1 Dynamic Sleeping Scheduling for Real-time Wireless Sensor Networks Department of EECS University of Tennessee, Knoxville Xiaodong Wang, Yanjun Yao

2 Introduction Low energy consumption  Lower energy consumption -> longer life time Equipment usually powered by batteries  Approaches to save power: Transmission power adjustment, supported by hardware, e.g., power level 1 – 31 on Tmote Invent Periodic sleeping Etc. Real-time requirement: to constrain the end-to-end delay of information relay  A lot of WSN applications require real time service quality: Wood fire monitoring Battle field application Border Intruder Monitoring Alarm System

3 Periodic Sleeping Periodic sleeping  Sender knows receiver’s sleeping scheduling  Sender wakes up when receiver wakes up Tradeoff between power consumption and real-time  Wake up more often -> less delay, more power :Sleeping Delay :Cycle Time :Retransmission Count Periodic sleeping incurs delay!!

4 Goals and Challenges Goal: develop a dynamic sleeping scheduling scheme  Provide end-to-end real-time guarantee for each data flow  Take advantage of periodic sleeping to save power. Challenges:  Model the end-to-end delay vs. sleeping scheduling on each node. Each flow constructed by several nodes. Approach to coordinate the nodes  The scheme for the dynamic change of sleeping scheduling. Centralized or distributed

5 Preliminary Plan Break end-to-end deadline to sub-deadline on each node  Decouple the coordination of sleeping scheduling on all nodes in the same flow.  Simplify the delay vs. sleeping schedule model establishment. Distributed approach to adjust sleeping scheduling by each node.  Adjust sleeping scheduling for each node to meet sub-deadline.  Using feed back control theory to adjust the sleeping scheduling Plan schedule:  By mid-term: Finish the model of delay vs. sleeping schedule. Implement feed back control on single hop  By final: Finish the coordination of the whole network by controlling all the nodes Using NS2 to do experiment

6 Q&A