Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
권호 표지
DOI QR코드

DOI QR Code

A Hexagon Tessellation Approach for the Transmission Energy Efficiency in Underwater Wireless Sensor Networks

  • Kim, Sung-Un (Dept. of Telecommunication Engineering, Pukyong National University) ;
  • Cheon, Hyun-Soo (Dept. of Telecommunication Engineering, Pukyong National University) ;
  • Seo, Sang-Bo (Dept. of Gyeongnam Mobile Network O&M Center Korea Telecom) ;
  • Song, Seung-Mi (Dept. of Communication R&D Center Samsung Thales) ;
  • Park, Seon-Yeong (Dept. of Telecommunication Engineering, Pukyong National University)
  • Published : 2010.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

The energy efficiency is a key design issue to improve the lifetime of the underwater sensor networks (UWSN) consisting of sensor nodes equipped with a small battery of limited energy resource. In this paper, we apply a hexagon tessellation with an ideal cell size to deploy the underwater sensor nodes for two-dimensional UWSN. Upon this setting, we propose an enhanced hybrid transmission method that forwards data packets in a mixed transmission way based on location dependent direct transmitting or uniform multi-hop forwarding. In order to select direct transmitting or uniform multi-hop forwarding, the proposed method applies the threshold annulus that is defined as the distance between the cluster head node and the base station (BS). Our simulation results show that the proposed method enhances the energy efficiency compared with the existing multi-hop forwarding methods and hybrid transmission methods

Keywords

References

  1. Jun Hong Cui, et al., Challenges: Building Scalable and Distributed Underwater Wireless Sensor Networks (UWSNs) for Aquatic Applications, Tech. Rep, UbiNet-TR05-02 (BECAT/CSE-TR-05-5), UCONN CSE, Jan., 2005.
  2. E. Cayirci, H. Tezcan, Y. Dogan, V. Coskun, Wireless sensor networks for underwater surveillance systems, Ad Hoc Networks, in press; doi:10.1016/j.adhoc.2004.10.008
  3. J. Jalbert, D. Blidberg, M. Ageev, Some design considerations for a solar powered AUV: Energy management and its impact on operational characteristics, Unmanned Systems 15 (4) (1997) 26-31.
  4. L. Badia, M. Mastrogiovanni, M. Zorzi, An Optimization Framework for Joint Sensor Deployment, Link Scheduling and Routing in Underwater Sensor Networks, The First ACM International Workshop on Underwater Networks, Los Angeles, California, USA, pp.56-63, September, 25, 2006. https://doi.org/10.1145/1161039.1161051
  5. D. Pompili, T. Melodia, I. F. Akyildiz, Deployment Analysis in Underwater Acoustic Wireless Sensor Networks, The First ACM International Workshop on Underwater Networks , Los Angeles, California, USA, pp.48-55, September, 25, 2006. https://doi.org/10.1145/1161039.1161050
  6. M. Ettus, System capacity, latency, and power consumption in Multi-hop routed SS-CDMA wireless networks, Proc. Radio and Wireless Conf, pp.55-58, Aug., 1998. https://doi.org/10.1109/RAWCON.1998.709135
  7. A. Mahapatra, K. Anand, D. P. Agrawal, QoS and energy aware routingfor real-time traffic, wireless sensor networks Computer Communications, Vol.29, pp.437-445, 2006. https://doi.org/10.1016/j.comcom.2004.12.028
  8. Y. Xu, J. Heidemann, and D. Estrin, Geography-Informed Energy Conservation for Ad Hoc Routing, Proc. Seventh Ann. Mobile Computing and Networking, July, 2001. https://doi.org/10.1145/381677.381685
  9. M. Zorzi, R.R. Rao, Geographic random forwarding (GeRaF) for adhoc and sensor networks: multihop performance, IEEE Transactions on Mobile Computing, 2, 2003. https://doi.org/10.1109/TMC.2003.1255648
  10. J, Heidemann, Underwater sensor networking: Research challenges and potential applications, Tech. Rep, ISI-TR-2005-603, USC/Information Sciences Institute, July, 2005.
  11. I. F. Akyildiz, et al, Challenges for Efficient Communication in Underwater Acoustic Sensor Networks, ACM Sigbed Review, Vol.1, No.2, July, 2004. https://doi.org/10.1145/1121776.1121779
  12. I. F. Akyildiz, et al, Underwater Acoustic Sensor Networks: Research Challenges, Elsevier's Journal of Ad Hoc Networks, Vol.3, Issue 3, May, 2005, pp.257-279 https://doi.org/10.1016/j.adhoc.2005.01.004
  13. R. J. Urick, Principles of Underwater Sound, Mcgraw-Hill, 1983.
  14. R. Jurdak, et al, Battery lifetime estimation and optimization for underwater sensor networks, IEEE Sensor Network Operations, Jun., 2004.
  15. Q. Xue and A. Ganz, Maximizing sensor network lifetime: Analysis and design guides. In Proceedings of MILCOM, 2004. https://doi.org/10.1109/MILCOM.2004.1495016
  16. Underwater Acoustic Modem. Available: www.link-quest.com (last accessed in April, 2007).

Cited by

  1. High-resolution temperature and salinity model analysis using support vector regression pp.1868-5145, 2018, https://doi.org/10.1007/s12652-018-0896-y