1. Tracking Murat Demirbas SUNY Buffalo

2. A Pursuer-Evader Game for Sensor Networks Murat Demirbas Anish Arora Mohamed Gouda

3. Pursuer-evader problem Evader is omniscient; Strategy of evader is unknown Pursuer can only see state of nearest node; Pursuer moves faster than evader ( ratio = f ) Required is to design a program for nodes and pursuer so that pursuer can catch evader (despite the occurrence of faults)

4. Model  Connected graph of sensor nodes  Transient faults; connectivity still maintained  Maximal parallelism in node actions

5. Two approaches Evader-centric program  move is costly, find is for free  sensor nodes communicate periodically with neighbors  stabilizes and tracks faster Pursuer-centric program  find is costly, move is for free  sensor nodes communicate with neighbors only upon request  minimizes number of messages and energy efficient Hybrid program  find & move are both tunable

6. Outline Evader-centric program Pursuer-centric program Hybrid program Extensions

7. Evader-centric program Nodes collectively maintain a tracking tree  ts.j : latest timestamp that j knows about detection of evader  p.j : parent of j in tree  d.j : distance of j from evader  root is the node where the evader resides {Evader resides at j} ---> p.j := j; ts.j :=clock.j; d.j :=0  every node sets its parent to be the nbr with maximum ts ts.k > ts.j ---> p.j :=k ; ts.j := ts.(p.j); d.j:= d.(p.j)+1 Find is for free: pursuer follows the tracking tree to its root

8. Evader-centric program (cont.) Tracking tree is dynamically rooted at the evader Parent of a node is closer to the evader

9. Evader-centric program (cont.) Tracking tree is dynamically rooted at the evader Parent of a node is closer to the evader

10. Evader-centric program (cont.) Tracking tree is dynamically rooted at the evader Parent of a node is closer to the evader

11. Evader-centric program (proof of stabilization) Tracking tree is rooted at the evader within D steps  soft-state stabilization The distance between pursuer and evader does not increase once the constructed tree includes the pursuer Starting from an arbitrary state pursuer catches evader in at most D+ 2D * f/(1-f) steps

12. Pursuer-centric program Move is for free: ts.j is maintained locally {evader detected at j} ---> ts.j := clock.j Nodes communicate with nbrs only at the request of pursuer; pursuer is directed to nbring node with highest recorded time {pursuer detected at j} ---> next.j :in {k | ts.k= max ts.nbr.j }; ts.j :=0 Pursuer action is to move to next.j  pursuer does a random walk until it reaches a node that evader has visited

13. Pursuer-centric program (cont.) If the pursuer reaches a node j with ts.j>0, pursuer catches evader within N*f/(1-f) steps

14. Pursuer-centric program (stabilization) Since ts.j is reset to 0 when pursuer visits j, bad values disappear From random walk result, pursuer reaches a node j, ts.j >0, within O( N 2 * log N ) steps Then, pursuer catches evader within N*f/(1-f) steps

15. Hybrid program Tracking tree is bounded to a depth R Pursuer-centric program is executed at nodes outside tracking tree

16. Hybrid program (stabilization) Since tracking tree is bounded to a depth R, soft-state stabilization is not available for nodes outside tree  Cycles are detected and removed by counting to R Starting from an arbitrary state pursuer finds the tracking tree in at most O( (N-n) 2 * log (N-n) ) steps  n : number of nodes in tracking tree Then, pursuer catches evader within R*f/(1-f) steps Hybrid program is tunable by assigning R appropriately

17. Outline Evader-centric program Pursuer-centric program Hybrid program Extensions

18. Asynchronous model : Readily available Faster convergence : Extended hybrid program Better scalability : Hierarchical tracking program

19. Asynchronous model Evader-centric program  instead of ts.j maintain val.j  val.j denotes the number of detections of the evader that j is aware of  when j detects evader it increments val.j  tracking tree is rooted at evader in 2D steps  we have implemented the asynchronous version for June 2002 DARPA/NEST demo Pursuer-centric program  readily available

20. Extended hybrid program Pursuer-centric program can be modified query a radius R p (instead of 1) s.t. R+ R p = D

21. Self-Stabilizing Hierarchical Tracking Service for Sensor Networks Murat Demirbas, Anish Arora, Tina Nolte, Nancy Lynch

22. STALK: Scalable tracking Maintain tracking structure  over fewer number of nodes  with accuracy inversely proportional to the distance from evader  communication cost of msg j,k = distance(j,k), delay= δ * distance(j,k)  nearby nodes (cheap to update) have recent & accurate info  distant nodes (expensive to update) have stale & rough info Local operations : — Cost of move proportional to the distance the evader moves — Cost of find proportional to the distance from the evader — Cost of healing proportional to the size of the initial perturbation To this end we employ a hierarchical partitioning of the network M. Demirbas, A. Arora, T. Nolte, and N. Lynch. A Hierarchy-based Fault-local Stabilizing Algorithm for Tracking in Sensor Networks. OPODIS, 2004.

23. Hierarchical clustering R: dilation factor of clustering to determine size at higher levels Radius at level L is ≈ R L M. Demirbas, A. Arora, V. Mittal, and V. Kulathumani. Design and Analysis of a Fast Local Clustering Service for Wireless Sensor Networks. BroadNets 2004.

24. evader Hierarchical tracking path evader Grow action for building a tracking path Shrink action for cleaning unrooted paths

25. Local find Searching phase:  A find operation at j queries j ’ s neighbors & j ’ s clusterhead at increasingly higher levels to find the tracking path Tracing phase:  Once path is found, operation follows the path to its root

26. evaderfind Examples of find A find for an evader d away incurs O(d) work/time cost  guaranteed to hit the tracking path at level log R d of hierarchy

27. A problem for move evader dithering between cluster boundaries may lead to nonlocal updates evader

28. Local move Lateral links to avoid nonlocal updates When evader moves to new location j:  a new path is started from j  the new path checks neighbors at each level to see whether insertion of a lateral link is possible Restricts lateral links to 1 per level in order not to deteriorate the tracking path  otherwise find would not be local since it could not hit the path at level log R d for an evader d away

29. evader Examples of move A move to distance d away incurs O(d*log R d) work/time cost  a level L pointer is updated at every  i L-1 R i distance; level L is updated d/ i L-1 R i times  update at L incurs O(R L ) cost

30. Local healing Local healing means work/time for recovery proportional to perturbation size & not the network size In the presence of faults a grow can be mistakenly initiated; shrink should contain grow a shrink can be mistakenly initiated; grow should contain shrink

31. Fault-containment Give more priority to the action that has more recent info regarding the validity of the path A shrink or grow action is delayed for longer periods as the level of the node executing the action gets higher j.grow-timer = g * R lvl(j) j.shrink-timer = s * R lvl(j) Catching occurs within a constant number of levels  For g=5δ, s=11δ, b=11δR grow catches shrink in 2 levels: log R ((bR–b+sR 2 –gR-δR)/(sR-gR-3δ)) shrink catches grow in 4 levels: log R ((bR–b+sR+gR-2s+3δR)/(gR-s-δ))

32. Seamless tracking Fault-containment does not affect responsiveness  Total delaying up to l is a constant factor of communication delay up to l, δR l Concurrent move operations  move occurs before tracking path is updated  a complete path is no longer possible; discontinuity in the path  give a bound on evader speed to maintain a reachable path Concurrent find operations  when find reaches a dead-end, search phase is re-executed  reachability condition guarantees that new path is nearby Cost of find & move unaffected find