1. HETEROGENEOUS WIRELESS SENSOR NETWORK DEPLOYMENT Yeh-Ching Chung Department of Computer Science National Tsing Hua University

2. Outline 2015/10/14 2  What is Wireless Sensor Network (WSN)?  Heterogeneous WSN  Irregular coverage model: polygon model  Irregular range model  Heterogeneous WSN deployment algorithm  Experiments  Conclusions

3. Wireless sensor network (1/2) 2015/10/14 3  A wireless network consists of spatially distributed autonomous devices using sensors to cooperatively monitor physical or environmental conditions [Wikipedia].  Resource-constrained sensor node: Low-power microcontroller Constrained memory Low transmission bandwidth Limited power source (battery, solar panel) Sensing Area phenomenon SinksWSN

4. Wireless sensor network (2/2) 2015/10/14 4  Enabling Technologies:  Embedded system Small form factor  Wireless networking WLAN, Bluetooth, ZigBee  Sensing Infrared, ultrasonic, temperature, acceleration, gas, … MICAz by Crossbow MICA2DOT by Crossbow

5. Applications of WSN 2015/10/14 5  Structural health monitoring  Industrial equipment monitoring  Petroleum facility  Semiconductor plant  Environmental monitoring  Volcano monitoring  Habitat monitoring  Others  Military applications: target detection, classification, and tracking  Health applications: collect physiological data  Air conditioner control in home/office buildings

6. Heterogeneous WSN 2015/10/14 6  WSN consists of sensor nodes with different characteristics:  Coverage area Different types of antennas and sensing devices result in various communication and sensing areas  Effective communication and sensing ranges Unavoidable variations for the same type of sensor nodes  Others Computing power: speed of microcontroller, size of memory Energy consumption: battery powered, unlimited power source

7. Deployment problems of heterogeneous WSN 2015/10/14 7  How to deploy a heterogeneous WSN to:  Maintain network connectivity  Get more sensing coverage rate  How to model the irregularity of sensor nodes?  Different shapes of coverage areas  Various effective communication and sensing ranges

8. Maintain network connectivity 2015/10/14 8  Two-way communication Heterogeneous WSNHomogeneous WSN Disconnected

9. Get more sensing coverage rate 2015/10/14 9  Reduce the overlapping between sensor nodes Lower sensing coverage rateHigher sensing coverage rate

10. Model the irregularity of sensor nodes (1/2) 2015/10/14 10  Disk model [Li et al. 2003]:  The communication/sensing area of a sensor node is represented by a circular area.  Not practical to a realistic sensor node. Helix antenna Infrared sensor Modeling? communication area sensing area

11. Model the irregularity of sensor nodes (2/2) 2015/10/14 11  Degree of irregularity (DOI) [He et al. 2005, Zhou et al. 2006]:  Based on the disk model, denote the irregularity of the radio propagation pattern: The maximum radio range variation per unit degree changed from 0° to 360°  The radius (effective communication/sensing range) varies between pre-defined upper and lower bound.

12. Contributions 2015/10/14 12  Contributions of our work:  Polygon model Represent different shapes of communication and sensing areas of sensor nodes  Irregular range model Represent various effective communication and sensing ranges for the same type of sensor nodes  Heterogeneous WSN deployment algorithm Topology control: maintain network connectivity Scoring process: improve sensing coverage gains

13. Polygon model (1/2) 2015/10/14 13  The polygon model is represented by a list of vertices:  Model poly = {vex i = (range i,  i ) | 1  i  m, m ≥ 3}, where the ith vertex of the polygon, vex i, is represented by the polar coordinate (range i,  i ) range i (radial coordinate): the default communication or sensing range of a sensor node at  i  i (angular coordinate): the counterclockwise angle from 0°  An example:  Model poly = {vex 1, …, vex 16 } = {(range 1,  1 ), …, (range 16,  16 )} = {(25, 0°), (20, 15°), (35, 30°), (50, 50°), (60, 70°), (65, 90°), (60, 110°), (50, 130°), (35, 150°), (20, 165°), (25, 180°), (15, 210°), (20, 230°), (10, 270°), (20, 310°), (15, 330°)}

14. Polygon model (2/2) 2015/10/14 14  Represent communication or sensing coverage area: The sensing area of sensor node S n : Area poly,S (S n ) = (Loc(S n ), {(Range S (S n ) 1,  1 ), …, (Range S (S n ) m,  m )}, Rot(S n )) = ((10, 20), {(25.3, 0°), …, (14.9, 330°)}, 30°) Where Range S (S n ) i is the effective sensing range of S n at  i S n 0° Rot(S n ) = 30° vex 1 Loc(S n ) = (10,20)

15. Irregular range model (1/3) 2015/10/14 15  The effective communication or sensing range of a sensor node S n at  i is defined as:  Range(S n ) i = range i + Rand(DOI), –3×DOI ≤ Rand(DOI) ≤ 3×DOI range i : the default communication/sensing range at  i DOI : the degree of irregularity of sensor node S n Rand(DOI): the normal distribution with the standard derivation DOI

16. Irregular range model (2/3) 2015/10/14 16  Why 3×DOI ?  The “68-95-99.7 rule” in normal distribution: 99.7% of the effective communication/sensing ranges are within three standard derivations (3*DOI) away from the mean value (the default communication/sensing range) The value of Range(S n ) is between 21 and 39

17. Irregular range model (3/3) 2015/10/14 17  The value of DOI determines the irregularity of coverage areas of sensor nodes. (a) DOI = 0 (b) DOI = 1 (c) DOI = 2 (d) DOI = 3

18. Sensor node connection 2015/10/14 18  The connection degree of a sensor node S n, Deg(S n ):  The number of two-way communication links to S n  The maximum connection degree, Deg max (S n ): The maximum number of sensor nodes that can be connected to S n

19. Communication and sensing signal strength (1/4) 2015/10/14 19  The degree of the received communication or sensing signal at a point from a sensor node.  Used by the proposed heterogeneous WSN deployment algorithm:  Topology control  Coverage rate calculation

20. Communication and sensing signal strength (2/4) 2015/10/14 20  Based on the Friis transmission formula [Friis 1946]:  Power r / Power t = Area r Area t / d 2 λ 2 Power t : the power fed into the transmitting antenna Power r : the power available at the receiving antenna Area r (or Area t ): the effective area of the receiving (or transmitting) antenna d: the distance between two antennas λ : the wavelength  Assume that Power t, Area r, Area t, and λ are constants, Power r  1/d 2

21. Communication and sensing signal strength (3/4) 2015/10/14 21  The communication and sensing signal strength of S n at a point P i are defined as:  Range C (S n, P i ) and Range S (S n, P i ): the effective communication and sensing range of S n at P i  d(S n, P i ): the Euclidean distance between S n and P i

22. Communication and sensing signal strength (4/4) 2015/10/14 22  The relationship between SSS(S n, P i ) and d(S n, P i ):  If d(S n, P i ) ≤ Range S (S n, P i ), it indicates that P i is covered by the sensing area of S n

23. Calculate the effective communication/sensing range (1/2) 2015/10/14 23  Calculate the effective sensing range of S n at P i, Range S (S n, P i ):  Q i is the intersection point of ray S n P i and line segment vex a vex b Note: 1. vex a = (Range S (S n ) a,  a +Rot(S n )) and vex b = (Range S (S n ) b,  b +Rot(S n )) 2. The area of ∆vex a S n vex b is the sum of the area of ∆vex a S n Q i and ∆Q i S n vex b 0° SnSn Rot(S n )  (S n, P i ) PiPi QiQi vex b vex a

24. Calculate the effective communication/sensing range (2/2) 2015/10/14 24  An example:  Given vex 4 = (50, 80°), vex 5 = (60, 100°), and  (S 1, P 1 ) = 90°  Since 80° <  (S 1, P 1 ) < 100°, Range S (S 1, P 1 ) = d(S 1, P 2 ) = (50∙60)∙sin(100°–80°) / [50∙sin(90°–80°) – 60∙sin(90°–100°)] ≈ 53.7 units

25. Calculate the sensing coverage rate 2015/10/14 25  The sensing coverage rate of the deployment area: N grid (Area deploy ): the number of grid points within the deployment area N cov (Area deploy ): the number of the grid point P i within the deployment area with SSS(P i ) ≥ 1 deployment area

26. Heterogeneous WSN deployment algorithm (1/9) 2015/10/14 26  Preliminaries  The sink node contains 1 communication device (without sensing device)  Each sensor node contains 1 communication and 1 sensing devices  The same type of communication/sensing devices may have different communication/sensing ranges based on the value of degree of irregularity

27. Heterogeneous WSN deployment algorithm (2/9) 2015/10/14 27  Given  A deployment area with obstacles  Multiple types of sensor nodes  Objectives  A communication-connected WSN  Higher sensing coverage rate with less sensor nodes

28. Heterogeneous WSN deployment algorithm (3/9) 2015/10/14 28  Step 1: Initialization  Deployment area  Deployable sensor nodes An initialized deployment area: 1 sink node (S 0 ) 1 pre-deployed sensor node (S 1 ) 2 obstacles with different shapes S0S0 S1S1

29. Heterogeneous WSN deployment algorithm (4/9) 2015/10/14 29  Step 2: Base node selection  Select from the deployed sensor nodes  Calculate deployment quota Starting from the sink node Traverse along the communication links Given Deg(S 0 ) = 1, Deg max (S 0 ) = 2 Deployment quota at S 0 = Deg max (S 0 ) – Deg(S 0 ) = 1 (Can deploy 1 more sensor node around S 0 ) S0S0 S1S1

30. Heterogeneous WSN deployment algorithm (5/9) 2015/10/14 30  Step 3: Candidate positions generation  Generate candidate positions around the base node  Based on the topology control mechanism S0S0 S1S1 P0P0 P1P1 P2P2 Given Max(CSS) = 4 and Max(SSS) = 2 P 0 is abandoned: CSS(S 0, P 0 ) = 4.5 > Max(CSS) P 1 is abandoned: SSS(S 1, P 1 ) = 2.5 > Max(SSS) For P 2, CSS(S 0, P 2 ) = 1.5, SSS(S 1, P 2 ) = 0 P 2 is selected as the candidate position around S 0

31. Heterogeneous WSN deployment algorithm (6/9) 2015/10/14 31  Step 4: Scoring and deployment  Score(S n, P i ): the increased sensing coverage if a deployable sensor node S n deployed at candidate position P i Given a deployable sensor node S 2 Put a square area of grid points centered at P 2, the length of edge = 2*Max(sensing range of S 2 ) N before (Area sq (S 2, P 2 )) = 250 (The number of grid points G i with SSS(G i ) ≥ 1) S0S0 S1S1 P2P2

32. Heterogeneous WSN deployment algorithm (7/9) 2015/10/14 32  Step 4: Scoring and deployment  Select a candidate position & deployable sensor node with the highest score Rotate 0° N after (Area sq (S 2, P 2 )) = 600 (points) Score(S 2, P 2 ) = 600 - 250 = 350 S0S0 S1S1 P2P2 S1S1 P2P2 S0S0 S2S2 S2S2 Rotate 200° N after (Area sq (S 2, P 2 )) = 950 (points) Score(S 2, P 2 ) = 950 - 250 = 700

33. Heterogeneous WSN deployment algorithm (8/9) 2015/10/14 33  Step 4: Scoring and deployment  Deploy a new sensor node around the base node S1S1 S2S2 S0S0

34. Heterogeneous WSN deployment algorithm (9/9) 2015/10/14 34  Steps  Initialization  Base node selection  Candidate positions generation  Scoring and deployment  Stop deployment  The limit of deployable sensor nodes is reached  No more sensor nodes can be deployed Stop conditions are not reached

35. Experiments 2015/10/14 35 Deploy different types of sensor nodes to an area with 9 obstacles Four types of sensor nodes used for deployment Type 1: loop antenna + infrared sensor Type 2: loop antenna + ultrasonic sensor Type 3: chip antenna + infrared sensor Type 4: chip antenna + ultrasonic sensor (a) loop antenna (b) chip antenna (c) infrared sensor (d) ultrasonic sensor

36. Representation of coverage areas Coverage areaDisk modelPolygon model loop antenna radius = 50.816 vertices: {(50.8, 9°), (50.8, 33.7°), (50.8, 56.3°), (50.8, 82°), (50.8, 98°), (50.8, 123.7°), (50.8, 146.3°), (50.8, 171°), (50.8, 189°), (50.8, 213.7°), (50.8, 236.3°), (50.8, 262°), (50.8, 278°), (50.8, 303.7°), (50.8, 326.3°), (50.8, 351°)} chip antenna radius = 50.826 vertices: {(50.8, 6.8°), (50.2, 21.4°), (47.6, 38°), (43.7, 54.5°), (38, 68.2°), (28.7, 81.9°), (10.5, 90°), (28.7, 98.1°), (38, 111.8°), (43.7, 125.5°), (47.6, 142°) (50.2, 158.6°), (50.8, 173.2°), (50.8, 186.8°), (50.2, 201.4°), (47.6, 218°), (43.7, 234.5°), (38, 248.2°), (28.7, 261.9°), (10.5, 270°), (28.7, 278.1°), (38, 291.8°), (43.7, 305.5°), (47.6, 322°), (50.2, 338.6°),(50.8, 353.2°)} infrared sensor radius = 47.73 vertices: {(47.7, 19.6°), (0, 180°), (47.7, 340.4°)} ultrasonic sensor radius = 40.111 vertices: {(40.1, 4.3°), (35.9, 17.9°), (31, 24.9°), (21.8, 35.8°), (13, 48°), (0, 180°), (13, 312°), (21.8, 324.2°}, (31, 335.1°), (35.9, 342.1°), (40.1, 355.7°)} 2015/10/14 36

37. Test sets of experiments  Case 1: single type of deployable sensor nodes  Includes test sets 1 to 4 with single type of sensor nodes  Case 2: multiple types of deployable sensor nodes  Includes test sets 5 to 9 with two or four types of sensor nodes SetSensor nodes 1Type 1 2Type 2 3Type 3 4Type 4 5Type 1 + Type 2 6Type 3 + Type 4 7Type 1 + Type 3 8Type 2 + Type 4 9Type 1 + Type 2 + Type 3 + Type 4 2015/10/14 37 Deployment parameters for each case: 1.Deployable sensor nodes: 600 for each type 2.DOI: 0, 2 3.The maximum connection degree: 6, ∞ 4.The rotation steps of coverage areas: 1, 4, 8 Case 1 Case 2

38. Experiment analysis 2015/10/14 38  After deployment, we compare the accuracy of the proposed polygon model with the disk model:  The sensing coverage rate  The number of deployed sensor nodes  The network connectivity  The network connectivity  The number of isolated networks An isolate network is a network in which sensor nodes of the isolated network cannot communicate with the sink node

39. Results of Case 1 (1/3) 2015/10/14 39 Disk model Polygon model (rotation steps = 1) Polygon model (rotation steps = 4) Polygon model (rotation steps = 8) Max. degree6∞6∞6∞6∞ SetDOI = 0 1 0.38270.38780.76300.82450.83230.89140.79520.9017 2 0.54800.55050.80900.89360.81040.91660.76380.9190 3 0.38270.38780.82670.91140.93910.96860.95150.9709 4 0.54800.55050.87780.92890.95470.98160.97160.9863 SetDOI = 2 1 0.35400.38140.76280.83340.88550.90860.82090.9100 2 0.47930.49620.83000.89880.89820.92950.89000.9404 3 0.35850.40640.79070.90810.96240.97170.97060.9792 4 0.49430.49940.77080.93810.89540.98760.98060.9917 sensing coverage rate Disk model Polygon model (rotation steps = 1) Polygon model (rotation steps = 4) Polygon model (rotation steps = 8) Max. degree6∞6∞6∞6∞ SetDOI = 0 1 116115276319279331261338 2 151153273319265331238331 3 116115378591418544434525 4 151153386571424549444536 SetDOI = 2 1 94105276330300335269333 2 127132277321293336285337 3 98112350550439520437521 4 133135334571384545456533 deployed sensor nodes The polygon model can deploy more sensor nodes and produces higher sensing coverage rate than the disk model for all test sets

40. Results of Case 1 (2/3) 2015/10/14 40 Disk model Polygon model (rotation steps = 1) Polygon model (rotation steps = 4) Polygon model (rotation steps = 8) Max. degree6∞6∞6∞6∞ SetDOI = 0 1 0.38270.38780.76300.82450.83230.89140.79520.9017 2 0.54800.55050.80900.89360.81040.91660.76380.9190 3 0.38270.38780.82670.91140.93910.96860.95150.9709 4 0.54800.55050.87780.92890.95470.98160.97160.9863 SetDOI = 2 1 0.35400.38140.76280.83340.88550.90860.82090.9100 2 0.47930.49620.83000.89880.89820.92950.89000.9404 3 0.35850.40640.79070.90810.96240.97170.97060.9792 4 0.49430.49940.77080.93810.89540.98760.98060.9917 sensing coverage rate Disk model Polygon model (rotation steps = 1) Polygon model (rotation steps = 4) Polygon model (rotation steps = 8) Max. degree6∞6∞6∞6∞ SetDOI = 0 1 116115276319279331261338 2 151153273319265331238331 3 116115378591418544434525 4 151153386571424549444536 SetDOI = 2 1 94105276330300335269333 2 127132277321293336285337 3 98112350550439520437521 4 133135334571384545456533 deployed sensor nodes Type 1 and Type 3 sensor nodes are identical under the disk model: The communication areas of the loop antenna and chip antenna are the same under the disk model.

41. Results of Case 1 (3/3) 2015/10/14 41  The WSN constructed under the polygon model are connected (no isolate networks) for all test sets.  The value of DOI affects the network connectivity under the disk model. Disk model Polygon model (rotation steps = 1) Polygon model (rotation steps = 4) Polygon model (rotation steps = 8) Max. degree6∞6∞6∞6∞ SetDOI = 0 1 00000000 2 00000000 3 1011000000 4 23000000 SetDOI = 2 1 84000000 2 00000000 3 3025000000 4 129000000 number of isolated networks Increased Type 3 and Type 4 sensor nodes use Chip antenna

42. The maximum connection degree 2015/10/14 42  The restriction of the maximum connection degree may block the deployment of new sensor nodes. The 25th deployed sensor node (S 25 ) has 7 neighbors (S 0, S 2, S 6, S 7, S 9, S 13, and S 35 ) Since the maximum connection degree = 6, it is not possible to deploy new sensor nodes around S 25. As a result, no sensor nodes can be deployed into the empty area.

43. Results of Case 2 (1/3) 2015/10/14 43 Disk model Polygon model (rotation steps = 1) Polygon model (rotation steps = 4) Polygon model (rotation steps = 8) Max. degree6∞6∞6∞6∞ SetDOI = 0 5 0.38270.38780.79880.88150.81140.91380.88990.9175 6 0.38270.38780.85300.92940.96670.97490.98310.9850 7 0.38270.38780.76300.82450.83230.89140.79520.9028 8 0.54800.55050.80900.89360.81040.91660.76380.9190 9 0.38270.38780.79880.88150.81140.91380.88990.9223 SetDOI = 2 5 0.35400.38140.85100.89360.90450.92740.90090.9341 6 0.35850.40640.84030.93640.91830.98750.97610.9904 7 0.37200.37960.76610.84930.86110.95430.93160.9582 8 0.49420.48180.89200.90460.93190.96460.94930.9700 9 0.37200.37960.86530.91680.93930.96350.88050.9707 sensing coverage rate SetDOI = 0 5 116115269314265337287328 6 116115360567432515464536 7 116115276319279331261338 8 151153273319265331238331 9 116115269314265337287341 SetDOI = 2 5 94105274323291336288335 6 98112369547407522445531 7 103104302365308397345396 8 128130340381345402358412 9 103104321394342402309403 deployed sensor nodes Similar to Case 1, the polygon model can deploy more sensor nodes and produces higher sensing coverage rate than the disk model for all test sets

44. Results of Case 2 (2/3) 2015/10/14 44 Disk model Polygon model (rotation steps = 1) Polygon model (rotation steps = 4) Polygon model (rotation steps = 8) Max. degree6∞6∞6∞6∞ SetDOI = 0 5 0.38270.38780.79880.88150.81140.91380.88990.9175 6 0.38270.38780.85300.92940.96670.97490.98310.9850 7 0.38270.38780.76300.82450.83230.89140.79520.9028 8 0.54800.55050.80900.89360.81040.91660.76380.9190 9 0.38270.38780.79880.88150.81140.91380.88990.9223 SetDOI = 2 5 0.35400.38140.85100.89360.90450.92740.90090.9341 6 0.35850.40640.84030.93640.91830.98750.97610.9904 7 0.37200.37960.76610.84930.86110.95430.93160.9582 8 0.49420.48180.89200.90460.93190.96460.94930.9700 9 0.37200.37960.86530.91680.93930.96350.88050.9707 sensing coverage rate SetDOI = 0 5 116115269314265337287328 6 116115360567432515464536 7 116115276319279331261338 8 151153273319265331238331 9 116115269314265337287341 SetDOI = 2 5 94105274323291336288335 6 98112369547407522445531 7 103104302365308397345396 8 128130340381345402358412 9 103104321394342402309403 deployed sensor nodes Only one type of sensor nodes are deployed in these test sets: Set 5: Type 1 Set 6: Type 3 (same as Type 1 in Disk model) Set 7: Type 1 Set 9: Type 1

45. Results of Case 2 (3/3) 2015/10/14 45  The polygon model is more accurate than the disk model while multiple types of sensor nodes are deployed.  The number of isolated networks under the disk model is increased while the value of DOI is changed from 0 to 2. number of isolated networks Disk model Polygon model (rotation steps = 1) Polygon model (rotation steps = 4) Polygon model (rotation steps = 8) Max. degree 6∞6∞6∞6∞ SetDOI = 0 5 00000000 6 1011000000 7 00000000 8 00000000 9 00000000 SetDOI = 2 5 84000000 6 3025000000 7 2316000000 8 67000000 9 2316000000 Set 6 consists of Type 3 and Type 4 sensor nodes that use Chip antenna

46. Conclusions 2015/10/14 46  The proposed irregular coverage model - polygon model, can represent different shapes of communication and sensing areas of sensor nodes.  The four-step heterogeneous WSN deployment algorithm can maintain the network connectivity and improve the sensing coverage gains.  Topology control mechanism and scoring process  According to the simulation results, the proposed polygon model is more accurate than the disk model.  Communication-connected WSN  Higher sensing coverage rate