Underwater Sensor Protocol for Time Synchronization and Data Transmissions using the Prediction Model

Unsynchronized data transmission within the underwater environment leads to an increase in several transmissions which results in a reduction of transfer efficiency. The primary tribulation of underwater sensor nodes is mobility where nodes have to be localized and then subsequently synchronized. Synchronized Underwater Communication using Auto-Regressive protocol (SUCAR) has been proposed which initially localizes the source and destination sensors to the sink. If the source sensor is successfully synchronized with the permissible delay, it initiates the transmission considering the metrics of bandwidth efficiency and propagation delay. Pairwise synchronization between source and destination transfers data only if the acoustic channel matches with the metrics of propagation delay and bandwidth efficiency. SUCAR transfers data with the autoregressive model until there is a deviation in transfer efficiency due to localization or the associated resources. Comparison of SUCAR has been made with simulation using aquasim patch where network-wide synchronization achieved when compared with existing protocols used in this work.

[1]  J. V. Anand,et al.  Regression based analysis of effective hydrocast in underwater environment , 2014, TENCON 2014 - 2014 IEEE Region 10 Conference.

[2]  Bhaskar Krishnamachari,et al.  Design and analysis of a propagation delay tolerant ALOHA protocol for underwater networks , 2011, Ad Hoc Networks.

[3]  John S. Heidemann,et al.  Time Synchronization for High Latency Acoustic Networks , 2006, Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications.

[4]  Milica Stojanovic,et al.  Underwater acoustic communication channels: Propagation models and statistical characterization , 2009, IEEE Communications Magazine.

[5]  W. H. Thorp Analytic Description of the Low‐Frequency Attenuation Coefficient , 1967 .

[6]  Shuo Li,et al.  Integrated Time Synchronization and Multiple Access Protocol for Underwater Acoustic Sensor Networks , 2019, IEEE Access.

[7]  Kaishun Wu,et al.  Simulation and Experimentation Platforms for Underwater Acoustic Sensor Networks , 2017, ACM Comput. Surv..

[8]  P. Sivanesan,et al.  Certain investigations of underwater wireless sensors synchronization and funneling effect , 2019 .

[9]  Sabu M. Thampi,et al.  Simulation Tools for Underwater Sensor Networks: A Survey , 2016, Netw. Protoc. Algorithms.

[10]  Shengli Zhou,et al.  Aqua-Sim: An NS-2 based simulator for underwater sensor networks , 2009, OCEANS 2009.

[11]  S. Titus,et al.  Energy Efficiency Analysis of Effective Hydrocast for Underwater Communication , 2017 .

[12]  Jun-Hong Cui,et al.  Aqua-3D: An underwater network animator , 2012, 2012 Oceans.

[13]  Anand Nayyar,et al.  Analysis of Simulation Tools for Underwater Sensor Networks (UWSNs) , 2018, International Conference on Innovative Computing and Communications.

[14]  Mandar Chitre,et al.  UnetStack: An agent-based software stack and simulator for underwater networks , 2014, 2014 Oceans - St. John's.