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

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

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

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

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

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

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

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

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

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

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