1. Optimal Tracking Interval for Predictive Tracking in Wireless Sensor Network
IEEE COMMUNICATIONS LETTERS, VOL. 9, NO. 9, SEPTEMBER 2005
Zhen Guo,
Mengchu Zhou, Fellow, IEEE, (周孟初,
and Lev Zakrevski, Member, IEEE
Presentation by
Cheng-Ta Lee
Optimal Tracking Interval for Predictive Tracking in Wireless Sensor Network

2. Outline Introduction Predictive Tracking Sensor Network Architecture
Power Optimization and Quantitative Analysis
Conclusion
Future Work
Optimal Tracking Interval for Predictive Tracking in Wireless Sensor Network

3. Introduction 1/3
Object tracking is an important application in wireless sensor networks
Terrorist attack detection
Traffic monitoring
Most of researchers concentrate on tracking objects and finding efficient ways to forward the data reports to the sinks
Optimal Tracking Interval for Predictive Tracking in Wireless Sensor Network

4. Introduction 2/3 Tracking Interval
As the tracking interval becomes lower↓, in other words ”more frequent↑”, the tracking power consumption is increased ↑
As it increases ↑, the miss probability increases ↑, thereby lowering the tracking quality ↓
Optimal Tracking Interval for Predictive Tracking in Wireless Sensor Network

5. Introduction 3/3 This paper intends to
propose a quantitative analytical model to find such an optimal tracking interval
study the effect of the tracking interval on the miss probability
propose a scheme called Predictive Accuracy-based Tracking Energy Saving (PATES) by exploiting the tradeoff between the accuracy and cost of sensing operation.
Optimal Tracking Interval for Predictive Tracking in Wireless Sensor Network

6. Predictive Tracking Sensor Network Architecture 1/2
Object Tracking Sensor Networks
An object tracking sensor network refers to a wireless sensor network designed to monitor and track the mobile targets in the covered area
Generally, each sensor consists of three functional units
Micro-Controller Unit (MCU)
Sensor component
RF radio communication component
Optimal Tracking Interval for Predictive Tracking in Wireless Sensor Network

7. Predictive Tracking Sensor Network Architecture 2/2
Predictive Accuracy-based Tracking Energy Saving (PATES)
In PATES, three modules must be in use.
Monitoring and tracking
Prediction and reporting
Recovery
The targets are missed, then the recovery module is initiated
ALL NBR recovery
ALL NODE recovery
Optimal Tracking Interval for Predictive Tracking in Wireless Sensor Network

8. Power Optimization and Quantitative Analysis 1/6
quadratic function
s: tracking interval
a, b, and c are the constants
missing probability P(s)
Optimal Tracking Interval for Predictive Tracking in Wireless Sensor Network

9. Power Optimization and Quantitative Analysis 2/6
m: number of the neighbor around the current node.
N: total number of sensors in whole network
Notification: when a neighbor nodes detects the target, it sends notification to the currect node
Optimal Tracking Interval for Predictive Tracking in Wireless Sensor Network

10. Power Optimization and Quantitative Analysis 3/6
T: Entire period
s: Tracking interval
Optimal Tracking Interval for Predictive Tracking in Wireless Sensor Network

11. Power Optimization and Quantitative Analysis 4/6
Optimal Tracking Interval for Predictive Tracking in Wireless Sensor Network

12. Power Optimization and Quantitative Analysis 5/6
a=0.0013, b=0.025, and c=0.062

13. Power Optimization and Quantitative Analysis 6/6
Fig. 2 shows the relationship between the power consumption and tracking interval
Optimal Tracking Interval for Predictive Tracking in Wireless Sensor Network

14. Conclusion
The power consumption with respect to tracking intervals can be minimized with a quadratic miss probability function under a given prediction algorithm
A predictive tracking scheme to optimize the power efficiency with two stages of recovery is proposed
The proposed scheme is demonstrated by the analytical results to be capable of successfully balancing the tradeoff between the prediction accuracy and tracking cost
Optimal Tracking Interval for Predictive Tracking in Wireless Sensor Network

15. Future Work 1/2
Propose an algorithm to automatically model and validate the real-time relationship between miss probability and tracking interval
Consideration three stages recovery or other recovery mechanism (for example, wake up all the two steps’ neighbor nodes around the current sensor in ALL_NBR recovery stage)
Optimal Tracking Interval for Predictive Tracking in Wireless Sensor Network

16. Future Work 2/2 Decrease missing probability
Because Erecovery = 9656mJ >> Esuccess = 42mJ
For example, (always) wake up all the neighbor nodes around the current sensor in next state (Optimal number of wake up the neighbor nodes around the current sensor in next state)
missing probability
Number of wake up the neighbor nodes
around the current sensor in next state
Power consumption
Number of wake up the neighbor nodes
around the current sensor in next state
Optimal Tracking Interval for Predictive Tracking in Wireless Sensor Network

17. Q & A
Optimal Tracking Interval for Predictive Tracking in Wireless Sensor Network