1. Jason A. Fuemmeler, and Venugopal V. Veeravalli Edwin Lei
Smart Sleeping Policies for Energy Efficient Tracking in Sensor Networks
Jason A. Fuemmeler, and Venugopal V. Veeravalli
Edwin Lei

2. Problem Description
Want to track a randomly moving object in a network of wireless sensors
To conserve energy, we can put sensors into sleep mode
Tradeoff between energy cost and tracking errors

3. Problem Assumptions Each sensor has a limited range
Network is sufficiently dense
Sensors in sleep mode cannot be awaken prematurely
Object described by a Markov chain whose statistics are assumed to be known a priori
Once the object leaves the network, it will not return
Central controller keeps track of the state of the network and assigns sleep times

4. Setup
At each time step
If the sensor is awake and the object is within its range, the sensor detects the object and sends this information to the central unit
The sensor receives a new sleep time (may be 0) that is decremented by one at each time step

5. General Idea
Use information about the state of system to set sleep time of each sensor

6. Time Information
Let rk,l denote the residual sleep time of sensor l at time k
Let uk,l denote the sleep time input supplied to sensor l at time k
I is an indicator function
Residual sleep time evolution is

7. Space Information Let bk denote the location of the object at time k
Let T denote the terminal state
Then the actual observable location is

8. Partially Observable Markov Decision Process
The total information available is therefore
Let pk denote the probability distribution of bk given Ik, then the state of the system is

9. Optimal and Suboptimal Solutions
Cost function
Optimization problem!
Optimal solution is intractable even for relatively small networks
Make unrealistic assumptions to simplify problem
Only made to generate a sleeping policy

10. Suboptimal Solutions First Cost Reduction (FCR) QMDP
Assumes we will have no future observations
QMDP
Assumes the location of the object will be known
Both methods separate problem into a sub-problem for each sensor so the global solution is just the local solutions applied together

11. Results and Conclusion
Suboptimal solutions still perform better than a random duty cycle
Detecting multiple objects and solving the problem without assuming the statistics of the object’s movement are two improvements to the current algorithm