Structures for In-Network Moving Object Tracking inWireless Sensor Networks

1. Structures for In-Network Moving Object Tracking inWireless Sensor Networks Chih-Yu Lin and Yu-Chee Tseng Broadband Wireless Networking Symp. (BroadNet), 2004.

2. Outlines • Introduction • Tree construction algorithms • DAT (Deviation-Avoidance Tree) • Z-DAT (Zone-based DAT) • Simulation • Results • Conclusion

3. Introduction • message-pruning tree • a logical weighted tree such that the total communication cost is as low as possible • weight of edge (a, b) : wT(a,b) • the minimum hop count between a and b in G • event rate • the frequency of objects traveling from one sensor to another in statistics • detected list : DLa = (L0, L1, . . . , Lk) • each node a in T maintains a detected list DLa • L0 is the set of objects currently inside the coverage of sensor a itself

5. Introduction(Cont.) • departure event : dep(o, a, b) • arrival event : arv(o, b, a) • par(u, v) : the root of the minimum subtree in T that includes both u and v. • distT (x, y) : the sum of weights of the edges on the path connecting x and y in T. • distT (F,K) = wT (F, I) + wT (I, J) + wT (J,K) = 3, although the minimum hop count between F and K is 2 in G.

6. cost function of Tby counting the number of events transmitted

7. STUN-Scalable Tracking Using Networked Sensors • Hierarchically record information about the presence of the objects. • Leaves : sensors Root : query point others : communication nodes. • Key action : Message pruning

8. DAB-Drain-And-Balance • Goal : building efficient tracking hierarchies In a bottom-up fashion  from leaves to root. • Draining threshold : event rate thresholds. • Balance : merge the adjacent tree. • Method : high rate subsets of leaves merge first. • Input : a sensor graph G(VG,EG,lG,w) • Out : a hierarchy tree T = (VT,ET,lT) • Draining threshold : H = {h1,h2,…,hk}

9. DAB - Example

11. Figure 4. Four possible message-pruning trees with the corresponding graph shown in Fig. 1(b). Those trees in (a), (c), and (d) are deviation-avoidance trees.

12. message-pruning trees • The tree in (b) is not a deviation avoidance tree (DAT), since distT(E,A) is 3 and distG(E,A) is 2. • The average values of distT (u, par(u, v)) + distT (v, par(u, v)) for each (u, v) ∈ EG are 3.591, 2.864, and 2.227 in (a), (c), and (d) respectively.

13. 2 message-pruning tree structures • DAT (Deviation-Avoidance Tree) • a greedy approach based on a deviation-avoidance idea • Z-DAT (Zone-based DAT) • a grid zone-based approach

14. DAT • To find a tree T that incurs a low C(T), from Eq. 1 and Eq. 2,we would expect • T is deviation-free • u deviates from its shortest path to the sink • each sensor’s parent is its neighbor • Only the tree shown in Fig. 4(d) satisfies this observation • an edge (u, v) with a higher wG(u, v) should be merged into T early and par(u, v) should be either u or v

15. DAT(Cont1.) • edge(u, v) will be included into T only if u and v belong to different subtrees in T. • edge(u, v) will be included into T only if at least one of them is a root of a subtree and the other node is on a shortest path in G from the former node to the sink • A link passing these checking will then be included into T

16. DAT(Cont2.) • As a result of our construction,T is always a subgraph of Gand wT (u, v) = 1 for all (u, v) ∈ ET • Theorem 2 : If G is connected, then the T constructed by the DAT algorithm is connected, deviation-free, and is a shortest path tree rooted at the sink.

18. Z-DAT (Zone-based DAT) • Assumes the sensing field to be a square area and takes two input parameters α and δ • T is constructed in an iterative manner. • Partition the sensing field horizontally and vertically into α strips. • for each boundary between strips, it is allowed to move up and down no more than a distance of δ from its original position • Totally partitioned into α × α square-like zones, α2 subtrees.

19. Figure 9. An example of ZDA algorithm with α = 4. (a) In the first iteration, we divide the field into α×α zones and adjust the boundary according to δ. (b) In the second iteration, we divide α by 2 until a single tree is obtained.

21. Simulation • deploy 4096 sensors • in a 64 × 64 grid network, one in each grid. • a 256 × 256 grid network, 4096 sensors are randomly deployed. • the sensing field to be a square of size r × r • level-1 regions : divides the sensing field into four sub-regions • probability p1 : leave its current leavel-1 region • probability 1 − p1 : stay in its current leavel-1 region

22. Simulation(Cont1.) • level-2 regions : each level-1 region is recursively divided into four smaller sub-regions • probability p2 : leave its current leavel-2 region • probability 1 − p2 : stay in its current leavel-2 region • Level-i region : an exponential probability • C is a positive constant • d is the total number of levels • a higher C will exhibit higher locality in movement

23. Simulation(Cont2.) • consider two performance metrics • updating cost C(T) • querying cost Q(T) : the cost spent on transmitting querying requests and replies when objects’ locations are inquired from the sink • only count the numbers of messages without considering message sizes

24. Simulation(Cont3.) • Compare 2 scheme • non-message-pruning tree scheme • always sends all location updates to the sink through the shortest path • the sink always has the most up-to-date information • has no querying cost, but may incur high updating cost

25. Simulation(Cont4.) • DAB • assumes that all sensors are leaf nodes of the message-pruning tree • there is a logical tree to connect these leaf nodes • assume that whenever a new subtree is formed by DAB, the sensor that is closest to the sink will be the root of the subtree

26. Results • compare the updating costs C(T) • generate 64 objects and adopt the 5-step DAB tree construction • Z-DAT, we set α = 20 and δ = 1 grid. • Fig. 10(a) (b) (c) (d) • A larger C, i.e. a higher moving locality, leads to lower updating cost. • The non-message-pruning scheme has the highest cost because each movement will incur a lot of update messages.

28. Results(Cont1.) • compare the querying cost Q(T) • Fig. 11 • The querying cost generally increases linearly with the querying rate. • There is no querying cost for the non-message-pruning scheme. • The querying costs for DAT and Z-DAT schemes are always the same • because querying messages are always transmitted along shortest paths • Not using shortest paths, DAB has higher Q(T) that depends on the tree structure in DAB.

30. Results(Cont2.) • combined updating and querying costs • Fig. 12 • as the querying rate becomes higher, using message-pruning tree will gradually lose its advantage • because non-message-pruning scheme has no querying cost • as C becomes smaller, objects will move more frequently, thus leading to more saving in using DAB/DAT/Z-DAT

31. Figure 12. Comparison of combined (updating plus querying) costs in a 64 × 64 grid: (a) sink at a corner and C = 1.0, (b) sink at the center and C = 1.0, (c) sink at a corner and C = 0.1, and (d) sink at the center and C = 0.1.

32. Conclusion • Present 2 message-pruning structures for moving object tracking in a sensor network.