Fuzzy based clustering and aggregation technique for Under Water Wireless Sensor Networks

The Under Water Sensor Network (UWSN) contains a set of underwater local area networks (UW-LAN) which is also called as clusters or cells. Inside the cluster, each sensor node can be linked with the sink via direct paths at multiple hops. Data gathering in UWSN is really a challenging task since energy is constrained and usually batteries cannot be recharged as solar energy cannot be exploited. Moreover, sensors are more vulnerable for failures due to pollution and corrosion. Retrieving information using the sensors manually is subject to very long delays. In this paper, we propose to design a fuzzy based clustering and aggregation technique for UWSN. In this technique the parameters residual energy, distance to sink, node density, load and link quality are considered as input to the fuzzy logic and based on the output of fuzzy logic module, appropriate cluster heads will be elected and will act as aggregator nodes. Simulation results show that the proposed technique reduces the average energy consumption and delay thereby improving the packet delivery ratio.

[1]  Biplab Sikdar,et al.  A Mobility Based Architecture for Underwater Acoustic Sensor Networks , 2008, IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference.

[2]  Kyoung-Don Kang,et al.  Hop-by-hop congestion control and load balancing in wireless sensor networks , 2010, IEEE Local Computer Network Conference.

[3]  Takumi Miyoshi,et al.  Adaptive Routing Protocol with Energy Efficiency and Event Clustering for Wireless Sensor Networks , 2008, IEICE Trans. Commun..

[4]  Xiuzhen Cheng,et al.  Silent Positioning in Underwater Acoustic Sensor Networks , 2008, IEEE Transactions on Vehicular Technology.

[5]  Nirvana Meratnia,et al.  MDS-Mac: A Scheduled MAC for Localization, Time-Synchronisation and Communication in Underwater Acoustic Networks , 2012, 2012 IEEE 15th International Conference on Computational Science and Engineering.

[6]  Jiejun Kong,et al.  The challenges of building mobile underwater wireless networks for aquatic applications , 2006, IEEE Network.

[7]  Jamil Y. Khan,et al.  Adaptive Token Polling MAC Protocol for Wireless Underwater Networks , 2009, 2009 4th International Symposium on Wireless Pervasive Computing.

[8]  F. Golatowski,et al.  Weighted Centroid Localization in Zigbee-based Sensor Networks , 2007, 2007 IEEE International Symposium on Intelligent Signal Processing.

[9]  Mayank Singh,et al.  Clustering Based on Node Density in Heterogeneous Under-Water Sensor Network , 2013 .

[10]  Sanguthevar Rajasekaran,et al.  PADP: Prediction assisted dynamic surface gateway placement for mobile underwater networks , 2012, 2012 IEEE Symposium on Computers and Communications (ISCC).

[11]  Jun-Hong Cui,et al.  Aqua-Net: An underwater sensor network architecture: Design, implementation, and initial testing , 2009, OCEANS 2009.

[12]  Wei Cheng,et al.  3D Underwater Sensor Network Localization , 2009, IEEE Transactions on Mobile Computing.

[13]  S. De,et al.  On the underwater wireless network clustering , 2012, 2012 National Conference on Communications (NCC).

[14]  Song Mao,et al.  Unequal clustering algorithm for WSN based on fuzzy logic and improved ACO , 2011 .

[15]  Fangchun Yang,et al.  Web service composition algorithm based on TOPSIS , 2011 .

[16]  Mari Carmen Domingo,et al.  A Distributed Clustering Scheme for Underwater Wireless Sensor Networks , 2007, 2007 IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications.

[17]  Dario Pompili,et al.  Underwater acoustic sensor networks: research challenges , 2005, Ad Hoc Networks.

[18]  Cheng Li,et al.  An Agreement-Based Fault Detection Mechanism for Under Water Sensor Networks , 2007, IEEE GLOBECOM 2007 - IEEE Global Telecommunications Conference.

[19]  Wenyu Liu,et al.  Minimum-Latency Aggregation Scheduling in Underwater Wireless Sensor Networks , 2011, 2011 IEEE International Conference on Communications (ICC).