1 efficient in-network moving object tracking in wireless sensor networks chih-yu lin, student...

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Efficient In-Network Moving Object Tracking in Wireless Sensor Networks

Chih-Yu Lin, Student Member, IEEE, Wen-Chih Peng, Member, IEEE, and

Yu-Chee Tseng, Senior Member, IEEE指導教授：林振緯教授報告人：周軒逸

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Outline 1. Introduction 2. PRELIMINARIES 3. TREE CONSTRUCTION ALGORITHMS 3.1. Algorithm DAT (Deviation-Avoidance Tree) 3.2. Algorithm Z-DAT (Zone-Based Deviation-

Avoidance Tree) 3.3. Algorithm QCR (Query Cost Reduction) 4. SIMULATION RESULTS 5. CONCLUSIONS

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1. Introduction （ 1/3,P.1） In the first category, the problem of accurately e

stimating the location of an object is addressed. In the second category, innetwork data processin

g and data aggregation for object tracking are discussed.

Object tracking typically involves two basic operations: update and query.

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1. Introduction （ 2/3,P.1） Drain-And-Balance (DAB) tree structure has two

drawbacks :

First, a DAB tree is a logical tree not reflecting the physical structure of the sensor network.

Second, the construction of the DAB tree does not take the query cost into consideration.

We take a two-stage approach. The first stage aims at reducing the update cost, while the second stage aims at further reducing the query cost.

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1. Introduction （ 3/3,P.2） Deviation-Avoidance Tree (DAT) Zone-based Deviation-Avoidance Tree (Z-DAT) Query Cost Reduction (QCR)

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2. PRELIMINARIES

Fig. 1. (a) The Voronoi graph of a sensor network. The arrival and departure rates between sensors are the numbers associated with arrows. (b) The graph G corresponding to the sensor network in (a). The number labeled on each edge represents its weight.

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2. PRELIMINARIES

Fig. 2. (a) An object tracking tree T, where the dotted lines are the forwarding path of a query for Car1. (b) The events generated as Car1 moves from sensor K to G and Car2 moves from H to C.

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2. PRELIMINARIES

TABLE 1 Summary of Notations

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3. TREE CONSTRUCTION ALGORITHMS 3.1 Algorithm DAT (Deviation-Avoidance Tree)（ 1/7,P4）

Therefore, the update cost U(T) of a tree T can be formulated by counting the average number of messages transmitted in the network per unit time:

（ 1）

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3. TREE CONSTRUCTION ALGORITHMS 3.1 Algorithm DAT (Deviation-Avoidance Tree) （ 2/

7,P4）

Theorem 1. Given any logical tree T of the sensor network G, we have：

（ 2） where Subtreee(v) is the subtree of T rooted at node v and p(v) is

the parent of v.

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3. TREE CONSTRUCTION ALGORITHMS 3.1 Algorithm DAT (Deviation-Avoidance Tree) （ 3/7,P4）

From (1) and (2), we make three observations about U(T): Equation (1) contains the factor Equation (2) contains the factor In (1), the weight will be multiplied by .For two edges and

such that it is desirable that

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3. TREE CONSTRUCTION ALGORITHMS

3.1 Algorithm DAT (Deviation-Avoidance Tree) （ 4/7,P5）

Fig. 3. Four possible location tracking trees for the graph in Fig. 1(b).

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Fig. 4. Snapshots of an execution of DAT. Solid lines are those edges that have been in

cluded into T.

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3.1 Algorithm DAT (Deviation-Avoidance Tree) （ 7/7,P6）

Theorem 2. If G is connected, the tree T constructed by algorithm DAT is a connected deviation-avoidance tree rooted at the sink.

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3. TREE CONSTRUCTION ALGORITHMS 3.2 Algorithm Z-DAT (Zone-Based Deviation-A

voidance Tree)（ 1/4,P6）

Fig. 5. Possible structures of subtrees with nine sensors.

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3.2 Algorithm Z-DAT(Zone-Based Deviation-Avoidance Tree)（ 2/4,P7）

Fig. 6. An example of the Z-DAT algorithm with α=4. (a) In the first iteration, we divide the field into αxα zones and adjust their boundaries according to δ. (b) In the second iteration, each 2x2 neighboring zones is combined into a larger zone.

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3. TREE CONSTRUCTION ALGORITHMS 3.2 Algorithm Z-DAT (Zone-Based Deviation-Avo

idance Tree)（ 3/4,P7）

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3.2Algorithm Z-DAT (Zone-Based Deviation-Avoidance Tree)（ 4/4,P7）

Theorem 3. If G is connected, the tree T constructed by algorithm Z-DAT is a connected deviation-avoidance tree rooted at the sink.

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3.3 Algorithm QCR (Query Cost Reduction) (1/7,P7)

DAT and Z-DAT only try to reduce the update cost. The query cost is not taken into account. QCR is designed to reduce the total update and query cost by adjusting the object tracking tree obtained by DAT/Z-DAT.

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3. TREE CONSTRUCTION ALGORITHMS 3.3 Algorithm QCR (Query Cost Reduction) (2/7,P7) To begin with, we define the query rate q(v) of each sensor v as the av

erage number of queries that refer to objects within the sensing range of v per unit time in statistics.

We make two observations on Q(T). First, because distT(p(v),sink) is always smaller than distT(v,sink),(3) indicates that placing a node as a leaf can save the query cost instead of placing it as a nonleaf.

Second,the second term in (3) implies that the value of distT (p(v),sink) should be made as small as possible.

(3)

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3. TREE CONSTRUCTION ALGORITHMS 3.3 Algorithm QCR (Query Cost Reduction) (3/7,P7) If a node v is not a leaf node, we can make it a leaf by cutt

ing the links to its children and connecting each of its children to p(v).

(4)

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3. TREE CONSTRUCTION ALGORITHMS 3.3 Algorithm QCR (Query Cost Reduction) (4/7,P

8) If a node v is a leaf node, we can make pevT closer to the si

nk by cutting v`s link to its current parent p(v) and connect v to its grandparent p(p(v)).

(5)

(6)

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8)

Fig. 7. Making a nonleaf node v a leaf node.

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8)

Fig. 8. Connecting a leaf node vi to p(p(vi))

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3. TREE CONSTRUCTION ALGORITHMS 3.3 Algorithm QCR (Query Cost Reduction) (7/7,P8)

Fig. 9. An execution example of algorithm QCR.

Fig. 9a (which is constructed from Fig. 1b), where the number labeled on each node is its query rate.

Only H is moved upward as shown in Fig. 9b. This updates the tree to Fig. 9c. D will become a leaf and all its children are connected to D’s parent

as shown in Fig. 9d.

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4. SIMULATION RESULTS(1/5,P9)

Fig. 10. Comparison of update costs. In the Z-DAT scheme, α=8 and δ =0. (a) Regular deployment, sink at a corner. (b) Regular deployment, sink at the center. (c) Random deployment, sink at a corner. (d) Random deployment, sink at the center.

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Fig. 11. Snapshots of tree T obtained by DAT and Z-DAT under regular and random deployments. There are 1,024 sensors with the sink at the lower-left corner. (α,δ )=(8,0) for Z-DAT.) (a) A DAT tree (regular deployment). (b) A Z-DAT tree (regular deployment). (c) A DAT tree (random deployment). (d) A Z-DAT tree (random deployment).

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Fig. 12. Comparison of update costs under different e; T for Z-DAT. Sinks are located at the center of the network. (a) Regular deployment, C = 0.1,4,096 sensors. (b) Random deployment, C = 0.1, 4,096 sensors. (c) Regular deployment, C = 0.1, 2,500 sensors. (d) Random deployment, C = 0.1,2,500 sensors.

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Fig. 13. Comparison of query costs. (C =1.0).(a)Regular deployment, sink at a corner.(b)Regular deployment, sink at the center.(c)Random deployment, sink at a corner. (d) Random deployment, sink at the center.

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Fig.14. Comparison of total costs (C =1.0). (a) Regular deployment, sink at a corner. (b) Regular deployment, sink at the center. (c) Random deployment, sink at a corner. (d) Random deployment, sink at the center.

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5. CONCLUSIONS （ P.10） Simulation results show that by exploiting the deviation-a

voidance trees,algorithms DAT and Z-DAT are able to reduce the update cost. By adjusting the deviation-avoidance trees, algorithm QCR is able to significantly reduce the total cost when the aggregate query rates is high, thus leading to efficient object tracking solutions.

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END

1 efficient in-network moving object tracking in wireless sensor networks chih-yu lin, student...

Documents

tree construction algorithms3

logical tree t

balance dab tree structure

sensor network g

object tracking

update cost ut

graph g

subtree of t