Local Data Collection in Geographic Routingfor Wireless Sensor Networks

Euisin Lee, Soochang Park, Fucai Yu, Taehee Kim, and Sang-Ha KimDepartment of Computer Engineering

Chungnam National University220 Gung-dong, Yuseong-gu, Daejeon, 305764, Republic of Korea

{eslee, winter, yufc, and thkim}@cc1ab.cnu.ac.krand shkim@cnu.ac.kr

Figure I. Two methods of data collection in a geographic routing: (a) datacollecti on without a local sink and (b) data collection with a local sink

concentratedly data or is interested by users. However, datacollection and aggregation of the region in wireless sensornetworks is important and necessary to save the energy ofsensor nodes [1, 5, 6], because data generated from the sourcesin the local and adjacent region are often redundant and highlycorrelated since the wireless sensor networks are denselydeployed by a large number of sensor nodes [6]. However, itgenerates various problems that a global sink collects andaggregates data generated from the region in as shown in fig.I(a). Firstly, the energy consumption increases with the numberof source nodes because their data is disseminated to the globalsink without the aggregation . Secondly, as many data are

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Index Terms-Sensor networks, Geographic routing, Datacollection and aggregation, Global sink, and Local sink

Abstract-Most existing geograph ic routing protocols on sensornetworks concentrates on finding ways to guarantee dataforwarding from the source to the destination, and not manyprotocols have been done on collecting and aggregating data ofsources in a local and adjacent region. However, data generatedfrom the sources in the local and adjacent region are oftenredundant and highly correlated. Accordingly, data collection andaggregation of the region in the sensor networks is important andnecessary to save the energy of sensor nodes. We introduce theconcept of a local sink to address this issue in geographic routing.The local sink is a sensor node in the region, in which the sensornode is temporarily selected by the global sink for collecting andaggregating data from source nodes in the region and deliveringthe aggregated data to the global sink, in geographic routing. Wealso design a model to determine an optimal location of the localsink and propose a mechanism to collect data through the localsink. Simulation results show that the proposed mechanism withthe local sink is more efficient in terms of the energy and the datadelivery ratio than the existing mechanism in a geograph icrouting.

I. INTROD UCTIO N

Geographic routing [2, 3, 4] has been considered as anefficient, simple, and scalable routing protocol since differentfrom topology-based routing, it exploits pure locationinformation instead of global topology information such as therouting table of the whole networks to route data packets. Inother words, it can route data packets if nodes are aware of theirgeographic information, one-hop geographic information ofneighboring nodes, and geographic information of a destinationnode. Accordingly, many geographic routing protocols havebeen proposed to route efficiently data in wireless sensornetworks because these wireless sensor networks are deployedby numerous sensor nodes and the sensor nodes are dynamic e.g.,node mobility, node failure, and node on/off.

However, most existing geographic routing protocols [2, 3,4]on sensor networks concentrates on finding ways to guaranteedata forwarding from the source to the destination, and not manyprotocols have been done on collecting and aggregating data ofsources in a local and adjacent region where generates

• Source nodes include their won location information intheir data because many applications in wireless sensornetworks require the location of source data, forexample , target tracking and habitat monitoring.

• Source nodes also include their won remaining energyin their data.

B. Terminology and Definition

A Target region is defined as the specific region where isgenerated concentratedly data from many source nodes bygenerating of events or is interested by a user. A Global sink isa sink (or base station) which collects data from whole sensorfields and delivers them to users in architecture of generalwireless sensor networks [1]. After receiving data with locationinformation from sources in a target region, this global sink canselect a general sensor node to function as a local sink and canreceive aggregated data from the local sink. A Local sink is anentity which collects locally data in a target region and deliversaggregated data to a global sink. This local sink is one sensornode selected by the global sink, based on location informationamong general sensor nodes in the target region.

C. Determination ofLocal Sink

We develop an analytical model to determine optimallocation of the local sink. The model determines a sensor nodein the target region as the local sink, in which the total energycost function E, about (1) data collection and (2) aggregateddata dissemination is minimized in a geographic routing.

To derive the total energy cost function , we assume thatsensor nodes are densely and uniformly deployed in a sensorfield and all sensor nodes have the same transmission range.Accordingly, the total energy consumed by transmitting a datapacket along a multi-hop path in a geographic routing isproportional to the Euclidean distance between a source nodeand a destination node. This assumption is justified by the factthat the Euclidean distance between two nodes in a dense anduniform wireless sensor network is approximately proportionalto the hop count between the same nodes [15]. We note thatsuch an energy model is also adapted by several existing

Figure 2. An example to select an optimal local sink

disseminated via similar paths in geographic routing methods,sensor nodes on the paths consume much energy and suffer datacongestion. Thirdly, if there are data in other regions in order tobe disseminated on the similar paths, they are difficult to bedisseminated owing to the data congestion [4]. Hence, it canreduce the energy consumption and the data congestion that datagenerated concentratedly in the region are collected locally in theregion and the aggregated data are disseminated to the globalsink, as shown fig.l (b).

To address this issue, we introduce the concept of a localsink in geographic routing. The Local sink is an entity whichcollects locally data in a local and adjacent region and deliversthe aggregated data to a global sink. This local sink is onesensor node selected by the global sink, based on locationinformation among general sensor nodes in the region. Figure 1shows two methods to collect data from the local and adjacentregion in geographic rouging: (a) data collection without alocal sink and (b) data collection with a local sink.

We also design the Local Sink Model for determiningoptimal location of the local sink. The Local Sink Model useslocation information which is included in data from sourcenodes in the local and adjacent region. The global sink selects asensor node located in the optimal location which can consumeminimum energy for collecting data in the region and deliveringthe aggregated data to the global sink, as the local sink. We alsopropose an efficient mechanism that collects data in the regionthrough the local sink and delivers the aggregated data to theglobal sink. In addition, we propose a scheme that the local sinkis changed into a new optimal local sink when new source nodesare added in the target region. We last show through simulationresults that the proposed mechanism with the local sink is moreefficient in terms of the energy and the data delivery ratio thanthe existing mechanism in a geographic routing.

The remainder of this paper is organized as follows. Wedesign the Local Sink Model in section II and describe theproposed mechanism in section III. Simulation results arepresented in Section IV to evaluate the effectiveness of theproposed mechanism with the Local Sink Model. Section Vconcludes the paper.

II. LOCAL SINK MODEL

In this section, we describe the Local Sink Model. First, wepresent assumptions and terminology for the proposed model.Next, we explain a method for determining the optimallocation of a local sink in the proposed model. Lastly, weextend a single local sink to multiple local sinks for providingscalability about the number of sources.

A. Assumptions

• Each sensor node is aware of its own location throughreceiving GPS signals or through techniques such as [7]and [8].

• Source nodes detected an event generate data of samesize and deliver their data to the global sink throughgeographic rouging [2,3,4].

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(3) An ellipse (4) A rectangle(a) Each example to divide each polygon into equal size parts (b) An example ofa circle to divide a target region into 4 equal size parts

Figure 3. Multiple loeal sinks in one target region

power-efficient data communication protocols in wirelesssensor networks [16].

However, since the local sink takes charge of a role tocollect data from all source nodes in the target region and todisseminate the aggregated data to the global sink, the role ofthe local sink consumes more energy than the role of othersource nodes in the target region. Accordingly, the local sinkneeds energy beyond thresholdmax value which is needed tocarry out the role. Hence, we include only source nodes havingenergy beyond threshold as candidates to become the local sink.

As shown in fig. 2, consider a set of source nodes S = {n.,nz,... ,nN} in the target region which generates concentratedlydata. We define the energy cost function of (1) data collectionE; in a node n, having energy beyond threshold.c; value in theset S as

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Ec(i) = L hopsi],j) xR2-4] X (e(+eJxpacket_ size , (1)} =lJ.i

where the hops(i, j) is the number of hop counts between two

nodes i and j, the R is a radio range of all sensor nodes, thepacket_size is a packet size of sensing data in a source node,and the e, and e, are energy to send and receive 1 bit packet ,respectively. The hops(i,j) is defined as

h ( ..) r d(i,j) l (2)ops I,] = R-(R%Mean _dis) ,

where d(i, j) is defined as the Euclidean distance between thenodes i and j, R is a radio range of all sensor nodes, and theMean_dis is mean Euclidean distance between any twoneighbor nodes in the dense and uniform sensor network.

We also define the energy cost function of (2) aggregateddata dissemination Ed in the node n, as

Eii) = hops(i,g) xR2-4] x(e(+er ) x aggregated_ ]X1Cktet _ size , (3)

where the hops(i, g) is the number of hop counts between the

node n, and the global sink, the R is a radio range of all sensornodes , the aggregatedpacketsize is a packet size of

aggregated data that the node n, aggregates the collected datafrom all other source nodes in the set S. The hops(i,g) is

defined as

h (. ) r d(i,g) l (4)ops i.g = R-(R%Mean _dis) ,

where d(i, g) is a Euclidean distance between the node nj andthe global sink, R is a radio range of all sensor nodes, and theMean_dis is a mean Euclidean distance between any twoneighbor nodes in the dense and uniform sensor network.

Accordingly, total energy cost function E( is defined as

E((i) = E, (i) + Ed (i) . (5)

The global sink determines a node n, among all source nodehaving energy beyond the thresholdmax value in the set S as thelocal sink, whose E( is minimal.

D. Multiple Local Sinks in One Target Region

Due to the peculiarity of a sensor node that it has smallmemory size, the buffer size of a local sink is limited in orderto store data packets. If the local sink receives data packetsbeyond its buffer size, it will drop them and hence the dataaccuracy will fall due to data aggregation with insufficient datapackets. Accordingly, it is unreasonable that only a local sinkis used to collect and aggregate data of many source nodes inlarge-scale target region.

Hence, in order to solve this issue, we a scheme to expandthe Local Sink Model into a multiple local sinks model takingthe buffer size and the number of source nodes intoconsideration in a large-scale target region . The proposedscheme first determines the number of local sinks according tothe buffer size and the number of source nodes in the targetregion, and next divides the target region into as many parts asthe number of local sinks, in which the parts have same size.This design principle is based on the reason that because everysensor node is deployed densely and uniformly in a wirelesssensor networks, the same size parts have the same number ofsource nodes and hence each local sink is in charge of the same

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Figure 4. Three phases of the proposed mechanism

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number of source node,Let Nbufle r denote the buffer size of a sensor node. (Here, the

N bufler varies according to a specification of sensor nodes usedin sensor networks.) The maximum number of packetsMaxpackets that a local sink can store is calculated as

M. -N / kt .axpackets - buffer pac e _ Size. (6)

Namely, the Maxpackets is the maximum number of source nodesthat a local sink can support to collect and aggregate packetswith its buffer size. Let N.w urce denote the number of sourcenode in the target region. Then , the number of local sinksN Local_sink that are needed in the target reg ion is also calculatedas

N local _sin ks = (Nsources / Maxpackets) +1. (7)

We next present how to divide the target region into as manysame size parts as the number of local sinks. First , the globalsink decides an optimal polygon among polygons in fig. 3(a) toinclude most of the source nodes based on their locationinformation deployed in the target region. (In order to decidean optimal polygon, a few source nodes can be excluded, butthey forward directly their data to the global sink, or belong tothe nearest one among the determined multiple local sinks fromtheir location and deliver their data to it.) Each polygon isconfigured through the location information of source nodes asfollows. A circle and a square are configured through thelongest distance between two nodes among all source nodes asa diameter of the circle and a diagonal of the square,respectively. A rectangle and an ellipse are configured by theproposed scheme in our previous paper [14].

In order to divide a target region into 4 multiple local sinks,fig 3(b) shows an example of a circle that is simple in terms oftime complexity. 360 degree of the circle is divided by 4 andeach local sink take charges a 90 degree sub region in the circletarget region. Each local sink in each sub region is calculatedby the Local Sink Model in the section II-C.

E. Benefit ofLocal Sink

In fig. l(a), there are 12 nodes inside the target region, sothere are 12 data flows between the global sink and the targetregion and they share the similar paths. The data collection

mechanism without using a local sink leads to excessive energyconsumption and data congestions in the path between theglobal sink and the target region. However, as shown in fig.I(b), the local sink aggregates the data packets received fromall the nodes inside the target region and sends the aggregateddata to the global sink. There is only one data flow between theglobal sink and the target region. Data collection mechanismwith a local sink can minimize the energy consumption andavoid data congestion. We verify the benefits of the local sinkthrough simulation results in the Section IV.

III. DATA COLLECTIO N M ECHA NISM WITH LOCAL SI NK

In this section , we describe a mechanism that collects datathrough a local sink and disseminates the aggregated data to aglobal sink. As shown in fig. 4, the proposed mechanism consistsof three phases: (a) Selection and announcement of local sink,(b) data collection and delivery of local sink, and (c) change oflocal sink.

A. Selection and announcement oflocal sink

1f a global sink recognizes congestion of sensing data in anylocal and adjacent region, namely a target region, throughlocation information contained in the sensing data, it selects asensor node to function as a local sink through the Local SinkModel in the section II. The global sink sends aLocal_Sink_Selection message with location information of thetarget region to the sensor node, and the sensor node receivedthe message becomes the local sink to collect data from sensornodes in the target region. The local sink disseminates aLocal_Sink_Announcement message included its locationinformation in the region through the well known geocastingprotocols [9, 10]. Through disseminating the messages, theother sensor nodes in the target region get to be aware oflocation information of the local sink collecting their data.Figure 4(a) shows the phase for selection and announcement ofthe local sink.

B. Data collection and delivery oflocal sink

After the source nodes in the target region receive theLocal_Sink_Announcement message, they send their sensing

data not directly to the global sink but to the local sink. After thelocal sink receives the Local_Sink_Selectionmessage, it collectsits data and data of source nodes in the target region, andaggregates all data and disseminates the aggregated data to theglobal sink. Since data aggregation is not the major concern ofthis paper, we only exploit the well known existing dataaggregation schemes [11] for aggregating data. Figure 4(b)shows that the local sink collects data from all source nodes inthe target region and delivers the aggregated data to the globalsink.

We here consider the case that neighbor sensor nodes of thetarget region become new source nodes, because any event canmove or diffuse in accordance with applications of sensornetworks. Accordingly, it is more energy efficiency that datafrom these source nodes are also collected by the local sink.However, these source nodes do not disseminate their data to theglobal sink because they can not know existence and location ofthe local sink.

To address this issue, we present the simple scheme that thenew source nodes can recognize existence and location of thelocal sink and send their data to the local sink. To support thisscheme, we assume each sensor node works in promiscuousmode and hence listens on the channel for activities of directneighbors. A source node in the target region sends its data tonext hop sensor node within its radio range to deliver them to thelocal sink. In that case, the next hop sensor node also sends thedata to next hop sensor node toward the local sink. On the otherhand, the other sensor nodes within radio range of the sourcenode overhear data packet of the source node and hence knowinformation of the event and location of the local sink. Thus,neighbor sensor nodes of the target region can deliver their datato the local sink if they detect the event in consequence itsmovement or diffusion.

C. Change oflocal sink

A local sink should be changed due to two conditions. Thefirst condition is a case that new source nodes are added to thelocal sink because of diffusion and movement of the event inthe target region. As a result, location of the present local sinkis no longer the most optimal to collect data from all sourcenodes in the target region and to deliver the aggregated data tothe global sink. In this case, through the Local Sink Model inthe section II, the local sink (old local sink) should be changedinto another sensor node (new local sink) which is located inoptimal location for collecting data in the target region.

However, changing from the old local sink to the new localsink consumes energy for disseminating aLocal_Sink_Selection message from the old local sink to thenew local sink and geocating a Local_Sink_Announcementmessage in the target region by the new local sink. Thisconsuming energy to change into the new local sink may bemore than the saving energy by collecting data through the newlocal sink. Accordingly, in this case, the old local sink hadbetter not be changed into the new local sink.

To address this issue, we propose a scheme that changes the

old local sink into the new local sink if and only if the savingenergy through using the new local sink is more than theconsuming energy for changing into the new local sink. Thescheme stands on the basis of the next equation.

Et(new)-Et(old) ~ Edisse(old, new) + Egeocast(tar _reg) (8)

The left part of the above equation is defined as the saving

energy through using the new local sink. The E,(new) and

E,(old) is calculated by the Local Sink Model in the section

II, respectively. The right part of the above equation is defmedas the consuming energy for changing into the new local sink.

The Edisse (old, new) is defined as the consuming energy

for disseminating a Local_Sink_Selection message from the oldlocal sink to the new local sink as follows

Edisse(old,new) =hops(old,new)x R2-

4] x(et+er)xselection_ size. (9)

The Egeocast (tar _ reg) is defined as the consuming energy

for geocasting a Local_Sink_Announcement message in thetarget region by the new local sink. In other words, theE t(tar reg) is defined as energy that the all sourcegeocas -node in the target region receive and transmit theLocal_SinkAnnouncement message only once as follows

Egeocast(tar _ reg) = n x R[2-4] x (e, + e,) x announce _ size. (10)

The second condition to change a local sink is a case that thelocal sink has energy below threshold.i; value. Thethresholdmin value is energy that the local sink can not performits duty due to an energy scarcity and hence must transfer itsduty to another source node in the target region. If the localsink has energy below the threshold.i; value, it (old local sink)is changed into an optimal location one (new local sink) amongcandidates except itself in the target region, through the LocalSink Model in the Section II.

If the old local sink finds the new local sink located in thetarget region, the old local sink sends a LocalSink_Selectionmessage with location information of the target region to thenew local sink and relapse into a general source node in thetarget region. The new local sink received theLocal_Sink_Selection message disseminates aLocal_Sink_Announcement message in the target regionthrough the well known geocasting protocols [9, 10]. Throughdelivering these messages, the new local sink collects datafrom the whole source nodes in the target region and deliversthe aggregated data to the global sink as shown in fig. 4(c).

IV. SIMULATION RESULTS

We performed simulations using the Qualnet ver.4.0 [12]with 802. 11 MAC. The energy model of sensor nodes wasadopt the MICA [13] specification. Table 1 describes thedetailed setup for our simulation. We simulate three targetregions which are located at coordinates point (500, 2000),(2000, 2000), and (2000, 500), respectively. The number ofsource nodes in each target region is varied 10, 20, 30, 40, and50 respectively. The Threshold.i.; Value and the Threshold.i;

Value are 50 % and 10 % of the initial power, respectively. Alocal sink has a buffer size of 1280 bytes to store only its dataand data from 19 sources. We use the well known GreedyPerimeter Stateless Routing Protocol (GPSR) [2] as ageographic routing protocol. In terms of the energyconsumption and the data delivery ratio, we compare our datagathering mechanism with local sinks to a general datagathering mechanism without local sinks. In all graphs, the'with local sink' represent s the data gathering mechanism withlocal sinks and the 'without local sink' represents the generaldata gathering mechani sm without local sinks.

TABLE l. SIMULATION ENVIRONMENT SETTINGS

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Parameters ValuesNetwork space 2500m x 2500m square

The number of nodes I global sink and 200 sensor nodesThe node placement Dense, Uniform

MAC protocol IEEE 802.11 DCI'The transmitting and receiving 42mW in a transmission

power consumption rates 29mW in a receiveTransmission range Omnidirection 100mThe initial power lOW

Data packet size of a sensor node 64 bytes

Figure 5 shows the energy consumption for the number ofsource nodes and data generations. The sign a in the figurerepresents the number of data generations. As the number ofsource nodes and data generations is 10 and 2, respectively , theproposed mechanism with the local sink decreases about 35percent than one without the local sink. As the number ofsource nodes and data generations increases the energyconsumption of the mechanism without the local sink increaseswith their number. On the other hand, the energy consumptionthe mechanism with the local sink increases small because itdoes not increase the number of transmissions from the localsink to the global sink.

Figure 6 shows the energy consumption for data packet sizesof s source node and data aggregation rates. The sign d in thefigure represents the data aggregation rate, and the higher itsvalue is, the smaller the aggregated data packet size is. Here,

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Th e numb er 0 1 source nod es

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Figure 6. The energy consumption for data packet sizes of s sourcenode and data aggregation rates

the data packet size and the data aggregation rate changeaccording to applications of sensor networks. As the datapacket size increases, the mechanism without the local sinkconsumes more energy based on the energy cost functionexpression (1). However, the energy consumption of themechanism with the local sink is small in comparison withincrement of the data packet size because it collects all datapacket through the local sink and disseminates the aggregateddata packet to the global sink. As the data aggregation ratioincreases, the mechanism with the local sink consumes lessenergy because the aggregated packet size to disseminate fromthe local sink to the global sink is smaller.

Figure 7 shows the data delivery ratio for the number ofsources nodes in the target region. The mechanism with thelocal sink is about 5.2 percent higher than one without the localsink as the number of source nodes is ten. The higher thenumber of source nodes is, the bigger a gap of performancebetween the two mechanisms is. It is because a possibility ofdata drop is higher as data of each source node in themechanism without the local sink are disseminated by similarpaths.

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v. CONCLUSION

In this paper, we introduce a Local Sink, which collects dataof source nodes in a local and adjacent area and disseminatesaggregated data to a Global sink in geographic routing forsensor networks. We also designed a model to determine anoptimal location of the local sink and proposed a mechanism todisseminate data through using the local sink. We also verifiedsuperiority of the proposed mechanism with the local sink interms of the energy consumption and the data delivery ratiothrough the simulation results.

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