oceans07 uwsn with dual communications, sensing ... - iuliu

An Underwater Sensor Networkwith Dual Connunications, Sensing, and

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Carrick Detweiller Juliu Vasilescu Daniela RusComputer Science and Artificial Intelligence Laboratory

Massachusetts Institute of TechnologyCambridge, 02139

Email: groups.csail.mit.edu/drl

Abstract This paper describes an underwatersensor network with dual communication and sup-port for sensing and mobility. The nodes in thesystem are connected acoustically for broadcastcommunication using an acoustic modem we de-veloped. The nodes are connected optically forhigher speed point to point data transfers usingan optical modem we developed. We describe thehardware details of the underwater sensor nodeand the communication and networking protocols.Finally, we present and discuss the results fromexperiments with this system.

I. INTRODUCTION

Underwater modeling, mapping, and monitoringfor marine biology, environmental, and security pur-poses are currently done manually or using expensivehard to maneuver underwater vehicles or individualinstruments. We would like to bring a new level ofautomation and capability to this domain in the formof versatile and easily deployable underwater sensornetworks. Just like the Berkeley Mica Mote [11], thecurrent industry standard for ground sensor networkshas fueled an explosion in the development of groundand aerial sensor network applications, our goal isto develop underwater technology that will enable asimilar level of automation.More than 70% of our planet is covered by water.

It is widely believed that the underwater world holdsideas and resources that will fuel much of the nextgeneration of science and business. However, anyunderwater operations are fraught with difficulty dueto the absence of an easy way to collect and monitordata. Underwater sensors exist but they are notnetworked and their use has many issues:

Deploying, retrieving, and using the sensors islabor intensive;

. Collecting the data is subject to very long de-lays;

. The manual aspects of using the sensors leads toerror;

. The spatial scope for data collection with indi-vidual sensor is limited;

. Individual sensors are unable to perform opera-tions that require cooperation, such as trackingrelative movement and locating events.

What is required is a low-cost, versatile, high-quality, easily deployable, self-configurable platformfor underwater sensor networks that will (a) auto-mate data collection and scale-up in time and space,(b) speed-up access to the collected data, and (c) beeasy to use.

In this paper we describe the underwater sensornetwork hardware we designed, built, and deployedin lakes, rivers, and ocean. The hardware consistsof static sensor network nodes and mobile robotsthat are dually networked optically (for point-to-point transmission at 330kb/s and acoustically forbroadcast communication over hundreds of metersrange at 300b/s). We discuss the communicationperformance of the network during experiments withthis system in the ocean, in rivers, and in lakes. Wedescribe the sensor network hardware, explain thecommunication protocols, and show results from fieldexperiments.The sensor nodes, which were developed in our

lab, are shown in Figure 1. These nodes packagecommunication, sensing, and computation in a smallcylindrical water-tight container. Each unit includesan acoustic modem and an optical modem imple-mented using green light and designed in our lab. Thesystem of sensor nodes communicates with a TDMAprotocol and is self-synchronizing. The system is

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capable of ranging and has a data rate of 300 b/sverified up to 400 meters in fresh water and in theocean. The sensors in the unit include temperature,pressure, and camera with inputs for water chemistrysensors.

Because the nodes are light and small, they areeasily deployed by manually throwing them over-board. Once deployed, the nodes are anchored withweights and form a static underwater network. Thisnetwork self-localizes using a range based 3D dis-tributed localization algorithm extension of the 2Ddistributed localization algorithm developed in ourprevious work [13].

A. Related WorkThere has been a growing interest in automating

oceanographic research applications. This researchis motivated by the vision of collaborative oceano-graphic research projects such as the AutonomousOcean Sampling Network II [1] and [2], [5], [6], [10].It is becoming more important for robots and sensinginstruments to be able to assist in the deployment ofmeasuring systems or to act as part of large-scaledata-collecting networks.

In designing the underwater network system, wedraw from important results in acoustic telemetry[4] and the design of sensor networks for aerial op-erations [11]. A cabled water operation system wasdescribed in [12]. We also build on the success of theWHOI acoustic modem [15] to develop a flexible, re-programmable acoustic model with new capabilitiessuch as reprogrammability, ranging, TDMA commu-nications, and self-synchronization.

II. THE UNDERWATER SENSOR NETWORKA. Hardware

Fig. 1. A picture of some sensor nodes drying.

In our previous work [14] we described our firstunderwater sensor network. This network had lim-ited acoustic capabilities. Building on this work, wedeveloped a second generation underwater sensornetwork that has retained the original goals andaddressed them with a different design and enhancedcapabilities.We have built a second generation underwater

sensor nodes called AquaNodes (see Figure 1). Sofar we have built 10 nodes. Each node is buildaround a CPU unit , based on the ATmegal28processor, with 128kbyte of program flash memory,4kbyte of RAM, and 512kbyte of flash memory fordata logging/storage. This board has temperatureand pressure sensors on it, and inputs for 6 other 24-bit analog or digital sensors. The underwater sensornode is contained in an acrylic watertight cylindricalcontainer with a radius on 15 cm and height of 25cm.The caps of the tube are molded to fit the electronicsthat need to be there (e.g. the optical receiver andtransmitter, the acoustic transducer, and cables).The bottom cap of the box has a winged systemthat allows the addition of free-standing measuringdevices and provides a suspension mechanism forweights.The mother board is interfaced to a special optical

communications board through a serial link. Theoptical board has its own processor and uses 532nmlight. It is capable of a range of 2.2m/8m1, within acone of 90/30 degrees and a maximum data rate of320kbits/s. Additionally, there is an acoustic commu-nication module using 30kHz FSK modulation andan in-house built transducer with a range tested upto 400m (we believe it can go farther) and a datarate of 330 bits/s.

For sensing, each node has a pressure sensor, tem-perature sensor, and a 640 x 480 color camera. Thetop side of the first generation sensor box containeda 170 mm rod with an LED beacon. The rod wasused by an AUV to locate the box, dock, and pick itup. Future versions of the second generation sensornode will contain such a docking rod. In addition, thenode will contain a XENON flash tube for increasingthe distance for reliable node location to about 20meters. The sensor node is powered by 7 2 amp/hourLithium Ion batteries. When all the components ofthe node run at full power (e.g. the communicationhardware is fully powered and operates continuouslyand the all sensors are also fully powered and sample

'The 8m range requires lenses on one of the devices andactively pointing it toward to other

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continuously) the battery provides 2 weeks of contin-uous operation. In sleep mode the battery provides1 year of continuous usage. The box is weighted tobe 20% negatively buoyant, and balanced such thatif dropped in water it always lands top up.The sensor node design has been planned for oper-

ations up to 200m of depth and has been tested in apressure chamber up to 50m. We have deployed thenodes in different ocean, lake and river environmentsup to 10m. The sensor node is deployed manually (bythrowing overboard) or it can be deployed with highprecision using a robot. The node has lines of man-ually adjustable length. The nodes stays suspendedwith weights attached to the line at the bottom ofthe water basin.

B. CapabilitiesThe sensor system was built in such a way that

the system is very easy to deploy. The nodes canjust be placed in the water and they will automati-cally localize themselves using ranges obtained fromthe acoustic modems. Before the nodes can localizethemselves they must decide on a communicationschedule. To do this we use a self-synchronizing timedivision multiple access (TDMA) scheme to schedulemessages (see Section III).The static localization algorithm is a robust lo-

calization algorithm we developed based on a 2Ddistributed localization algorithm developed in [13].This algorithm requires the set of ranges between thesensor nodes as input.The ranges are obtained using the acoustic

modems (see Section III.) The ranges are obtainedusing two different methods. The first is to measurethe round trip time of a message between a pair ofnodes. This give us a ranges with an accuracy of ±3cm.The second method is to synchronize the clocks on

the nodes and then use a schedule to determine wheneach node should send a message. The modems havetemperature compensated oscillators with about onepart per million drift which allows sub-meter rangingaccuracy for about thirty minutes before the clocksneed to be synchronized again.

In both of these cases we are only able to obtaina single range measurement at any one time. Inthe case where the ranging is taking place on amoving node such as the robot, this implies thatbetween each range measurement the robot will havemoved and this movement needs to be compensatedfor. We are currently able to obtain a single rangemeasurement every one to four seconds.

III. COMMUNICATION

In this section we describe the acoustic protocolsthat give our underwater sensor network nodes thecapabilities described in Section II-B.The sensor nodes are networked dually. Optical

communication allows line-of-sight fast data transfersas described in [14]. The data rate is 320 Kb/s andthe nodes. Direct line of site within a 90 degree coneat 2 m (extensible to 8m using lenses) is required foroptical communication.The acoustic communication enables broadcast at

lower data rates of 330 kbits/s at distances of over400 m2. In this section we describe the details of theacoustic communication system.

A. Acoustic Modem Hardware

The acoustic modem is built around a AnalogDevice Blackfin BF533 fixed point DSP processorrunning at 600Mhz. For transmition the processorgenerate a PWM signal which is amplified by a lOWD class amplifier, operating at about 90transducerdeveloped in house. For reception, the signal fromthe same transducer is passed through a band passfilter, and variable gain amplifier which drives a 12bitA/D sampling the signal at 250 k samples per second.The modem uses a FSK modulation on a 30Khz

carrier frequency. The symbol size is 3ms, consistingof Ims transmition and 2ms pause. The numberswere determined experimentally. We aimed to reach agood trade off between inter-symbol interference andfrequency resolution on the receiver side. Each datapacket consists of a synchronization pulse followedby 16 bytes of data. The synchronization pulse isa linear frequency sweep of 5ms recognized on thereceiver side by a matched filter. The data consistsof 10 bytes of payload, 2 byte CRC, 1 byte for thesource ID, 1 byte for the destination ID, 1 byte forthe packet type and 1 byte for the slot number (usedby the TDMA scheduler).

B. A TDMA Protocol

A typical network deployment starts with initial-izing the number of slots at deployment type. Typi-cally, we start with the a number of slots equal tothe number of nodes in the system. We can addslots on the fly or at any time over the durationof the network. The additional slots are allocated tosupport more frequent communication for the moving

2We have only tested the system up to 400 m but we believethe range is greater and have plans to test this in the nearfuture.

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nodes, or for base stations. Nodes are allocated a slotnumber at the beginning of deployment. However,any node can send a command to another node torelease its time slot.Our TDMA protocol uses 4 s time slots. Each time

slot is divided into a 2 s master packet for the slotowner, and 2 s response time. The response timemay be one of two categories, based on the owner'srequest. The owner may request communication to asingle node, or communication to multiple nodes. Inthe case of communication to a single specific node,the response includes data. However, the responsepacket is also used to compute the range between thetwo nodes (e.g., the roundtrip time offset by 2 sec.)In the case of multiple destinations, the responses arespaced at 200 ms intervals and are used primarily tocompute multiple ranges within one communicationslot. This communication modality does not supportmuch data transfer, but it enables a very efficient wayof estimating multiple ranges. This feature is impor-tant for applications that use the sensor network asan external localization system to localize and tracka moving node, for example using the algorithm in [7], as well as for applications that require the networkto self-localize and establish a system of coordinates,for example using the algorithm extensions of [13].

In our TDMA implementation, a node can ownanywhere between 0 and N time slots. The ownersof the slots can be changed dynamically in real time.

C. Self-synchroniizationA key feature of our sensor network system is

the ability of the system to self-synchronize withoutaccess to an external clock source such as a GPS orto very high-precision clocks.The self-synchronization algorithm works as fol-

lows. The nodes are initialized with the total numberof slots and each node knows its slot number.When node n with allocated slot N is deployed in

water, it waits to hear a correct master packet forN x 4 s. If the node hears a message in this interval,it decodes the slot number from the message andcomputes the its own time to talk based on this slotnumber. So, for example if N = 4 and n hears amessage from the node whose allocated slot numberis 3, node n knows that its time to communicate is4 s later. This method allows nodes to synchronizetheir internal clock.

If node n allocated to slot N does not hear any-body during its first N x 4 s, n starts transmittingmessages. Eventually, one of the nodes will startcommunicating first and all the other nodes will

synchronize to it. A possible deadlock happens ifall the nodes start talking at once. This is a low-probability event. During over 100 trials, we havenever encountered this situation. We can reduce thisprobability to a very small value if one of the nodesis allocated two time slots.Once the nodes synchronize upon deployment,

they continue to transmit during their own timeslots. Every time a node hears a master packet withcorrect CRC, the node re-synchronizes clocks. Thisprocedure keeps the network synchronized over time,and enables robustness to clock drifts.

IV. MOBILE NODES

The underwater sensor network supports mobilenodes such as our underwater robot called theAutonomous Modular Optical Underwater Robot(AMOUR), shown in Figure 2. The robot is 15kg.with a maximum speed of 1.5m/s. It has a batterylife of 8 hours.

Fig. 2. A picture of AMOUR and some sensor nodes.

The robot has all of the capabilities of the sensorboxes as well as a more advanced camera system foruse in local obstacle avoidance. One of the designgoals of the robot was to be inexpensive, so it doesnot have an expensive inertial measurement unit(IMU). Instead we rely heavily on the range measure-ments we obtain to the sensor nodes to determine itstrajectory through the waterThe job of the robot is to travel around, download

data from the senor nodes optically and relocate thesensor nodes. Additionally, it gives the network dy-namic sampling capabilities. If an event is happeningof interest the robot can move to that area to provide

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denser sensor sampling. We envision having manyrobots in the final system to provide highly dynamicsampling and faster download of the data from thestatic nodes.To be able to find the sensor nodes the robot must

know precisely where it is at all times. A passivelocalization and tracking algorithm we developed [7]has been implemented on this sensor network systemand used to localize and track the moving robot.

V. EXPERIMENTS

We have deployed the sensor nodes and the robotin the ocean (Moorea, French Polynesia), in the river(Charles River, MA) and in a lake (Otsego, NY)and collected extensive networking and localizationdata for this system. We have done over 100 exper-iments with the sensor network. Typical data fromsuch deployments in shown in Figures 4, 3, and 5.These figures show the performance of a four nodec[eployment.

Fig. 3. The ranges computed betweena moving sensor node in meters. Thesurement number. The y-axis shows tWe have performed tests at 4 differentm, 16.15 m, and 24.7 m. The y values slestimates.

Fig. 4. The ranges computed by four sensor network nodes toa moving node over time. The x-axis shows time. The y-axisshows distance in meters.

The fourth set of measurements was taken at 24.7 m.The average estimate by the sensor network node was24.35m.

Figure 4 shows the ranges to a moving node (therobot) computed by a sensor network of 4 nodes. Inthis experiment the robot was commanded to movein the field of sensor nodes. The gaps in the graphdenote communications that were not successful. Fig-

.. ure 5 shows the details of the communication success.For sensor network node 1, 58 % of the messages werereceived correctly. In the case of node 2, 64 % of themessages were received correctly. For node 3 64 %of the messages were received correctly. Finally, fornode 4 the communication success rate was 50 %. The

200 250 3'00 message loss was due to changing water conditionsand lake bottom profile as the moving node traveled

a fixed sensor node and across the lake. We need to understand better howx-axis shows the mea- the lake geography and composition affects acoustic-he estimated distance.ditncs 4.5m.96 communication. We believe that a success rate ofdistances: 4.75 m, 9.67how the sensor network over 50% is sufficient for the type of monitoring and

tracking applications we wish to perform with thissystem.

Figure 3 shows the ranges computed between afixed sensor node anchored in a lake at a depth of 3 mto a sensor network node moved in a straight line bythe underwater robot. The first set of measurementswere taken at a distance of 4.75 m. The averagesensor network node estimated the range at 5.01m. The second set of measurements were taken at9.67 m. The average estimated range was 9.72m. Thethird set of measurements was taken at 16.15 m. Theaverage estimated range by the network was 16.19 m.

VI. CONCLUSION

In this paper we described the hardware andcommunications support for an underwater sensornetwork designed and implemented in our lab. Ourexperiments show that this is a capable and usableplatform for water applications in shallow waters atdepths less than 100m. We are especially interestedin using this system to provide automated datacollection for marine biology applications related to

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understanding and modeling coral reefs. This sensornetwork could sustain operations at greater depthsby replacing the acrylic enclosure with a glass ortitanium enclosure.

ACKNOWLEDGEMENTS

We are grateful to the NSF and ONR (underthe PLUS-Net project) for supporting in part thisresearch.

[8] M. Dunbabin, I. Vasilescu, P. Corke, and D. Rus. Datamuling over underwater wireless sensor networks using anautonomous underwater vehicle. In Proceedings of the 2006International Conference on Robotics and Automations,Orlando, FL, May 2006.

[9] E. Fiorelli, N. E. Leonard, P. Bhatta, D. Paley, R. Bach-mayer, and D. M. Fratantoni. Multi-AUV control andadaptive sampling in Monterey Bay. In Proceedings ofthe IEEE Autonomous Underwater Vehicles: Workshop onMultiple AUV Operations, Sebasco, ME, USA, June 2004.

[10] HEIDEMANN, J., Li, Y., SYED, A., WILLS, J., AND YE,W. Underwater sensor networking: Research challengesand potential applications. Tech. Rep. ISI-TR-2005-603,USC/Information Sciences Institute, July 2005.

[11] HILL, J., BOUNADONNA, P., AND CULLER, D. Activemessage communication for tiny network sensors. In IN-FOCOM (2001).

[12] HOWE, B. M., McGINNIS, T., AND KIRKHAM, H. SensorNetworks for Cabled Ccean Observatories. EGS- AGU-EUG Joint Assembly, Abstracts from the meeting held inNice, France, 6- 11 April 2003, abstract #12598 (Apr.2003), 12598-+.

[13] David Moore, John Leonard, Daniela Rus, and SethTeller. Robust distributed network localization with noisyrange measurements. In Proc. 2nd ACM SenSys, pages50-61, Baltimore, MD, November 2004.

[14] I. Vasilescu, K. Kotay, D. Rus, P. Corke, and M. Dun-babin. Data collection, storage and retrieval with anunderwater sensor network. In ACM Sensys, 2005.

[15] Whoi modem: http://acomms.whoi.edu/micromodem/.

REFERENCES

[1] Autonomous Ocean Sampling Network (AOSN)II, collab-orative project. http://www.princeton.edu/dcsl/aosn/.

[2] I. Akyiditz, D. Pompli, and T. Melodia. Underwater sensornetworks: research challenges. Ad-hoc Networks, 2005 (toappear) .

[3] K. Audenaert, H. Peremans, Y. Kawahara, and J. VanCampenhout. Accurate ranging of multiple objects usingultrasonic sensors. In Proc. IEEE Int. Conf. Robotics andAutomation, pages 1733-1738, Nice, France, May 1992.

[4] A. B. Baggeroer. Acoustic telemetry an overview. IEEEJ. Ocean Engineering, OE-9(4):229-235, October 1984.

[5] J. G. Bellingham. In E. Bovio and R. Tyce, editors,Autonomous Underwater Vehicle and Ocean ModellingNetworks: GOATS 2000 Conference Proceedings. NATOSACLANT Undersea Research Centre, La Spezia, Italy,2000.

[6] T. Curtin, J. G. Bellingham, J. Catipovic, and D. Webb.Autonomous ocean sampling networks. Oceanography,6(3):86-94, 1993.

[7] Carrick Detweiler, John Leonard, Daniela Rus, and SethTeller. Passive mobile robot localization within a fixed bea-con field. In Proc. of the 2006 International Workshop onAlgorithmic Foundations of Robotics, New York, August2006. Springer-Verlag.

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