Dual Prediction-based Reporting for Object Tracking Sensor Networks
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Dual Prediction-based Reporting for Object Tracking Sensor Networks
Yingqi Xu, Julian Winter, Wang-Chien LeeDepartment of Computer Science and Engineering, Pennsylvania State U
niversity
International Conference on Mobile and Ubiquitous Systems: System and Services (MobiQu
itous 2004)
Speaker: Hao-Chun Sun
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Outline
Introduction Related Work Dual Prediction Based Reporting Performance Evaluation Conclusion
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Introduction -background-
Object Tracking Sensor Network (OTSN)Energy conservation is the most critical issue.
Monitoring Reporting
OTSN
Base Station
T seconds
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Introduction -background-
Object Tracking Sensor Network (OTSN)Sensor Fusion Problem
Deciding the states of the tracked objects may need several sensor nodes to work together.
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Introduction -background-
Factors impact on the energy consumptionNetwork workloadReporting frequencyLocation modelsData precision
OTSN
Base Station
T seconds
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Related Work -PES-
Prediction-based Strategies for Energy Saving in Object Tracking Sensor Networks (IEEE MDM 2004)
RF Radio
Sensor MCU
Sensor Node
OTSN
Base Station
T seconds
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Related Work -PES-
Basic monitoring schemes Naïve
Space: All sensor nodes Time: All time
Scheduled Monitoring (SM) Space: All sensor nodes Time: activated for X (s), sleep for (T-X) (s)
Continuous Monitoring (CM) Space: One sensor node Time: All time
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Related Work -PES-
Base Station
Monitored region
SM
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Related Work -PES-
Base Station
Monitored region
SM
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Related Work -PES-
Base Station
Monitored region
CM
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Related Work -PES-
Monitoring Solution Space
IdealScheme
Energy consu
mption decre
ases
Missing ra
te incre
ases
NaiveSM
CM
Number of Nodes
Sampling Frequency
1
S
LowestFrequency(=1)
HighestFrequency(=T/X)
Legend
Basic schemes
Possible schemes
Legend
Basic schemes
Possible schemes
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Related Work -PES-
Prediction Model— Heuristics INSTANT
Current node assumes that moving objects will stay in the current speed and direction for the next (T-X) seconds.
Heuristics AVERAGE By recording some history, the current node derives the
object’s speed and direction for the next (T-X) seconds from the average of the object movement history.
Heuristics EXP_AVG Assigns different weights to the different stages of history.
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Dual Prediction based Reporting
Reporting energy conservation
OTSN
Base Station
T frequency
RF Radio
Sensor MCU
Sensor Node
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cb
Dual Prediction based Reporting
Dual Prediction based Reporting
f
d
a
Base Station
Instance Prediction
Model
e
Instance Prediction
Model
OTSN
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Dual Prediction based Reporting
Location Models Indirectly affect the accuracy of the prediction
models.Two categories
Geometric location model Symbolic location model
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Dual Prediction based Reporting
Location ModelsSensor Cell(SS)Triangle(ST)Grid(SG)Coordinate(SG)
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Performance Evaluation
ComparisonNaïve schemePREMON scheme
Prediction-based reporting mechanism
Base Station
PredictionModel
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Performance Evaluation
Simulator: CSIM
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Performance Evaluation
Workload—Total Energy Consumption
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Performance Evaluation
Workload—Prediction Accuracy
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Performance Evaluation
Moving Duration—Total Energy Consumption
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Performance Evaluation
Moving Duration—Prediction Accuracy
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Performance Evaluation
Moving speed—Total Energy Consumption
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Performance Evaluation
Moving speed—Prediction Accuracy
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Performance Evaluation
Reporting period—Total Energy Consumption
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Performance Evaluation
Reporting period—Prediction Accuracy
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Performance Evaluation
Location Model—Total Energy Consumption
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Performance Evaluation
Location Model—Prediction Accuracy
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Conclusion
OTSN energy consumptionMonitoring and Reporting
Dual Prediction Reporting (DPR)Prediction ModelLocation Model
DPR is able to minimize the energy usage of OTSNs efficiently under various condition.
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Conclusion
Mobile objects have less impact on the low granular location models than the high granular one.
The longer reporting period is adverse to the prediction-based schemes with high granular location models, but improves the prediction accuracy for the location models with low gutturality by eliminating the granularity effect.