1. Coverage Issues in Wireless Sensor Networks Youn-Hee Han yhhan@kut.ac.kr Korea University of Technology and Education Internet Computing Laboratory http://icl.kut.ac.kr

2. Introduction 2/50

3. 3/50 Change of Research Issues in Sensor Networks Hardware (2000) CPU, memory, sensors, etc. Protocols (2002) MAC layers Routing and transport protocols Applications (2004) Localization and positioning applications Management (2005) Coverage and connectivity problems Coverage and connectivity problems Power management Power management Etc. Etc. From Dr. Yu-Chee Tseng (Associate Dean), College of Computer Science, National Chiao- Tung University

4. Coverage Problem In general, determine how well the sensing field is monitored or tracked by sensors. Objectives of the problem Determine the coverage hole (or targets) Minimize the number of sensors deployed Make the whole area covered by three or more sensors  Location determination by “Triangulation” Maximize the network lifetime  [Def.] Sensor Network Lifetime The time interval that all points (or targets) in the given area is covered by at least one sensor node. Etc. Study of Coverage Problem 4/50

5. Sensor Deploy Method Deterministic (planned) vs. Random Coverage Types Area coverage vs. Target (Point) coverage Problem Design Criteria (1/3) 5/50 6 5 4 3 2 1 7 8 R S2S2 S1S1 S4S4 S3S3 t3t3 t1t1 t2t2

6. Coverage Modeling Binary Model vs. Probability Model Communication Range ( ) & Sensing Range ( ) vs. vs. Homogeneous vs. heterogeneous? Problem Design Criteria (2/3) 6/50 Binary, unit disc sensing model Probabilistic sensing model

7. Algorithm Characteristics 1) Centralized 2) Distributed 3) Self-*  Self-determination free choice of one’s own acts without external compulsion  Self-organization (Self-configuration) a process of evolution where the effect of the environment is minimal, i.e. where the development of new, complex structures takes place primarily in and through the system itself  Self-healing For example, a mobile sensor can move to an area with a coverage hole or routing void and significantly improve network performance. Problem Design Criteria (3/3) 7/50

8. Review: Art Gallery Problem Victor Klee (1973) Place the minimum number of cameras such that every point in the art gallery is monitored by at least one camera Chvátal's art gallery theorem (1975) guards (cameras) are always sufficient and sometimes necessary to guard a simple polygon with vertices 42 vertices  upper bound: 8/50

9. Review: Disk Covering Problem Given a unit disk, find the smallest radius required for equal disks to completely cover the unit disk. Zahn (1962). 9/50

10. Review: Sensor Node Architecture System architecture of a typical wireless sensor node i) a computing subsystem consisting of a microprocessor or microcontroller ii) a communication subsystem consisting of a short range radio for wireless communication iii) a sensing subsystem that links the node to the physical world and consists of a group of sensors and actuators iv) a power supply subsystem, which houses the battery and the dc-dc converter, and powers the rest of the node.

11. Review: Power Saving Make the sensor node sleep!!! [13] Modes * 2Mb/s IEEE 802.11 Wireless LAN Tx Rx Idle Sleep Energy Consumption Rockwell’s WINS Nodes TxRxIdleSleep 0.38 ~ 1 W0.75 W0.72 W0.4 W Medusa II Nodes TxRxIdleSleep 22 ~ 24 mW22 mW6 mW0.02 mW http://www.inf.ethz.ch/personal/kasten/research/bathtub/energy_consumption.html It is highly recommended to “schedule” the wireless sensor nodes to alternate between active (Tx, Rx, Idle) and sleep mode

12. Review: Power Saving Make the sensor node intelligent!!! [13] The ratio of the energy spent in sending one bit of information to the energy spent in executing one instruction.  1500~2700 for Rockwell’s WIN nodes  220~2900 for the MEDUSA II nodes  1400 for the WINS NG 2.0 So, local data processing, data fusion and data compression are highly desirable.

13. Coverage 13/50

14. Coverage Modeling Binary Model [1] Each sensor’s coverage area is modeled by a disk Any location within the disk is perfectly monitored by the sensor located at the center of the disk; otherwise, it is not monitored by the sensor. Probability Model [2] An event happening in the coverage of a sensor is either detected or not detected by the sensor depending on a probability distribution Hence even if an event is very close to a sensor, it may still by missed by the sensor. 14/50

15. Binary Model: K-coverage in 2-D K-coverage (only within Binary Model) [Definition] covered  A location in an area is said to be covered by if it is within 's sensing range. [Definition] k-covered (location or area)  A location in an area is said to be k-covered if it is within at least K sensors' sensing ranges.  “k” is called coverage level Why K>1? Fault-tolerance in case of the dismissal of some sensors Power saving and enlarge network lifetime Triangulation: getting location of a targeted object Uplift the confidence level on gathering information 15/50

16. Binary Model: K-coverage in 2-D Problems about K-coverage [1] [Definition] k-NC problem  Given a natural number k, the k-Non-unit-disk Coverage (k-NC) problem is a decision problem whose goal is to determine whether all points in an area are k-covered or not. [Definition] k-UC problem  Given a natural number k, the k-Unit-disk Coverage (k-UC) Problem is a decision problem whose goal is to determine whether all points in an area are k-covered or not, subject to the constraint that r 1 = r 2 = · · · = r n. 16/50 k-NC (k=1) k-UC (k=1)

17. So this area is not 1-covered! 1-covered means that every point in this area is covered by at least 1 sensor 2-covered means that every point in this area is covered by at least 2 sensors This region is not covered by any sensor! Is this area 1-covered? This area is not only 1- covered, but also 2- covered! What is the coverage level of this area? Coverage level = k means that this area is k-covered Binary Model: K-coverage in 2-D 17/50

18. Binary Model: K-coverage in 2-D Algorithm to determine coverage level, k, in a given sensor network? [1] [Definition] k-perimeter-covered  Consider any two sensors s i and s j. A point on the perimeter of s i is perimeter-covered by s j if this point is within the sensing range of s j [Theorem]  An area A is k-covered iff each sensor in A is k-perimeter-covered. 2 차원 문제를 1 차원 문제로 바꾸어 해결 Partially self-determination, but a central node determines the coverage level (k) finally. 18/50

19. Binary Model: Coverage Configuration in 2-D Coverage Configuration Protocol (CCP) [3] 1) a coverage level (k) is allocated to all sensors 2) all sensors are deployed randomly at the target area 3) Each sensor makes itself sleep or active to achieve the coverage level [Theorem]  A given area is “k-covered” if the following conditions are satisfied 1) All intersection points between each pair of sensors are "k- covered" 2) All intersection points between each sensor and boundary of the area are "k-covered” 19/50 Active nodes Intersection points

20. Binary Model: Coverage Configuration in 2-D Coverage Configuration Protocol (CCP) [3] A node becomes “sleep” if all intersection points inside its coverage is already K-covered by other active nodes in its neighborhood. A node becomes “active” if there exists an intersection point inside its sensing circle that is not K-covered by other active nodes. 20/50 Active nodes Sleeping nodes Intersection points active?

21. Binary Model: K-coverage in 3-D K-coverage in 3-D [4] [Definition] k-BC Problem  Given a natural number k, the k-Ball-Coverage (k-BC) Problem is a decision problem whose goal is to determine whether all points in a 3-D cuboid sensing area are k-covered or not. How to determine k?  (3D  2D) Determine whether the sphere of a sensor is sufficiently covered  (2D  1D) Determine whether the circle of each spherical cap of a sensor intersected by its neighboring sensors is covered 21/50

22. Probability Model Why Probability Coverage Model? [2] Quality of sensor surveillance may be much affected by sensing distances, signal propagation characteristics, obstacles, and environmental factors. Probability coverage model may be more realistic! Methodology Simple Model [5] Signal-strength-based Model [2] 22/50 임의의 센서와 가까운 지역이 특수한 요인 ( 장애물 ) 에 의하여 센싱이 되지 않을 수 있거나 그 센서와 먼 지역이 특수한 요인 ( 다수의 센서의 감지 ) 에 의하여 센싱이 될 수도 있다.

23. Probability Model Simple Model [5] : the probability that a sensor can sense a event happened at a location : the detection probability contributed by the sensors 23/50

24. Probability Model Signal-strength-based Model [2, 6] : the probability that a sensor can sense a event happened at a location  Path Loss (in dB),, at a distance 24/50 Tx Power – Rx Power = http://en.wikipedia.org/wiki/Log-distance_path_loss_model

25. Probability Model Signal-strength-based Model [2, 6] : the probability that a sensor can sense a event happened at a location  Path Loss (in dB),, at a distance 25/50

26. Probability Model Signal-strength-based Model [2, 6] : the probability that a sensor can sense a event happened at a location  Path Loss (in dB),, at a distance : the detection probability contributed by the sensors 26/50 Q-Function:

27. Probability Model: Probabilistic Coverage Algorithm [Definition] Effective Coverage [2] Effective coverage range,, of a sensor is defined as distance of the target from the sensor beyond which the detection probability is negligible. That is, an area where is over a given threshold [Definition] Sufficiently Covered [2] : Desired Detection Probability, DDP A location in region A is said to be sufficiently covered if its cumulative detection probability, due to sensors located within the effective coverage range of this location, is equal to or greater than the detection probability desired by the application. Probabilistic Coverage Algorithm (PCA) [2] Check whether the current whole area is sufficiently covered or not 27/50

28. Probability Model: Evaluation of Sensor Networks The probability of location estimation by a sensor [6] : The probability that sensor estimates that the location of is at 28/50

29. Probability Model: Evaluation of Sensor Networks The probability of location estimation by all sensors : When the real location of event is, the normalized probability that all sensors predict that the location of the object is at The error of location estimation by all sensors : When the real location of event is, the weighted error that the sensor network predicts that the estimated location of the object is 29/50

30. Probability Model: Evaluation of Sensor Networks The accumulated error of location estimation by all sensors : When the real location of event is, the accumulated weighted error at all possible estimated locations  임의의 센서 집단 배치에 대한 특정 위치 의 감지 실패를 평가할 수 있음 The overall error by all sensors : the overall error degree for the sensor network to monitor a given area  전체 위치에 대해 임의의 센서 집단 배치가 얼마나 잘 되었는가를 평가할 수 있음 30/50

31. Probability Model: Evaluation of Sensor Networks 31/50 The real location of event (or object):

32. Probability Model: Evaluation of Sensor Networks 32/50

33. Probability Model: Evaluation of Sensor Networks Scheme to deploy sensors in an area [6] [Step 1] randomly select one location to deploy the first sensor [Step 2] greedily add one more sensor to the location such that is maximum. 33/50

34. Probability Model: Evaluation of Sensor Networks SLEEP and AWAKE protocols [6] 34/50

35. References 1. C.-F. Huang and Y.-C. Tseng, The Coverage Problem in a Wireless Sensor Network, In ACM International Workshop on Wireless Sensor Networks and Applications (WSNA), pp. 115–121, 2003. 2. N. Ahmed, S. S. Kanhere and S. Jha, Probabilistic Coverage in Wireless Sensor Networks, in Proceedings of the IEEE Workshop on Wireless Local Networks (WLN, in conjunction with LCN 2005), Sydney, Australia, pp. 672-679, November 2005. 3. X. Wang, G. Xing, Y. Zhang, C. Lu, R. Pless, and C. Gill, Integrated coverage and connectivity configuration in wireless sensor networks, In ACM International Conf. on Embedded Networked Sensor Systems (SenSys), pp. 28–39, 2003. 4. C.-F. Huang, Y.-C. Tseng, and L.-C. Lo, The Coverage Problem in Three-Dimensional Wireless Sensor Networks, Journal of Interconnection Networks, Vol. 8, No. 3, pp. 209-227. Sep. 2007. 5. Y. Zou and K. Chakrabarty, "Sensor deployment and target localization based on virtual forces," in Proceedings of INFOCOM 2003, March 2003. 6. S.-P. Kuo, Y.-C. Tseng, F.-J. Wu, and C.-Y. Lin, A Probabilistic Signal-Strength-Based Evaluation Methodology for Sensor Network Deployment, International Journal of Ad Hoc and Ubiquitous Computing, Vol. 1, No. 1-2, pp. 3-12, 2005 35/50

36. References 7. Honghai Zhang and Jennifer C. Hou, ``On deriving the upper bound of a-lifetime for large sensor networks,'' Proc. ACM Mobihoc 2004, June 2004 8. S. Megerian, F. Koushanfar, G. Qu, G. Veltri, M. Potkonjak. "Exposure In Wireless Sensor Networks: Theory And Practical Solutions," Journal of Wireless Networks, Vol. 8, No. 5, ACM Kluwer Academic Publishers, pp. 443-454, September 2002 9. M. Cardei and D.-Z. Du, "Improving Wireless Sensor Network Lifetime through Power Aware Organization," ACM Wireless Networks, Vol. 11, pp. 333-340, 2005. 10. M. Cardei, M. T. Thai, Y. Li, and W. Wu, "Energy-efficient Target Coverage in Wireless Sensor Networks," In IEEE Infocom 2005, vol. 3, pp. 1976-1984, 2005. 11. 김용환, 이헌종, 한연희, " 무선 센서 네트워크 수명 연장을 위한 에너지 인지적 스케줄링 알고리즘," 한국정보과학회 학술발표논문집 2008 년도 가을, 2008 년 10 월 12. C.-F. Huang, L.-C. Lo, Y.-C. Tseng, and W.-T. Chen Decentralized Energy-Conserving and Coverage-Preserving Protocols for Wireless Sensor Networks, ACM Trans. on Sensor Networks, Vol. 2, No. 2, pp. 182-187, 2006. 13. V. Raghunathan, C. Schurgers, S. Park, and M. B. Srivastava, Energy-Aware Wireless Microsensor Networks, IEEE Signal Processing Magazine, 19 (2002), pp 40-50. 36/50