A Mobile Delay-Tolerant Approach to Long-Term Energy-Efficient Underwater Sensor Networking

Underwater environment represents a challenging and promising application scenario for sensor networks. Due to hard constraints imposed by acoustic communications and to high power consumption of acoustic modems, in underwater sensor networks (USN) energy saving becomes even more critical than in traditional sensor networks. In this paper the authors propose delay-tolerant data dolphin (DDD), an approach to apply delay-tolerant networking in the resource-constrained underwater environment. DDD exploits the mobility of a small number of capable collector nodes (namely dolphins) to harvest information sensed by low power sensor devices, while saving sensor battery power. DDD avoids energy-expensive multi-hop relaying by requiring sensors to perform only one-hop transmissions when a dolphin is within their transmission range. The paper presents simulation results to evaluate the effectiveness of randomly moving dolphins for data collection.

[1]  Frank Harary,et al.  Graph Theory , 2016 .

[2]  Zygmunt J. Haas,et al.  The shared wireless infostation model: a new ad hoc networking paradigm (or where there is a whale, there is a way) , 2003, MobiHoc '03.

[3]  J. Heidemann,et al.  Underwater Sensor Networking : Research Challenges and Potential Applications , 2006 .

[4]  John S. Heidemann,et al.  Low-power acoustic modem for dense underwater sensor networks , 2006, WUWNet '06.

[5]  Jean-Yves Le Boudec,et al.  Perfect simulation and stationarity of a class of mobility models , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[6]  Kevin R. Fall,et al.  A delay-tolerant network architecture for challenged internets , 2003, SIGCOMM '03.

[7]  Jean-Yves Le Boudec,et al.  Perfect simulations for random trip mobility models , 2005, 38th Annual Simulation Symposium.

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

[9]  Vinton G. Cerf,et al.  Delay-tolerant networking: an approach to interplanetary Internet , 2003, IEEE Commun. Mag..

[10]  Milica Stojanovic,et al.  Shallow water acoustic networks , 2001, IEEE Commun. Mag..

[11]  Philippe Jacquet,et al.  Comparative Study of CBR and TCP Performance of MANET Routing Protocols , 2002 .

[12]  Bhaskar Krishnamachari,et al.  Sharp thresholds For monotone properties in random geometric graphs , 2003, STOC '04.

[13]  Ellen W. Zegura,et al.  A message ferrying approach for data delivery in sparse mobile ad hoc networks , 2004, MobiHoc '04.

[14]  Jiejun Kong,et al.  Analysis of Aloha Protocols for Underwater Acoustic Sensor Networks , 2006 .

[15]  Sheldon M. Ross Introduction to Probability Models. , 1995 .

[16]  A.B. Baggeroer,et al.  The state of the art in underwater acoustic telemetry , 2000, IEEE Journal of Oceanic Engineering.

[17]  Donald B. Olson,et al.  Behavioral Assumptions in Models of Fish Movement and Their Influence on Population Dynamics , 2004 .

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

[19]  W. Mccoll,et al.  Analytical inversion of general tridiagonal matrices , 1997 .

[20]  S. Singh,et al.  The WHOI micro-modem: an acoustic communications and navigation system for multiple platforms , 2005, Proceedings of OCEANS 2005 MTS/IEEE.