DNAR: Depth and Noise Aware Routing for Underwater Wireless Sensor Networks

Recently, the underwater wireless sensor networks (UWSNs) have been proposed for exploration of the underwater resources and to obtain information about the aquatic environment. The noise in UWSNs challenges the successful transmission of packets from a sender to a receiver. There are many protocols in literature that address noise reduction/avoidance during underwater communication. However, they require localization information of each sensor nodes that itself is a challenging issue. In this paper, the minimum channel noise is considered and the depth and noise aware routing (DNAR) protocol is proposed to send the packets reliably from a sender node to a surface sink. In the DNAR protocol, more energy is assigned to the sensor nodes that have depth level \(\le \)150 m. Therefore, the sensor nodes that deployed are nearby to the sink node have more capability of transmission and will not die quickly. Also, the proposed protocol selects the forwarder candidate that have lowest depth and minimum channel noise at the receiver. As compared to some existing schemes, the proposed scheme requires no geographical information of the nodes for data routing. The DNAR protocol is validated by Matlab simulation and compared it with the DBR scheme. The simulation results show that the DNAR has better results in-terms of packet delivery ratio (PDR), total energy consumption, and the network lifetime.

[1]  Yunsi Fei,et al.  QELAR: A Machine-Learning-Based Adaptive Routing Protocol for Energy-Efficient and Lifetime-Extended Underwater Sensor Networks , 2010, IEEE Transactions on Mobile Computing.

[2]  Milica Stojanovic,et al.  Focused beam routing protocol for underwater acoustic networks , 2008, Underwater Networks.

[3]  Nadeem Javaid,et al.  SMIC: Sink Mobility with Incremental Cooperative Routing Protocol for Underwater Wireless Sensor Networks , 2016, 2016 10th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS).

[4]  Guangjie Han,et al.  Geographic multipath routing based on geospatial division in duty-cycled underwater wireless sensor networks , 2016, J. Netw. Comput. Appl..

[5]  Deshi Li,et al.  A Link-State Based Adaptive Feedback Routing for Underwater Acoustic Sensor Networks , 2013, IEEE Sensors Journal.

[6]  Faisal Karim Shaikh,et al.  Underwater Sensor Network Applications: A Comprehensive Survey , 2015, Int. J. Distributed Sens. Networks.

[7]  Jun-Hong Cui,et al.  DBR: Depth-Based Routing for Underwater Sensor Networks , 2008, Networking.

[8]  Jun-Hong Cui,et al.  Improving the Robustness of Location-Based Routing for Underwater Sensor Networks , 2007, OCEANS 2007 - Europe.

[9]  Nadeem Javaid,et al.  DEAC: Depth and Energy Aware Cooperative Routing Protocol for Underwater Wireless Sensor Networks , 2016, 2016 10th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS).

[10]  Peng Xie,et al.  VBF: Vector-Based Forwarding Protocol for Underwater Sensor Networks , 2006, Networking.

[11]  Habib Rostami,et al.  Energy efficient spherical divisions for VBF-based routing in dense UWSNs , 2015, 2015 2nd International Conference on Knowledge-Based Engineering and Innovation (KBEI).

[12]  Marc Pastre,et al.  System-Level Design Considerations for Carbon Nanotube Electromechanical Resonators , 2013, J. Sensors.

[13]  Md. Ashraf Uddin,et al.  Noise aware level based routing protocol for underwater sensor networks , 2016, 2016 International Workshop on Computational Intelligence (IWCI).

[14]  Guangjie Han,et al.  A Reliable Depth-Based Routing Protocol with Network Coding for Underwater Sensor Networks , 2016, 2016 IEEE 22nd International Conference on Parallel and Distributed Systems (ICPADS).

[15]  Jun-Hong Cui,et al.  Efficient Multipath Communication for Time-Critical Applications in Underwater Acoustic Sensor Networks , 2011, IEEE/ACM Transactions on Networking.

[16]  Qingwu Li,et al.  Human Visual System based Automatic Underwater Image Enhancement in NSCT domain , 2016, KSII Trans. Internet Inf. Syst..

[17]  Mandar Chitre,et al.  A high-frequency warm shallow water acoustic communications channel model and measurements. , 2007, The Journal of the Acoustical Society of America.

[18]  Milica Stojanovic,et al.  On the relationship between capacity and distance in an underwater acoustic communication channel , 2006, Underwater Networks.

[19]  HanGuangjie,et al.  Geographic multipath routing based on geospatial division in duty-cycled underwater wireless sensor networks , 2016 .

[20]  Dongkyun Kim,et al.  DFR: Directional flooding-based routing protocol for underwater sensor networks , 2008, OCEANS 2008.

[21]  Dario Pompili,et al.  Three-dimensional routing in underwater acoustic sensor networks , 2005, PE-WASUN '05.

[22]  Roberto Petroccia,et al.  CARP: A Channel-aware routing protocol for underwater acoustic wireless networks , 2015, Ad Hoc Networks.

[23]  Mohd Murtadha Mohamad,et al.  Greedy Routing in Underwater Acoustic Sensor Networks: A Survey , 2013, Int. J. Distributed Sens. Networks.

[24]  Dongkyun Kim,et al.  A reliable and energy‐efficient routing protocol for underwater wireless sensor networks , 2014, Int. J. Commun. Syst..

[25]  John R. Potter,et al.  Viterbi Decoding of Convolutional Codes in Symmetric α -Stable Noise , 2007, IEEE Transactions on Communications.

[26]  Pinaki Mazumder,et al.  Design of Highly Selective Metamaterials for Sensing Platforms , 2013, IEEE Sensors Journal.

[27]  Enzo Baccarelli,et al.  P-SEP: a prolong stable election routing algorithm for energy-limited heterogeneous fog-supported wireless sensor networks , 2017, The Journal of Supercomputing.

[28]  Tolga M. Duman,et al.  Cooperative underwater acoustic communications [Accepted From Open Call] , 2013, IEEE Communications Magazine.

[29]  M. Chitre,et al.  Optimal and Near-Optimal Signal Detection in Snapping Shrimp Dominated Ambient Noise , 2006, IEEE Journal of Oceanic Engineering.