Shih-Chieh Lee 1

Detection and Tracking of Region-Based Evolving

Targets in Sensor Networks

Chunyu Jiang, Guozhu Dong, Bin WangWright State University

ICCCN 2005

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AgendaAgenda

IntroductionIntroduction BackgroundBackground R-tree Sensor Network TopologyR-tree Sensor Network Topology In-network DetectionIn-network Detection In-network TrackingIn-network Tracking Experimental EvaluationExperimental Evaluation ConclusionsConclusions

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IntroductionIntroduction Region-based targets/events

Contiguous spatial regions where objects of interest are present

Applications: toxic leak region, pollution area, area of forest fires, formations of tanks

Issues: detection of event shape tracking of shape evolving targets communicating spatial objects in-network processing of spatial information efficient processing of spatial queries

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IntroductionIntroduction

This work designs efficient network topologies and algorithms for detecting and tracking region-based targets and events

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BackgroundBackground

Definition: A region-based target is a spatial region where objects/phenomenon of interest exist. We will assume that regions can be described by polygons. Each region is represented by a chain-of-vertices (COV) description, which consists of the vertices of the underlying polygon of the region listed in clockwise manner.

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Chain-of-Vertices (COV) = 41-27-31-39-47-51-(41)

BackgroundBackground

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R-tree Sensor Network TopologyR-tree Sensor Network Topology

A 2-D space2 sensor field is divided into grids: grid is a basic spatial unit of a square shape at least one sensor exists in each grid Using R-tree structure to organize sensor nodes

into a distributed hierarchical topology. R-tree

(1) effectively represent spatial information (2) obtain fast response time and accuracy for

query execution (3) achieve minimal energy consumption for shape

evolving target detection and tracking

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R-tree Sensor Network TopologyR-tree Sensor Network Topology

A 2-D space2 sensor field is divided into grids: grid is a basic spatial unit of a square shape at least one sensor exists in each grid Using R-tree structure to organize sensor nodes

into a distributed hierarchical topology. R-tree

(1) effectively represent spatial information (2) obtain fast response time and accuracy for

query execution (3) achieve minimal energy consumption for shape

evolving target detection and tracking

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R-tree Sensor Network TopologyR-tree Sensor Network Topology

R-tree satisfies the following properties: The root has at least two children unless it is a

leaf Every non-leaf node has at least 0.5p children

unless it is the root All leaves appear on the same level

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In-network DetectionIn-network Detection Base Algorithm: Forward-All

Algorithm each intermediate node simply passes the

information received from its children to its parent

incurs a large amount of communication/energy overhead since

Forward-Description Algorithm Boundary detection algorithm Merging algorithm: Description improvement algorithm:

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In-network DetectionIn-network Detection Boundary detection algorithm

Used for nodes at a certain level of the R-tree in a distributed manner

Determines the sensors on the boundary of the event Graham’s scan [11] for computing convex hull : O(n2) Suggest boundary detection algorithm at level 3 or

higher level

COV = 41-34-27-28-29-30-31-39-47- 46-45-44-43-51-42-(41)

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convex hull The convex hull of a set of points is the The convex hull of a set of points is the

smallest convex set that includes the smallest convex set that includes the points. For a two dimensional finite set points. For a two dimensional finite set the convex hull is a convex polygon.the convex hull is a convex polygon.

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In-network DetectionIn-network Detection Merging algorithm:

Used by a node to merge the event regions obtained from its child nodes

41-27-31-47-51-(41) 88-90-99-106-104-(88)

47-51-41-27-31-88-90-99-106-104-(47)

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In-network DetectionIn-network Detection Description improvement algorithm:

reducing the unnecessary details in COV can help reduce communication cost.

Simplification 41-34-27-28-29-30-31-39-47-51-(41)

=> 41-27-31-47-51-(41) Smoothing

When the length of an edge is much shorter than the lengths of its two neighboring edges, we can remove this edge by removing one of the two points of this edge from the COV description.

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In-network DetectionIn-network Detection Smoothing

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In-network TrackingIn-network Tracking

Concerned with determining various dynamic characteristics of the event region moving direction/speed/rate of change

(expansion or shrinking) in area center(t) , area(t)

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Experimental EvaluationExperimental Evaluation 32 * 32 sensor grids sensors are organized into a 6-level R-tree with

a branching factor p of 4. Event Detection cases:

Event 1: a circular shaped event of radius 4 involving 52 grids located at the center of the sensor field.

Event 2: a circular shaped event of radius 5 involving 76 grids located at the center of the sensor field

Event 3: the same size and shape as event 1, but is located on the right side of the sensor field

Event 4: a small irregular shaped event involving 63 grids.

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Experimental EvaluationExperimental Evaluation Event 5: a large irregular shaped event

involving 200 grids, and has several concave segments.

Event 6: a moon shaped event involving 79 grids, and has a concave side and a convex side.

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ConclusionsConclusions

Developed in-network algorithms for the detection of spatial, regionbased event, for simplification of event descriptions to reduce communication cost, and for tracking of region-based events/targets.

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ConclusionsConclusions