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  • 1. UNDERWATER ACOUSTIC SENSOR NETWORK FOR EARLY WARNING GENERATION by Srija Physics and Mathematics Dept. Whitman College, WA, USA Co-authors:  Prashant Kumar and Preetam Kumar, Electrical Engg. Dept. Indian Institute of Technology Patna, India  Poonam Priyadarshini, Electronics and Comm. Engg. Dept. Birla Institute of Technology Patna, India IEEE/MTS OCEANS’12 Hampton Roads, Virginia Oct. 14-19
  • 2. BRIEF OUTLINE  Problem Statement  Flaw in Existing Systems  Our Contribution  Challenges in Implementation  Performance Metrics  Scope of further improvement  Conclusion  References
  • 3. PROBLEM STATEMENT  Tsunami is actually a series of ocean waves produced by earthquakes or underwater landslides that can travel at speeds averaging 450 miles per hour in the open ocean.  A tsunami warning system (TWS) is used to detect tsunamis in advance and issue warnings to prevent loss of life and damage.  The evolution of TWS shows a significant development from seismic- centered to multi-sensor system architectures.  Two equally important components: a network of sensors to detect tsunamis and a communications infrastructure to issue timely alarms to permit evacuation of coastal areas.  This presentation highlights the physical layer challenges in establishing a reliable, low power consuming and long life underwater wireless sensor network (UWSN) system for such early warning generation.
  • 4. EXISTING SYSTEMS  A sensor network capable of detecting an oceanic earthquake and an impending tsunami is feasible, but will be useless unless backed by improved communications infrastructure in the countries in greatest peril.  The devastating death toll and damage caused by the tsunami in 2004 has prompted urgent calls for an early warning system.  European Union funded project Distant Early Warning System (DEWS) has aims at developing an advanced interoperable Tsunami early warning system for strong early warning capacities for Indonesia.  The project detects and analyzes seismic events in the Indian Ocean, the rapid assessment of their potential to unleash a Tsunami, and warning at-risk countries by means of a network of detectors made up of broadband seismometers, land and ocean-surface based GPS instruments, tide gauges, and ocean bottom pressure control devices. Using satellites, the data obtained by these instruments is sent to a central station in Jakarta, Indonesia for processing.
  • 5. INCOIS Tsunami Early Warning Centre is a part of Indian Nation Centre For Ocean Information Services (INCOIS). It has a warehouse of ocean related information gathered from institutions in India involved in Marine Data, Ocean Observation and Atmospheric sciences.  INCOIS translate it into deliverable products to a range of users - Fishing community, State Fishery Department Officers, Planning Commission, Shipping Industry, Navy, Coast Guards, Pollution Control Board, etc for timely dissemination of advisories following a standard operating procedure.  Seismic and sea-level data are continuously monitored in the warning centre using a custom-built DSS software application that generates alarms/alerts in the warning centre whenever a preset threshold is crossed. The software solution built entirely using GIS techniques enables operations (i) display of locations of seismic sensors, tide gauges, bottom pressure sensors, (ii) retrieve real-time data, (iii) online plotting, (iv) overlay tsunami travel times by picking up the right scenario from the database, (v) warning generation and dissemination, (vi) system monitoring, administration, back up, data retrieval and play back.
  • 6. INCOIS
  • 7. DART  An early warning system for tsunamis is already in operation in the Pacific Ocean and consists of a network of seismograph and tidal gauges linked via satellite to monitoring centers based in Alaska, US, and Hawaii.  Deep Ocean Assessment and Reporting sensors use deep-sea pressure detectors that measure changes in water depth as a tsunami wave passes overhead.  The sensors then transfer the information to a surface buoy, which relays it to the monitoring stations by satellite.  The DART system prevented a false alarm on Hawaii just a month after its activation, following a tremor in Alaska.  DART is also less vulnerable to earthquake damage than tide gauges but experts insist that multiple detection systems are essential.
  • 8. DART
  • 9. GITEWS  GITEWS-developed, GPS based component offers possibility for detection of co-seismic land mass movements and tsunami waves on the ocean, it covers station design, data transfer, near real time data processing and warning center operator desk. This German Indonesian project was started in 2004.  The project detects and analyzes seismic events in the Indian Ocean, the rapid assessment of their potential to unleash a Tsunami, and warning at-risk countries by means of a network of detectors made up of broadband seismometers, land and ocean-surface based GPS instruments, tide gauges, and ocean bottom pressure control devices.  Using satellites, the data obtained by these instruments is sent to a central station in Jakarta, Indonesia for processing.
  • 10. GITEWS
  • 11. FLAW IN THE EXISTING SYSTEMS  90 % a tsunami is generated by an earthquake but also volcanic eruptions and landslides may be the triggering events.  The early warning part of Tsunami hazard management till date relies upon the measurements of sea level and computer models to characterize the Tsunami waves.  Scientists rely on ocean based buoys and models to track and predict the path of a Tsunami.  Geospatial technology has immensely helped in the design of early warning system for tsunami.  Use of model simulations as well as water level data from tide gauges for generation of tsunami bulletins has definite advantage in bringing down the number of false alarms.  The observation need to be made at the site and not at the sea level.
  • 12. OUR CONTRIBUTION  Sensor networks that measure seismic activity from remote locations can provide Tsunami warnings to coastal areas, or study the effects of submarine earthquakes (seaquakes).  Underwater sensor networks have the potential to pave the way for unexplored applications and to help observe and predict the ocean.  We combine the underwater acoustic sensor network with satellite and terrestrial communication systems together with the internet to generate early warning.  This type of UWSN technology integrated with the internet technology would make the data and warning accessible to one and all.
  • 14. UWSN BASED EARLY WARNING SYSTEM  The present design considers a method of exploiting underwater acoustic sensor network with nodes spread over the entire ocean bed under coverage.  It has space-based monitoring with satellite technology in addition to other terrestrial communication technologies. There are three levels in the design.  The 1st level deals with communication inside the harsh underwater environment where only acoustic communication is possible. The sensor nodes are organized in clusters and these nodes communicate over short distances while the cluster heads convey the data collected to the surface stations closest to them.  In the 2nd level the surface station collects the information and forwards it to the coastal data collection centre through line of sight (LOS) microwave terrestrial transmission; there may be a master surface station which is more capable in terms of power and communication.  In the 3rd level the information is either directly sent to a universal data collection centre through satellite by the surface station or the coastal data
  • 16.  Power and energy optimizations are especially critical for UWANs because: 1) acoustic communications will consume more energy than RF channels, and 2) energy harvesting is much more difficult because major harvesting sources such as solar and wind energy are not available in the underwater environment.  Since UWANs are battery operated, lowering the transmission power may extend network life time but at the cost of increased bit error rate (BER), as signal to noise ratio (SNR) might not be high enough to ensure satisfactory information transmission.  Motivated by these constrains UWANs design require low power consuming, good BER system with least complexity. CHALLENGES IN IMPLEMENTATION
  • 17. CHALLENGES IN IMPLEMENTATION  Doppler Spread-depends on the ocean environment (chemical-physical properties of the water medium such as temperature) under consideration and varies from ocean to ocean.  These variations, together with the wave guide nature of the channel cause the acoustic channel to be temporally and spatially variable.  Routing Techniques-A common practice used in terrestrial sensor network applications is to design routing algorithms that minimize the communication power and consequently increase the sensor lifetime. This requirement needs to be ported to underwater as well as space applications.  Modulation Schemes-Among the important design parameter are bit error rate, peak to average power ratio, number of subcarriers, signal bandwidth, block duration, guard interval, subcarrier spacing, pilot carriers and null subcarriers.
  • 18. PERFORMANCE METRICS  The system will be judged by the reliability (correct and prompt warning) so that public can be targeted warned and evacuation measures can be initiated. State of art Ocean Instrumentation  Instruments are installed on the coast, in the ocean or on the ocean floor measuring the sea level fluctuations both on the ocean as well as on the coasts  Analysis of different measurements at a very early stage with the help of seismometers, GPS instruments, tide gauges and buoys as well as ocean bottom pressure sensors. The recording and analysis of bathymetric data for determining the topography of the ocean floor is an essential basis for modeling. Warning Centre  Here the data of the particular sensor systems are received and analyzed.  By means of a Decision Support System and based on simulations and pre- tailored hazard and risk maps the information is to be delivered to governmental institutions, local disaster management, action forces and media.
  • 19. SCOPE OF FURTHER IMPROVEMENT Better Simulations:  As the WSN supplies data at few points only, computer simulations are needed, in order to synthesize an overall picture of the situation. Taking help of model for the ascertainment of arrival times and wave heights as well as information on the inhabitants and infrastructure, fast risk estimations can be reached. Better Warning Centre and Decision-making Support:  A better Decision Support System (DSS) is always required. Rigorous Tsunami Modeling: Modeling of Tsunami can be divided into three stages: Generation, Propagation and Run-up (inundation).  The use of numerical modeling to determine the potential run-ups and inundation from a local or distant Tsunami is recognized as useful and important tool, since data from past Tsunamis are usually insufficient to plan future disaster mitigation and management plans. Models can be initialized with potential worst case scenarios for the Tsunami sources or for the waves just offshore to determine corresponding impact on near by coast.
  • 20. CONCLUSION  The development of the early warning generation system based on UWANs will undoubtedly contribute to save a lot of human lives facing natural disasters.  It can be concluded that reliable communication, low-power design and efficient resource management will remain the major challenges for UWSN based early warning generation system designs.  Sensor webs for early warning will prove to be a powerful technology for environmental and ecological research in addition to saving human life.  However in deploying such a system it should be taken care that the aquatic ecological balance is not disturbed.  Education is another key element in the tsunami warning system. Coastal areas must have designated tsunami inundation zones and marked evacuation routes to assist residents and visitors to higher ground. Emergency management officials should distribute tsunami education information, conduct community meetings and workshops, and many more awareness activities.
  • 21. REFERENCES [1] J. J. Makela, P. Lognonné, H. Hébert, T. Gehrels, L. Rolland, S. Allgeyer, A. Kherani, G. Occhipinti, E. stafyeva, P. Coïsson, A. Loevenbruck, E. Clévédé, M. C. Kelley, J. Lamouroux, (2011). “Imaging and modeling the ionospheric airglow response over Hawaii to the Tsunami generated by the Tohoku earthquake of 11 March”, Geophys. Res. Lett., 38. [2] [3] [4] [5] Erol-Kantarci, M.; Mouftah, H.; Oktug, S., (2011). “A survey of architectures and localization techniques for underwater acoustic sensor networks”. IEEE Commun. Surv. Tutor., vol.13, no.3, pp.487- 502. [6] [7] M. C. Domingo, (2008). “Overview of channel models for underwater wireless communication networks,” Physical Communication, vol. 1, no. 3, pp.163–182. [8] M. Stojanovic, (2006). “Low Complexity OFDM Detector for Underwater Acoustic Channels,” IEEE Oceans Conf., pp.1-6. [9] C. R. Berger, S. Zhou, J. Preisig, and P. Willett, (2010). “Sparse channel estimation for multicarrier underwater acoustic communication: From subspace methods to compressed sensing,” IEEE Trans. Signal Processing, pp.1708-1721. [10] B. Li, S. Zhou, M. Stojanovic, L. Freitag, and P. Willett, (2008). “Multicarrier communication over underwater acoustic channels with nonuniform Doppler shifts,” IEEE J. Ocean. Eng., vol. 33, no. 2, pp.198-209. [11] D. B. Kilfoyle, J. C. Preisig, and A. B. Baggeroer, (2005). “Spatial modulation experiments in the underwater acoustic channel,” IEEE Journal of Oceanic Engineering, vol. 30, no. 2, pp.406-415. [12]
  • 22. Thank you!