Coverage Issues in Wireless Sensor Networks

Youn-Hee Hanyhhan@kut.ac.kr

Korea University of Technology and EducationInternet Computing Laboratory

http://icl.kut.ac.kr

Introduction

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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 problemsCoverage and connectivity problems Power managementPower management Etc.Etc. From Dr. Yu-Chee Tseng

(Associate Dean), College of Computer

Science, National Chiao-Tung University

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

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Sensor Deploy Method Deterministic (planned) vs. Random

Coverage Types Area coverage vs. Target (Point) coverage

Problem Design Criteria (1/3)

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6

54

3

2

1

7

8 R

S2

S1

S4S3

t3

t1

t2

Coverage Modeling Binary Model vs. Probability Model

Communication Range ( ) & Sensing Range ( ) vs. vs. Homogeneous vs. heterogeneous?

Problem Design Criteria (2/3)

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Binary, unit disc sensing model Probabilistic sensing model

CR SRC SR R C SR R C SR R

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)

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Review: Art Gallery ProblemVictor 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

3n

n

42 vertices upper bound:42 123

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

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( )r nn

Review: Sensor Node ArchitectureSystem 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.

Review: Power SavingMake the sensor node sleep!!! [13]

Modes

* 2Mb/s IEEE 802.11 Wireless LAN

TxRx

Idle

Sleep

Ener

gy C

onsu

mpt

ion

• Rockwell’s WINS Nodes Tx Rx Idle Sleep

0.38 ~ 1 W 0.75 W 0.72 W 0.4 W

• Medusa II Nodes Tx Rx Idle Sleep22 ~ 24

mW 22 mW 6 mW 0.02 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

Review: Power SavingMake 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.

Coverage

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Coverage ModelingBinary 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.

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Binary Model: K-coverage in 2-DK-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

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Binary Model: K-coverage in 2-DProblems 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.

16/50k-NC (k=1) k-UC (k=1)

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

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Binary Model: K-coverage in 2-DAlgorithm 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.

2 차원 문제를 1 차원 문제로 바꾸어 해결

Partially self-determination, but a central node determines the coverage level (k) finally.

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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”

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Active nodesIntersection points

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.

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Active nodesSleeping nodesIntersection points

active?

Binary Model: K-coverage in 3-DK-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? (3D2D) Determine whether the sphere of a sensor is

sufficiently covered (2D1D) Determine whether the circle of each spherical cap of a

sensor intersected by its neighboring sensors is covered

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Probability ModelWhy 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]

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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 .

Probability ModelSimple Model [5]

: the probability that a sensor can sense a event happened at a location

: the detection probability contributed by the sensors

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kisiPr

5, 3er r

NPr

Probability ModelSignal-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

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kisiPr

( )PL d ( , )id d s k

Tx Power – Rx Power =

http://en.wikipedia.org/wiki/Log-distance_path_loss_model

Probability ModelSignal-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

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kisiPr

( )PL d ( , )id d s k

Probability ModelSignal-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

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kisiPr

( )PL d ( , )id d s k

Q-Function:

00

( ) 10 logPr{ ( ) }

dx PL d nd

PL d x Q

PrN

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

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is

iPr

effectR

reqd

effectRPr

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

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( )ilg e is l

e

1( )lg e[2, 2]l 1[2,2]([4,1])g

1[2,2]([3,3])g

1[2,2]([2, 2])g

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

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( )lG e l

e

1( , )E l e

e

l

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

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2 ( )E l l

SE

l

Probability Model: Evaluation of Sensor Networks

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[20,35]l The real location of event (or object):

1 [15, 25]s

1

2

[15,25][35,25]

ss

1

2

3

[15, 25][35,25][40,45]

sss

( )lG e

Probability Model: Evaluation of Sensor Networks

2 ( )E l

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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.

l2 ( )E l

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Probability Model: Evaluation of Sensor Networks

SLEEP and AWAKE protocols [6]

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References1. 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

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References7. 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.

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