range of applications for
underwater acoustic sensor networks.
Fig.1 Underwater networking
2. RELATED WORK ALGORITHM
Input:
N: the wireless sensor network
nn: the total number of nodes in N
R: the expected number of clusters for each round
e: e node in N
C: a randomly selected value for becoming a CH candidate
chance (e): the chance of the node to be CH, calculated
based on current Energy and distance from BS
probability(e): true for the node which has chance(e) value
above threshold bucket
(e): the node a is a member for random selection of CH
candidate
“UNDERWATER ACOUSTIC WSN: ROUTING PROTOCOL”
Shri Amit Dahiya1, Shri Vipin Vats2
1Department of ECE, IITB College, Jhundpur, Dcrust, Sonipat
2Head of Department, IITB College, Jhundpur, Dcrust, Sonepat

(e): a is a candidate for cluster head
Output: cluster
(e): the CH of the node, which is a node from among nn
nodes
Function:
broadcast(data, range of distance);
send(data, receiver);
minmax(currentEnergy, distance );
findMinDist(nodesX1[], nodesY1[], nwSize1, nodesX2[],
nodesY2[], nwSize2,
nodeIndex, clusterIndex) ;
FOR EVERY CLUSTERING ROUND
Clustering procedure this procedure is responsible for
forming the cluster scheme by using the clustering
algorithm. The basic idea of this algorithm is that each
sensor node, when the deployment is finished, sends a
control packet seeking for a CH. If the sensor node accepts
an ACK then it connects to the specific CH otherwise it
enters a different state such as the retry or the sleeping
(suspension) state.
1 start
2 start timer t1
3 initialize a packet retry counter (C_count)
4 repeat
5 send a control packet cont_C *// control packet is the
request packet
6 if no ACK then 7 C_count=C_count+1
8 if C_count>limit then *// limit: maximum
transmission retries a packet can do
9 sensor sleep *// suspension time
10 endif
11 else
12 calculate sensor distance from head
13 if (sens_dist<=max_dist) and (sens_dist>=min_dist)
14 then
15 sensor is a head
16 else
17 sensor is a client
18 endif
19 endif
20 to t1>T1 * // T1:
maximum time for the cluster procedure
21 end
A sensor node can spend a significant amount of time
seeking a CH. Therefore, to avoid the total consumption of
the sensor node’s energy, after the retry state, where a
sensor node retransmits the control packet, it enters in a
suspension mode. The suspension time is the period where
a sensor node sleeps without sending or receiving any
signals and therefore without spending any energy. For
further research, the suspension time can be altered by the
user.
Communication procedure
Generally a communication procedure has to deal with the
receiving, gathering and sending data. However, it must
take into account the two states that a sensor node can be:
the client state where a sensor node is a simple node,
gathering data from the environment and the cluster head
state, where a sensor node is a CH gathering data from
both the environment and the other sensor nodes of the
cluster team.
1 start
2 call gather data procedure
3 call transmit data procedure
4 if no ACK then
5 call cluster procedure
6 endif
7 end
This procedure is also responsible for the time period
where a sensor node is allowed to wait for an ACK until
retransmission and how many retries a sensor node can
do. The number of retries it is not limited but it can be
changed by the user for a further research purpose. The
time period which a sensor node is allowed to wait for an
ACK must be greater than the Round Trip Time (RTT). In
our case, RTT is the length of time it takes for a packet to
be sent plus the length of time it takes for an ACK of that
packet to be received.
1 start
2 call check buffer procedure
3 if control packet received then
4 if target_id==sensor_id then // target_id, sensor_id :
sensor’s packet fields
5 ACK received
6 else
7 if target_id=-1 then // if -1 is the value of the field, then
the sensor is a head
8 sensors is a cluster head - Send an ACK
9 else
10 discard data
11 endif
12 endif
13 endif
14 end
3. CONCLUSION
a) Average Delay:
Fig 5.1 Average end to end delay

b) Drop Packet:
Fig 5.2 Number of Dropped data
c) Energy Consumption
Fig.5.3 Energy Consumption
d) Normalized Routing Load
Fig 5.4 Normalized Routing load
e) Packet Delivery Ratio
Fig 5.5 Packet Delivery Ratio

f) Average Throughput
Fig 5.6 Average Throughput
In this Dissertation are: transmission range management,
re-cluster process activation for each group separately,
sensor node sleeping mode and unwanted information
rejection. The simulation results show that the proposed
Cluster Based Routing Protocol (SEEC) achieved a better
performance than Base protocol, in terms of metrics such
as the network’s throughput, the packet delivery ratio, the
average routing overhead, the offered load and the average
energy consumption for data transmission. Thus the
feasibility of the SEEC for UWSNs is verified.
The next step of this research was to investigate the
assumption of further optimization of the SEEC. During the
evaluation stage of the SEEC it was found that the
algorithm cannot efficiently manage CH transmission with
the Monitoring services. Moreover it was also found that
both proposed and base algorithm cannot properly
operate when sensor nodes are placed outside of the
predefined cluster boundaries.
The analysis of the simulation results shows that the Ad-
SEEC is more energy efficient and can effectively enhance
network survivability capacity in the event of cluster-head
node almost successfully achieve but performance is not
satisfied , than the scheme with the optimized algorithm of
SEEC and the base algorithm.
In the final stage of this study, Ad-SEEC with the support of
the bridge CH activation process was used again, to
address the second issue described above. In the case of a
mobile sensor node that is close to a cluster (near to
ordinary sensor nodes) but not in the range of a CH, the
Ad-SEEC changes the status of the nearest sensor node to a
CH (bridge CH) and then it establishes a communication
link between them. Simulation tests show that once more
the Ad-SEEC ensures the connectivity of the network
without sacrificing the energy efficiency of the network.
REFERENCES:
[1] T. Hu and Y. Fei, “QELAR: A machine-learning-
baseadaptive routing protocol for energy-efficient and
lifetime-extended underwater sensor networks,” IEEE
Trans. Mobile Comput., vol. 9, no. 6, pp. 796–809, Jun.
2010.
[2] F. Yuan, Y. Zhan, and Y. Wang, “Data density correlation
degree clustering method for data aggregation in WSN,”
IEEE Sensors J., vol. 14, no. 4, pp. 1089–1098, Apr. 2014.
[3] H. Lin and H. Uster, “Exact and heuristic algorithms for
data-gathering cluster-based wireless sensor network
design problem,” IEEE/ACM Trans. Netw., vol. 22, no. 3, pp.
903–916, Jun. 2014.
[4] J. Wu, L. Zhang, Y. Bai, and Y. Sun, “Cluster-based
consensus time synchronization for wireless sensor
networks,” IEEE Sensors J., vol. 15, no. 3, pp. 1404–1413,
Mar. 2015.

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