Coverage and Scheduling in Wireless Sensor Networks

1. Coverage and Scheduling in Wireless Sensor Networks Yong Hwan Kim cherish@kut.ac.kr Korea University of Technology and EducationLaboratory of Intelligent Networks http://link.kut.ac.kr

2. Scheduling • Basic Policy • Sensor should be active or sleep? • Scheduling (related to the coverage issue) • An interval: is active • Another interval: is active • So, the battery power can be saved

3. Scheduling • Scheduling Type • Centralized • All sensors send “their location information” to the centralized sink node. • The sink node performs “its scheduling algorithm” for the sensors • The sink node broadcasts “the scheduling information” to all sensor nodes • Each sensor becomes active or sleep according to the information • Distributed • Each sensor self-determies its scheduling time • # of messages reduced

4. Energy-efficient target coverage in wireless sensor networks

5. Centralized Scheduling • The contributions of this paper • 1. Introduce a new model of maximizing the network lifetime of the target coverage problem • Define the MSC(maximum set covers) problem • Prove that MSC is NP-complete • 2. Design two target coverage heuristics for solving the MSC • Linear programming • Greedy techniques • 3. Analyze the performance of two approach through simulation

6. Centralized Scheduling • MSC (Maximum Set Covers) • Given a collection C of subsets of a finite set R, find a family of set covers with time weights in [0,1] such that to maximize and for each subset s in C, s appears in with a total weight of at most 1, where 1 is the life time of each sensor [Definition] Maximum Set Covers Problem

7. Centralized Scheduling • MSC (Maximum Set Covers) • For Example, • By MSCScheduling • Network Lifetime: 2.5 t1 s1 s3 s4 t2 t3 s2 active time=0.5 active time=0.5 active time=1 active time=0.5

8. Centralized Scheduling • Integer Programming Formulation of the MSC Problem

9. Centralized Scheduling • Integer Programming Formulation of the MSC Problem

10. Centralized Scheduling • Integer Programming  Linear Programming

11. Centralized Scheduling • Greedy algorithm of the MSC Problem Critical target iscovered by minimum number of sensors • Cover a larger number of uncover targets • Have more residual energy available

12. Centralized Scheduling • MSC (Maximum Set Covers) • Existing Algorithms • Linear Programming • Greedy (Complexity: ) i: # of setcovers, m: # of targets, n: # of sensors

13. Power-Saving Scheduling for Multiple-Target Coverage in Wireless Sensor Networks

14. Centralized Scheduling • The problem of previous work • It has been assumed that sensors consume the same amount energy when transmitting the collected data, regardless of how many targets they observed. (a)case 1 (b)case 2

15. Centralized Scheduling • The contributions of this paper • 1. Consider the transmitting energy according to the number of targets covered by the sensor • 2. Removes the redundancy of overlapped targets

16. Centralized Scheduling • Overlapped target examples

17. Centralized Scheduling • Integer Programming Formulation of the MTC Problem • The first constraint indicates the limited energy of sensors • The second constraint guarantees that all the targets must be covered by at least one sensor in each joint set for

18. Centralized Scheduling • RSSA(Responsible Sensor Selecting Algorithm) of the MTC Problem

19. Centralized Scheduling • Condition of sensor selection • 1. Sensor does not cover the critical target • 2. Sensor monitors a smaller number of targets t1 s1 s1 s3 t1 s2 s4 t2 t2 s3 t3 s2 t3 s4

20. Centralized Scheduling • Simulation result • Conventional scheme vs. Proposed scheme

21. Minimum Coverage Breach and Maximum Network Lifetime in WSN

22. Centralized Scheduling • Additional consideration • Limited bandwidth -> Coverage breach • If Bandwidth is less than the number of sensors in a sensor cover • Bandwidth : the total number of time slots/channels • Coverage breach : the state that targets are not covered • Example ( bandwidth = 2 ) • Set1 = {s1, s2, s3}, |Set1| = 3 • Set2 = {s4}, |Set2| = 1 • Coverage breach occurs in Set1

23. Centralized Scheduling • The contributions of this paper • 1. Introduce coverage problems under bandwidth constraints • Define the MCBB(Minimum Coverage Breach under Bandwidth constraints) problem • Define the MNLB(Maximum Network Lifetime under Bandwidth constraints) problem • Prove that MCBB and MNLB are NP-hard • 2. Design two target coverage heuristics for solving the problems • Linear programming • Greedy techniques • 3. Analyze the performance of two approach through simulation

24. Centralized Scheduling • MCBB(Minimum Coverage Breach under Bandwidth constraints) [Definition] Problem MCBB

25. Centralized Scheduling • MNLB(Maximum Network Lifetime under Bandwidth constraints) [Definition] Problem MNLB

26. Centralized Scheduling • Integer Programming Formulation of the MSC Problem Energy constraint Coverage constraint Bandwidth constraint Lifetime constraint

27. Centralized Scheduling • Integer Programming Formulation of the MSC Problem

28. Centralized Scheduling • Integer Programming  Linear Programming

29. Centralized Scheduling • GREEDY-MSC of the MCBB Problem

30. Centralized Scheduling • Binary Search Algorithm for MNLB

31. Distributed Scheduling

32. Distributed Scheduling • 1-Coverage Preserving Scheduling (1-CP) • For Example Init Phase: 1) Each sensor exchange its location and Ref. value 2) Each sensor get its schedule (active) time The set of intersection points within ‘s area Trnd=20 The set of sensorscovering the target p Ref1=2, Ref2=9, Ref3=11

33. Distributed Scheduling • 1-Coverage Preserving Scheduling (1-CP) 2 16.5 5.5 9 11

34. Appendix

35. Virtual GRID • L is the set of all grid-point of a virtual grid on the unit square region

36. Virtual GRID

37. Virtual GRID