1. Mobility Improves Coverage of Sensor Networks Benyuan Liu*, Peter Brass, Olivier Dousse, Philippe Nain, Don Towsley * Department of Computer Science University of Massachusetts - Lowell

2. Outline  background and motivation  mobility improves coverage  summary and future work

3. What is coverage ? r coverage: quality of surveillance of sensor network m how well sensors cover a region of interest ? m how effective sensor network detect intruders ? m many different measures: area coverage, barrier coverage, detection coverage, etc r important for surveillance sensor net applications m battlefield, infrastructure security

4. Mobile sensor networks  coverage of stationary sensor network intensively studied  sensors can be mobile: mounted on robots or move with environments Q: How does sensor mobility affect coverage?

5. Previous work [Howard 02, Zou04, Wang 04]  sensors move to reach stationary configuration with better area coverage  several approaches proposed, different in how to compute desired locations for sensors (e.g., potential field, virtual force, etc)

6. Our work  different perspective: coverage resulting from continuous movement of sensors 1. mobility increases covered area  stationary sensors: covered area doesn’t change over time  mobile sensors: uncovered area may be covered later, more area covered over time we are interested in area coverage  area covered at specific time instant t  area covered over time interval [0, t)  fraction of time a location is covered

7. Our work 2. mobility improves intrusion detection  stationary sensors: intruder won’t be detected if not move or moves along uncovered path  mobile sensors: may be detected by moving sensors we are interested in detection time  time before an intruder is first detected  measure how quickly sensors detect intruders  consider stationary and mobile intruders

8. Our work 3. how should sensors and intruder move?  intruder moves to maximize its detection time  sensors minimize the maximum detection time we are interested in optimal mobility strategies  for both sensors and intruders  game theoretic approach

9. Network model  initial configuration  sensors are deployed uniformly at random  sensor density: sensing range: r  mobility model  each sensor chooses a random direction  [0, 2  ) according to distribution  speed v s  [0, v s max ] according to simple model to obtain insight

10. Area coverage  area coverage at any given time instant unchanged t  uncovered region will be covered, more area will be covered for a time interval [0,t)

11. Tradeoff: covered area and covered time  location alternates between covered and uncovered  appropriate for delay-tolerant applications fraction of time a point is covered  uncovered time: covered time

12. Detection time: stationary intruder  intruder can be detected by moving sensors  detection time: time before first being detected, X  divide sensors into different classes according to direction  time takes to be first hit (detected) by a class i sensor: Vs

13. Detection time: stationary intruder  to guarantee expected detection time smaller than T 0 can tradeoff sensor density with speed  detection time: smallest hit times among all classes  result:

14. Mobile intruder: detection time  convert to reference system where intruder is stationary  detection time:

15. Mobile intruder: optimal strategy  target maximizes its lifetime  sensors minimize the maximum detection time a minimax optimization problem

16. Optimal strategy: special cases  sensors: move in same direction  intruder: moves in same direction with same speed as sensor  sensors: choose direction uniformly in [0, 2  )  intruder: stay stationary  intuition: if intruder moves, will hit oncoming sensors sooner

17. Optimal strategy: solution  sensors choose direction uniformly  target stay stationary  intuition: if not uniform, intruder will move in direction of highest probability density, resulting in longer detection time ?

18. Summary and future work  define coverage resulting from sensor mobility  derive analytical results to provide insight  future work:  more general mobility and detection model  collaboration among sensors

19. Thank you!