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AquaNode: A Solution for Wireless Underwater Communication Ryan Kastner Department of Electrical and Computer Engineering University of California, Santa Barbara CREON & GLEON Workshop March 30, 2006
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Monitoring in Moorea Establish monitoring sites in lagoons and on fore reefs surrounding Moorea Response variables measured: Weather Tides, Currents and Flows Ocean Temperature & Color Salinity, Turbidity & pH Nutrients Recruitment & Settlement Size & Age Structure Species Abundance Community Diversity Lagoon Fore reef Underwater wireless enabling technology for Moorea
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Why Use Wireless Underwater? Wired underwater not feasible in all situations Temporary experiments Tampering/breaking of wires Significant cost for deployment Experiments over longer distances Ocean observatories ORION, LOOKING, MARS, NEPTUNE Not ideal for coral reefs, lakes AquaNode can easily be used in conjunction with observatories Why not use radios and buoys? Common use is buoy with mooring – commercial radio on buoy to satellite, shore, … Buoys/equipment get stolen Cable breakage, ice damage Underwater wireless will enable new experiments & complement existing technologies
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Scenario for WetNet for Eco-Surveillance Deploy Ad hoc wireless (acoustic) network in lagoon Network consists of AquaNodes with Conductivity, Temperature, Depth (CTD) sensors (and many others) Ad hoc network allows AquaNodes to relay data to a dockside collector AquaNode requirements: Low cost, low power wireless modems Integral router Integral CTD sensor suite Additional nitrate, oxygen chemical sensors Real-time data from Moorea available on Web lagoon MOOREA Lab Aquanodes Collection station with acoustic sensor array Ad hoc network between Aquanode sensors.
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Underwater Acoustic Channel AquaNodes with acoustic modems/routers, sensors. Dock Severe multipath - 1 to 10 msec for shallow water at up to 1 km range Doppler Shifts Long latencies – speed of sound underwater approx 1500 m/sec
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WetNet using Aquanodes Dock Dockside acoustic/RF comms and signal processing. Cabled hydrophone array Wi-Fi or Wi-Max link CTD, currents, nutrient data to Internet. Adaptive sampling commands to AquaNodes. Data collection sites with acoustic modems/routers, sensors, mooring and underwater floats
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Float Sensors Batteries Transducer Router Software Defined Acoustic Modem Sensor Interface Modem Circuitry Mooring AquaNode
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Hardware Platform Ideal: One piece of hardware for all sensor nodes Hardware is wirelessly updatable: no need to retrieve equipment to update hardware for changing communication protocols, sampling, sensing strategies Reconfigurable Hardware Platform Transducer CTD Sensor
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Hardware Platform Interfaces Sensor Interface: Must develop common interface with different sensors (CTD, chemical, optical, etc.) and communication elements (transducer) Wide (constantly changing) variety of sensors, sampling strategies Reconfigurable Hardware Platform Transducer CTD Sensor Communication Interface: Amplifiers, Transducers Signal modulation Hardware: Software Defined Acoustic Modem (SDAM) Reconfigurable hardware known to provide, flexible, high performance implementations for DSP applications
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Complex, computationally intensive communication protocols Limited power/energy Ease of use: Good design tools, plug-n-play, reprogrammable Acoustic Modem Requirements Reconfigurable Hardware Platform Transducer Mapping CTD Sensor Communication Protocol Plug-N-Play
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Design Considerations for SDAM Multipath Spread – Range of 1 to 10 milliseconds for shallow water at up to 1 km range Larger bandwidths reduce frequency dependent multipaths Transducers Size/weight/cost proportional to wavelength Acceptable propagation losses at 100 meter ranges Waveform M-FSK signaling Datasonics/Benthos modems (used in Seaweb, FRONT) Narrowband thus sensitive to frequency-selective fading. Use more tones – increasing sensitivity to Doppler spread. Walsh/m-sequence signaling (Direct-sequence) Provides frequency diversity due to wide bandwidth Can be detected noncoherently
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What about existing modems? Commercial modems: (Benthos, Linkquest…) Too expensive, power hungry for Eco-Sensing. Proprietary algorithms, hardware. M-FSK (Scussel, Rice 97, Proakis 00) does use frequency diversity, but requires coding to erase/correct fades. Navy modems: Need open architecture for international LTER community – precludes military products. Direct-sequence, QPSK, QAM, coherent OFDM Great deal of work on DS, QPSK for underwater comms. But equalization, channel estimation are difficult. (Stojanovic 97, Freitag, Stojanovic 2001, 2003.) MicroModem (WHOI) Best available solution for WetNet. FSK/Freq. Hopping relies on coding to correct bad hops. But can we do better? Less power? Wider bandwidth?
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AquaModem Data Sheet Signal and Data Parameters Data rate: 133 bps Chip duration T c =.2 msec. Symbol duration T sym = 11.2 msec. Time guard interval T c = 11.2 msec. M-sequence length L pn = 7 chips. Walsh sequence length N w = 8 Bandwidth = 5 kHz Carrier Frequency f c = 25 kHz Nominal range 100 – 300 m. Power Consumption Overview Load Tx State Rx State Sleep State CPU 440 mW 440 mW.30 mW CPU I/O 420 mW 420 mW.15 mW Flash Memory 165 mW 165 mW.10 mW Power Amp. 7.2 W.05 mW.05 mW Battery Total 9.3 W 2.1 W 10 mW Battery Life (Based on 20 amp-hours) Tx Duty Cycle Rx Duty Cycle Days.1%.2 % 624.5% 1 % 189 1% 2% 101 Power Amp and Transducer Matching Network TI 2812 DSP with CompactFlash, ADC, DAC < 1 meter Sonatech Transducer
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Walsh/m-Sequence Waveforms Chip rate – 5 kcps, approx. 5 kHz bandwidth. Uses 25 kHz carrier. Use 7 chip m-sequence c per Walsh symbol, 8 bits per Walsh symbol b i. Composite symbol duration is thus T = 11.2 msec. (Longer than maximum multipath spread.) Symbol rate is 266 bps, or 133 bps using 11.2 msec. time guard band for channel clearing. 11 msec.
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Transmitted Signal 111 11 1 1 111
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Walsh/m-sequence Signal Parameters 111 11 1 1 111
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8 Walsh Symbols
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UWA Walsh/m-sequence GMHT-MP Modem Note: 112 Nyquist samples/symbol + 112 samples for channel clearing. Matching Pursuit Core Matching Pursuit Core Matching Pursuit Core Matching Pursuit Core arg min i Generalized multiple hypothesis test (GMHT)
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Acoustic Modem Performance N f : # paths assumed by MP estimation N : Number of paths present MP identifies major paths using one symbol of information True multipath intensity profile (MIP)
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Acoustic Modem Performance Symbol Error Rate (SER) Signal to noise ratio (E s /N 0 ) N f : # paths assumed by MP estimation N : Number of paths present > 4 dB gain over FSK @.5 x 10 -3 SER
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10dB = 90% reduction in amplifier power for all links less than 450 meters Transmit power control Adapt automatically to field conditions, Use only enough to get reliable links Often use small % of amplifier capacity → Significant reduction in system energy use Required Transmit Power
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Energy used while “ asleep ” < 10% of total Energy used per bit transceived ≈ constant Energy Usage For all links up to 400 meters, projected energy use is ≤ 50 mJ per bit In most cases CPU power dominates (when using low transmit power)
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Battery life System example uses alkaline D cells (low self discharge, good J ∕ $) 16 or 32 cells = 1.3 or 2.6 MJ respectively At 50 mJ per bit, with 16 cell battery, endurance [days] = 300 ∕ rate [bps]
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AquaModem Air Tests UCSB Engineering 1 Hallway 233’ 18’ 7’ 5’ 11’ 6’ 7’ 10’ 233’ 18’ 7’ 5’ 11’ 6’ 7’ 10’ Transmitter Location Receiver Location 233’ 18’ 7’ 5’ 11’ 6’ 7’ 10’ # Symbols Sent: 144 # Packets Sent: 36 Symbol Error: 1.4% Packet Error: 5.6% # Symbols Sent: 360 # Packets Sent: 90 Symbol Error: 1.1% Packet Error: 4.4% # Symbols Sent: 192 # Packets Sent: 48 Symbol Error: 10% Packet Error: 20.1%
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Challenges Power Communication Transducer size/weight/cost proportional to wavelength Adaptive power control Computation Microprocessors extremely power hungry Move towards FPGA, ASIC Cost Communication Current transducer ~ 3K US $ Fish finders? (< 100 US $) Computation Data rates aren’t particularly high → simple microprocessors Communication protocols complex → DSP, FPGAs Low power/energy will cost money → FPGA, ASIC Ease of use Plug-n-play interfaces to sensors Change network/communication protocols Adjust sampling strategies
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Credits Investigators: Ron Iltis, Hua Lee, Ryan Kastner ExPRESS Lab – http://express.ece.ucsb.edu/http://express.ece.ucsb.edu/ Telemetry Lab – http://telemetry.ece.ucsb.edu/ AquaNode Research Team: Research Tech – Maurice Chin PhD Students – Bridget Benson, Daniel Doonan, Tricia Fu, Chris Utley Undergrads – Brian Graham http://aquanode.ece.ucsb.edu/ Sponsor:
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