A Localization and Routing Framework for MobileUnderwater Sensor Networks

Melike Erol, Sema Oktug

Istanbul Technical University, Computer Engineering Department, Istanbul, Turkeymelike.erol@itu.edu.tr, oktug@itu.edu.tr

AbstractIn this paper, we propose a framework to establishlocalization and routing in mobile underwater sensor networks.Localization and routing are done at two consecutive rounds.Localization messages include localization specific data andadditional fields which are used in the routing decision. Theproposed catch up or pass forwarding algorithm is a greedygeo-forwarding scheme. It benefits from the uncontrolled motionof the underwater nodes. The underwater nodes use position andvelocity information to decide whether to carry the packet untilthey catch up with a sink or pass it to a faster or slowerrelay.

I. INTRODUCTIONUnderwater Sensor Networks (USN) are emerging as pop-

ular tools in ocean observations. Currently, there are fewtestbeds. The wired ocean bottom network, NEPTUNE [1]and the wireless acoustic Seaweb [2] are two examples. AsUSNs are in the early development stage, most of the real lifeexperiments are concerned with the physical layer issues. Onthe other hand, research is widely based on simulation studiesfor synchronization, localization, medium access and routingprotocols.

In this paper, we focus on two fundamental problems:localization and routing. Sensed data are meaningful with timeand location information, e.i, localization is essential for datatagging. Sending the data efficiently to a sink is another majorissue in sensor networks. In USNs, more than one sink maybe employed and the underwater nodes may act as relays forrouting the data from source to sink. In this work, we combinethese two significant tasks in a framework where they mutuallyhelp each other. We use a limited number of Mobile Beaconand Sink (MBS) nodes. MBSs are able to move vertically inthe water column. The rest of the nodes stay under the waterand drift with the currents. MBSs periodically descent to thesame depth with the underwater sensors to localize them andcollect their data. Then, MBSs surface to receive coordinatesfrom the GPS and upload the data that has been collected bythe underwater sensors to a ground station. MBSs can ascentand descent with modifying density. Real life implementationof such floats are used in Argo project [3]. A similar techniquecan be applied to MBS nodes to achieve vertical movement.

Localization is done iteratively. Initially MBS nodes arelocalized via GPS. They periodically announce their coor-dinates while diving to the deepest level of the underwaternetwork. Upon hearing from several beacons (at least threebeacons for 2D and four beacons for 3D) a node is localized.

A localized underwater node becomes an active node andhelps in localization. It acts as a beacon and distributes selfcoordinates. This localization phase has a fixed duration whichis also announced in the localization message. It can becalibrated according to the depth of the underwater networkand the speed of the MBS. Alternatively, after each dive, theduration of the interval may be uploaded via satellite to theMBS nodes.

Routing starts after the localization round. Sensors that havedata to send, pick an MBS and forward their data towards thatsink. The proposed routing scheme benefits from the messagessent in the localization phase. The location and the velocityof the MBS node and the neighbours are learnt during thelocalization phase. The routing algorithm chooses the bestrelay according to the position and relative motion of the MBSand the sensor nodes.

II. NETWORK ARCHITECTURE AND THE MOBILITYMODEL

We design our framework for a mobile underwater sensornetwork with acoustic communications. Underwater nodesstay at a certain depth and drift with the currents. MBS nodesascent and descent to provide location service and to deliverdata from the sensors to the ground station via satellite orradio links. The example network is illustrated in Fig. 1.

Most of the existing mobility models for ad hoc networksassume that the sensors move independently from each other[4]. However, in a fluid medium strong correlations nearthe same velocity field are expected. In the ocean, mobilityis determined by advection from the ocean currents. Thesubsurface current is a jet-like current, meandering betweenrecirculating vortices. In Fig. 1(a), the sensors either movewith the meandering jet or they are captured to eddies andalmost stay at the same place. This model is verified in [5]for Gulf Stream measurements and used for underwater sensornetworks in [6]. In this work, we simplify the motion to ahorizontal drift. In Fig. 1(b), the simplified mobility modelis shown. Here, we assume that the sensors in the jet areseparated from the ones in the eddies with straight linesand there is no crossing between two regions. We define thevelocities in the horizontal and vertical axis with vx and vy,respectively. For the nodes in the jet, vx is chosen betweenvjetmin < vx < vjetmax and for the nodes outside the jetveddymin < vx < veddymax where veddymax < vjetmin. The

This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE INFOCOM 2008 proceedings.

978-1-4244-2219-7/08/$25.00 2008 IEEE.

Fig. 1. (a) Network architecture and the mobility, (b) simplified mobilitymodel

simplified model assumes that vyi = 0, for i = 1, 2, .., N,where N is the total number of underwater nodes. Moreover,the jet is divided into velocity zones similar to highway lanes.The nodes in the central part drift with the highest velocityand the nodes in the two surrounding velocity zones drift withlower velocity values.

This USN architecture employs passive moving (drifting)underwater nodes and MBS nodes which have controlledmovement along the vertical axis. Note that, MBSs also driftwith the currents along x-y axis.

The framework includes two consecutive rounds: localiza-tion and routing. These rounds start after MBS nodes descentto the level of the underwater nodes. The MBS nodes arecalibrated to stay underwater for enough time to at leastaccomplish localization. After localization, the remaining timeis used for sending data. We assume that all the nodes are clocksynchronized. Synchronization can be assumed for short termmissions. Sensor networks that need to work underwater forseveral weeks require an additional mechanism for synchro-nization.

III. LOCALIZATION

MBS nodes learn their coordinates via GPS while theyare floating on the surface. Then, they descent and sendlocalization messages [7]. Localization message includes theID, the coordinates, the velocity of the MBS node and atimestamp field. The timestamp field is used to determine thedistance via Time of Arrival (ToA) method. Nodes measure

Fig. 2. MBS localization packet format

Fig. 3. Active Underwater node localization packet format

the range with ToA and do lateration to estimate their location.The underwater nodes are able to locate themselves withat least three localization messages sent from non-collinearpositions. z-coordinate is the depth and it is derived from thepressure sensor. In our scheme we use four MBS nodes todecrease the estimation error. The format of the localizationmessage sent by the MBS is given in Fig. 2. Localizationmessage also includes the length of each round. This maybe pre-configured or downloaded to MBS nodes via satellite.Nodes forward localization messages during the localizationperiod. After this period, nodes that are localized and havedata to sent start sending their data. They are allowed to senduntil the end of the routing period.

To decrease the number of the MBSs, we use iterativelocalization. Successfully localized underwater nodes becomeactive and announce self coordinates. Localization message ofactive nodes include additional fields: the total number of hopsto MBS nodes, the estimated error in localization, MBS ID,MBS coordinates, MBS time stamp and MBS velocity of thefour MBS nodes that the node has used in localizing itself.The format of the localization message of the active nodes isgiven in Fig. 3. The total number of hops to the MBS nodesand the estimated error are used in iterative localization. Ifa node hears from more than four MBS nodes then the onewith least hop count and error is selected. The position andthe velocity of the MBS nodes are used in routing.

The localization success and the error of the iterativelocalization scheme is given in Fig. 4. for a static underwaternetwork with the number of underwater sensor nodes varyingbetween 50-200 and 25 MBS nodes. The preliminary resultson mobile scenarios show that more than 80% of the nodesmay be localized with this technique. The simulations areperformed in Qualnet Simulator [8] using an acoustic physicallayer.

This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE INFOCOM 2008 proceedings.

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Fig. 4. Localization success and error for the number of underwater nodesbetween 50-200 and 25 MBSs.

IV. ROUTINGThe underwater nodes learn the beginning of the routing

round and the ID of the potential sinks at the localizationround. Each node independently selects an MBS and forwardits data towards that MBS. The forwarding decision is takenaccording to the relative position and velocity of the MBSnode. The underwater nodes benefit from mobility and try tocatch up with the MBSs. According to the catch up or pass(C/P) algorithm, a node either carries the packet until it catchesup with the selected MBS or, if the MBS and the node is notlikely to catch up then it forwards the packet to a slower orfaster relay.

The algorithm works as follows. The source node comparesits position and velocity with the MBS. If MBS is behind andthe velocity of the MBS is larger than the node (vMBS >vN ), then, MBS has a chance to catch up with the node. Thepacket is delayed for some time. Delay is a function of thedistance in between and the difference between the velocities,i.e., delay = f(distance, vMBS , vN ). If MBS is behind andvMBS vN , the MBS will never catch up, so the packet ispassed to a slower neighbour. Note that, the velocity valuesof the neighbours are learnt during localization. If MBS isahead of the forwarding node and vMBS < vN , C/P delaysthe packet on the node again because this time the node cancatch up with MBS. However, if vMBS vN then, the nodeis unable to reach MBS and it passes the packet to a fasterneighbour.

When the packet is carried, waiting for a possibility ofcatching up, it is delayed until the forwarding zone of theMBS (see Fig1(b)). When the node is within the forwardingzone, geographic coordinates are used for forwarding. Becausethe velocities indicate the horizontal displacement (vyi = 0)and the packet is delayed until the x coordinates of the nodeand the MBS are close enough, in the vertical axis they maybe further away. In the forwarding zone, only y coordinatesare used to reach the MBS. The node with the least verticaldistance is selected as the relay.

Note that, the delay value is bounded by some thresholdbecause even though the direction and the velocities of thenodes may allow each other to catch up, the relative speed of

the nodes may be so low that given a limited routing round,the nodes may not have the chance to meet.

C/P works in corporation with the localization scheme.It does not need extra messages for position or velocityinformation exchange. Moreover, C/P delays the packets onthe nodes until they meet with the sink. This is cost and energyefficient for USNs since the low bandwidth, high bit error rateand propagation delay [9] enforces limited messaging.

V. CONCLUSIONIn this work, we propose a framework for localization and

routing in mobile underwater sensor networks. We use mobilebeacons both as location servers and sinks. Their verticalmovement enable GPS driven coordinates to be distributedin underwater. Moreover, they deliver data from the depthsof the network to the surface of the ocean. Our routingalgorithm uses position and velocity of the sensors. Thisapproach can be used in VANETs for a highway scenario,as well. In this paper, we introduce the simplest frameworkand enhancements will be made as future work. Due to highpropagation delay in underwater, using predicted positionsrather than the coordinates sent in packets will be considered.Moreover, routing loops, voids and ping-pong effect will beanalyzed.

REFERENCES[1] Neptune project, http://www.neptunecanada.ca/about/index.html[2] J. A. Rice, Us navy seaweb development, in Proc. of WuWNet, pp.

3-4, Montreal, QC, Canada, 2007.[3] Argo project, http://www.argo.ucsd.edu[4] C. Bettstetter, Mobility Modeling, Connectivity, and Adaptive Cluster-

ing in Ad Hoc Networks, Utz Verlag, 2004.[5] A. S. Bower, A simple kinematic mechanism for mixing fluid parcels

across a meandering jet, J. Phys. Ocean., vol. 21(1), pp. 173180, 1991.[6] A. Caruso, F. Paparella, L. Vieira, M. Erol, and M. Gerla, Meandering

current model and its application to underwater sensor networks, in toappear in INFOCOM08, 2008.

[7] M. Erol, L. F. M. Vieira, and M. Gerla, Localization with divenrise(dnr) beacons for underwater acoustic sensor networks, in Proc. ofWuWNet, pp. 97-100, Montreal, QC, Canada, 2007.

[8] Qualnet Network Simulator, http://www.scalable-networks.com/[9] J. Heidemann, W. Ye, J. Wills, A. Syed, and Y. Li, Research challenges

and applications for underwater sensor networking, in IEEE WirelessCommunications and Networking Conference, Las Vegas, Apr. 2006.

This full text paper was peer reviewed at the direction of IEEE Communications Society subject matter experts for publication in the IEEE INFOCOM 2008 proceedings.

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