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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 1

A Smart Sensor Web for Ocean Observation: Fixedand Mobile Platforms, Integrated Acoustics, Satellites

and Predictive ModelingBruce M. Howe, Member, IEEE, Yi Chao, Payman Arabshahi, Senior Member, IEEE, Sumit Roy, Life Fellow, IEEE,

Tim McGinnis, and Andrew Gray

Abstract—In many areas of Earth science, including climatechange research and operational oceanography, there is a needfor near real-time integration of data from heterogeneous andspatially distributed sensors, in particular in situ and space-basedsensors. The data integration, as provided by a smart sensor web,enables numerous improvements, namely, 1) adaptive samplingfor more efficient use of expensive space-based and in situ sensingassets, 2) higher fidelity information gathering from data sourcesthrough integration of complementary data sets, and 3) improvedsensor calibration. Our ocean-observing smart sensor web pre-sented herein is composed of both mobile and fixed underwater insitu ocean sensing assets and Earth Observing System satellite sen-sors providing larger-scale sensing. An acoustic communicationsnetwork forms a critical link in the web, facilitating adaptive sam-pling and calibration. We report on the development of variouselements of this smart sensor web, including (a) a cable-con-nected mooring system with a profiler under real-time controlwith inductive battery charging; (b) a glider with integratedacoustic communications and broadband receiving capability;(c) an integrated acoustic navigation and communication network;(d) satellite sensor elements; and (e) a predictive model via theRegional Ocean Modeling System interacting with satellite sensorcontrol.

Index Terms—Acoustic communication, acoustic navigation,mission planning, moored profilers, inductive power transfer,underwater gliders.

Manuscript received October 26, 2009; revised April 05, 2010; accepted April28, 2010. This work was supported in part by three projects. The integratedunderwater/satellite sensor network science and technology development wasfunded by the NASA Earth Science Technology Office’s Advanced Informa-tion Systems Technology (AIST) Program under Award AIST-05-0030. Themooring work was funded by the National Science Foundation (NSF) OceanTechnology and Interdisciplinary Coordination (OTIC) program, Grant OCE0330082. The acoustic Seaglider work was funded by the Office of Naval Re-search (ONR), Grant N00014-05-1-0907. Part of the research described in thispaper was carried out at the Jet Propulsion Laboratory (JPL), California Instituteof Technology, under contract with the National Aeronautics and Space Admin-istration (NASA). Computing support from the JPL Supercomputing Project isacknowledged.

B. M. Howe is with the Department of Ocean and Resources Engi-neering, University of Hawaii at Manoa, Honolulu, HI 96822 USA (e-mail:[email protected]).

Y. Chao is with the Jet Propulsion Laboratory, California Institute of Tech-nology, Pasadena, CA 91109 USA. He is also with the Joint Institute for Re-gional Earth System Science and Engineering, University of California, LosAngeles, CA 90095 USA.

P. Arabshahi and T. McGinnis are with the Applied Physics Laboratory, Uni-versity of Washington, Seattle, WA 98105 USA.

S. Roy and A. Gray are with the Department of Electrical Engineering, Uni-versity of Washington, Seattle, WA 98195 USA.

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/JSTARS.2010.2052022

I. INTRODUCTION

E ARTH’S oceans are under-sampled and efforts are un-derway to rectify this situation. On global scales, the Inte-

grated Ocean Observing System (IOOS) within the Global EarthObservation System of Systems (GEOSS) is in the process ofbringing together satellite, in situ observations, and modeling toprovide products on various time and space scales. Satellite ob-servations include sea surface height altimetry for heat contentand near surface currents, scatterometry for wind speed and di-rection, and infrared radiometers for sea surface temperature. insitu elements include a near-global distribution of Argo profilingfloats to provide sparse (incoherent, 300 km spacing) in situtemperature and salinity data, volunteer observing ships (typ-ically temperature profile data), and arrays of moorings, e.g.,the TAO-TRITON array in the equatorial Pacific primarily forEl Nino monitoring. Numerical modeling is just now reachingthe state of being able to assimilate with adequate resolutionthe satellite altimetry and in situ data, to produce a four-dimen-sional ocean state that is dynamically consistent. However, thereare still major discrepancies when one looks at the total heatand fresh water budget [1]—various models and independentdata driven results for the fraction of sea level rise attributableto ocean thermal expansion and to ice melting are inconsistentwithin their respective formal error bars. Even just for oceanheat content change, different analysis groups produce estimatesthat differ by more than the formal error bars, with a spread thatis about half the nominal change in heat content over the lastfive decades [2], [3]. In a research-oriented effort, the NationalScience Foundation (NSF) has initiated the Ocean Observato-ries Initiative (OOI) to provide leading edge infrastructure forlong-term sustained observations at a few selected sites. Thereare many other efforts to develop and sustain long-term oceanobserving capability, to complement the satellite data collectedby NASA and other space based Earth observing systems.

We are developing a smart sensor web that combines manyof the essential elements of an ocean observing system: a mixof fixed and mobile in situ sensors and satellite sensors that to-gether can perform a combination of spatial and temporal sam-pling; and an ocean model, embodying all our best and cur-rent knowledge of the physics, embedded in a data assimila-tion framework, that can be used in an adaptive sampling modeto jointly optimize sampling and resource allocation for im-proved science data [4]–[6]. For all the pieces to work together,the power, communications, and timing network infrastructure

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2 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING

must be in place, linking the web between the in situ and space-based sensors. (We note the field of smart sensor webs is devel-oping and definitions thereof vary.)

Constructing and demonstrating such a sensor web is a majortask, and is only possible by building on the efforts of severalcomplementary projects: (a) cabled, profiler mooring (theALOHA-MARS Mooring (AMM) system) intended for theNSF OOI, (b) acoustic Seagliders with integrated sensors andmodems talking to each other and other platforms, includingbottom nodes and gateway buoys, (c) an integrated acousticnavigation and communication network, (d) satellite sensors,and (e) a predictive model via the Regional Ocean ModelingSystem (ROMS) that is used to control adaptive sampling,including redirection of satellite assets. The system composedof the above is illustrated in Fig. 1. It should be noted that theglider and mooring system described here are but one of manyvariants. For example, propeller-driven autonomous underseavehicles (AUVs) transiting between bottom nodes are regardedas conceptually very similar. Here we report on our progress todate in these areas.

II. SMART SENSOR WEB

A. Mooring Sensor System

The basic mooring system is illustrated in Fig. 1 with a blockdiagram in Fig. 2. The current hardware implementation wasdeployed and operated on the Seahurst Observatory in 40 mwater depth in Puget Sound, just west of Sea-Tac InternationalAirport. In the future it will be deployed at the MARS cabledobservatory in Monterey Bay in 1000 m water depth and at theALOHA Cable Observatory site 100 km north of Oahu in 5000m water depth. Here the emphasis is on a system description [7].

The basic mooring concept is to provide the infrastructureto distribute power, communications, and precise and accuratetiming throughout the water column. The mooring systemconsists of three main components: a near-surface float at adepth of 165 m with a secondary node and suite of sensors, aninstrumented motorized moored profiler moving between theseafloor and the float that will mate with a docking station justbeneath the float for battery charging, and a secondary nodeon the seafloor with a suite of sensors. Both secondary nodeshave remotely operated vehicle (ROV)-mateable connectorsavailable for guest instrumentation. The profiler has real-timecommunications with the network via an inductive modem thatprovides some remote control functions to allow the samplingand measurement capabilities to be focused on the scientificfeatures of greatest interest. The power, two-way real-timecommunications and timing provided by cabled seafloor obser-vatories will enable this sensor network, the adaptive samplingtechniques, and the resulting enhanced science. The samplingand observational methods developed here will be transferableto ocean observatories elsewhere in the world.

Seafloor Cable and EO-Converters: ROV-mateable con-nectors on the MARS Observatory primary node will provide375 V, 48 V, 100Base-T Ethernet, and a 1 pulse-per-second(PPS) precise timing signal (the same is available on NEP-TUNE Canada, the future NSF OOI Regional Scale Nodes,and the Aloha Cabled Observatory). The mooring system is

Fig. 1. The ALOHA-MARS mooring system, using acoustic Seagliders andsatellite data to extend the spatial sampling footprint.

designed for a maximum of 1200 W with 320 W for the profilercharging and 270 W for guest users. In-line electro-opticalmedia converters (Fig. 3) are required to convert electricalcommunication and timing signals to optical form for transmis-sion over any significant distance using optical fibers and backagain. The seafloor and mooring riser cables both have foursingle mode fibers. One optical fiber is used for the Ethernetcommunications, and one for the PPS/RS-422 time distribu-tion and two are spares. Wave division multiplexers (WDMs)allow bidirectional data transmission using 1310 and 1550 nmwavelengths on the fibers. The 1.5-km cable between theprimary node and the seafloor secondary node junction box isa 12.7-mm diameter electrical/optical cable with six electricalconductors and the optical fibers in a 1.2-mm stainless steeltube. ROV-mateable connectors allow connection of the cable



HOWE et al.: A SMART SENSOR WEB FOR OCEAN OBSERVATION 3

Fig. 2. Mooring system block diagram.

to the primary and secondary nodes. The seafloor cable (withEO converters and connectors) is installed by ROV with a reelmounted in the cable laying tool sled on the ROV; the spool willbe left on the seafloor at the end of the cable laying process. Inthe future, EO-converters can be miniaturized and combinedwith the connectors at each end. When hybrid electro-opticalconnectors become more reliable and reasonable cost, theEO-converters could be eliminated.

Secondary Nodes: The AMM has two secondary nodes thatprovide the same connectivity functions that are available at theprimary observatory nodes, though power and communicationsclearly are now shared and (more) limited resources. Much ofthe design is based on the MARS power system (e.g., bus struc-ture, PC-104 node controller, switching and monitoring of ports,and ground fault monitoring; see [8]).

The seafloor secondary node serves as the terminus for theseafloor EOM cable that runs from the MARS node to thebase of the mooring. The node includes a frame, electronicshousing, and ROV-mateable electrical connector receptacles.The mechanical design of the node was done in consultationwith the ROV pilots at the Monterey Bay Aquarium ResearchInstitute (MBARI), the operators of the MARS system. Thereare two guest ports in addition to ports for the seafloor cable

Fig. 3. ROV-mateable connectors and the electro-optical converters (in beryl-lium copper pressure cases) attached to each end of the (black) seafloor cable.

from the primary nodes, the mooring cable (to the float node),and the instrument package. Syntactic foam buoyancy is addedto make the unit just slightly negative, so the ROV can pick it




up and move it around if necessary. There are receptacles forlead weights, once it is in place.

The subsurface float secondary node is connected viathe mooring cable to the seafloor node next to the base ofthe mooring anchor. It is also connected to the AMM floatinstrument package and has two unused guest ports withROV-mateable receptacles. In addition it has the electronics forthe inductive power coupler, the Sea-Bird inductive modem forcommunication with the profiler, an internal attitude sensor, theacoustic Doppler current profiler (ADCP), and video cameraand light (looking at the profiler docking station below thefloat).

Science Instrument Interface Module (SIIM): To minimizethe number of ROV-wet-mateable connectors used, an interme-diate multiplexer/SIIM is used to first connect all the sensorsat one location together (using inexpensive dry-mate connec-tors); then the SIIM is connected to the secondary node housingusing a single (expensive) ROV-mateable connector. This SIIMhas a mix of the following features: eight ports (dry-mate con-nectors), power at required instrument voltages (48 Vdc or 12Vdc), an eight-port Ethernet switch, Ethernet or RS-232 to Eth-ernet conversion (to connect to network), and individual soft-ware controlled load switching and deadface switching. Muchof this is accomplished with a custom, easily modified, four-channel printed circuit board, a “SIIM board.” Each channelhas a DigiConnectME embedded module, a FET switch, anddeadface relays. The DigiConnect module provides a 10/100BaseT network interface (i.e., an IP address), one high-speedRS-232 serial interface, 2 MB Flash memory, and 8 MB RAM.It provides an extremely convenient way to convert instrumentRS-232 to Ethernet. It is the only “smart” device in the SIIM,and can, for instance store and forward sensor metadata. On thefloat and at the base of the mooring, the SIIM board is housed ina titanium pressure case rated for 5000 m. A SIIM board also re-sides in the float secondary node for the attitude sensor, ADCP,and Sea-Bird inductive modem. The units work well with manydifferent oceanographic sensors connected: conductivity, tem-perature, depth, and oxygen (CTDO ), optical backscatter, hy-drophone, acoustic modem, ADCP, attitude/orientation sensor,camera/light and inductive modem. Most use RS-232 but some(e.g., the hydrophone) use Ethernet. The acoustic modem andhydrophone have access to the precise pulse-per-second timingsignal.

Sensors and Instruments: The Sea-Bird 52 MP/43 F pumpedCTDO is used throughout, two each (for redundancy) on thesubsurface float and at the base of the mooring, and one on theprofiler. These have titanium pressure cases rated for 6000 m.The WetLabs BBF2 sensor measures optical backscatter at470 nm and 700 nm, and chlorophyll fluorescence withinthe same volume. There is one each on the float and seafloorinstrument packages and on the MMP. The ADCP on thesubsurface float is a RD Instruments Workhorse Quartermaster150 kHz. It is mounted permanently on the float with a dry mateconnector to the float secondary node electronics case. TheADCP has an integral attitude sensor package. The acousticcurrent meter (ACM) on the profiler is a Falmouth Scientific4 axis device measuring a 3-D velocity vector. There is a

Fig. 4. (top) Detail of the hydrophone (small black cylinder inside the cleartube) and the WHOI micromodem transducer (larger black cylinder). (bottom)The complete instrument package on the deck of the R/V Thompson prior to de-ployment at the Seahurst Observatory; the micromodem electronics is strappedin the corner. The SIIM pressure case is visible with a yellow APL sticker. TheCTDs and BB2F are behind the SIIM. The white block is syntactic foam tomake the package near neutral buoyancy. The float secondary node is the Delrincylinder to the right (all the connectors are on the hidden end).

broadband hydrophone (Naxys eHyd) on the subsurface floatand Woods Hole Oceanographic Institution (WHOI) acousticmicromodems [9], [10] on the two instrument packages; theseare shown mounted on the subsurface float instrument packagewith SIIM in Fig. 4.

A gyro enhanced orientation sensor in the float secondarynode is installed to better understand the float/mooring dy-namics, related stresses, and impact on the optical fibers; itwill help answer the question, is a swivel necessary in futuremooring systems like this? If no swivel is needed, a major en-gineering simplification in the mooring can be made. There isa color video camera with lights mounted below the subsurfacefloat looking at the profiler dock to monitor the MMP dockingand undocking.

Moored Profiler: The McLane Moored Profiler (MMP, [11])has been modified for this application. The changes include:new motor, gearbox, and wheel for use with the larger EOMcable; interface for Wet Labs and Seabird sensors; interfaceAPL-UW moored profiler controller (MPC) to the MMP con-troller to offload data after every profile; replace primary Libattery pack with rechargeable 860 W-h Li-ion battery bankmounted in glass sphere; mount for inductive charging couplerand electronics; and use extended length McLane housing withadditional glass sphere for rechargeable battery bank and for




increased buoyancy. With these changes, there is a data col-lecting-to-battery charging duty cycle of 95% (4 days operationwith 4 h charging). The MPC (a Persistor CF-2 processor) col-lects the BB2F optical data (backscatter and fluorescence), inter-faces with and downloads data from the MMP (CTDO , ACM,engineering data), interfaces with and transfers data/commandsto/from the shore server, and supervises the charging of the bat-tery pack.

The appropriate MMP software to enable adaptive samplinghas only recently become available (“patterns” and schedule filetransfers at the end of any profile). In the future the profilerwill be modified to be ROV serviceable. By this we mean anROV can remove and install it without disturbing the deployedmooring. There are three reasons for this: 1) reliability of theMMP is not yet as good as desired, 2) any profiler is a mechan-ical device prone to failure, and 3) irrespective of profiler relia-bility, one may want to or need to change out the profiler with itsload of sensors, either because of sensor calibration/biofouling,or for a different payload. With this capability, the basic mooringsystem, with expected long life, can remain in place.

Inductive Power System (IPS): The inductive power system(IPS) to charge the batteries on the profiler is a key new tech-nical development of the project. The McLane Mooring Profiler(MMP) will periodically connect or “dock” beneath the mooringfloat to charge its battery pack. Due to the fact that the systemis submerged in conducting seawater, the connection must notutilize any contacts that allow an electrical connection to con-tact the seawater. Wet-mateable connectors that have enclosed,oil-bathed contacts have some potential for this but they typi-cally require a relatively high mating force and have a limitednumber of mate/de-mate cycles. The technique that has beenselected is to use inductive coupling for the power. S&K En-gineering has a significant amount of experience in the elec-tric automobile industry and was contracted to make the induc-tive power coupler (the “dock”) and the associated drive andcharging electronics.

A block diagram of the charging system is shown in Fig. 5.The float side has a high-voltage dc-power source that can rangefrom 150 to 400 Vdc. A boost DC-DC converter is used to createa pre-regulated voltage at 375 Vdc for the second stage. Themain power transfer through the inductive coupler is driven by ahalf-bridge inverter driving a series resonant tank circuit. The in-verter operates at a constant switching frequency of 50 kHz thatis above the resonant frequency. The inductive coupler has a pri-mary side that is fixed to the mooring cable and a secondary sidethat is mounted on the profiler. Because of the need to operatefor an extended period of time in the presence of biofouling, thecoupler has been designed with an annular gap of 2 mm. Be-cause of this relatively large gap, the coupler has a much largercross-sectional area than a typical power transformer. The cou-pler has 10 turns on the primary and 3 turns on the secondary.Because of the relatively large gap (2 mm) and the separation ofthe windings, the coupler functions as a poorly coupled trans-former and produces significantly less voltage or current in thesecondary than an ideal transformer. The seawater also appearsas a moderate impedance load that reduces the power conversionefficiency. The secondary side has a full-wave rectifier that may

Fig. 5. Inductive power system block diagram. A series resonant converterdrives a constant frequency square wave voltage across the inductive couplerthat is rectified and regulated on the secondary side.

Fig. 6. The inductive power system (IPS) components.

be shunted by a FET switch. The system is designed to deliver250 W at 16.4 Vdc on the secondary side, with an efficiency

70%. Fig. 6 shows the system components, Fig. 7 shows theprofiler being deployed in Puget Sound and in the docking po-sition on the mooring. The charging cycle is shown in Fig. 8;about two-thirds of the charge is accomplished in 2 hours and afull charge of the batteries (849 W-h Lithium Ion) takes 5 hours.




Fig. 7. (top) Deploying the MMP. The inductive power system primary is atthe very top on the mooring cable, the secondary is on top of the profiler. Theacoustic current meter has the four arms in the middle. The CTD sensors arewrapped in antifouling copper tape. (bottom) The MMP on the mooring in PugetSound in the docking position. One electro-optical converter is above the dockfollowed by a D-plate and the electro-mechanical swivel.

Fig. 8. Plot of the battery voltage and current during charging, measured at thebattery.

Future versions will have a more robust ferrite construction, asthe current one chipped in places. The coupling mechanism will

Fig. 9. The subsurface float. The instrument module with two CTDs is on theleft; TM is plugging in the ROV mateable connector, connected to the secondarynode in the left background. The ADCP is top right. A titanium post fits into theslot in the float.

be reversed so the spring-loaded portion is hanging down on themooring wire. More detail can be found in [12].

Inductive Modem (IM) Communications: The Sea-Bird In-ductive Modem (IM) system is used for communications be-tween the float and the MMP. Communications rate is nominally150 bytes/s; with forward error correction and other overheadthe effective rate is 90 bytes/s. This slow link is a bottleneckand will be replaced with a newer 19.2 kbaud system that canalso provide 30 s timing accuracy.

Mooring Riser Cable: The 22-mm (0.85-inch) diametermooring cable has six 18 AWG conductors with polypropyleneinsulation, four loose fibers in a 1.3-mm diameter steel tube(in center), a Kevlar strength member, and a steel mesh forfish bite protection, all enclosed in a polyurethane jacket. Thiscable, connecting the seafloor secondary node to the subsurfacefloat and the float secondary node, has electrical connectorterminations and EO converters identical to the seafloor cableconnecting the MARS primary node to the seafloor secondarynode. Initial pull tests of the cable with the mechanical termi-nations failed; the terminations were improved and the testswere completed successfully.

Mooring Float: The subsurface float tensions the mooringriser cable and serves as an instrument platform. Fig. 9 showsthe float and structure with the float instrument package andADCP. The 300-m depth rated syntactic foam float is 1.8 m di-ameter, 0.8 m high, weighs 2052 lbs in air, and has a buoyancy of2375 lbs. The float structure is made from 6061-T6 painted alu-minum. An electromechanical swivel/slip ring assembly is usedat the top end of the mooring cable just beneath the subsurfacefloat. The swivel has 16 slip rings in an oil-filled, pressure-com-pensated housing with an external pressure compensator (vis-ible in the top of the lower panel of Fig. 7). The stainless steelswivel is rated at three metric tonnes (6600 lbs) working load.




Fig. 10. Temperature as a function of depth (vertical axis) and time (horizontal axis). Warm (and salty) bottom intrusions associated with tidal flow are clear.

Fig. 11. Hydrophone spectrogram showing two strong modem signals (saturated, aliased here) and the ADCP transmissions at 1-second intervals.

Software: The successful operation of the mooring sensornetwork depends on software. The mooring system uses ascaled-down version of the MARS power monitoring andcontrol system (PMACS, [8]) with the secondary node con-troller (SNC) serving a similar role as the MARS node powercontroller. The SNC (a PC-104 stack) monitors load currentand bus voltage, allows for the setting of per-load currentlimits, and provides circuit-breaker and ground-fault moni-toring capabilities. The PMACS server communicates withthe SNC via an XML-RPC interface. The shore server (SS)runs a dedicated process for each sensor (an instrument serverprocess). Each process interfaces to its respective sensor overthe network and archives the sensor data on the local disk. Allsensor configuration tasks are handled through the SS. In total,there are 36 IP addresses required.

The profiler has required a significant amount of softwareassociated with the transfer of data from the MMP controller,communications over the inductive modem, acquisition of datafrom non-MMP sensors (the BB2F), and the battery charging/docking process.

Testing in Puget Sound: The mooring system was testedin Puget Sound on several occasions between June 2007 andAugust 2008 with success. An example of a temperature timeseries from the profiler is shown in Fig. 10. The profiler wasprogrammed to transit vertically every 15 minutes. An exampleof hydrophone data is shown in Fig. 11. The hydrophonebandwidth is from 5 Hz to 30 kHz. At the Seahurst Observatory(a high school marine center) the sampling was limited to12 kHz because of storage and communications bandwidthlimitations between the shore station and APL. This figureshows a representative spectrogram with both acoustic modemactivity (the two thick bands) and the ADCP going off everysecond.

B. Seaglider

The basic Seaglider developed at the University of Wash-ington’s Applied Physics Laboratory (UW/APL) can dive to1000 m while moving horizontally at about 1/2 knot using 1/2 Wof power. Glider missions have now exceeded 737 dives, cov-ering 4900 km over ground and 279 days. Gilders routinely col-




Fig. 12. The acoustic Seaglider. The hydrophone is in tail cone at the top, the acoustic modem transducer beneath it. The body contains (left to right) an acousticemergency locator transponder/altimeter, electronics and low voltage batteries, high voltage batteries on a mass shifter for changing pitch and roll, buoyancy engine(oil pump) with internal and external oil bladder, and GPS and Iridium antenna.

Fig. 13. Acoustic Seaglider being recovered after a Monterey Bay mission(courtesy J. Curcio).

lect temperature and conductivity (salinity) data during a dive.We have adapted the Seaglider to carry a broadband hydrophone(10 Hz–30 kHz) and a WHOI acoustic micromodem [9]; seeFigs. 12 and 13. The latter operates in the 23–27 kHz band.

C. Acoustic Communications and Network Infrastructure

There is currently much activity within the oceanographiccommunity to develop integrated underwater sensor networksthat include mobile, fixed, autonomous, and cabled nodes.A significant difficulty in this effort is that the underwaterocean environment, especially in shallow water, is in generala very challenging medium in which to reliably commu-nicate information. The reasons are well documented [13],[14]. Since radio frequency (RF) waves attenuate extremelyrapidly underwater, acoustic signaling is the preferred methodof underwater wireless communication. Low acoustic soundspeed (1500 m/s) introduces long propagation delays andextensive time spreading of the received signal. The shallowocean environment is a dense scattering environment and isgenerally highly time varying. Acoustic signals attenuate veryquickly as frequency increases; hence, the underwater channelis bandwidth limited. Furthermore, underwater Seagliders, suchas those used in this project, are low power battery operateddevices. This imposes practical constraints on the complexityof communications hardware [15], [16].

Testing has taken place of acoustic communications betweengliders and between gliders other platforms, both fixed and mo-bile, at the surface, in the water column, and on the bottom. Inone such test in Puget Sound, a glider sat on the bottom and

communicated with a glider moving out in range; see Fig. 14.Travel time and thus range was routinely measured. Frequencyshift-key (FSK) signal coding at 80 bits per second (b/s) wasused, with reliable results to 4 km and less reliable results to7 km. In another test, an acoustic modem was installed on theAMM bottom node at 30 m water depth as part of the mooringtesting at the Seahurst Observatory in Puget Sound (just westof Sea-Tac airport). Using a boat deployed transducer and deckbox, ranges to 2.5 km were obtained (see Fig. 15); the lowerranges than in the first mentioned test were likely a result of themuch shallower bottom. In more recent testing, phase-shift-key(PSK) signal coding (coherent versus the incoherent FSK) wasused. In this case 240 b/s and 5200 b/s were obtained betweena glider and a surface gateway buoy with a modem suspendedbeneath; this modem has a four-element hydrophone-receivingarray. Further, a go-to-surface command was sent to the gliderto demonstrate real-time vehicle control via the acoustic com-munications channel. In all cases, one-way travel times wereobtained from which range is obtained. In summary, these testresults confirm that the acoustic modem can perform adequatelyboth from a communications perspective as well as for naviga-tion. This demonstrates a necessary capability for the NSF OOI,to communicate between fixed and moving infrastructure.

In related work, the acoustic recording system on the gliderwas used to collect marine mammal data during an experimentin Monterey Bay in 2006. Many blue, fin, humpback, and spermwhale calls were detected, as well as birds and sea lions [17].In another mission off the Hawaiian Island of Kauai, the gliderrecorded transmissions from a 75 Hz bottom mounted acousticsource (part of the Acoustic Thermometry of Ocean Climate/North Pacific Acoustic Laboratory project [18], [19]). Coherentsignal processing with near-theoretical gain was achieved withpositive ray identification. This indicates that gliders can coher-ently average these low frequency signals over 10 minutes, andestimate Doppler velocity very accurately. Thus, they can serveas mobile tomography receivers. These results are examples ofusing basic acoustics infrastructure for navigation and commu-nication, as well as science and other uses. Further, the Kauairesults indicate that the same low-frequency sources can serveas underwater GPS transmitters for basin scale navigation andcommunication.

D. Satellite Sensors

Of most direct relevance for determining the physical stateof the ocean from space are sensors that determine sea surfaceheight and wind stress. Sea surface height is measured with an




Fig. 14. Signal quality versus range and depth. Signal quality ranges from 253 (high signal-to-noise ratio) to 100 (low SNR). One glider is sitting on the bottomcommunicating with another glider making multiple dives. Data from Puget Sound tests conducted summer 2007.

altimeter, e.g., Jason-2, and provides a measure of depth inte-grated density; from this can be inferred heat content and upperocean currents via geosptrophy. Wind stress is estimated fromwind speed as determined by a scatterometer, e.g., SeaWindson the QuikSCAT satellite, which infers wind speed from sur-face roughness. The SeaWinds scatterometer is a specializedmicrowave radar that measures near-surface wind velocity (bothspeed and direction) under all weather and cloud conditionsover Earth’s oceans. SeaWinds collects data in a continuous 1800-km-wide band, making approximately 400,000 measure-ments and covering 90% of Earth’s surface in one day. Further,satellite measurements of sea surface temperature provide addi-tional observations.

In the future, a wide-area swath altimeter system (SurfaceWater and Ocean Topography, SWOT) will provide orders ofmagnitude more data on the ocean surface topography. In othermissions, the estimation of wind speed will be improved withthe Extended Ocean vector Winds Mission (XOVWM) and sur-face salinity (affecting ocean density among other things) willbe measured by the Aquarius mission [20]. These and manyother space-based sensors will be used in smart sensor webs asthey develop over time.

E. Regional Ocean Modeling System (ROMS)

Despite the recent advance of the ocean observing tech-nology, sampling the ocean on both the global and regionalscales remains a challenging task. While satellite sensors canonly measure the surface properties of the ocean (includingthe surface manifestations of subsurface processes, such aswith altimetry) with imperfect, though improving, time–spaceglobal sampling, there are usually large temporal and spatialgaps between in situ sensors. Three-dimensional dynamicalmodels are therefore needed to combine these in situ and

satellite measurements in a process known as data assimilation.The goal is to estimate the state of the ocean today and then topredict its future evolutions.

We are using the Regional Ocean Modeling System(ROMS), a community based and openly available software(http://www.myroms.org). ROMS solves the three-dimensionaloceanic equations of momentum, temperature, and salinity [21].The model uses a vertical coordinate following the bottom to-pography [22]. Compared with the traditional sigma-coordinatesystem, this vertical coordinate system provides more flexibilityin choosing vertical levels in specific vertical domains, such asthe bottom boundary layer or surface mixed layer. The modelexplicitly represents the time evolution of the free surface andhas an open lateral boundary condition to allow the exchangeof information through boundaries [23]. The Flather boundarycondition [24] is used to allow the propagation of barotropictidal signals into the domain.

The ROMS configuration used here includes a fully resolvedtidal component. ROMS uses data from the Topex-PoseidenGlobal Inverse Solution version 6.0 (TPXO.6) developed at theOregon State University [25]. The tides are provided as complexamplitudes of earth-relative sea-surface elevation and tidal cur-rents for eight primary harmonic constituents (M2, S2, N2, K2,K1, O1, P1, Q1), on a 1/4 degree resolution grid. These har-monics are introduced in ROMS through the open boundariesusing the Flather condition (see [23]). This open boundary con-dition combines the Sommerfeld equation (with surface gravitywave phase speed) with a 1-D version of the continuity equa-tion applied in the outwardly normal direction at each openboundary. The volume is automatically conserved in the domainand variations due to physical forcing such as tides (but also theother subtidal components) are introduced through the externaldata. Preliminary results from the ROMS solutions with tidal




Fig. 15. Acoustic modem results from Seahurst tests: (top) signal quality versus time, (middle) travel time versus time, (bottom) quality versus range.

forcing show that the coastal ocean is dominated by the semi-di-urnal (M2) tide with an error less than 5% for both amplitudeand phase [26].

ROMS can be implemented in a multi-nested grid configura-tion that allows for telescoping from the large-scale down to re-gional and local region at very high resolution (on the order of 1km). Fig. 16 shows a three-domain nested ROMS configurationcentered around Monterey Bay, California. The spatial resolu-tions are 15 km, 5 km, and 1.5 km, respectively. The MontereyBay ROMS configuration has been tested in two major field ex-periments: Adaptive Sampling Ocean Network (AOSN) in 2003[27], and Adaptive Sampling and Prediction (ASAP) in 2006.

ROMS has the ability to assimilate both in situ and satellitedata using a three-dimensional variational data assimilation(3DVAR) method [28], [29]. During both field experiments,the ROMS modeling and data assimilation system was run

daily in real-time, assimilating satellite data as well as in situtemperature and salinity vertical profile data (e.g., derivedfrom CTD measurements on moorings, ships, gliders, andautonomous underwater vehicles or AUVs). The current ROMSconfiguration produces nowcasts every six hours. Forced withthe mesoscale atmospheric forecast [30], the current ROMSconfiguration makes a 48-hour forecast every 24 hours. Initialresults show significant skill in the ROMS model in describingand predicting the coastal circulation and variability [31].

During the 2006 ASAP field experiment, we have demon-strated the significant positive effect of adaptive sampling usinggliders to improve the model simulation and forecast [31]. Thiseffectiveness may be maximized if the observations are “tar-geted” based on objective guidance that is derived from models(Fig. 17). Such guidance can answer the question: “What arethe optimal sampling locations in the next two days, in order




Fig. 16. Sea surface temperature as simulated by the three nested ROMS models with a spatial resolution of 15 km (left), 5 km (middle), and 1.5 km (right),respectively.

Fig. 17. ROMS-derived guidance aimed at improving a 1-day ROMS prediction within the blue rectangle region. Darker shading corresponds to preferred loca-tions for sampling targeted observations of (left) temperature and (right) salinity.

to reduce the errors in the ROMS forecast within a region ofinterest?” Fig. 17 shows an example of the guidance showingthe uncertainty and dynamics of the flow that can be derivedfrom the ROMS ensembles. Formally, the guidance representsthe reduction in prediction error variance within a given region.Based on this guidance, we have identified optimal locations foradditional sampling. One additional glider was deployed to col-lect more data around these optimal locations. Fig. 18 showsthe RMS differences in temperature and salinity between allglider measurements within the observational box and the co-lo-cated ROMS reanalysis values. Two curves are shown, one forthe ROMS reanalysis that does not assimilate this additionalglider data and another for the reanalysis that does assimilatethis glider data. The reanalysis that includes this adaptively de-ployed glider data shows a significant improvement (i.e., smallerRMS errors), at least in part, due to the adaptive sampling.

During 13–23 October 2008, another field experiment wasconducted in Monterey Bay (MB08). The major science goalwas to detect and predict extreme blooms in Monterey Bay using

a smart sensor web-integrating in situ and remote sensing obser-vations with predictive models. A unique innovation of MB08was to bring the adaptive sampling concept to the satellite plat-form, for the first time. The objective was to demonstrate theability to detect and predict extreme blooms using an observa-tory concept consisting of both in situ and remote sensing plat-forms as well as real-time ocean and atmospheric forecastingmodels.

Recent efforts have included feasibility pilot tests to trackoceanographic events using sensor web technologies. Wide cov-erage satellite assets such as MODIS and MERIS have oceancolor products useful in studying algal blooms. However thesewide coverage sensors have lower spatial resolution. Ideallythese datasets would be combined with point-and-shoot satellitedata with less spatial coverage but higher spatial (and possiblyspectral) resolution.

During MB08, the Earth Observing 1 (EO-1) satellite wastasked to automatically deliver oceanographic science dataproducts for scientist evaluation. The EO-1 Hyperion high-res-




Fig. 18. ROMS reanalysis RMS errors in (A) temperature and (B) salinity before (solid line) and after (dashed line) the adaptive sampling deploying a gliderfollowing the ROMS derived guidance.

olution hyperspectral imager is capable of resolving 220spectral bands (from 0.4 to 2.5 m) with a 30-m resolution.The instrument can image a 7.5-km by 100-km area per image,and provide detailed spectral mapping across all 220 channelswith high radiometric accuracy. Specifically in this case, EO-1Hyperion acquisitions were made in coordination with theEO-1 Sensorweb team on 10 days in September and October2008 with five scenes during the MB08 deployment. EO-1 usedautomated workflows to process and deliver the data to theMB08 science and operations team along with two derivativescience products, Fluorescence Line Height (FLH) and Max-imum Chlorophyll Index (MCI) linear baseline data products.These automated sensor web workflows were triggered by thecampaign tag associated with the acquired scenes and invokedperl/IDL processing to produce the desired science products.The resultant science products were automatically delivered tothe scientists through a collaboration web portal. The systemposted web updated the portal with links to files downloadablefrom EO-1 servers. This capability was later generalized toenable sftp and email delivery of products as well. These ef-forts demonstrated the utility of automated science processingand delivery of EO-1 products to support science operationsand also provided guidance on areas of improvement neededbefore operationally useful products could be delivered. Keyoperational areas identified for future work include improvedinstrument and atmospheric correction.

The ultimate goal is to eventually develop automated pro-cessing flows that deliver alerts and science classification prod-ucts to interested scientists and authorities. These automatedalerts could then be used to deliver data to interested authori-ties and also request subsequent data to track the evolving phe-nomena.

III. DISCUSSION

The foregoing presentation of sensor web elements and re-sults is part of an ongoing, evolving effort to improve the oceanobserving system. The current generation of satellite and in situobservations are a great improvement over a decade ago, butthey are inadequate for future needs. Improvements in satellite

sampling have been called for as part of the NASA DecadalPlan, for example (the SWOT and XOVWM mentioned abovewere examples of planned missions [20]), and some of theseare being funded. These missions will better sample the oceansurface in space and time but there are fundamental limits tohow deep into the ocean this boundary condition informationcan propagate, given the uncertainties in our knowledge ofthe physics and the uncertainties of the measurements. in situmeasurements will always be required to keep models on track.In the International Ocean Observations 2009 Conference [2],there was a call to improve the present system with elementssuch as glider fleets and tomographic sampling of the deepocean and under ice-covered water (e.g., the Arctic Ocean andoff Antarctica) [32], [33].

Quantification of the overall efficacy of any particular oceanmeasurement or measurement type with associated instrumentplatform is a ongoing effort within the ocean modeling and dataassimilation community. It is only now that the models and dataassimilation tools have the fidelity and sophistication to capturethe important information content of a data type. Simulationand actual experiments to determine the efficacy of the mooringplus glider combination have not been conducted. It is known,though, that glider data is significant in this regard, because,even though slow, a glider can move several times faster thantypical ocean advection speeds and thus can capture some of thedynamics. In general, a fixed-point measurement by itself is lesseffective because of the high levels of high frequency and highwavenumber “noise” (e.g., internal waves) relative to the oceanmesoscale that is typically the main signal of interest in oceanobserving. However, the fixed individual platforms typically cancollect data that are of more interest for process studies or long-term climate studies. Further, when fixed platforms are in anarray configuration, such as the TAO-TRITON array across theequatorial Pacific, they too can synoptically capture the large-scale features of interest.

We see the primary use of acoustic communications in oceansensor webs being the transfer of data and commands betweenfixed and mobile subsea platforms and the surface to shoreand back. Because the air–sea interface is so inhospitable,the number of gateways through the interface will always be




limited. Rather, much of the sensor web will remain below thesurface in more hospitable domains and rely on specializedplatforms to provided the gateway capability. Further, throughthe transfer of data between elements of the web, the samplingcan be made more efficient, either using truly autonomousin situ decision making, or by relying on more centralizedcommand and control.

Sensor web elements such as we have described here cancontribute to these future efforts, evolving with time. Forinstance, the same cabled mooring systems can host acoustictransceivers, serving the role of “underwater GPS satellites” forlong(er) range navigation as well as communications. Whileproviding the more conventional temperature and salinity pointprofile data, seagliders (or other fixed or mobile platforms) canserve as acoustic tomography receivers providing complemen-tary data. As an integrated web, instruments and platforms canserve multiple purposes.

A sensor web as envisioned and developed here complementsexisting and planned space science missions. Specifically thesensor web integrates space-based sensor data with in situ data;these are integrated via the ROMS model, the output of whichcan used for achieving a set of scientific objectives, includingenhancing the science products of stand-alone missions suchas described for the MB08 example. The ROMS model is alsouseful in planning future space-based missions and in situ ob-serving programs dedicated to climate change science.

IV. CONCLUDING REMARKS

The ocean-observing smart sensor web presented herein iscomposed of (a) a cable-connected mooring system with a pro-filer under real-time control with inductive battery charging; (b)a glider with integrated acoustic communications and broad-band receiving capability; (c) satellite sensor elements; (d) anintegrated acoustic navigation and communication network; and(e) a predictive model via the Regional Ocean Modeling System(ROMS). The acoustic communications network forms a crit-ical link in the web between the in situ sensors and shore con-trol, and thus space-based sensors, and facilitates adaptive sam-pling and calibration. As an example of the power of the sensorweb concept, during MB08 we were able to adjust the lookingangle of the Hyperion sensor on board the EO-1 satellite as itpassed over Monterey Bay. This is the first time that satellitemeasurements were guided by in situ observations and modelsimulations and forecasts in real time without manual interven-tion, therefore closing a complete loop of a smart sensor webfrom ocean observing to model forecast leading to future guidedobservations.

ACKNOWLEDGMENT

Thanks are given to the many scientists, engineers, techni-cians, and students who have contributed to this work.

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Bruce M. Howe (M’05) received the B.S. degree inmechanical engineering and the M.S. degrees in en-gineering science in 1978 from Stanford University,Stanford, CA, and the Ph.D. degree in oceanographyfrom the Scripps Institution of Oceanography, Uni-versity of California, San Diego, in 1986.

From 1986 to 2008, he worked at the AppliedPhysics Laboratory and is presently a Professor inthe Ocean and Resources Engineering Department atthe University of Hawaii at Manoa. While at StanfordUniversity, he developed laser Doppler velocimetry

(LDV) instrumentation for air–sea interaction experiments. From 1979 to 1981,he was a Research Associate with the Institut für Hydromechanik, UniversitätKarlsruhe, Germany, working on LDVs for use in the atmospheric boundarylayer. While at the Scripps Institution and since then, he has worked on manyocean acoustic tomography projects, including Moving Ship Tomography,Acoustic Thermometry of Ocean Climate (ATOC) and the North PacificAcoustic Laboratory (NPAL). He helped establish ongoing Ocean Observa-tories efforts, and is working on fixed infrastructure (e.g., cable systems andmoorings), mobile platforms (e.g., gliders as navigation/communications nodesand acoustic receivers), and hybrids (e.g., moored vertical profilers). A goal

is to integrate acoustics systems in ocean observing to provide navigation,communications, timing, and science applications.

Yi Chao received the M.A. degree in geophysicalfluid dynamics and the Ph.D. degree in atmosphericand oceanic science, both from Princeton University,Princeton, NJ, in 1990 and 1987, respectively.

He is a Principal Scientist with the Jet PropulsionLaboratory (JPL), California Institute of Technology,Pasadena. He is the Supervisor of the Ocean-Atmos-phere Interaction group at JPL’s Earth Science Sec-tion. Since 2003, he has also been the Project Sci-entist for the ESSP Aquarius satellite mission (mea-suring sea surface salinity from space) to be launched

in March 2009. He has been with the Jet Propulsion Laboratory since 1993. Heis also an Adjunct Professor with the Department of Atmospheric and OceanicSciences at UCLA.

For his work, Dr. Chao has received the NASA Exceptional AchievementMedal, (2005), the Best Paper Award at the Supercomputing Conference, SanJose, CA (1997), and the Lew Allen Award for Excellence, JPL (1996).

Payman Arabshahi (SM’07) is a Senior ResearchScientist with the University of Washington’s Ap-plied Physics Laboratory, and Associate Professorof electrical engineering at the University of Wash-ington. From 1994 to 1996, he served on the facultyof the Electrical and Computer Engineering Depart-ment at the University of Alabama in Huntsville.From 1997 to 2006, he was on the senior technicalstaff of NASA’s Jet Propulsion Laboratory (JPL),in the Communications Architectures and ResearchSection. While at JPL he also served as affiliate

graduate faculty at the Department of Electrical Engineering at Caltech, wherehe taught the three-course graduate sequence on digital communications. Hehas a strong 12-year track record of successful design, implementation, andmanagement of large, complex technology projects; building and maintainingR&D relationships with academia, government, and industry; and strategicplanning and technology roadmapping. His research interests are in wirelesscommunications and networking, sensor networks, signal processing, datamining and search, and biologically inspired systems.

Sumit Roy (LF’07) received the B.Tech. degreein electrical engineering from the Indian Instituteof Technology, Kanpur, in 1983, and the M.S. andPh.D. degrees in electrical engineering from theUniversity of California at Santa Barbara in 1985and 1988, respectively, as well as the M.A. degree instatistics and applied probability in 1988.

Presently he is Professor and Associate Chair ofElectrical Engineering, University of Washington,Seattle, where his research interests include anal-ysis/design of wireless communication and sensor

network systems with a diverse emphasis on various technologies: wirelessLANs (802.11) and wireless MANs (802.16), multi-standard wireless inter-net-working and cognitive radio platforms, vehicular and underwater networks,and sensor networking involving RFID technology.

He spent 2001–2003 on academic leave at Intel Wireless Technology Lab as aSenior Researcher engaged in systems architecture and standards developmentfor ultra-wideband systems (wireless PANs) and next generation high-speedwireless LANs. During January–July 2008, he was the Science Foundation ofIreland’s E.T.S. Walton Awardee for a sabbatical at University College, Dublin.

Dr. Roy’s activities for the IEEE Communications Society (ComSoc) in-cludes membership of several technical and conference program committees,notably the Technical Committee on Cognitive Networks. He currently serveson the Editorial Board for IEEE TRANSACTIONS ON COMMUNICATIONS, IEEEINTELLIGENT TRANSPORTATION SYSTEMS and Wiley Journal of Wireless Com-munications and Mobile Computing. He was elevated to IEEE Fellow by theCommunications Society in 2007 for his “contributions to multi-user commu-nications theory and cross-layer design of wireless networking standards”.




Tim McGinnis joined Applied Physics Laboratory,University of Washington, Seattle, in 2001. He iscurrently working on the Regional Scale Nodescomponent of the NSF Ocean Observatories Ini-tiative. His main interest and expertise is in deepocean engineering and equipment design. For over20 years, he has been involved in a variety of towedand bottom landing vehicle development projectsand at-sea operations for mapping, imaging, andsampling the seafloor in water depths to 5000 meters.He has worked on power systems and moorings for

cabled ocean observatories.

Andrew Gray received the M.S. degree in electricalengineering from the Johns Hopkins University, Bal-timore, MD, in 1997, and the Ph.D. degree in elec-trical engineering and the M.B.A. from the Univer-sity of Southern California, Los Angeles, in 2000 and2004, respectively.

He has been with the Jet Propulsion Laboratory,Pasadena, CA, since 1998. He is currently DeputyManager of Commercial Programs and affiliategraduate faculty of the University of Washington,Seattle. From 1998 to 2007 at JPL, he was with

the Communications Architectures and Research Section where he served assenior research engineer, project manager, and technical group supervisor ofthe Advanced Signal Processing Projects Group. During this period he wasproject manager or principal investigator on numerous multi-disciplinary R&Dprojects in the areas of signal processing, wireless sensor networks, RF andoptical communications, radar, and reconfigurable hardware/firmware/softwaresystems. During this period he also contributed to and lead Mars missionarchitecture studies, supported the Outer Planet Missions decadal survey,and served as communications systems engineer on the pre-phase-A missionMars Science Orbitor. Previously he was a contractor with NASA’s GoddardSpace Flight Center, Microelectronics and Signal Processing Branch and wasthe primary architect of NASA’s 600 Mbps all digital receiver for bandwidthefficient satellite communications. He has over sixty publications and patentsin the areas of signal processing, communications, VLSI architectures forcommunications systems, and applications of decision science. His researchinterests include reconfigurable computing, sensor networks, decision anal-ysis, wireless communications, parallel and multirate digital processing, andintelligent systems.


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