A Novel Geographic Routing For

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Published in IET Communications

Received on 18th March 2009

Revised on 9th October 2009

doi: 10.1049/iet-com.2009.0197

ISSN 1751-8628

Elastic routing: a novel geographic routing formobile sinks in wireless sensor networksF. Yu

1S. Park

2E. Lee

2S.-H. Kim

2

1Key Lab of Broadband Optical Fiber Transmission and Communication Networks, UESTC, Chengdu, China2

Department of Computer Engineering, Chungnam National University, Daejeon, Republic of KoreaE-mail: [email protected]

Abstract: Geographic routing has been considered as an efficient, simple and scalable routing protocol for

wireless sensor networks, since it exploits pure location information instead of global topology information to

route data packets towards a static sink. Recently, a number of research works have shown that mobile sinks

can achieve high energy efficiency and load balance than static ones. In order to receive data packets

continuously, a mobile sink must update its location to the source frequently. However, frequent location

updates of mobile sinks may lead to both rapid energy consumption of the sensor nodes and increased

collisions in wireless transmissions. The authors propose a novel geographic routing for mobile sinks to

address this issue. The proposed scheme takes advantage of wireless broadcast transmission nature of

wireless sensor nodes. When a sink moves, the new location information is propagated along the reverse

geographic routing path to the source during data delivery. Analysis and simulation results indicate that elasticrouting is superior to other protocols in terms of control overhead, data delivery delay and energy consumption.

1 Introduction

Wireless sensor networks (WSNs) have been proposed forobservation of events and environments in sensing areasover long periods of time. A large number of sensor devicescommunicate over short range wireless interfaces to deliverthe observations over multiple hops to sinks. With theseproperties, WSNs are considered for many applicationscenarios including battlefield surveillance, habitat

monitoring, traffic monitoring, security applications andso on. Sensor nodes are small, simple and powered bybatteries. Since the sensor network consists of a largenumber of sensor nodes, recharging them is ofteninfeasible. Therefore to minimise the unnecessarytransmission becomes one of the key challenges in protocoldesign.

Geographic routing has been considered as an attractiveapproach, since it exploits pure location information insteadof global topology information to route data packets, andthis location-based strategy makes it a more efficient,

simple and scalable routing protocol in WSNs. In ageographic routing protocol, a source obtains the locationof a sink by some location service [15] in advance and

encapsulates the location of the sink in each data packet.After receiving a data packet, a node sends it to the one-hop neighbour that is geographically closest to the sink.Numerous geographic routing protocols have beenproposed for WSNs [611], most of which are designedfor static sinks, and assume that both sensor nodes andsinks do not change their location after deployment. Thus,once a source obtains the location of a static sink, it cancontinuously send data packets to the sink by geographic

routing. Recently, mobile sinks have been introduced into WSNs because of two main reasons. First, mobile sinkshave been utilised as mechanical data carriers to prolongthe network lifetime. In static WSNs, the sensor nodesclose to a static sink will deplete their energy quicklybecause they have to forward messages originating frommany other nodes, and thus shorten the lifetime of theentire network [12]. If a sink can move, the sensor nodesin the network can take turns to become the neighbours ofthe sink, and thus the energy can be evenly consumedamong all the sensor nodes, and consequently, the lifetimeof the entire network can be prolonged [13]. Second, some

applications need the support of mobile sinks, such assoldiers equipped with personal digital assistants (PDA)move in a battlefield for enemy detection and a rescuer who

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is equipped with a PDA move in a disaster area for searchingsurvivals. In general, sensor nodes do not have a prioriknowledge of the moving speed and direction of the mobilesinks. How to propagate the location of a mobile sink tothe source in real-time manner with low overhead is adifficult issue.

In this paper, we propose a novel geographic routingtermed elastic routing, which can superiorly support mobilesinks in WSNs. Elastic routing exploits the greedyforwarding [6] as the data delivery protocol. An importantfeature of wireless communication is that a node canoverhear transmitting packets in the neighbourhood even ifthe packets are not destined for it. Elastic routing ismotivated by this overhearing feature of wirelesstransmission. First, a source obtains the location of amobile sink by some location service and forwards datapackets to the sink by greedy forwarding. When the sink

moves, the last hop forwarding node detects the newlocation of the sink and resets the sink location informationin a received data packet to the new location of the sink.Data transmission of the modified data packet from lasthop forwarding node to the sink can be overheard by thelast second hop forwarding node. Then the last second hopforwarding node changes the sink location information ineach subsequently received data packet and forwards themtowards the new location of the sink by greedy forwarding.

As a result, with continuous data transmission, the newlocation of the mobile sink will be finally propagated to thesource. The elastic routing works like an elastic band withone end tied to a source and another end tied to a mobilesink. When the sink moves, the path between the sink andthe source is inflected, but finally converged to a line pathlike the shrinkage of an elastic band. No significantoverhead is generated for propagating the location of amobile sink.

The rest of this paper is organised as follows. Weintroduces the related works which also focus on the issueof efficient data delivery to mobile sinks by geographicrouting scheme in Section 2. Section 3 describes theproposed elastic routing in detail, and Section 4 discusseselastic routing in special cases. We analyse the performance

of elastic routing and compare the performance with otherprotocols by simulations in Section 5. Section 6 concludesthe paper.

2 Related works

Existing solutions for addressing the issue of updatingthe location of a mobile sink can be classified into fourcategories: full flooding-based [1416], local flooding-based [17], rendezvous points-based [18] and grid-basedsolutions [19]. Full-network flooding can be furtherdivided into source-triggered full-network flooding and

sink-triggered full-network flooding. The source-triggeredfull flooding is that a source continuously floods the senseddata throughout the entire network. This scheme ensures

that the mobile sink receives data packets from the sensornodes, but the data delivery rate may be very low because ofdrastically increased collisions in transmitting floodingpackets from the sensor nodes [14]. The sink-triggered fullflooding is that a mobile sink consecutively informs its newlocation information to the entire network by flooding,

such as dynamic routing protocol (DRP) [15] and gradientbroadcast (GRAB) [16], so that all the sensor nodes getupdated with the direction of sending future data reports.However, frequent location updates from mobile sinks canalso lead to both large energy consumption of the sensornodes and increased collisions in wireless transmissions.

Adaptive local update-based routing protocol (ALURP)[17] is a local flooding-based location update scheme,

which utilises an adaptive area for location update of amobile sink. A sink only floods it location to the nodes

within the adaptive area if the sink only moves within the

adaptive area. If the sink moves beyond the adaptive area, itstill needs to flood its location information throughoutthe entire network and constructs a new adaptive area.

Although ALURP can significantly reduce unnecessarytransmissions and energy consumptions by local flooding,the full-network flooding is also required if the sink movesbeyond the adaptive area.

To reduce the flooding overhead, a rendezvous points-based solution, termed line proxy target detection (LPTD)[18], was proposed for updating the location of a mobilesink. LPTD assumes that all the sensor nodes in thenetwork are time synchronised and achieve the sametemporal-based hash function. A network is divided intocells (square areas). All cells in the same row or columnbecome rendezvous points (line proxies) at some momentdepending on the temporal-based hash function. Therendezvous points (line proxies) are alternated over time fornetwork load balance. When a sink wants to query someevent, it first calculates the rendezvous points by the sametemporal-based hash function and sends an interestregistration message to the rendezvous points, and theinterest registration message is flooded to all sensors withinthe same row or column. When a source detected someevent, it sends targets registration messages to the same

rendezvous points by the same hash function. The cell where the interest registration and targets registration areoverlapped forwards the target report to the sink. The sinkmay directly send a message to ask the source forperforming continuous reporting. If the sink moves too faraway from its previous location, it achieves the sameprocess mentioned above. LPTD drastically avoids the full-network flooding for location update of a mobile sink.However, flooding interest registration message within arow or a column band area across the entire network alsogenerates high overhead. The time synchronisation andhash function of sensors are also difficult issues in WSNs.

Two-tier data dissemination (TTDD) [19] is a grid-basedapproach which avoids the entire network flooding for

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location update of mobile sinks. TTDD periodicallyconstructs per-source-based global grid structures, eachgrid point is associated with a dissemination node andeach dissemination node is aware of its upstream anddownstream dissemination nodes. A sink floods a dataquery message within about a grid cell size area to find the

nearest dissemination node. Then the query message isrelayed by a series of dissemination nodes and eventuallyreceived by the source. Then the source sends data packetsto the sink along the reverse path of the query message.

When a sink moves into another cell, it achieves the sameprocess described above. Flooding query messages only

within about a grid cell size is an efficient way, however,the bigger the cell size, the wider the local flooding area,thus more flooding overhead, whereas small grid size incursmore overhead for the grid construction and maintenance.Periodic per-source-based global grid constructions alsogenerate additional overhead.

We summarise above discussions as follows. Geographicrouting is an efficient routing protocol for WSN. Ingeographic routing, the location information of a sink isencapsulated in each data packet. To support mobile sink,the geographic routing requires the location of the sink tobe efficiently updated to the source. The existing solutionsfor addressing this issue suffer from more or less overheadsand excessive energy consumptions. Unlike wired networks,the wireless sensor nodes forwards data packets in broadcastmanner, thus data transmission can be overheard by allneighbour nodes. Motivated by the overhearing feature of

wireless transmission, this paper proposed a novel approachtermed elastic routing to address this issue. Elastic routingtakes advantage of the broadcast nature of wirelesstransmission to propagate the new location of a mobile sinkto source along the reverse path of geographic routingduring data delivery. The property of stationarity of sensornodes makes the location propagation scheme executable.

The overhearing has been widely employed by previousresearch for varying applications such as network timesynchronisation [20, 21], malicious packet-modifyingattacks detection [22] and reducing redundanttransmissions [23]. To our best knowledge, elastic routingis the first scheme which exploits the overhearing feature of

wireless transmission to propagate the location of a mobilesink to a source.

3 Elastic routing

We assume in this work that all sensors remain static, butsinks can move freely in the network; each node can obtainits own location information by global positioning systemor other location services [2426]; each node can obtain

its one-hop neighbour list and their locations by beaconmessages [6]. Without any specification, the greedyforwarding referenced in the following sections is referredas greedy perimeter stateless routing[6].

3.1 Tracing of mobile sink

To facilitate discussion, we additionally assume that all sensornodes have the same unit disk radio range. If two nodes arelocated in the radio range of each other, then the channel inbetween is bidirectional (heterogeneous radio range and radioirregularity problem will be further discussed in Section 4.1).

In greedy forwarding, a node selects only the neighbournode that is closest to the destination as the next hop.Since we have assumed that all sensor nodes are static, thusthe entire routing path between the source and the sinkremains static except that some intermediate forwardingnode dies, and which leads to the change of routing path.Fig. 1a shows a scenario of greedy forwarding. The currentlocation of the sink has been obtained by the source bysome sink location service. The LOOP problem which wasmentioned in [27] arises if the sink moves. In particular,

when a sink moves from its original location, beforeobtaining the new location, the source still encapsulates the

original location of the sink in each data packet andforwards them towards the original location of the sink.If the sink moves to a new location and another nodebecomes a closest one to the original location of the sink,then this situation is misunderstood as a local maximumby greedy forwarding. In this case, the perimeter mode istriggered to resolve this problem. However, in thissituation, the packets normally get dropped unless the sinkcomes back to near the original location and becomes theclosest node to the destination location of the packet again.Perimeter mode generates wasteful loops in this situation.

To solve this LOOP problem, a destination location

prediction (DLP) scheme was proposed in [27]. WithDLP, each node searches its neighbour list for the sinkbefore it makes a packet forwarding decision based on the

Figure 1 Tracing of a mobile sink



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location information of sink. If the sink exists in theneighbour list and is located within the transmission rangeof the packet holder, the packet is forwarded directly to thesink without further calculation for finding a closestneighbour to the destination. LOOP problems can beovercome by utilising the ID of nodes as well as location

information. A significant amount of lost packets and wasted network resources can be saved by avoidingmisjudgement on the local maximum situation. The DLPscheme is efficient if the sink moves within the radio rangeof the last hop forwarding node A as shown in Fig. 1a.Considering the situation in Fig. 1b, when the sink movesoutside of the radio range of the last hop forwarding nodeA, the new location of the sink becomes unknown to nodeA. DLP fails in this situation. Forwarding the data packetto the node which is located closest to the location wherethe sink disappeared is an intuitive but unreliable waybecause of the uncertain mobility of the sink. To solve this

problem, in elastic routing, besides sending periodic beaconmessages to neighbour nodes, a sink also informs itscurrent location to the node from which it received the lastdata packet by greedy forwarding. In Fig. 1b, when thesink moves outside of the radio range of node A, it updatesits new location to node Aby greedy forwarding, then nodeA resets the sink location information in the data packet tothe new sink location and selects node B as the next hopforwarding node to forward the subsequent data packets. Inthis situation node Bbecomes the last hop forwarding nodeto the sink. The mobility of the mobile sink can be tracedby this way. The mobile sink tracing scheme discussedabove can guarantee continuous data delivery to a mobilesink. The function of a mobile sink is given as follows:

1. Sends beacon messages to announce its current location toneighbour sensor nodes.

2. Checks whether it has moved outrange of the last hopforwarding node; if so, informs its current location to thelast hop forwarding node by unicasting.

However, mobile sink also leads to detour path problem asthe real-line path shown in Fig. 1c. The detour path problemleads to increasing of data delivery delay and total energyconsumption. An intuitive solution is that the mobile sinkupdates its new location to source in real-time manner, sothat the source can encapsulate the new location of sink

into each data packet and forwards data packets to the sinkalong the dashed line path, as shown in Fig. 1c. Theexisting solutions [1419] for supporting mobile sinksmay generate more or less overhead and delay. In the nextsection, we describe a new location propagation scheme

which takes advantage of overhearing feature of wirelesstransmission.

3.2 Sink location propagation scheme

An important feature of wireless communication is thata node can overhear transmitting packets in the

neighbourhood even if the packets are not destined for it.Elastic routing is motivated by the overhearing feature of

wireless transmission, where if the channel between twonodes is bidirectional, the data transmission from one nodecan be overheard by the other node.

As shown in Fig. 2, we assume that the source hasobtained the location of the mobile sink by some locationservice and forwards continuous data packets to the sinkby greedy forwarding. The mobile sink may move in threecases: (i) the sink moves within the radio range of the lasthop forwarding node A, but outside of the radio range of

the last second hop forwarding node B, as shown inFig. 2a; (ii) the sink moves into the radio range of lastsecond hop forwarding node B, as shown in Fig. 2b and(iii) the sink moves outside of the radio range of bothnodes A and B, as shown in Fig. 2c. In the first case, thesource sends data packets along the path source! C!B! A! sink by greedy forwarding. When the sinkmoves to a new location inside the radio range of node Abut outside of the radio range of node B as shown in

Figure 2 Location propagation of a mobile sink



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Fig. 2a, from the perspective of the source, since themovement of the sink is unknown to the source, the sourcestill encapsulates the original location of the sink in eachdata packet and forwards them towards the originallocation of the sink. Since we have assumed that all sensornodes are static, the data packets are sent along the original

path source! C! B!A until received by node A.From the perspective of node A, since the sink moves

within its radio range, the new location of the sink can beobtained by the beacon message of the sink, thus node Afirst resets the location information of the sink in thereceived data packet to the new location of the sink andthen forwards it to the sink directly by the DLP schemeproposed in [27]; that is, if the sink exists in the neighbourlist and is located within the transmission range of thepacket holder, the packet is forwarded directly to the sink

without further calculation for finding a closest neighbourto the destination. We have assumed that the channel

between two neighbour nodes is bidirectional, so whennode A forwards a data packet which contains the newlocation of the sink to the sink, the transmission can beoverheard by node B, thus node B obtains and caches thenew location of the sink. Node B then resets the sinklocation information in the subsequently received datapackets to the cached new location of the sink and forwardsthem towards the sink by the greedy forwarding, as shownin Fig. 2d. Node C can also obtain the new locationinformation of the sink by overhearing the transmission ofnode B, as shown in Fig. 2e. Finally, the new locationinformation of the sink can be propagated to the sourcestep-by-step during data forwarding. Then the sourceencapsulates the new location of the sink in each datapacket and directly forwards them towards the sink asshown in Fig. 2f.

In the second case, if the sink moves into the radio range ofthe last second hop forwarding node B, as shown in Fig. 2b,then node Bcan directly detect the location of the sink andresets the location information of the sink in the receiveddata packet to the new location and directly sends themto the sink by DLP scheme. Node C overhears thistransmission and also resets the location information of the

sink in the received data packet to the new location anddirectly sends them to the sink by greedy forwarding. Inthe third case, when the sink moves outside of the radiorange of both nodes A and B, as shown in Fig. 2c, the sinkdetects this situation and sends its new location to node Aby greedy forwarding. Then node A resets the sink location

information in the received data packets to the newlocation of the sink and then forwards them to the sink bygreedy forwarding. The following processes are the same asother two cases described above.

The above presentations only discuss the actions of thesensor nodes which have participated in data forwarding,for example, nodes A, B and C in Fig. 2. How about theother sensor nodes which are located in the radio range ofthese forwarding node? From the above discussions we cansee that the new location of the sink is propagated tosource along the reverse path of greedy forwarding. Thus,

the nearer to the sink, the fresher the sink locationinformation is. In elastic routing, when a node overhears atransmission, it first extracts the sink location informationin the overheard packet, then compares whether it is fartherto that location than the originator of the transmission. Ifso, it records the sink location information in memory,otherwise, it just discards it. For instance, node Moverhears a transmission from node N and the locationinformation in the overheard data packet is D. IfMD. ND, this means node N is closer to D than nodeM, thus node M believes that node N has a fresher sinklocation than itself, and so node M records location Din memory. Otherwise, node M just discards the overheardpacket. This location propagation scheme is crucial if acertain greedy forwarding node fails. Detailed explanationis given in Section 4.2. The function of sensor nodes forlocation propagation of mobile sinks is summarised asfollows:

1. Checks whether the sink is located in its radio range; if so,records the sink ID and location.

2. On overhearing a transmission, for instance, node Moverhears a transmission from node N (Fig. 3).

Figure 3 Location propagation algorithm of elastic routing



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4 Discussions

In this section, we extend the elastic routing to heterogeneoussensor networks and discuss the robustness of elastic routingprotocol.

4.1 Elastic routing in heterogeneoussensor networks

The preceding descriptions are based on the assumption thatall sensor nodes have uniform unit disk radio range, that is,the channel between two neighbour nodes is bidirectional.

A practical sensor network may have heterogeneous nodes with different radio ranges, which leads to unidirectionalchannels between neighbour sensor nodes. Anotherphenomenon termed radio irregularity [28] may also leadto unidirectional channels between neighbour sensor nodeseven though they have the same radio range. The proposed

location propagation scheme is achieved by overhearing thenext hop transmission to obtain the new location of amobile sink. The unidirectional channels may lead to anode failure in overhearing the next hop transmission, thusfails to propagate the new sink location towards the source.

To solve this problem, a symmetric geographic forwarding(SGF) mechanism, which has been proposed in [28], canbe exploited by elastic routing. SGF allows a node to addthe IDs of all its neighbours it has discovered into a beaconmessage. When a node receives a beacon message, itregisters the sender as its neighbour in its local neighbourtable, and then checks whether its own ID is in thebeacon message. If the receiver finds its own ID inthe neighbour list in the beacon message, then it marks thechannel connecting it to the sender as bidirectional.Otherwise, it marks the channel between them asunidirectional. Whenever a node needs to forward a packet,it selects only those neighbouring nodes with which it isconnected through bidirectional channels. By using SGFscheme, elastic routing forwards data packets only throughbidirectional channels, thus can guarantee that eachtransmission can be overheard by the previous forwardingnode.

4.2 Failure of an intermediate forwarding node

We have discussed that, for a static sensor network, thelocation of a mobile sink can be propagated to source alongthe reverse path of greedy forwarding step-by-step. Itmight happen that an intermediate forwarding node failsbecause of exhaustion of energy or physical destruction, forexample, node D in Fig. 4. When the sink moves to a newlocation, the new location of the sink is propagated tosource along the path A! B! C step-by-step. However,the propagation is terminated because of the failure of nodeD. The shaded disks in Fig. 4 indicate the radio range of

corresponding forwarding nodes. All sensor nodes withinthe shaded area covered by these disks have obtained thenew location of the mobile sink by overhearing the

transmission of corresponding forwarding nodes, asdiscussed in Section 3.2. Since the location propagation ofthe mobile sink was terminated because of the failure ofnode D, thus the source still encapsulates the originallocation of the sink in each data packet and forwards themtowards the original location of the sink. Node E detectsthe failure of node D and selects node Fas a new next hop

forwarding node. Since the original location of the sinkis inside the shadow area, the new greedy forwarding pathfrom node F certainly crosses with the boundary of theshadow area. Thus, when a node inside the shadow area(node G in Fig. 4) receives the data packets, it resets thesink location information in each packet to the newlocation and forwards them towards sink. At that time, thepropagation process of the new sink location can becontinued from node G towards the source.

4.3 Location update of a continuousmoving sink

In this section, we discuss the location propagation processof a continuous moving sink. In Fig. 5, only the nodes

which need to reset the location information in the receiveddata packets are drawn out. The thin black arrowheadedlines indicate the greedy forwarding path towards theoriginal location of the sink. When the sink moves to sink0,the location of sink0 is propagated towards the source alongthe path A! B! C step-by-step. We assume that thesink moves to sink00 when the location of sink0 is propagatedto node D. Node D resets the location information in thereceived data packet to sink0 and forwards it to sink0 directly.However, at this time, the sink moves to sink00 and the new

location is detected by node F. Thus when node F receivedthe data packet from node D, it resets the sink locationin the packet to location of sink00 and forwards the datapackets to sink00. At this time, the location of sink00 ispropagated to node G by node F. In Fig. 5, the thick realarrowheaded lines show the data forwarding paths along

Figure 5 Location update of a continuous moving sink

Figure 4 Location propagation of a mobile sink in the case

of failing of an intermediate forwarding node



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which, the sink location in all data packets wereset to sink0;thethick dotted arrowheaded lines show the data forwardingpaths along which, the sink location in all data packets wereset to sink00. In short, the location of sink0 is propagatedto source along the path A! B! C! D!E and thelocation of sink00 is propagated to source along the path

F! G! H!I!J. Finally, the source obtains thelocation of sink00 and encapsulates the location of sink00 ineach data packet and forwards them towards sink00 directly.From the above discussion, we can see that the elasticrouting works like an elastic band with one end tied to asource and another end tied to a mobile sink. When the sinkmoves, the path between the sink and the source is inflected,but finally converged to a line path like the shrinkage of anelastic band.

5 Analysis and performance

evaluationsIn this section, we analyse and evaluate the performance ofelastic routing with ALURP [17], LPTD [18] and TTDD[19]. Elastic routing exploits Column Row LocationService (XYLS) [5] location service to discover the locationof a sink initially. XYLS is a quorum-based location servicefor WSNs, where a source sends a sink location querymessage along eastwest direction and a sink sends a sinklocation announcement message along the south northdirection, respectively, throughout the entire network, and theintersection node informs the source about the sink location.

We use the following metrics for performance analysis andevaluations: the control overhead is defined as the totalnumber of control packets. The average delivery delay isdefined as the average time between the moment when asource sends a packet and the moment when a sink receivesthe packet. The energy consumption is defined as the totalenergy consumption for location update of mobile sinks anddata delivery. The convergence time is defined as the timerequired by a source to obtain the new location of a mobilesink. The success delivery ratio is defined as the ratio of thenumber of data packet successfully received by the sink to thenumber of data packets generated by the source. Owing tothe maximum length limitations, we do not analyse ALURP,

LPTD and TTDD in this paper. Detailed analysis of themcan be found in [1719]. This section focuses on analysingelastic routing and makes intuitive comparisons with otherprotocols. The analysis results are validated by simulations.

5.1 Simulation environments

We implemented simulations in a network simulator Qualnet3.8 and utilised IEEE 802.15.4 as the MAC protocol. Thetransmission range of sensor nodes is set to 15 m. The sizeof the sensor network is set to 250 250 m2, where 2000nodes are randomly distributed. For all simulations, we use

one sourcesink pair for performance evaluation. The gridsize of TTDD is set to 30 m. The width of rendezvouspoints (line proxies) of LPTD is set to 20 m and LPTD

triggers one-time location update if the sink moves 20 maway from its previous location. The adaptive area of

ALURP is set to be a circle with radius of 20 m. In elasticrouting, the sink sends location announcement beaconmessages once it moves 1 m away from its previouslocation. Elastic routing exploits XYLS [5] for initial

location discovery of the mobile sink.

5.2 Analysis and simulation resultswith different moving speeds

In elastic routing, a mobile sink sends periodic beaconmessages to announce its current location and unicasts itscurrent location information to the last hop forwarding node

with a short distance. No other special messages are used forlocation propagation of a mobile sink. Location propagationis done by overhearing the transmission of data packets, thuselastic routing does not generate significant overhead. In this

simulation scenario, a source which is randomly selected inthe network generates and forwards 128 bytes data packetsat a constant rate of 10 packets/s. We vary the maximumsink moving speed from 1 to 10 m/s; a sink moves with arandom speed between 1 m/s and the maximum speed andfollows a random waypoint model. The simulation durationis set to 500 s.

In Fig. 6a, the control overhead of elastic routing consistsof the overhead of XYLS location service for initial locationdiscovery of the mobile sink and a small quantity of beaconmessages sent by the sink for location announcement. Thus,

the control overhead of the elastic routing nearly remainsunchanged with the sinks moving speed increasing. Thecontrol overhead of TTDD consists of the overhead ofsource-based grid construction and maintenance, and localflooding within the area of about one grid size for the nearestdissemination node discovery if the sink moves from one cellto another. With the increasing moving speed, the number ofcells traversed by the sink also increases, thus the moving sinkrequires more frequent local flooding for location update.In LPTD, once a sink moves 20 m away from its previouslocation, it needs flooding in its new location within therendezvous points (line proxies). In our simulation, therendezvous point area is larger than the grid size of TTDD.

So the local flooding overhead of LPTD is higher than thatof TTDD. However, TTDD spends more overhead for gridstructure maintenance. Thus, the total overhead of TTDD iscomparative as that of LPTD. The overhead of ALURP issignificantly higher than that of other protocols because offrequent full-network flooding. If the sink moves within theadaptive area, it just announces its location by local flooding

within the adaptive area, whereas if the sink moves out of theadaptive area, it needs to flood throughout the entire network.

The metric of energy consumption is closely related to thetotal transmission. Elastic routing exploits the overhearing

feature of wireless transmission for location propagation.However, overhearing is not a special feature of elasticrouting, but a general feature of WSNs. In general, when a



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node forwards a packet, all nodes within the node radio rangeneed to overhear the transmission and check whether thepacket is destined for them. A node just discards theoverheard packet if the node is not a desired receiver or aforwarding candidate of the packet. A little difference ofthe overhearing action in elastic routing is that a nodemight extract the sink location information in an overheard

packet before discarding it. This extraction action does notconsume significant energy. In fact, excessive energyconsumption is incurred because of the overhead of otherprotocols for location update of a mobile sink. We evaluatethe energy consumption by using the parameter values in[29], where the energy consumption to transmit and receivea packet during one slot are 0.0100224 and 0.0113472 mJ,respectively. Fig. 6b shows the energy consumption of thefour protocols. Fig. 6b is similar to Fig. 6a, but thegradient of the curves in Fig. 6b are more gentle than thatof Fig. 6a. This is because most energy is consumed fordata packets forwarding in all these protocols.

The average delivery delay of geographic routing is mostlyaffected by two factors: path length and per-hop forwarding

delay. The per-hop forwarding delay is an inherentcharacteristic in sensor networks; hence we do not discussthe per-hop forwarding delay in this paper and just assumethat it is a fixed value tp. The average delivery delay can berepresented as follows

TDelayP

k1 ntp

k (1)

where k is the total number of forwarded data packets and n isthe hops between a source and a sink, thus ntp present the timeduring which a packet was sent from the source to the sink.

TTDD forwards data packets along grid structure, and thepath length is at most

p2 times the length of that of a

straight line. Thus, when the other three protocols forwardsdata packets along a nearly straight-line path, the ntp of

TTDD is significantly longer than that of the other threeprotocols. In both ALURP and LPTD, if a sink moves witha relative short distance from the previous location, data

packets are sent to the previous location and then sent to thesink in detour manner. If the sink moves far away from itsprevious location, then the sink location update procedure is

Figure 6 Simulation results with different moving speedsa Control overhead impacted by sink mobilityb Energy consumption impacted by sink mobilityc Average delivery delay impacted by sink mobilityd Success delivery ratio impacted by sink mobility



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triggered. While in elastic routing, the routing path tends to beshrunk to a straight-line path even though the sink only moves

with a short distance, thus the ntp of elastic routing is furthershorter than that of ALURP and LPTD. The simulationresults in Fig. 6ccan validate this analysis result.

The success delivery ratio is mostly affected by networkcollisions and the invalid location information of mobilesinks. In other words, before obtaining the new location ofa mobile sink, a source still encapsulates the known originallocation of the sink in each data packet, and this invalidlocation information may lead to unsuccessful data delivery.Fig. 6d shows the success delivery ratio of these protocolsaffected by the sink mobility. Both TTDD and LPTDexploit agent nodes to trace a mobile sink, thus the successdelivery ratio is mostly affected by collisions. In TTDD, amobile sink only floods within about a grid size area to findthe nearest dissemination node if the sink moves to another

grid. Although the flooding frequency is proportional tothe sink moving speed, but only flooding within a smalllocal area does not generate too many collisions, thus

TTDD achieves a higher success delivery ratio. Theflooding areas of LPTD are two zonal areas which spreadacross the whole networks, and this leads to a widercollision area compared to TTDD. In elastic routing, themobility of a sink can be traced by overhearing feature ofthe last hop forwarding node if the sink does not move toofast, and no flooding messages are required to support thesink mobility. Thus elastic routing achieves higher successdelivery ratio. With a higher moving speed, the sink maymove out of the radio range of the last hop forwardingnode with a higher probability. In this case, the sink has toinform its location to the last hop forwarding node byunicasting, and the data packets forwarded by the last hopforwarding node during this period are all dropped, thusleads to degradation in success delivery ratio. In ALURP,once the sink moves out of the adaptive area, it has toconstruct a new adaptive area by full-network flooding, andthus leads to a wider collision area. The data packetsdelivered during this period are all dropped, thus ALURPachieves the worst success delivery ratio among theseprotocols.

5.3 Simulation results with different packet generation rates

We define the packet generation rate as the rate of the packetsgenerated and sent by a source. Among the four protocols,only elastic routing exploits the overhearing feature of

wireless transmission to propagate the location of a mobilesink to the source. The overhead for location updateof a mobile sink in ALURP, LPTD and TTDD isindependent of the packet generation rate. In elasticrouting, a node obtains the sink location by overhearing thenext hop data transmission. If a sink moves with a relativehigher speed and the data packets are generated by a source

with a relative lower rate, then it might happen frequentlythat a sink moves out of the radio range of the last hopforwarding node during the interval between two timesdata transmissions. In this case, the sink must sendits current location to the node from which it received the

last data packet by unicasting, and this process maygenerate a little overhead. In addition, since the location ofa sink is propagated to a source by overhearing the datatransmission, thus a lower packet generation rate may leadto a longer location propagation time. During the locationpropagation time, the sink may move to another location ifthe sink moves with a relative higher speed. As a result,data packets might be delivered to the sink in a flexuralpath, thus leads to a higher packet delivery delay. To verifythe above discussion, we set a source at the centre of thenetwork and a sink moves around the source with arandom changing speed between 1 and 10 m/s. Thedistance between the source and the sink is set to 100 m

fixedly. The source generates and forwards 128 bytes datapackets at a varying rate from 10 to 0.2 packet/s. Thesimulation duration is set to 500 s. Each simulation is doneten times, and we take the average value as the simulationresults.

Fig. 7ashows the control overhead impacted by the packetgeneration rate. In Fig. 7a, the packet generation rate doesnot affect the control overhead of ALURP, LPTD and

TTDD, whereas the control overhead of elastic routingincreases a little if the packet generation rate is less than

Figure 7 Simulation results with different packet generation rates

a Control overhead impacted by packet generation rateb Average delivery delay impacted by packet generation ratec Success delivery ratio impacted by the packet generation rate



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1 packet/s. In elastic routing, the location of the mobilesink is propagated to source by overhearing feature of datatransmission. With a higher packet generation rate, thesink location can be propagated several times within a shortmoving distance, thus the sink do not require to inform itslocation to the last hop forwarding node by unicasting, and

only the periodic beacon messages are required for the sinkto announce its location to the neighbour nodes. With alower packet generation rate, the sink may move out of theradio range of the last hop forwarding node with ahigher probability during the interval between two datatransmissions, thus the sink need to frequently inform itslocation to the last hop forwarding node. The lower thepacket generation rate is, the higher the probabilitybecomes. This is the reason why the control overhead ofelastic routing increases a little with the decreasing packetgeneration rate. This unicasting overhead is not significant,because the distance between the sink and the last hop

forwarding node is not too long even though with a lowerpacket generation rate.

Fig. 7b shows the average delivery delay impacted bythe packet generation rate. In ALURP, LPTD and TTDD,the data delivery path are independent of the packetgeneration rate, thus keep static with the packet generationrate, whereas the average delivery delay of elastic routingsignificantly degrades with decreasing of packet generationrate. The reason is that, in elastic routing, a source usuallyencapsulates the original location of a sink into each datapacket until obtaining the new location of the sink. FromFig. 5 we can see that if the sink is moving continuously,the data packets are usually delivered in a flexural path. Themore flexural the path is, the higher the average deliverydelay becomes. In elastic routing, an intermediateforwarding node directly sends data packet to a sink onceit obtains the new sink location, thus the fast the locationpropagation process is, the less flexural the path is. Sincethe elastic routing propagates the sink location byexploiting the overhearing feature of wireless transmission,so a lower packet generation rate leads to a lower locationpropagation speed, thus leads to a high packet deliverydelay. From Fig. 7b we can see that the elastic routing

works well in terms of average delivery delay in case of high

packet generation rate. Supporting mobile sink withcontinuous data delivery is a key design object of elasticrouting.

Fig. 7c shows the success delivery ratio affected by thepacket generation rate. The success delivery ratio of

ALURP, LPTD and TTDD are independent of packetgenerated rate. While in elastic routing, by using theoverhearing feature of wireless data transmission forlocation propagation of a mobile sink, a lower packetgenerated rate leads to infrequent location propagation ofthe mobile sink. Thus, each time the sink moves out of the

radio range of the last hop forwarding node, the datapackets sent by the last hop forwarding node during themoment are all dropped. From Fig. 7cwe can see that the

success delivery ratio of elastic routing declines withdecreasing packets generation rate. The simulation resultsof TTDD, ALURP and LPTD in Fig. 7 undulate just alittle because of the random changing speed of the mobilesink.

5.4 Convergence time with differentdistances and packet generation rates

In elastic routing, we term the time required by a sourceto obtain the new location of a mobile sink as pathconvergence time t. If a sink moves to a new location,before obtaining the new location information, the sourcestill encapsulates the original location of the sink in eachdata packet, thus data packets are sent along a flexural pathto the sink until the source has obtained the new locationof the sink. Given a source and a mobile sink, the hopbetween the source and the sink travelled by greedy

forwarding is m and the data packets transmission rate is npackets/s. According to Fig. 2, convergence time t can becalculated as follows

t

m

n(the case in Fig: 2a)

m 1

n(the case in Fig: 2b)

m 1

n(the case in Fig: 2c)

8>>>>>>>>>:

(2)

The reason is that the location of a sink can be propagated

with one hop progress towards the source with each timeforwarding a data packet. Thus m hops propagationrequires m times data transmission. Thus, the convergencetime t can be calculated by (2). From (2) we can see thatthe convergence time t is directly proportional to the hopdistance between a source and a sink and is inverselyproportional to the packet generation rate. To verify theabove discussion, in the first simulation scenario, we set thedistance between a source and a sink to 50, 100 and150 m, respectively, and vary the packet generation ratefrom 1 to 10 packets/s to evaluate the convergence time. Inthe second simulation scenario, we set the packet deliverrate to 1, 5 and 10 packet/s, respectively, and vary the

distance between a source and a sink from 20 to 200 m toevaluate the convergence time. In both the simulationscenarios, the source sends data packets to the sink in asteady-state, and then the sink moves 30 m distance aroundthe source with a speed of 5 m/s. The convergence time iscalculated as the time between the moment when the sinkmoves and the moment when the source obtains the finallocation of the sink. From Figs. 8a and b, we can see thatthe convergence time t is directly proportional to the hopdistance between a source and a sink and is inverselyproportional to the packet generation rate. In summary, alower packet generation rate leads to a longer convergence

time and a higher data delivery delay, but we believe thatthe location propagation scheme is well worth the gain incontrol overhead and energy consumption.



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6 Conclusion

In this paper, we proposed a novel geographic routing schemetermed elastic routing for supporting mobile sinks in WSNs.Elastic routing takes advantage of the broadcasttransmission nature of wireless sensor nodes. The locationpropagation of mobile sink is achieved by overhearing thedata transmission of the next hop forwarding node. Thelocation of mobile sink is propagated to source along thereverse path of geographic routing during data delivery. Nosignificant overhead is generated for location propagation ofa mobile sink. Elastic routing works like an elastic band withone end tied to a source and another end tied to a mobilesink. The routing path between a source and a sink adapts

the movement of the sink in real time manner. Elasticrouting is superior to other protocols in terms of controloverhead, data delivery delay and energy consumption.

7 References

[1] LI J., JANNOTTI J., COUTO D., KARGER D., MORRIS R.: A scalable

location service for geographic ad hoc routing. ACM

MOBICOM, 2000, pp. 120 130

[2] XUE Y., LI B., NAHRSTEDT K.: A scalable location management

scheme in mobile ad-hoc networks. IEEE Annual Conf. Local

Computer Networks (LCN), 2001, pp. 102111

[3] HE T., HUANG C., BLUM B., STANKOVIC J., ABDELZAHER T.: Range-

free localization schemes for large scale sensor networks.

ACM MOBICOM, 2003, pp. 8195

[4] SEADA K., HELMY A.: Rendezvous regions: a scalable

architecture for service location and data-centric storage

in large-scale wireless networks. IEEE/ACM IPDPS FourthInt. Workshop on Algorithms for Wireless, Mobile, Ad Hoc

and Sensor Networks (WMAN), 2004, pp. 218225

[5] STOJMENOVIC

I., LIU D., JIA X.: A scalable quorum-basedlocation service in ad hoc and sensor networks,

Int. J. Commun. Netw. Distrib. Syst., 2008, 1, (1), pp. 7194

[6] KARP B., KUNG H.T.: GPSR: greedy perimeter stateless

routing for wireless networks. Proc. Sixth Annual Int. Conf.

Mobile Computing and Networking, 2000, pp. 243254

[7] BASAGNI S., CHLAMTAC I., SYROTIUK V., WOODWARD B.: A

distance routing effect algorithm for mobility (DREAM).

ACM MOBICOM, 1998, pp. 7684

[8] KO Y., VAIDYA N.: Location-aided routing (LAR) in mobile

ad hoc networks, Wirel. Netw., 2000, 6, (4), pp. 307321

[9] KRANAKIS E., SINGH H., URRUTIA J.: Compass routing

on geometric networks. Proc. 11th Canadian Conf. on

Computational Geometry, August 1999, pp. 5154

[10] KUHN F., WATTENHOFER R., ZOLLINGER A.: Worst-case optimal

and average-case efficient geometric ad-hoc routing.

ACM MOBIHOC, 2003, pp. 267 278

[11] YU Y., GOVINDAN R., ESTRIN D.: Geographical and energy

aware routing: a recursive data dissemination protocol for

wireless sensor networks. UCLA Computer Science

Department Technical Report UCLA/CSD-TR-01-0023Report, May, 2001

[12] AL-KARAKI J.N., KAMAL A.E.: Routing techniques in wireless

sensor networks: a survey, IEEE Trans. Wirel. Commun.,2004, 11, (6), pp. 628

[13] GANDHAM S.R., DAWANDE M., PRAKASH R., VENKATESAN S.:

Energy efficient schemes for wireless sensor networks

w ith m ultiple m obile bas e s ta tions. P roc. I EEE

GLOBECOM, San Francisco, CA, 2003, pp. 377381

[14] SHAH D., SHAKKOTTAI S.: Oblivious routing with mobile

fusion centers over a sensor network. Proc. IEEE

INFOCOM, Anchorage, AK, USA, 2007, pp. 15411549

[15] COFFIN D., HOOK D.V., MCGARRY S., KOLEK S.: Declarative ad-

hoc sensor networking. SPIE Integrated Command

Environments, 2000, pp. 109120

Figure 8 Convergence time with different packet generation rates and distances between the source and the sink

a Convergence time impacted by packet generation rateb Convergence time impacted by the distance between the source and the sink



www.ietdl.org



12/12

[16] YE F., ZHONG G., LU S., ZHANG L.: Gradient broadcast:

a robust data delivery protocol for large scale sensor

networks, ACM Wirel. Netw., 2005, 11, (3), pp. 285298

[17] WANG G., WANG T., JIA W., GUO M., LI J.: Adaptive location

updates for mobile sinks in wireless sensor networks,

J. Supercomput., 2009, 47, (2), pp. 127145

[18] LEE J., YU W., FU X.: Energy-efficient target detection in

sensor networks using line proxies, Int. J. Commun. Syst.,

2008, 21, (3), pp. 251275

[19] YE F., LUO H., CHENG J., LU S., ZHANG L.: A two-tier data

dissemination model for large-scale wireless sensor

networks. ACM MOBICOM, 2002, pp. 148159

[20] CHENG K., LUI K., WU Y., TAM V.: A distributed multihop time

synchronization protocol for wireless sensor networks using

pairwise broadcast synchronization, IEEE Trans. Wirel.Commun., 2009, 8, (4), pp. 17641772

[21] NOH K., SERPEDIN E., QARAQE K.: A new approach for time

synchronization in wireless sensor networks: pairwise

broadcast synchronization, IEEE Trans. Wirel. Commun.,

2008, 7, (9), pp. 33183322

[22] SSU K., CHOUA C., CHENG L.: Using overhearing technique

to detect malicious packet-modifying attacks in wireless

sensor networks, Elsevier Comput. Commun., 2007, 30,

(1112), pp. 23422352

[23] LE H., GUYENNET H., FELEA V.: OBMAC: an overhearing

based MAC protocol for wireless sensor networks. Int.

Conf. Sensor Technologies and Applications, 2007,

pp. 547553

[24] YU K., GUO Y.J.: Anchor-free localization algorithm and

performance analysis in wireless sensor networks, IETCommun., 2009, 3, (4), pp. 549560

[25] JI W.-W., LIU Z.: Locating ineffective sensor nodes in

wireless sensor networks, IET Commun., 2008, 2, (3),

pp. 432439

[26] WANG W.D., ZHU Q.X.: RSS-based Monte-Carlo localization

for mobile sensor networks, IET Commun., 2008, 2, (5),

pp. 673681

[27] SON D., HELMYA.: The effect of mobility-induced location

errors on geographic routing in mobile ad hoc and sensornetworks: analysis and improvement using mobility

prediction, IEEE Trans. Mobile Comput., 2004, 3, (3),

pp. 233245

[28] ZHOU G., TIAN H., KRISHNAMURTHY S., STANKOVIC J.A.: Models

and solutions for radio irregularity in wireless sensor

networks, ACM Trans. Sensor Netw. (TOSN), 2006, 2, (2),

pp. 221262

[29] Chipcon Products: CC2420: 2.4 GHz IEEE 802.15.4/ZigBee-ready RF transceiver (Texas Instruments, 2007)



www.ietdl.org

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