Abstract
Sea depth monitoring is a critical task for ensuring safe operation of harbors. Traditional schemes largely rely on labor-intensive work and expensive hardware. This study explores the possibility of deploying networked sensors on the surface of the sea, measuring and reporting the sea depth of given areas. We propose a Restricted Floating Sensors (RFS) model in which sensor nodes are anchored to the sea bottom, floating within a restricted area. Distinguished from traditional stationary or mobile sensor networks, the RFS network consists of sensor nodes with restricted mobility. We construct the network model and elaborate the corresponding localization problem. We show that by locating such RFS sensors, the sea depth can be estimated without the help of any extra ranging devices. A prototype system with 25 Telos sensor nodes is deployed to validate this design. We also examine the efficiency and scalability of this design through large-scale simulations.
- Ahn, G.-S., Miluzzo, E., Campbell, A. T., Hong, S. G., and Cuomo, F. 2006. Funneling-MAC: A localized, sink-oriented MAC for boosting fidelity in sensor networks. In Proceedings of the ACM Conference on Embedded Networked Sensor Systems (SenSys). Google Scholar
Digital Library
- Bahl, P. and Padmanabhan, V. N. 2000. RADAR: An in-building RF-based user location and tracking system. In Proceedings of the IEEE International Conference on Computer Communications (INFOCOM).Google Scholar
- Beyer, W. H. 1987. CRC Standard Mathematical Tables. CRC Press, Boca Raton, FL.Google Scholar
- Bulusu, N., Heidemann, J., and Estrin, D. 2000. GPS-less low cost outdoor localization for very small devices. IEEE Pers. Commun. Mag.Google Scholar
Cross Ref
- de Berg, M., Kreveld, M. V., Overmars, M., and Schwarzkopf, O. 2000. Computational Geometry: Algorithms and Applications. Springer-Verlag, Berlin Heidelberg. Google Scholar
Digital Library
- Dellaert, F., Fox, D., Burgard, W., and Thrun, S. 1999. Monte Carlo localization for mobile robots. In Proceedings of the IEEE International Conference on Robotics and Automation.Google Scholar
- Goldenberg, D., Bihler, P., Cao, M., Fang, J., Anderson, B., Morse, A. S., and Yang, Y. R. 2006. Localization in sparse networks using sweeps. In Proceedings of the ACM International Conference on Mobile Computing and Networking (MobiCom). Google Scholar
Digital Library
- Guo, D., Wu, J., Chen, H., Yuan, Y., and Luo, X. 2010. The dynamic bloom filters. IEEE Trans. Knowl. Data Eng. 22, 1, 120--133. Google Scholar
Digital Library
- He, T., Huang, C., Blum, B. M., Stankovic, J. A., and Abdelzaher, T. F. 2003. Range-free localization schemes in large scale sensor networks. In Proceedings of the ACM International Conference on Mobile Computing and Networking (MobiCom). Google Scholar
Digital Library
- Hightower, J. and Borriello, G. 2001. Location systems for ubiquitous computing. IEEE Comput. 34, 8. Google Scholar
Digital Library
- Hu, L. and Evans, D. 2004. Localization for mobile sensor networks. In Proceedings of the ACM International Conference on Mobile Computing and Networking (MobiCom). Google Scholar
Digital Library
- Karenos, K. and Kalogeraki, V. 2006. Real-time traffic management in sensor networks. In Proceedings of the IEEE Red-Time Systems Symposium (RTSS). Google Scholar
Digital Library
- Li, M., Cheng, W., Liu, K., He, Y., Li, X., and Liao, X. 2011. Sweep coverage with mobile sensors. IEEE Trans. Mobile Comput. 10, 1, 1534--1545. Google Scholar
Digital Library
- Li, M. and Liu, Y. 2010. Rendered path: Range-free localization in anisotropic sensor networks with holes. IEEE/ACM Trans. Netw. 18, 1, 320--332. Google Scholar
Digital Library
- Li, M. and Liu, Y. 2009. Underground coal mine monitoring with wireless sensor networks. ACM Trans. Sensor Netw. 5, 2. Google Scholar
Digital Library
- Li, S., Wang, X., Li, M., and Liao, X. 2008. Using cable-based mobile sensors to assist environment surveillance. In Proceedings of the IEEE International Conference on Parallel and Distributed Systems (ICPADS). Google Scholar
Digital Library
- Liu, J., Zhang, Y., and Zhao, F. 2006. Robust distributed node localization with error management. In Proceedings of the ACM International Conference on Mobile Computing and Networking (MobiHoc). Google Scholar
Digital Library
- Liu, Y., Zhu, Y., and Ni, L. M. 2011. A reliability-oriented transmission service in wireless sensor networks. IEEE Trans. Parallel and Distrib. Sys. 22, 2100--2107. Google Scholar
Digital Library
- Ni, L. M., Liu, Y., Lau, Y. C., and Patil, A. 2003. LANDMARC: Indoor location sensing using active RFID. In Proceedings of the IEEE International Conference on Pervasive Computing and Communication (PerCom). Google Scholar
Digital Library
- Niculescu, D. and Nath, B. 2003. Ad hoc positioning system (APS) using AOA. In Proceedings of the IEEE International Conference on Computer Communications (INFOCOM).Google Scholar
- Niculescu, D. and Nath, B. 2003. DV based positioning in ad hoc networks. J. Telecommu. Syst.Google Scholar
Digital Library
- Polastre, J., Szewczyk, R., and Culler, D. 2006. Telos: Enabling ultra-low power wireless research. In Proceedings of the IEEE/ACM Conference on Information Processing in Sensor Networks (IPSN). Google Scholar
Digital Library
- Savvides, A., Han, C., and Strivastava, M. B. 2001. Dynamic fine-grained localization in a ad-hoc networks of sensors. In Proceedings of the ACM International Conference on Mobile Computing and Networking (MobiCom). Google Scholar
Digital Library
- Seidel, S. Y. and Rappaport, T. S. 1992. 914 MHz path loss prediction models for indoor wireless communications in multifloored buildings. IEEE Trans. Antennas Propagation 40, 209--217.Google Scholar
Cross Ref
- Welzl, E. 1991. Smallest enclosing disks (balls and ellipsoids). In New Results and New Trends in Computer Science. Lecture Notes in Computer Science, vol. 655, Springer-Verlag, Berlin, Heidelberg, 359--370.Google Scholar
- Xing, G., Lu, C., Zhang, Y., Huang, Q., and Pless, R. 2005. Minimum power configuration in wireless sensor networks. In Proceedings of the ACM International Conference on Mobile Computing and Networking (MobiHoc). Google Scholar
Digital Library
- Yang, Z., Li, M., and Liu, Y. 2007. Sea depth measurement with restricted floating sensors. In Proceedings of the IEEE Real-Time Systems Symposium (RTSS). Google Scholar
Digital Library
Index Terms
Sea depth measurement with restricted floating sensors
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