1. Chapter 16:
Underwater Sensor Networks

2. WHY UNDERWATER SENSOR NETWORKS. I. F. Akyildiz, D. Pompili, T
WHY UNDERWATER SENSOR NETWORKS? I.F. Akyildiz, D. Pompili, T. Melodia, “Underwater Acoustic Sensor Networks: Research Challenges”, Ad Hoc Networks (Elsevier) Journal, March 2005
Traditional approach for ocean-bottom or ocean column monitoring:
Deploy UW instruments to record data during monitoring mission and then recover them after several months and evaluate the collected data!!

3. WHY UNDERWATER SENSOR NETWORKS?
Problems:
No real-time monitoring
No on-line system configuration (no tuning or reconfiguring the instruments)
No failure detection
Limited storage capacity
DEPLOY UNDERWATER SENSOR NETWORKS TO ENABLE
REAL-TIME MONITORING
REMOTE CONFIGURATION
INTERACTIONS WITH ON-SHORE HUMAN OPERATORS!!!

4. Applications Ocean Sampling Networks
Pollution Monitoring and other environmental monitoring (chemical, biological)
Monitoring of ocean currents and winds
Detecting climate change
Understanding effects of human activities on marine ecosystems
Tracking fishes or micro-organisms
Disaster Prevention
Sensor networks that measure seismic activity from remote locations and provide “tsunami” warnings to coastal areas

5. Applications Assisted Navigation
Locate dangerous rocks or shoals in shallow waters, mooring positions, submerged wrecks
Distributed Tactical Surveillance
Monitor areas for surveillance, reconnaissance, targeting and intrusion detection
Mine Reconnaissance

6. Typical UW Sensor Tasks
MEASURE
Quality of the water
Light radiation and harmful algal blooms
Temperature
Density
Salinity
Acidity
Chemicals
Conductivity
Turbidity
Hydrogen
pH Oxygen
Sulfide Oxidation

7. UNDERWATER SENSOR NETWORKS 2D Architecture for OCEAN BOTTOM MONITORING (for environmental monitoring or monitoring of UW plates in tectonics)

8. UNDERWATER SENSOR NETWORKS 3D STATIC Architecture for Ocean Column Monitoring (for surveillance applications or monitoring of ocean phenomena (ocean bio-geo-chemical processes, water streams, pollution)

9. UNDERWATER SENSOR NETWORKS 3D DYNAMIC Architecture using Autonomous Underwater Vehicles (AUVs)

10. UNDERWATER SENSOR NETWORKS 3D Dynamic Architecture using AUVs
Inexpensive AUV submarines equipped with multiple UW sensors that can reach any depth in the ocean; e.g., Odyssey class AUVs
Drifters
Use local currents to move and take measurements at preset depths
Gliders
Battery powered and move 25cm/s (0.5 knots)
On the surface they can be detected by GPS
Onshore operators can interact with Gliders
Depth 200m-1500m with lifespans (few weeks to several months)
 Integration of AUVs with UWSNs is UNEXPLORED !!

11. Ocean Sampling Sensors
Acoustic Transponders
Precision Marine Geodetic Systems
Spread Spectrum Modem

12. Ocean Sampling Sensors
Point measurements in upper water column 10 and 25 mi off Moss Landing
Drift buoy: Path followed by surface currents
Surface station

13. Autonomuos Underwater Vehicles (AUVs)
CARIBOU by Bluefin Robotics Corporation
Equipped with state-of-the-art sensors (side-scan sonar and sub-bottom profiler), and can collect high-quality data for:
Archaeological remote sensing
Multi-static acoustic modeling
Fisheries resource studies and
Development of concurrent mapping and localization techniques.

14. Autonomous Underwater Vehicles (AUVs)
Solar recharged AUV
Phantom HD2 ROV

15. Terrestrial vs. Underwater Sensors
Underwater Acoustic Modem
Terrestrial Wireless Sensor
Mica Mote MPR300CB
Short-range
Medium-range
Acoustic Frequency
Speed
4 MHz
kHz
54-89 kHz
Flash
128K bytes
Data Rate
7 kbit/s
14 kbit/s
Radio Frequency
916MHz or 433MHz
(ISM Bands)
Transmit Power
1 W
6 W
Data Rate
40 kbits/s (max)
Receive Power
0.75 W
1 W
Transmit Power
0.75 mW
Sleep Power
8 mW
12 mW
Radio Range
100 feet
Radio Range
1000 feet
3000 feet
Power
2 x AA batteries

16. Terrestrial vs Underwater Sensor Networks
Cost
Deployment
Power

17. Terrestrial vs Underwater Sensor Networks
Available bandwidth is severely limited
UW channel is severely impaired (in particular due to multi-path and fading)
Very long (5 orders of magnitude higher than in RF terrestrial channels) and extremely variable propagation delays
Very high bit error rates and temporary losses of connectivity (SHADOW ZONES)
Battery power is limited and usually batteries cannot be recharged; no solar energy!!
Very prone to failures because of fouling, corrosion, etc.

18. Acoustic vs Radio and Optic Waves
Radio Waves
Propagate at long distances through conductive sea water only at extra low frequencies (30-300Hz)
 large antennae and high transmission power
(e.g., Berkeley MICA motes  transmission range of 120 cm in UW at 433Mhz)
Optical Waves
No high such attenuation but scattering problem
High precision in pointing the narrow laser beams
Links in Underwater Networks 
Acoustic wireless communications

19. Effects on Acoustic Communication
Path Loss
Noise
Multipath
Doppler Spread
High and Variable Propagation Delays
UW Acoustic Channel very limited and dependent on both range and frequency

20. UW Acoustic Communication Links
Classified according to their range
Available bandwidth for different ranges in UW-A Channels
Range [km]
Bandwidth [kHz]
Very Long
1000
<1
Long
10-100
2-5
Medium
1-10
~ 10
Short
0.1-1
20-50
Very Short
< 0.1
>100
Can also be classified as VERTICAL or HORIZONTAL LINKS according to the direction of the sound ray with respect to the ocean bottom

21. Factors Influencing Acoustic Communications
Transmission (Path) Loss
Attenuation
Provoked by absorption due to conversion of acoustic energy into heat, also by scattering, reverberation, refraction, and dispersion
Increases with varying frequency and distance
Geometric Spreading
Spreading of sound energy as a result of the expansion of the wave-fronts;
Increases with distance and independent of frequency
Spherical (deep water) and cylindrical (shallow water <100m) types

22. Transmission Loss Attenuation due to geometric spreading
Attenuation due to absorption
Attenuation due to multipath
TL increases with increasing freq. & distance

23. Factors Influencing Acoustic Communications
Noise
Man Made Noise:
Machinery noise (pumps, reduction gears, power plant) and shipping activity
Ambient Noise:
Hydrodynamics (currents, storms, wind, rain), seismic, and biological phenomena
Multi-Path Propagation
Generates Inter-Symbol Interference (ISI), especially in shallow waters

24. Factors Influencing Acoustic Communications
High Delay and Delay Variance
The propagation speed in the UW-A channel is five orders of magnitude lower than in the radio channel (0.67 s/km)
Doppler Spread  Can be significant in UW-A and causes ISI

25. Factors that Affect Sound Velocity
The sound velocity increases with the increase of temperature, pressure and salinity
Salinity
Pressure
Temperature
Depth
Depth
Depth

26. Typical Ocean Sound Velocity Profile
Speed of Sound (meters/sec)
1480
1500
1520
Surface Layer
Seasonal Thermocline
Permanent Thermocline
1000
Depth of Water (meters)
Deep Isothermal Layer
2000
3000

27. Sound Bends Down When Water Grows Cooler With Depth
Warm Water
Depth
Shadow Zone
Cool
Water

28. Sound Bends Sharply Up
Cool Water
Depth
Shadow Zone
Warm Water

29. Sound Beam Splits When Temperature is Uniform at Surface and Cool at Bottom
Depth
Isothermal
Shadow
Zone
Temperature
Cool

30. FOLLOWING TCP/IP PROTOCOL STACK
Application Layer
Transport Layer
Task Management Plane
Network Layer
Mobility Management Plane
Power Management Plane
Data Link Layer
Physical Layer

31. PHYSICAL LAYER
Until 1990  Non-coherent FSK modulation schemes (high power but low BW efficiency)
In recent years  Fully coherent schemes such as PSK, and QAM (long range and high throughput)
Also  OFDM (high throughput, robust, high spectral efficiency)
YEAR RATE[kbps] Band[kHz] Range[km]
FSK (d)
PSK (d)
PSK (sh)
DPSK (d)
QAM (sh)

32. PHYSICAL LAYER RESEARCH CHALLENGES:
Need to develop inexpensive transmitter/receiver modems
Low complex sub-optimal filters for real-time communication with minimum energy expenditure
Overcome the stability problem in coupling between PLL and DCE (decision feedback equalizer)
Channel estimation techniques

33. ERROR CONTROL ARQs  not useful!!
FECs  the way to go but which one????
Hybrid ARQ

34. OPTIMAL PACKET SIZE M. C. Vuran and I. F
OPTIMAL PACKET SIZE M. C. Vuran and I. F. Akyildiz, ``Cross-layer Packet Size Optimization for Wireless Terrestrial, Underwater, and Underground Sensor Networks,’’ to appear in IEEE INFOCOM'08, Phoeniz, AZ, April 13-18, 2008.
Deep water
Shallow water

35. MAC LAYER FDMA MACs Not suitable for UW-ASN
Narrow bandwidth in UW channels
Limited spectrum bands are vulnerable to fading and multi-path

36. MAC LAYER
TDMA MACs
Not easy to achieve precise synchronization due to variable delays
High delay and delay variance  large guard times  limited efficiency

37. MAC LAYER
CSMA MACs
Impractical due to “Idle Listenings” and “Collisions”

38. CSMA MAC with Sleep Schedules V. Rodoplu and M. K
CSMA MAC with Sleep Schedules V. Rodoplu and M. K. Park, “An Energy-Efficient MAC Protocol for Underwater Wireless Acoustic Networks,” in Proc. IEEE/MTS OCEANS 2005, September 2005
The objective is to save energy based on sleep periods with low duty cycles
However,
Solution tied to the assumption that nodes follow sleep periods (synchronization?)
It mainly aims at efficiently organizing the sleep schedules

39. SLOTTED FAMA M. Molins and M
SLOTTED FAMA M. Molins and M. Stojanovic, “Slotted FAMA: A MAC Protocol for UW Acoustic Networks”, Proc. of IEEE OCEANS, Sept 2006.
Combines Carrier Sensing and a handshake between sender and receiver
Time slotting eliminates long control packets  energy saved
PROBLEM:
Handshaking  low system throughput in very high propagation delay UW networks
CS may sense the channel idle while a transmission may still be going on

40. Why CDMA? Robust to frequency-selective fading
Compensates for the effect of multipath (Rake filters)
Receivers can distinguish signals simultaneously transmitted by multiple devices
Increases channel reuse
Reduces packet retransmissions
Decreases energy consumption
Increases network throughput

41. CDMA MAC Protocol L. Freitag, M. Stojanovic, S. Singh, and M
CDMA MAC Protocol L. Freitag, M. Stojanovic, S. Singh, and M. Johnson, “Analysis of Channel Effects on Direct Sequence and Frequency-hopped Spread-Spectrum Acoustic Communication,” IEEE Journal of Oceanic Engineering, Oct
Two SS-CDMA access techniques in shallow water are compared
Direct Sequence Spread Spectrum (DSSS)
Frequency Hopping Spread Spectrum (FHSS)
FHSS is shown to lead to a higher BER than DSSS

42. UW-MAC D. Pompili, T. Melodia, I. F
UW-MAC D. Pompili, T. Melodia, I.F. Akyildiz, ``A distributed CDMA MAC Protocol for Underwater Acoustic Sensor Networks,’’ IFIP Med Hoc Network Conf, Corfu, Greece, June 2007.
Challenges:
Harsh characteristics of the underwater medium, e.g., multipath, fading, limited bandwidth, shadow zones….
A Distributed CDMA MAC Protocol
Maximizes the network throughput
Minimizes the access delays
Minimizes the required energy to successfully transmit data packets

43. UW-MAC: Main Features
Unique solution for different architecture scenarios (2D and 3D)
Distributed closed-loop algorithm to set the optimal transmit power and code length
Distributed (no centralized entity to select codes and transmit power)
Intrinsically secure (use of chaotic codes)
Supports multicast transmissions (codes decided at the transmitter)
Robust against inaccurate node position and interference information

44. Network Layer
Existing routing solutions are unsuitable for UW Sensor Networks !!!
Proactive (DSDV, OLSR)
Reactive (AODV; DSR)
Geographical

45. Proactive Approaches
Large signaling overhead to establish routes for the first time and each time the network topology is modified because of mobility or node failures
Updated topology information has to be propagated to all nodes in the network

46. Reactive Approaches
Latency for source-initiated flooding of control packets to establish paths amplified underwater by the slow propagation of acoustic sound
The topology in UW-ASNs is unlikely to vary dynamically on a short-time scale

47. Geographical Routing
Very promising for their scalability feature and their limited required signaling
However, GPS receivers do not work in the UW properly
Need to develop localization algorithms for underwater to be able to associate each information measured by sensor with the position of the sensing node !

48. Existing Solutions
G. Xie and J. H. Gibson. “A network layer protocol for UANs to address propagation delay induced performance limitations,” Proc. of IEEE OCEANS’01, Honolulu, Nov
E. M. Sozer, M. Stojanovic, and J. G. Proakis, ``UW Acoustic Networks,’’ IEEE Journal of Oceanic Eng, Jan
P. Xie, J.-H. Cui, and L. Lao, “VBF: Vector-Based Forwarding Protocol for Underwater Sensor Networks,” in Proc. Networking 2006, May 2006
D. Pompili and I. F. Akyildiz, "Overview of Networking Protocols for Underwater Wireless Communications," to appear in IEEE Communications Magazine, January 2009.

49. NETWORK LAYER CHALLENGES
Develop routing algorithms which can cope with disconnections due to shadow zones, failures, mobility or battery replacement
Develop algorithms to deal with possible intermittent connectivity (SHADOW ZONES)

50. NETWORK LAYER CHALLENGES
Develop algorithms to provide strict or loose latency bounds for time-critical applications
NOTE: Delay depends on the distance while delay jitter also depends on the nature of the link, e.g., (horizontal links > vertical links)

51. TRANSPORT LAYER It is totally unexplored so far except:
Peng Xie and Jun-Hong Cui, ``SDRT: A Reliable Data Transport Protocol for Underwater Sensor Networks,’’ to be published in Elsevier Ad Hoc Networks, Special Issue on Underwater Networks, July 2007

52. TRANSPORT LAYER
Classical end-to-end reliability notion is not applicable!
Very high and very variable RTT
Window Based or Rate Based TCPs will not work
Need to distinguish between congestions and errors
A New End-to-End Reliability notion is needed

53. APPLICATION LAYER
It is totally unexplored so far.

54. Further Research MULTIMEDIA UNDERWATER SENSOR NETWORKS
Surveillance using sensor/AUV hybrid networks
Multimedia (Audio, Video, Data, Still Image) Traffic
Reconstruction of underwater 3D images and videos
Computer Vision, Stereovision
Channel Allocation and Scheduling (Multimedia Traffic Management)

55. Further Research MULTIMEDIA UNDERWATER SENSOR NETWORKS
Protocols, Algorithms and Architectures to maximize the network lifetime while providing QoS required by the applications
Distributed Data Mapping
Convert “raw” readings into metrics of direct user interest
In-network processing (network & node levels) using classification methods
Synchronization (intra-media, inter-media)