[lecture notes in computer science] ubiquitous computing systems volume 4239 || dynamic clustering...

H.Y. Youn, M. Kim, and H. Morikawa (Eds.): UCS 2006, LNCS 4239, pp. 200 – 209, 2006. © Springer-Verlag Berlin Heidelberg 2006

Dynamic Clustering for Object Tracking in Wireless Sensor Networks

Guang-yao Jin, Xiao-yi Lu, and Myong-Soon Park*

Dept. of Computer Science and Engineering, Korea University Seoul 136-701, Korea

{king, felicity_lu, myongsp}@ilab.korea.ac.kr

Abstract. Object tracking is an important feature of the ubiquitous society and also a killer application of wireless sensor networks. Nowadays, there are many researches on object tracking in wireless sensor networks under practice, how-ever most of them cannot effectively deal with the trade-off between missing-rate and energy efficiency. In this paper, we propose a dynamic clustering mechanism for object tracking in wireless sensor networks. With forming the cluster dynamically according to the route of moving, the proposed method can not only decrease the missing-rate but can also decrease the energy consump-tion by reducing the number of nodes that participate in tracking and minimiz-ing the communication cost, thus can enhance the lifetime of the whole sensor networks. The simulation result shows that our proposed method achieves lower energy consumption and lower missing-rate.

1 Introduction

Recently, the development of the technology for embedded processor and low energy cost sensors which are equipped with RF communication module, make the killer application for object tracking in wireless sensor network possible. Because of the sensor’s low price and its ability of detecting at anytime and anywhere, the object tracking sensor network has great potential of being widely used in many domains like business and military area. But the limitation of sensor’s powers, which are usu-ally un-rechargeable batteries, as well as the attribute of highly distributed collabo-rated work, the instability of wireless communication and application specific requirements, really contribute the complexity to the design of a sensor network.

There have already been many researches on using sensor network to monitor the environment, and many of them can reach effective result. But however, the charac-teristic of the sensor network used for object tracking is completely different from the one used for simple environmental monitoring and thus we should take deep (special) research into this area.

In sensor network, many sensor nodes need to keep on collaborating with other nodes in order to track the moving target. For an important characteristic of sensor network is its limited power source, the most important issue for developing object * Corresponding author.

Dynamic Clustering for Object Tracking in Wireless Sensor Networks 201

tracking sensor network is to track the target with lowest energy consumption, espe-cially prevent some certain nodes over consuming energy, while maintain low miss-ing-rate, thus prolong the lifetime of the whole sensor network.

Fig. 1. Object traverses the sensor network and results the detection

Recently, the researches focus on object tracking sensor network become very ac-tive. These researches may be induced into 3 following aspects:

1. Using the position of the sensor which is the nearest to the target to be ap-proximate position of the target’s actual location.

2. Dynamically form cluster along the target’s moving direction using several sensor nodes near the target.

3. Predict the next possible position of the target and check the information of the sensor nodes on the moving route of the target.

The second part of this paper gives the example of target tracking and analyzes the existing methods for it in sensor network and shows their problems. In the third part, we proposed a Dynamic Clustering mechanism base on the travel status of the target. We are going to prove the efficiency of the proposed mechanism in part 4 through simulations.

2 Related Work

Existing researches about the object tracing in wireless sensor networks can be mainly classified into the 5 following schemes, which are Naïve, Scheduled Monitoring, Continuous Monitoring, Dynamic Clustering and Prediction-based. Among them, the Continuous Monitoring, Dynamic Clustering and Prediction-based are specially stud-ied for solving the object tracking problem in wireless sensor network while both the Naïve and Scheduled Monitoring are just focus on general applications of wireless sensor network. Each scheme can be described as follows:

Sensor Node

Cluster Head

Initial Cluster

Clusters

Object Path

202 G.-y. Jin, X.-y. Lu, and M.-S. Park

Naive: In this scheme, all the sensor nodes stays in active mode and monitor their detection areas all the time and sends the detected data to the base station periodically. Therefore, the energy consumption is high. [1]

Scheduled Monitoring: This scheme assumes that all the sensor nodes and base station are well synchronized, and all the sensor nodes can turn to sleep and only wake up when it comes to their time (turn) to monitor their detection areas and report sensed results. Thus, in this scheme, all the sensor nodes will be active for X second then go to sleep for (T − X) seconds. The advantage of this scheme is that it minimize the time for sensors in active mode and let sensors stay in sleep mode as long as they can. However, actually the assumption that all sensor nodes and base station are well synchronized is very difficult to be realized. And the number of sensor nodes in-volved in object tracking is more than necessary. Hence the energy consumption is high. [2][3]

Continuous Motoring: In this scheme, instead of having all the sensor nodes in the sensor network wake up periodically to sense the whole area, only one node which has the object in its detection area will be activated. Whenever a node wakes up, it will keep on monitoring the object until it enters a neighboring cell. The advantage of this scheme is that it involves only one sensor node to monitor each moving object while other sensor nodes turn to sleep and thus save energy. However, be-cause there is only one node monitor the object, the missing rate will be high when the object moves with a high speed. In order to reduce the missing rate, the active sensor has to stay awake as long as the object is in its detection area. This causes the unbalanced energy consumption among the sensor nodes and thus reduces the network lifetime. [4]

Dynamic Clustering: The network is formed with powerful CH nodes and low-end SN nodes. The CH node which has the strongest sensed signal of target becomes the cluster head and organizes nearby SN nodes to form a cluster. The SN nodes in the cluster transmit the sensed data to the cluster head, and then the cluster head sends the digested data to the base station after data aggregation. In order to prevent miss-ing the target, all CH nodes need to monitor continuously. Since only the CH nodes can be the cluster head, object tracking becomes very difficult in some areas where the CH nodes are sparsely distributed. And after a cluster is formed, there is no rotation of the cluster head, so the higher speed the target object travels at, the higher energy the cluster head would consume. So in this scheme, the energy con-sumption among the CH nodes is unbalanced, and thus reduces the network life-time. [5][6][7][8]

Prediction-based: In this scheme, a sensor node uses prediction model to predict the next location of the target object. Then, based on the prediction result, the sensor uses the Wake-up Mechanism to wake up an adjacent sensor node before the target leaving its own detection field and entering the adjacent field. If the system misses the target object, it would perform the recovery throughout the whole sensor network. Accord-ing to the different requirements of the application, the Wake-up Mechanism will be chosen from followings.


• Awakes only one sensor nearby the destination. • Awakes all the nodes on the route of the moving object from current location

to the destination. • Awakes all the nodes on and around the route of the moving object from cur-

rent location to the destination.

However, different problems exist with different Wake-up Mechanisms. The prob-lem with the first Wake-up Mechanism is that it is difficult to guarantee high accuracy of the prediction in reality, so the missing rate would be very high as well as the over-head caused by recovery procedure. And even if the prediction of the target location is 100% accurate and the awaked sensor can provide enough information for monitoring the target object, it would still have a problem of un-balanced energy consumption among the nodes, for there is only one sensor monitor the target and the energy con-sumption of this node would be high. In the second Wake-up Mechanism, though it has relatively smaller missing rate than the first one, there still will be un-ignorable increase of missing rate when the target change its moving direction beyond the pre-diction, because only the sensor nodes which are on the predicted route of the target would wake up and monitor the target continuously. If the missing rate increases, the overhead caused by missing recovery will increase too. And for all the sensor nodes on the object’s traveling route are supposed to be active to monitor the target, the energy consumption would be high. In the case that the third Wake-up Mechanism is chosen, since all the sensor nodes surrounding the route of the target’s movement would wake up to monitor the target at the same time, although it can reduce the miss-ing rate, the energy consumption would be higher.

Different from other researches, the goal of our paper is to reduce the missing rate and improve energy efficiency in wireless sensor network by performing clustering dynamically along the route that the target moves, and thus prolong the lifetime of the whole network.

3 Dynamic Clustering with Considering the Moving Information

The dynamic clustering mechanism proposed in this paper, as shown in figure 1, per-forms the clustering along the route of the target movement with minimum numbers of sensor nodes to track the target object, thus can reduce the energy consumption and prolong the lifetime of the whole sensor network. The proposed mechanism consists of the 3 following functionalities.

3.1 Efficient Clustering Mechanism

Although there have been many researches on object tracking in wireless sensor net-works, most of them are based on static clustering, hence it is difficult to apply them to object tracking application. The existing dynamic clustering mechanism in object tracking sensor networks is a kind of mechanism which first selects a sensor node with the strongest sensed signal strength to be the cluster head, and then form the cluster at the cluster composition stage. Sensor nodes need to communicate with each other several times to determine which one has the strongest sensed signal strength and can be selected as the cluster header. However, these methods could not be very


useful without guarantying the optimal communication cost of the sensor nodes. And also in order to track a fast moving object, a simple, rapid and energy efficient cluster-ing mechanism is needed.

In this paper, we considered all these features of the object tracking sensor net-works and developed an energy efficient clustering mechanism which forms the clus-ter with optimal communication cost. The procedure of clustering consists of two stages which are initial clustering stage and re-clustering stage.

3.1.1 Initial Clustering At initial time, all sensors in the network are in sleeping mode. After receiving a tracking command from the server, all the sensors wake up and do the sensing for a short time period. The sensor nodes which can not detect the target will go to sleep again as soon as possible, and those with the detection of the target will form a cluster and enter the target tracking state.

We define the collection of the sensor nodes which detected the target as D. All the sensor nodes in collection D will do sensing twice again, which means every sensor node in D would do sensing three times continuously. For a sensor node can know the distance, which is defined as d here, between the target and itself after doing sensing one time, by doing sensing three times, we can get three relationships between the distance d and time t, here we use R to stand for the collection of the three relation-ships. All sensor nodes in D which have finish sensing three times and successfully get collection R will wait for a short random time, and the first one, we say S1 whose timer expires will broadcast a solicitation packet, which contains its resident energy, its location and its collection R to the neighboring nodes and then go into listening mode. Other sensor nodes which receive the solicitation packet from the S1 will wait for another short random time, and then the second node, we say S2, whose timer first expires within these nodes will broadcast another solicitation packet to the neighbor-ing nodes and then go into listening mode. The procedure is similar with S1. In this way, all the sensor nodes in D except S1 and S2 will get R collections, two from S1 and S2 and one from themselves. Then all nodes except S1 and S2 in D would then wait for another short random time, and the first one whose timer expires will broad-cast a notification message to its neighbors and elect itself as the cluster head. It is easy to know that, a target location can be estimated from the data sensed by three different nodes at the same time, so the cluster head can get three locations of the target at three different times using the three R collections received, and thus the mov-ing speed and acceleration of the target can be estimated. Then, with the moving in-formation of the target, the cluster head can get the next possible location of the target by prediction. For every node is supposed to know the location information of all its one-hop neighbors, the elected cluster head will then go into schedule mode and send scheduling information to its neighbors to form a cluster.

3.1.2 Cluster Re-forming Cluster re-forming is a little different from the initial cluster forming procedure. When initialing a cluster, all the sensor nodes in the sensor network are active and can detect the target location after receiving an object tracking command from the base station, and then they will calculate the target moving information by collaborating with other adjacent sensors and volunteer themselves as cluster head to form the


cluster. In cluster re-forming procedure, after a new cluster head is successfully elected, a new cluster will be formed around it. The new cluster head is selected by the former cluster head through broadcasting a confirming packet. The sensor nodes of the old cluster which receive the confirming packet will first check out whether they are the neighbor nodes of the new cluster head, if so, they would go into listening mode and wait for getting new scheduling information from the new cluster head and if not they would go to sleeping mode immediately. After receiving the confirming packet from the former cluster head, the new cluster head will wait for a short random time, and when its timer expires it will broadcast a re-clustering command packet which contains the new scheduling information to the neighboring sensors and then go into listening mode. The neighboring nodes will use the new schedule to detect after receiving this packet, and thus a new cluster is formed.

3.1.3 Cluster Head Election Cluster head election is one of the core issues of clustering in wireless sensor net-works. How to elect the cluster head directly affects not only the cluster’s energy efficiency, but also the lifetime of the whole network. In most of the existing papers related to clustering in general wireless sensor networks, a sensor node with the high-est residual energy will be selected as the cluster head after a cluster has been already formed, with considering the routing information and the distribution of the sensor nodes. And most of the researches on clustering in object tracking sensor networks just simply select a node with the strongest sensed signal strength to be the cluster head. However, in the real environment, a target may travel at a varying speed all the time, so the existing methods may not be suitable for object tracking applications in real sensor network.

Fig. 2. New cluster head election

As shown in figure 2, this paper is focusing on the object target which moves at a varying speed. A cluster head is selected with the consideration of the next possible location of target gained by prediction, the distribution of the sensor nodes which participate in tracking and the residual energy of these sensors. When the predicted location of the target is on the boundary of the current cluster, the current cluster head would send a solicitation packet to the sensor nodes around the predicted location, and select the first one which replies the message to be the new cluster head, and send

Sleeping Sensor

Wake-up Sensor

ClusterHead

Object Object Path

Sensor Node

@Time 1 @ Time 2


it a confirming packet. After the new cluster head receiving the confirming message, it will broadcast the re-clustering command to the neighboring sensor nodes.

4 Experimental Results

We set up simulation environment in NS2. In the simulation, there is a 100m x 100m square area with sensors uniformly and randomly deployed. The radio model adopted in this experiment is based on [12]. Thus, to transmit k-bit message a distance d, the radio expends:

⎪⎩

⎪⎨⎧

≥+

<+=

+=

−−

−

−−

crossoverampwaytwoelec

crossoverampfrisselec

ampTxelecTxTx

dddkkE

dddkkE

dkEkEdkE

,***

,***

),()(),(

4

2

ε

ε

and to receive this message, the radio expends:

kEkEkE elecelecRxRx *)()( == − For the experiments in this paper, we adopted the same value given in [12] as

42 //0013.0//10,/50 mbitpJandmbitpJbitnJE ampwaytwoampfrisselec === −−− εε

and each node is initialed with only 2J energy. The radio range of sensors is 10m and the distance between each sensor is 5m.

In order to simplify the experiment without losing generality, we suppose an object is moving into the surveillance area from the left bottom with an initial speed of 1m/s, and the initial speed angle is 150 relative to X-axis. And we suppose the acceleration of the target changes after every tΔ , but stays as a constant within tΔ .

Fig. 3. Simulation Environment Setting

100m

5m

R=10m

15o

SensorTarget

100m


Simulation on missing rate

0

2

4

6

8

10

12

14

16

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

Varying period of the acceleration (seconds)

Rec

ove

ry t

imes

in

5 m

inu

tes

Dynamic

Predict-Dest

Predict-Route

Predict-All

Proposed

Fig. 4. Simulation result for experimentation on missing rate

In object tracking sensor network, the energy consumption is proportional with the missing rate, that is to say, the higher the missing rate is, the more missing recovery would take place, and thus the energy consumed by recovering the tracking will be higher. So in the simulation, we compare our proposed mechanism with the existing dynamic clustering mechanism and the prediction-based mechanism which can be categorized as Predict-Dest, Predict-Route, and Predict-All, and we evaluate our pro-posed mechanism by comparing the missing rate caused in 5 minutes and the total energy dissipation in 5 hours.

From figure 4 we can see the existing dynamic clustering method can achieve the lowest missing rate, and the result is not much influenced by the changing of target movement status. That is because in this method, all the sensors keep on sensing con-tinuously no matter whether the target is in their sensing area. However, for all the nodes in the sensor network, including those which are far from the target are in-volved in tracking continuously, although it can reach low missing rate, the energy consumption will be high as shown in figure 5.

Among the three categories of prediction method, Prediction-Dest is under the strongest affection by the changing target’s movement status. Comparing figure 4 with figure 5 we can see that, when the target’s movement status changes smoothly, the missing rate will relatively decrease and the energy consumption will decrease too.

When using prediction-based mechanism, the whole energy consumption will be less if the target’s moving status changes smoothly, for the accuracy of prediction is affected by the changes of target’s movement status. Because our proposed mecha-nism is also based on prediction, so the energy consumption will be less when target


Simulation on energy consumption

0

1

2

3

4

5

6

0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

Varying period of the acceleration (seconds)

To

tal

ener

gy

dis

sip

atio

n i

n 5

ho

urs

Dynamic

Predict-Dest

Predict-Route

Predict-All

Proposed

Fig. 5. Simulation result for experimentation on energy consumption

travels with a smoothly changing status, that is to say, our proposed mechanism is more suitable for tracking the target with smoothly changing movement status. In our simulation, we use the changing of acceleration to indicate the changing of target’s movement status.

As shown in figure 4 and figure 5, the advantage of our proposed mechanism is more obvious when the acceleration of the target changes smoothly.

5 Conclusions and Future Work

In this paper, we proposed an efficient dynamic clustering mechanism which can reduce missing rate by prediction and prolong the lifetime of the whole sensor net-work by minimizing energy consumption. And the energy consumption is mainly reduced in two ways: first, minimizing the number of nodes involved in tracking by constructing cluster dynamically along the target’s traveling route. Second, minimiz-ing the communication cost between sensor nodes when forming a cluster.

For future work, we are going to study an energy efficient scheduling method based on the known moving information of the target, which can minimize both the missing rate and the energy consumption in dynamic cluster structure.

Acknowledgement

This work was supported by the Korea Research Foundation Grant funded by the Korea Government (MOEHRD) (KRF-2005-211-D00274).


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[lecture notes in computer science] ubiquitous computing systems volume 4239 || dynamic clustering...

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