1. Towards Optimal Sleep Scheduling in Sensor Networks for Rare-Event Detection Qing Cao, Tarek Abdelzaher, Tian He, John Stankovic Department of Computer Science, University of Virginia, Charlottesville, VA 229904 IPSN 2005 Xin Hong Lin

2. Outlines Introduction Sleep schedule optimization Detection Delay Optimization Efficient Implementation Algorithm Analysis End-to-End Delay Optimization Evaluation Conclusion and future work

3. Introduction Energy Saving in Sensor Network Full coverage Partial coverage,[1],[2].  Random sleep schedule  Synchronized sleep schedule

4. Introduction Near-optimal deterministically rotating sensory coverage Partial coverage Any point eventually sensed within a finite delay bound Localized distributed protocol To minimize average detection delay  Average detection delay  Average time elapsed between event occurrence at a point and its detection by a nearby sensor

5. Introduction Minimizing packet delivery latency to a common base-station Partial synchronization Optimized environment Rare events  Mission lifetime must be appropriately rate

6. Introduction Goal Minimizing the surveillance delay

7. General Framework- Level 1

10. General Framework- Level 2

11. T d : sleep time T on : wake up time Average detection delay: (T d +T on )/2 0 T d +T on

12. General Framework- Level 2 α: average number of primary nodes a point is sensed on average not more than once every T d /αtime units average detection delay no lower than T d /2α

13. Sleep schedule optimization Detection Delay Optimization End-to-End Delay Optimization

14. Stage 3 : The wakeup time in finalized. Stage 2 : Each node re-calculates its wakeup time, exactly once in each iteration Tc, based on the most recently updated neighbor schedules. If one node does not receive any updates within an iteration and has not changed its own wakeup time, processed to stage 3. Stage 1 : Each node chooses a wakeup time regardless of other nodes Receive neighbor beacon update neighbor wakeup time Detection Delay Optimization 1 2 3 1 2 3

15. Derivation of an Optimal Wakeup Time

16. Average detection delay (D) F i (t): an expression for average detection delay within the sensory range of node i. Average detection delay for event arrivals in the interval x i : x i /2 Each multiplied by the probability of arriving within that respective interval: x i /(T d +T on ) D equals the sum of, 1<=j<=n

17. optimality curve for point A

18. Example: Computing the Optimality Curve t 2 -1.85t+1.0225, 0.6<=t<1 t 2 -1.85t+0.4225, 0<=t<0.25 t 2 +0.15t+0.1725, 0.25<=t<0.6

19. Efficient Implementation

20. Result No update Two nodes found that they have each adjusted their sleeping times independently No ACK

21. Algorithm Analysis Cost Analysis Storage requirement at most M+1 entries are needed in the global result table. (M: neighbors) Computational requirement Overall cost is proportional to the product of grid resolution and the number of neighbors, M. On the Convergence of the Algorithm terminate after a finite number of steps.

22. End-to-End Delay Optimization At low duty cycle, the fraction of nodes that are awake at any given time do not necessarily form a connected network. First-level scheduling, the resulting primary nodes have many neighbors within their communication range Second-level scheduling, not all neighbors will be awake at the same time. It is desired to synchronize duty cycles of nodes into a streamlined sequence to pipe the data efficiently.

23. Streamlined wakeup

24. End-to-End Delay Optimization Step1: hop count Pipe flag run stage 1 Step2: run stage2 Step3: established streamlined data pipe Setp4: start duty cycle. HC=1 HC=2 HC=3 HC=4 HC=5

25. Evaluation Simulation Setup The area: 100m*100m Sensing range: 10m Nodes: 300 Nodes for each scenarios: 76 Scenarios: 10

26. Evaluation-Detection Delay Optimization

32. Conclusion and future work Conclusion Minimizing surveillance delay subject Detection delay Delivery delay A flexible framework in which application designers can trade-off energy versus latency of event detection Future work Toward more general model Optimizing detection delay for moving target

33. End Thank you!