Coverage Issues in Wireless Sensor Networks

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

2. Introduction

3. 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 • Power management • Etc. FromDr. Yu-Chee Tseng(Associate Dean),College of Computer Science, National Chiao-Tung University

4. Study of Coverage Problem • 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.

5. Problem Design Criteria (1/3) • Sensor Deploy Method • Deterministic (planned) vs. Random • Coverage Types • Area coverage vs. Target (Point) coverage t1 S1 S3 S4 t2 t3 S2 1 8 R 2 7 6 3 4 5

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

7. Problem Design Criteria (3/3) • 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.

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:

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).

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. Tx Rx * 2Mb/s IEEE 802.11 Wireless LAN Energy Consumption Idle Sleep Modes Review: Power Saving • Make the sensor node sleep!!! [13] • Rockwell’s WINS Nodes • Medusa II Nodes It is highly recommended to “schedule” the wireless sensor nodes to alternate between active (Tx, Rx, Idle) and sleep mode http://www.inf.ethz.ch/personal/kasten/research/bathtub/energy_consumption.html

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

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 toa sensor, it may still by missed by the sensor.

15. BinaryModel: 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 • WhyK>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

16. BinaryModel: 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 r1 = r2 = · · · = rn. k-UC (k=1) k-NC (k=1)

17. BinaryModel: K-coverage in 2-D Is this area 1-covered? So this area is not 1-covered! This region is not covered by any sensor! This area is not only 1-covered, but also 2-covered! What is the coverage level of this area? 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 Coverage level = k means that this area is k-covered

18. BinaryModel: 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 si and sj. A point on the perimeter of si is perimeter-covered by sj if this point is within the sensing range of sj • [Theorem] • An area A is k-covered iff each sensor in A is k-perimeter-covered. • 2차원 문제를 1차원 문제로 바꾸어 해결 • Partially self-determination, but a central node determines the coverage level (k) finally.

19. BinaryModel: 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” Active nodes Intersection points

20. BinaryModel: 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. active? Active nodes Sleeping nodes Intersection points

21. BinaryModel: 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

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] 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

24. http://en.wikipedia.org/wiki/Log-distance_path_loss_model 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 Tx Power – Rx Power =

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

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 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

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

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

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

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

32. Probability Model: Evaluation of Sensor Networks

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.

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

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