1. Target Tracking

2. Target tracking problem
Problem statement
A varying number of targets
Arise at random in space and time
Move with continuous motions
Persist for a random time and possibly disappear
Positions of targets are sampled at random intervals
Measurements are noisy and
Detection probability < 1.0
False alarms
Goal: detect, alert, and track for each target

3. Frisbee model

4. Issues in Frisbee model
Power savings with wake-up
Can be waked up by neighbors
Be able to form a “wakeup wavefront” that precedes the target
Localized algorithm for defining the Frisbee boundary
Each node autonomously decide if it is in the current Frisbee
Adaptive fidelity

5. Sensing model Sensor detection model
Object always detected in rage R-e
Object never detected out of range R+e
Object possibly detected in range [R-e, R+e]
e≈ 0.1R e R
Comments:
Binary detection model is most simple and reliable.
Location resolution is the sensing range for one sensor, however, by combining multiple sensors, resolution is improved significantly.
The sensing range don’t have to be circular.

6. Sensing model
We express the general sensing model S at an arbitrary point p for a sensor s as:
where d(s,p) is the Euclidean distance between the sensor s and the point p, and positive constants  and K are sensor technology dependent parameters

7. A Cooperative tracking algorithm
When the object enters the region where multiple sensors can detect it, its position is within the intersection of the overlapping sensing ranges.
Algorithm:
Each node records the duration for which the object is in its range.
Neighboring nodes exchange these times and their locations.
For each point of time, the object’s estimated position is computed as the weighted average of the detecting nodes’ locations.
A line fitting algorithm is run on the resulting set of points.

8. Weight assignments
Sensors that are closer to the path of the target will stay in sensor range for a longer duration.

9. Weight assignments R: sensor radius v: estimated speed
Equal weight
Proportional weight (r)
Logarithmic weight
R: sensor radius
v: estimated speed
ti: detection duration
f: sampling frequency

10. Tracking methods One sensor at a time Minimal sensor (binary) model
Each time, only the best sensor conducts tracking
Minimal sensor (binary) model
1: target in range
0: target out of range
Hierarchical method, clusters
Acoustic sensors (delay-based collaboration)
More than 3 sensors track a target jointly
Tree-based group collaboration

11. IDSQ Information-driven sensor query Procedures
Each sensor performs detection by comparing measurement with a threshold (aka, likelihood ratio test)
Detecting nodes elect a leader
The leader suppresses the other nodes to prevent multiple tracks for the same target
The leader initializes the belief state and reports the sensory data to the sink

12. IDSQ
DETECTION
message

13. IDSQ
Timestamp
Likelihood ratio

14. IDSQ
SUPPRESSION
message

15. IDSQ
HANDOFF
message
New leader reports sensory data to sink

16. Acoustic target tracking
Context
Delay based sound source locating algorithm, requires large number of redundant sensors for accuracy
Tiny wireless sensors to real-world acoustic tracking applications
Tracking only impulsive acoustic signals, such as foot steps, sniper shots, etc. No concept of tracking motion

17. Acoustic target tracking
Two subsystems
Acoustic target tracking subsystem
Communication subsystem

18. System Overview Acoustic target tracking subsystem Sensor (mica motes)
Sensors belong to clusters with singular cluster head.
Cluster head knows the locations of its slave sensors. Raw data gathered from sensors are processed in cluster head to generate localization results
Cluster Head (mono-board computer)

19. Acoustic target tracking subsystem
Reference-broadcast synchronization (RBS)
Physical layer broadcast

20. RBS Reference broadcasts do not have an explicit timestamp
Receivers use reference broadcast’s arrival time as a point of reference for comparing nodes’ clocks
Receivers synchronize with one another using the message’s timestamp (which is different from one receiver to another)

21. RBS illustration
Transmitter A broadcasts a reference packet to two receivers (e.g., 1 and 2)
Each receiver records the time that the reference was received, according to its local clock
The receivers (1 and 2) exchange their observations A 1 3 2 4

22. Cross Correlation (to find out delays)
Locate sound source location
Detect interesting sound
Broadcast sound signature
Cluster Head
Cross-correlation to detect local arrival time
Report local arrival time
Slave Sensor

23. Final position fixed
Sensor (mica motes)
Sensors belong to clusters with singular cluster head.
Cluster head knows the locations of its slave sensors. Raw data gathered from sensors are processed in cluster head to generate localization results
Cluster Head (mono-board computer)

24. Communication subsystem
Quality-driven redundancy suppression and contention resolution (QDR)
Overlapping of clusters’ monitoring areas (redundant areas)
CSMA MAC
interval: time unit
Q: 0 is the highest quality

25. Scenario Sensor Router Cluster Head Sink/Pursuer Sink/ Pursuer

26. Multi-parent sink tree routing
Communication Subsystem: route back the reports generated by cluster heads to sink
Multi-parent sink tree routing
Sink
cluster covered area
cluster head
router (mica motes)

27. Dynamic convoy tree-based collaboration (DCTC)
Hierarchical (tree)
Refer to relevant slides for details

28. references
K. Mechitov, S. Sundresh, Y. Kwon, G. Agha, “Cooperative Tracking with Binary-Detection Sensor Networks,” Technical Report UIUCDCS-R , Computer Science, UIUC, Sept. 2003
Juan Liu, Jie Liu, James Reich, Patrick Cheung, Feng Zhao: Distributed Group Management for Track Initiation and Maintenance in Target Localization Applications. IPSN 2003:
Qixin Wang, Wei-Peng Chen, Rong Zheng, Kihwal Lee, and Lui Sha, Acoustic Target Tracking Using Wireless Sensor Devices, Proc. of the 2nd Workshop on Information Processing in Sensor Networks (IPSN03), April 2003
Fine-Grained Network Time Synchronization using Reference Broadcasts, Jeremy Elson, Lewis Girod and Deborah Estrin, In Proceedings of the Fifth Symposium on Operating Systems Design and Implementation (OSDI 2002)

29. Sensor coverage and sleeping

30. Assumption
Sensing effectiveness diminishes as distance increases (monotonic)
E.g.,
Homogeneous sensor nodes
Non-directional sensing technology
Centralized computation model

31. How well can the field be observed ?
Coverage Formulation
How well can the field be observed ?
Worst Case Coverage: Maximal Breach Path
Best Case Coverage: Maximal Support Path
The “paths” are generally not unique. They quantify the best and worst case observability (coverage) in the sensor field.

32. Maximal Breach Path (PB)
Given: Field A instrumented with sensors; areas I and F.
Problem: Identify PB, the maximal breach path in S, starting in I and ending in F.
PB is defined as a path with the property that for any point p on the path PB, the distance from p to the closest sensor is maximized.

33. Voronoi diagram
The plane is partitioned by assigning every point in the plane to the nearest site

34. Voronoi diagram
A Voronoi Line consists of points which are equidistant to two sites in the plane.

35. Enabling Step: Voronoi Diagram
By construction, each line-segment maximizes distance from the nearest point (sensor).
Consequence: Path of Maximal Breach of Surveillance in the sensor field lies on the Voronoi diagram lines.

36. Graph-Theoretic Formulation
Given: Voronoi diagram D with vertex set V and line segment set L and sensors S
Construct graph G(N,E):
Each vertex viV corresponds to a node ni N
Each line segment li L corresponds to an edge ei E
Each edge eiE, Weight(ei) = Distance of li from closest sensor sk S
Formulation: Is there a path from I to F which uses no edge of weight less than K?

37. Finding Maximal Breach Path
Algorithm
Generate Voronoi Diagram
Apply Graph-Theoretic Abstraction
Search for PB
Check existence of path I --> F using binary search and BFS

38. Delaunay triangulation
The Delaunay triangulation of a point set is a collection of edges satisfying an "empty circle" property
For each edge we can find a circle containing the edge's endpoints but not containing any other points

39. Delaunay Triangulation
The Delaunay triangulation is a triangulation which is equivalent to the nerve of the cells in a Voronoi diagram

40. Maximal Support Path Given: Delaunay Triangulation
of the sensor nodes
Construct graph G(N,E):
The graph is dual to the Voronoi graph previously described
Formulation: what is the path from which the agent can best be observed while moving from I to F? (The path is embedded in the Delaunay graph of the sensors)
Solution: Similar to the max breach algorithm, use BFS and Binary Search to find the shortest path on the Delaunay graph.

41. PEAS: probing environment and adaptive sleeping

42. Basic Approach and assumption
Exploit the redundancy
Keep a necessary subset of nodes working; turn off others into sleeping
Sleeping nodes replace failed ones as needed
Assume nodes can control the transmitting power to reach a given radius
Variable tx power available in Berkeley motes

43. Probing Environment working No REPLY is heard
Each node sleeps for a random time ts
ts follows an exponential distribution f(ts) = e- ts
The PROBE message is within a radius Rp (given by applications)
Rp < maximum tx range Rt
Working nodes send back REPLY when hearing the PROBE (also within radius Rp)
working
No REPLY is heard
Broadcast a PROBE within Rp
(probing range)
sleeping
probing
Upon hearing a REPLY
(, probing rate, is adjusted)

45. Design rationale
Adjacent working nodes keep appropriate distances (at least Rp)
Redundancy in sensing and communicating function at appropriate levels
Probing avoid per-neighbor state about topology information maintenance
Randomized sleeping times
Spread over time to reduce “gap”, avoid prediction of a working node’s active time

46. Adaptive Sleeping
Goal: keep the aggregate PROBE rate on a desired level _d (specified by the application)
independent from node densities at different locations, over time
Probing rate decides how quick a dead node can be replaced
Unnecessary overhead if too frequent
Long gaps if too slow
Basic idea
working node measures the aggregate PROBE rate
piggybacks the info in REPLY
probing nodes adjust their rates accordingly.

47. How it works … Time K wakeups Example:
A working node keeps
Counter C
Last measurement time t0
Increase the counter each time a PROBE is heard
Calculate aggregate PROBE rate _a and includes it in REPLYs
Each probing neighbor adjusts its rate accordingly Ts …
Time t0 t
K wakeups
Measure aggregate rate:
_a = K / (t - t0)
Each probing one adjusts:
_new =  (_d / _a )
Example:
An application wants _d = 6 times/min.
Working node A has 5 sleeping neighbors, each probes at  =6.
Node A measures aggregate _a = 30.
Each sleeping one adjusts to _new = 6(6/30)=1.2, thus new _a = 6

48. Maximum distance between working neighbors
Working node A puts nodes in cell 4, 5, 6 into sleep
To put node C in cell 2 into sleep, node B’s maximum distance to A is (1+5)Rp
Otherwise, C will be working
When there’s at least one node in each cell, distance between working neighbors is bounded
Theorem 3.1: when Rt > (1+5)Rp, and conditions in Blough’s Theorem 2 hold (mobicom ’02), working nodes are connected asymptotically. Rp 3 B C 4 5 A C 2 6 1

49. references
Seapahn Meguerdichian, Farinaz Koushanfar, Miodrag Potkonjak, Mani Srivastava. "Coverage Problems in Wireless Ad-Hoc Sensor Networks." IEEE Infocom 2001, Vol. 3, pp , April 2001.
Seapahn Meguerdichian, Farinaz Koushanfar, Gang Qu, Miodrag Potkonjak. "Exposure in Wireless Ad Hoc Sensor Networks." Procs. of 7th Annual International Conference on Mobile Computing and Networking, pp , July 2001
Fan Ye, Gary Zhong, Jesse Cheng, Songwu Lu, Lixia Zhang, "PEAS: A Robust Energy Conserving Protocol for Long-lived Sensor Networks", in ICDCS'03, 2003