Wireless Sensor
Networks in Tsunami
TCOM 510 Wireless Networking
Soumya Sen, Prerit Gupta, Redwan Kabir
Tsunami Detection System (TDS)
Previous Underwater Sensor Networks
Current Research on Tsunami Detection
Acoustic Sensor Networks in TDS
Tsunami is a series of waves generated when a body of
water such as a lake or ocean is rapidly displaced on a
massive scale. Primary causes of Tsunami are Earthquakes
 Underwater landslides
 Underwater volcanic eruptions
 Meteoric impact
The Asian Tsunami of 2004
Tsunami Detection System
A Tsunami Detection System is a system to detect
tsunamis and issue warnings to prevent loss of life. This
system uses seismic data as its starting point, but then
also takes into account oceanographic data when
calculating possible threats. It consists of two equally
important aspects:
 A network of sensors to detect tsunamis
 A communications infrastructure to issue timely
alarms to permit evacuation of coastal areas
Previous UW Sensor Networks
The traditional approach for ocean-bottom or oceancolumn monitoring is to deploy underwater sensors that
record data during the monitoring mission, and then
recover the instruments. The problems with this approach
in the detection of tsunamis are manifold.
 No real time monitoring
 No online system reconfiguration
 No failure detection
 Limited Storage Capacity
Current Tsunami Warning Systems
The current tsunami warning systems being deployed
all over the world have two essential components. We
suggest the third component, a global positioning
system for faster and more accurate determination of
earthquake magnitudes, which is essential for timely
tsunami warnings.
 Buoy – Bottom Pressure Recorder System
 Satellite N etwork
 Global Positioning System
Buoy – Bottom Pressure Recorder
The BPRS uses a quartz crystal
resonator to measure ambient pressure
and temperature. The resonator uses a
thin quartz crystal beam, electrically
induced to vibrate at its lowest
resonant mode. It communicates these
measurements to the surface buoy
through an acoustic modem.
GPS in Tsunami Detection
Currently, estimating the magnitude of earthquakes
accurately takes around 1 hour or more. In the case of
tsunami detection, where time is of essence, measuring
seismic activity as quickly as possible is of utmost
importance. An earthquake's true size and tsunami
potential can be determined using Global Positioning
System (GPS) data up to only 15 min after earthquake
initiation, by tracking the mean displacement of the
Earth's surface associated with the arrival of seismic
Satellite Network
The surface buoys are
connected to a satellite
network, which is used to
relay information and
commands from the
BPRS to a Tsunami
warning center or vice
Communication Network Design
Challenges for Acoustic Sensor
Networks (ASN)
Battery power is limited
Available bandwidth is limited
Undesired channel characteristics,
delay variance.
High bit error rates, attenuation,
Underwater sensors are prone to
failure because of fouling,
corrosion etc.
ASN Architecture
B/W Ranges of UWA channels
Design issues: Physical layer
ASK- attenuation!
 PSK- coherent detection! (difficult PLL)
- Differential PSK (solves the coherent detection problem
partially, but error probability is higher)
 FSK- non-coherent energy detection based (guard
bands needed)
 OFDM- good one (use bit loading)
Modern technology can use QAM & PSK with feedback
channels (Decision Feedback equalizers)
Data Link Layer
FDMA not very suitable due to narrow b/w and
vulnerability of limited band systems to fading and
TDMA has limited b/w efficiency because of long time
guards, synchronization issue.
CSMA can prevent collisions at Tx side, but not on
receiver side, inefficient protocol.
RTS/CTS-impractical (large delays, synchronization
CDMA –at last! Robust to freq selective fading, less
retransmissions, less power needed
FEC (as ARQ is inefficient here)
Network layer
Routing information
-Proactive: DSDV (too much overhead & memory req)
-Reactive: AOVD (too slow, requires flooding!)
-Geographical routing protocols (localization information,
GPS isn’t too accurate for UWSN)
-Centralized network manager (polling)
Multi-hop routing requires less energy than single hop in
UW scenarios.
Use of Virtual Circuits for UW-ASNs.
Transport Layer
Still no good protocols proposed!
 Flow control, congestion control needed.
 But traditional end-to-end guarantee may
not feasible here- RTT too high!
 Research directions: integrating these at
the lower layers where we have channel
Application layer
Not explored yet!
 Directions:
SRB (storage Resource broker), a clientserver middleware that provides uniform
interface for connecting to heterogeneous
data resources over a network, and
accessing replicated data sets based on
their attributes or logical names rather
than physical location or name.
Deployment of BPRS
Deep-Ocean Assessment and
Reporting of Tsunamis (DART)
sized buoys are generally large,
weighing over 4000 kg. They
require the use of larger boats
that have A-frame structures and
cranes. Buoys must be serviced
every 1-2 years.
The Front-Resolving Observational Network with
Telemetry (FRONT)
–Univ. of Connecticut
 accomplish data telemetry and remote control for a set
of widely spaced oceanographic sensors by using
through-water acoustic signaling (telesonar) to form an
undersea wireless network (Seaweb)
List of FRONT experiments done
Present UWSN:
Deep-ocean Assessment and
Reporting of Tsunamis
In light of the events of the 2004 tsunami in South
Asia, there has been an increasing concern about
future tsunami threats, and with it, growing interest in
tsunami detection and prevention systems.
This presentation has shown that Wireless Sensor
Networks can be used for successful and timely
detection of tsunami.
We presented the basic concepts, challenges, design
issues and research directions in UWSN.
Underwater acoustic sensor networks: research challenges,
Ian F. Akyildiz , Dario Pompili, Tommaso Melodia
Rapid determination of earthquake magnitude using GPS
for tsunami warning systems, Geoffrey Blewitt, Corné
Kreemer, William C. Hammond, Hans-Peter Plag, Seth Stein
and Emile Okal
Thank you!
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