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Target Tracking in Sensor NetworksTarget Tracking in Sensor Networks

Choong Seon HongChoong Seon Hong

Kyung Hee UniversityKyung Hee University cshong@khu.ac.krcshong@khu.ac.kr

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TrackingTracking

One of the most important applications of sensors is target tracking.

Each node can sense in multiple modalities such as acoustic, seismic and infrared.

The type of signals to be sensed are determined by the objects to be tracked

Given a sensor network, use the sensors to determine the motion of one or more targets

Canonical domain for DSNs - much of what we have seen so far is applicable data routing, query propagation, wireless protocols

Typically requires more cooperation among entities than other examples we have seen Compare: “is there an elephant out there?” vs. “where has that

particular elephant been?”

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Objectives to be satisfiedObjectives to be satisfied

Collaborative Signal Processing (CSP)Distributive processingGoal oriented, on-demand processing Information fusionMulti-resolution processing

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Collaborative Signal Processing (CSP)Collaborative Signal Processing (CSP)

To facilitate detection, identification and tracking of targets, global information in both time and space must be collected and analyzed over a specified space-time region

However individual nodes provide spatially local information only

CSP provides data representation and control mechanisms to collaboratively process and store sensor information, respond to external events and report results

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Distributive processingDistributive processing

Raw signals are sampled and processed at individual nodes but are not directly communicated over the wireless channel

Instead each node extracts relevant summary statistics from the raw signal, which are typically smaller in size

The summary statistics are stored locally in individual nodes and may be transmitted to other nodes upon request.

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Goal oriented, on-demand processingGoal oriented, on-demand processing

To conserve energy, each node should perform signal processing tasks that are relevant to the current query

In the absence of a query, each node should retreat into a standby mode to minimize energy consumption

A sensor node should not automatically publish extracted information, but should forward information only when needed

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Information fusionInformation fusion

To infer global information over a certain space-time region, CSP must facilitate efficient hierarchical information fusion

High bandwidth time series data must be shared between neighboring nodes for classification purposes

Lower bandwidth data may be exchanged between more distant nodes for tracking purposes.

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Multi-resolution processingMulti-resolution processing Depending on the nature of the query, some CSP

tasks may require higher spatial resolution involving a finer sampling of sensor nodes, or higher temporal resolution involving higher sampling rates

Example: Reliable detection is achievable with relatively coarse space-time resolution, whereas classification typically requires higher resolution

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Tracking ChallengesTracking Challenges

Data dissemination and storageResource allocation and controlOperating under uncertaintyReal-time constraintsData fusion (measurement interpretation)Multiple target disambiguationTrack modeling, continuity and predictionTarget identification and classification

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Tracking DomainsTracking Domains

Appropriate strategy depends on the sensors’ capabilities, domain goals and environment Requires multiple measurements? Bounded communication? Target movement characteristics? No single solution for all problems

For example… Limited bandwidth encourages local processing Limited sensors requires cooperation

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Why Not Centralized?Why Not Centralized?

Scale!Data processing combinatoricsResource bottleneck (communication,

processing)Single point of failure Ignores benefits of locality

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Why Not (fully) Distributed?Why Not (fully) Distributed?

Redundant information and computationCan increase uncertaintyLack of unified view High communication costs

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Different Approaches of TrackingDifferent Approaches of Tracking

Tree-BasedCluster-BasedPrediction-Based

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Scalable Tracking Using Networked Sensors (STUN)Scalable Tracking Using Networked Sensors (STUN)

Tree based

- H. T. Kung and D. Vlah. “Efficient Location Tracking Using Sensor Networks.” WCNC, March 2003.

- Chih-Yu Lin and Yu-Chee Tseng “Structures for In-Network Moving Object Tracking in Wireless Sensor Networks” BROADNETS’04

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STUN (cont’d)STUN (cont’d)

The method will need to handle a large number of moving objects at once

This method uses a hierarchy to connect the sensors The leaves are sensors The querying point as the

root The other nodes are

communication nodes

1 2

X

3 4

Y

Z

Example of message pruning hierarchy. Consider the direction messages from sensors that detect the arrival of a car. Sensor 1’s message will update the detected sets of all its ancestors. The message from sensor 2 and 4 do not update the detected sets of their parents and thus will be pruned there. The message from sensor 3 updates only its parent Z and thus will be pruned at X

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STUN (cont’d) -- ExampleSTUN (cont’d) -- Example

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STUN (cont’d)STUN (cont’d)

Advantage Message pruning Routing

Disadvantage Building the tree (the structures of the tree)

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Dynamic Convoy Tree-Based Collaboration (DCTC)Dynamic Convoy Tree-Based Collaboration (DCTC)

Wensheng Zhang and Guohong Cao, “DCTC: Dynamic Convoy Tree-Based Collaboration for Target Tracking in Sensor Networks” IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 3, NO. 5, SEPTEMBER 2004

Wensheng Zhang and Guohong Cao, “Optimizing Tree Reconfiguration for Mobile Target Tracking in Sensor Networks” INFOCOM 2004

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DCTC (Cont’d) - IntroductionDCTC (Cont’d) - Introduction

DCTC relies on a tree structure called “convoy tree”

The tree is dynamically configured to add some nodes and prune some nodes as the target moves.

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DCTC (cont’d) – Basic IdeaDCTC (cont’d) – Basic Idea

When a target shows up for the firsttime, an initial convoy tree is constructed

The root collects data from nodessurrounding the target, and process data

When the target moves, themembership of the tree is changed

The structure of the tree is reconfiguredif necessary

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Cluster-BasedCluster-Based

Wei-Peng Chen, Jennifer C. Hou, and Lui Sha, Fellow, IEEE “Dynamic Clustering for Acoustic Target Tracking in Wireless Sensor Networks” IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 3, NO. 3, JULYSEPTEMBER 2004

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Dynamic Clustering for Acoustic Target TrackingDynamic Clustering for Acoustic Target Tracking

A CH volunteers to become active When it detects that the strength of a received acoustic

signal exceeds a predetermined threshold The signal matches one of the signal patterns which the

system intends to track. The tasks of an active CH

Broadcasting a packet that contains the energy and the extracted signature of the detected signal to sensors

Receiving replies from sensors Estimating the location of the target based on replies Sending the result to subscriber(s).

Energy-Based Localization The fundamental principle applied in the energy-based

approaches is that the signal strength (i.e., energy) of a received signal decreases exponentially with the propagation distance

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Dynamic Cluster – The Continuous ObjectsDynamic Cluster – The Continuous Objects

Continuously distributed across a region

Occupy a large area Trend to diffuse, increase

in size, change in sharp, split into multiple relatively smaller continuous objects

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Prediction-BasedPrediction-Based

Please go through the following papers Yingqi Xu Winter, J. Wang-Chien Lee “Prediction-

based strategies for energy saving in object tracking sensor networks” 2004 IEEE International Conference on Mobile Data Management, 2004.

Xu, Y.; Winter, J.; Lee, W.-C. “Dual prediction based reporting for object tracking sensor networks” MOBIQUITOUS 2004

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Organization-Based TrackingOrganization-Based Tracking

Use structure, roles to control data and action flow

Can be static, or dynamically evolvedMaintain an organizational hierarchy for

achieving energy efficient tracking solution

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Distributed Target Classification and Tracking in Distributed Target Classification and Tracking in Sensor Networks --Proceedings of the IEEE, vol. 91, Sensor Networks --Proceedings of the IEEE, vol. 91,

no. 8, pp. 1163-1171, 2003.no. 8, pp. 1163-1171, 2003.

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Problem DomainProblem Domain

Single targetFixed, acoustic sensorsRequires multiple measurementsLimited ad-hoc wireless networkTrack and classify target

(classification, which uses a supervised learning technique, is not discussed here)

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Location-Centric TrackingLocation-Centric Tracking

Control and data flow at each node: Initialization: disseminate sensor information Receive candidates: describe approaching

targets Local detections: gather measurements Merge detections: form track, compare

candidates Determine confidence: estimate uncertainty Estimate track: predict future target location Transmit track: notify relevant sensors

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Location-Centric TrackingLocation-Centric Tracking

“Closest point of approach” (CPA) measurements

Target detection causes cell formation Cells formed around the target’s estimated

location Intended to include relevant sensors

Manager is selected Node with greatest signal strength

Manager collects local CPA’s Linear regression over CPA node locations

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Location-Centric-TrackingLocation-Centric-Tracking

Estimated location compared to prior tracks Projections from candidate tracks

Cell created for tracking in new area Size is a function of target velocity Tracking information propagated to cell

Tracking repeats…

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Location-Centric AdvantagesLocation-Centric Advantages

Avoids combinatorial explosion of track association Centralized: n targets, n candidate locations = n2

Distributed: 1 target, n candidate locations = nReduces communication costs (multi-hop ad hoc)

Saves energy

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Using and Maintaining Organization in a Using and Maintaining Organization in a Large-Scale Sensor NetworkLarge-Scale Sensor Network

Bryan Horling, Roger Mailler, Mark Sims and Victor Lesser

Multi-Agent Systems LabUniversity of Massachusetts

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Problem DomainProblem Domain

Fixed doppler radars

Requires multiple, coordinated measurements

Multiple targets

Shared 8-channel RF communication

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Sensor CharacteristicsSensor Characteristics

Hardware Fixed location,

orientation Three 120° radar heads Agent controller

Doppler radar Amplitude and

frequency data One (asynchronous)

measurement at a time

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Organizational ControlOrganizational Control

Use organization to address scaling issuesEnvironment is partitioned

Constrains information propagation Reduces information load Exploits locality

Agents take on one or more roles Limits sources of information Facilitates data retrieval

Other techniques also built into negotiation protocol and individual role behaviors

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Typical Node LayoutTypical Node Layout

• Nodes are arranged or scattered, and have varied orientations.• One agent is assigned to each node.

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Partitioning of NodesPartitioning of Nodes

• The environment is first partitioned into sectors.• Sector managers are then assigned.

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Competition for Sensor AgentsCompetition for Sensor Agents

• Sector members send their capabilities to their managers.• Each manager then generates and disseminates a scan schedule.

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Track Manager SelectionTrack Manager Selection

• Nodes in the scan schedule perform scanning actions.• Detections reported to manager, and a track manager selected.

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Managing Conflicted ResourcesManaging Conflicted Resources

• Track manager discovers and coordinates with tracking nodes.• New tracking tasks may conflict with existing tasks at the node.

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Data Fusion (Track Generation)Data Fusion (Track Generation)

• Tracking data sent to an agent which performs the fusion.• Results sent back to track manager for path prediction.

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Sector SizeSector Size

This one parameter affects many things…Sector manager load

Smaller sector –› smaller manager directory Larger sector –› better sector coverage

Track manager actions Smaller sector –› fewer update messages Larger sector –› larger manager directory

Communication distance, agent activity, Empirical evaluation of varying these

parameters

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Recommended ReadingRecommended Reading Efficient in-network moving object tracking in wireless sensor networks Chih-Yu Lin; Wen-Chih

Peng; Yu-Chee Tseng;Mobile Computing, IEEE Transactions on Volume 5,  Issue 8,  Aug. 2006 Page(s):1044 – 1056

Self-organizing sensor networks for integrated target surveillance Biswas, P.K.; Phoha, S.; Computers, IEEE Transactions on Volume 55,  Issue 8,  Aug. 2006 Page(s):1033 – 1047

CollECT: Collaborative Event deteCtion and Tracking in Wireless Heterogeneous Sensor Networks Kuei-Ping Shih; Sheng-Shih Wang; Pao-Hwa Yang; Chau-Chieh Chang; Computers and Communications, 2006. ISCC '06. Proceedings. 11th IEEE Symposium on 26-29 June 2006 Page(s):935 – 940

 Wireless sensor network based model for secure railway operations Aboelela, E.; Edberg, W.; Papakonstantinou, C.; Vokkarane, V.; Performance, Computing, and Communications Conference, 2006. IPCCC 2006. 25th IEEE International 10-12 April 2006

Adaptive tracking in distributed wireless sensor networks Lizhi Yang; Chuan Feng; Rozenblit, J.W.; Haiyan Qiao; Engineering of Computer Based Systems, 2006. ECBS 2006. 13th Annual IEEE International Symposium and Workshop on 27-30 March 2006

A Monte Carlo Method for Joint Node Location and Maneuvering Target Tracking in a Sensor Network Miguez, J.; Artes-Rodriguez., A.; Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on Volume 4,  2006

Target Tracking in a Two-Tiered Hierarchical Sensor Network Vemula, M.; Bugallo, M.F.; Djuric, P.M.; Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on Volume 4,  2006

Localization and Tracking in Sensor Systems Manley, E.D.; Al Nahas, H.; Deogun, J.S.; Sensor Networks, Ubiquitous, and Trustworthy Computing, 2006. IEEE International Conference on Volume 2,  2006 Page(s):237 – 242

Efficient Online State Tracking Using Sensor NetworksHalkidi, M.; Kalogeraki, V.; Gunopulos, D.; Papadopoulos, D.; Zeinalipour-Yazti, D.; Vlachos, M.; Mobile Data Management, 2006. MDM 2006. 7th International Conference on 10-12 May 2006 Page(s):24 – 24

 Achieving Real-Time Target Tracking UsingWireless Sensor NetworksTian He; Vicaire, P.; Ting Yan; Liqian Luo; Lin Gu; Gang Zhou; Stoleru, R.; Qing Cao; Stankovic, J.A.; Abdelzaher, T.; Real-Time and Embedded Technology and Applications Symposium, 2006. Proceedings of the 12th IEEE 04-07 April 2006 Page(s):37 - 48

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Thanks !Thanks !