1. College of Engineering Grid-based Coordinated Routing in Wireless Sensor Networks Uttara Sawant Major Advisor : Dr. Robert Akl Department of Computer Science and Engineering Uttara Sawant Major Advisor : Dr. Robert Akl Department of Computer Science and Engineering

2. 10/30/2015 Outline Wireless Sensor Networks Overview Grid-based Coordinated Routing Simulation Results Future Work Wireless Sensor Networks Overview Grid-based Coordinated Routing Simulation Results Future Work 1

3. 10/30/2015 Wireless Sensor Networks Overview Introduction to Sensor Networks Sensor Routing Protocols Motivation Objectives Introduction to Sensor Networks Sensor Routing Protocols Motivation Objectives 2

4. 10/30/2015 Wireless Sensor Networks Overview Introduction to Sensor Networks Distributed networks Sensing, communication, computation Introduction to Sensor Networks Distributed networks Sensing, communication, computation 1

5. 10/30/2015 Features Ad hoc networks Low-power and battery-operated Sensors and radio Self-organizing Harsh environmental conditions Node mobility Node failure Dynamic topology Node heterogeneity Unattended operation Large scale deployment Ad hoc networks Low-power and battery-operated Sensors and radio Self-organizing Harsh environmental conditions Node mobility Node failure Dynamic topology Node heterogeneity Unattended operation Large scale deployment 3

6. 10/30/2015 Applications Video surveillance Traffic monitoring Environmental monitoring Structure and system health monitoring in buildings and aircraft interiors Video surveillance Traffic monitoring Environmental monitoring Structure and system health monitoring in buildings and aircraft interiors 4

7. 10/30/2015 Wireless Sensor Networks Overview Hardware Crossbow Motes – MICA2, MICA2DOT, MICAz, Cricket Intel Motes with Bluetooth support Software TinyOS – a component-based operating system for Motes EmStar – software system for Linux-based platforms nesC – programming Motes Middleware TinyDB – sensor database system Hardware Crossbow Motes – MICA2, MICA2DOT, MICAz, Cricket Intel Motes with Bluetooth support Software TinyOS – a component-based operating system for Motes EmStar – software system for Linux-based platforms nesC – programming Motes Middleware TinyDB – sensor database system 5

8. 10/30/2015 Wireless Sensor Networks Overview Sensor Routing Protocols Routing Protocols – data-centric, hierarchical, location-based, network flow approach Flooding Sending data to all neighbors Duplication of packets, packet congestion, more energy Sending identical information of overlapped regions LEACH – cluster-based PEGASIS Hierarchical-PEGASIS Sensor Routing Protocols Routing Protocols – data-centric, hierarchical, location-based, network flow approach Flooding Sending data to all neighbors Duplication of packets, packet congestion, more energy Sending identical information of overlapped regions LEACH – cluster-based PEGASIS Hierarchical-PEGASIS C1C2C3C4C5 C1C2C3C4C5 C3C5 C3 6

9. 10/30/2015 Wireless Sensor Networks Overview Location-based protocols MECN and SMECN AFECA, GAF, Span Ascent, GEAR Location-based protocols MECN and SMECN AFECA, GAF, Span Ascent, GEAR 7

10. 10/30/2015 Wireless Sensor Networks Overview Data-centric protocols SPIN – one of the most dominant data- centric routing protocol for sensor networks Directed diffusion Data-centric Named data Selecting paths, caching and managing data in-network Rumor routing, gradient-based routing Data-centric protocols SPIN – one of the most dominant data- centric routing protocol for sensor networks Directed diffusion Data-centric Named data Selecting paths, caching and managing data in-network Rumor routing, gradient-based routing 8

11. 10/30/2015 Motivation Energy consumption in sensor networks Network connectivity Network partition - define Energy consumption in sensor networks Network connectivity Network partition - define 9

12. 10/30/2015 Objectives Design grid-based coordinated routing protocol Extend network lifetime, prolong partition Maintain connectivity Compare with traditional flooding algorithm Design grid-based coordinated routing protocol Extend network lifetime, prolong partition Maintain connectivity Compare with traditional flooding algorithm 10

13. 10/30/2015 Grid-based Coordinated Routing Flooding Grid-based coordinated routing Link model Coordinator election Grid size estimation Load balancing Flooding Grid-based coordinated routing Link model Coordinator election Grid size estimation Load balancing 11

14. 10/30/2015 Grid-based Coordinated Routing Flooding Every node rebroadcasts packets after receive Information is disseminated across entire network Duplicate packets, infinite loops Results in tree structure rooted at the source Flooding Every node rebroadcasts packets after receive Information is disseminated across entire network Duplicate packets, infinite loops Results in tree structure rooted at the source 12

15. 10/30/2015 Grid-based Coordinated Routing 13

16. 10/30/2015 Grid-based Coordinated Routing Based on flooding Randomly placed sensor nodes – limited energy Fixed source and sink – infinite energy Square-shaped grids of specific width One coordinator per grid square Based on flooding Randomly placed sensor nodes – limited energy Fixed source and sink – infinite energy Square-shaped grids of specific width One coordinator per grid square 14

17. 10/30/2015 Grid-based Coordinated Routing 15

18. 10/30/2015 Grid-based Coordinated Routing 16

19. 10/30/2015 Grid-based Coordinated Routing Link model Dynamic and lossy wireless links Deterministic link model: Link model Dynamic and lossy wireless links Deterministic link model: 17

20. 10/30/2015 Link Model Probabilistic Link Model 18

21. 10/30/2015 Link Model Log Normal Shadowing Model Variations in environmental clutter Link model with log normal distribution Log Normal Shadowing Model Variations in environmental clutter Link model with log normal distribution Zero mean Gaussian distributed random variable with std. dev. σ 19

22. 10/30/2015 Grid-based Coordinated Routing Coordinator election Random node ID Coordinator = maximum node ID Grid size estimation Coordinator election Random node ID Coordinator = maximum node ID Grid size estimation 20

23. 10/30/2015 Grid-based Coordinated Routing Load balancing Nodes are ranked based on energy available Coordinator nodes with energy greater than 25 % of battery – rank + 1 Coordinator nodes with energy less than 25 % of battery – rank + 2 Current coordinators are replaced by lower ranked nodes in respective grid squares Equal distribution of routing load Load balancing Nodes are ranked based on energy available Coordinator nodes with energy greater than 25 % of battery – rank + 1 Coordinator nodes with energy less than 25 % of battery – rank + 2 Current coordinators are replaced by lower ranked nodes in respective grid squares Equal distribution of routing load 21

24. 10/30/2015 Simulation Assumptions Source and sink nodes have infinite energy Sensor nodes have limited energy Sensor field = 1000 m X 1000 m Number of nodes = 1000 Transmit power = -2 dBm, 1 dBm, 4 dBm Sensitivity = -87 dBm, -90 dBm, -93 dBm Node energy = 100 units, 250 units, 500 units Path loss exponent = 3.5 Transition region width = 60 m Grid width = 50 m, 100 m, 150 m, 200 m, 250 m Assumptions Source and sink nodes have infinite energy Sensor nodes have limited energy Sensor field = 1000 m X 1000 m Number of nodes = 1000 Transmit power = -2 dBm, 1 dBm, 4 dBm Sensitivity = -87 dBm, -90 dBm, -93 dBm Node energy = 100 units, 250 units, 500 units Path loss exponent = 3.5 Transition region width = 60 m Grid width = 50 m, 100 m, 150 m, 200 m, 250 m 22

25. 10/30/2015 Energy Consumption Model RangeLAN2 7401/02 PC card 300 mA – transmit 150 mA – receive Average 5 mA - doze mode RangeLAN2 7401/02 PC card 300 mA – transmit 150 mA – receive Average 5 mA - doze mode 23

26. 10/30/2015 Energy Consumption Assumptions: A node spends 0.5, 1.0, and 2.0 units of battery energy for transmission when transmit power of -2 dBm, 1 dBm, and 4 dBm resp. A node spends 0.5 unit of battery energy for reception An active coordinator spends 0.5 unit of battery energy if it is within the radio range of transmitting coordinator Assumptions: A node spends 0.5, 1.0, and 2.0 units of battery energy for transmission when transmit power of -2 dBm, 1 dBm, and 4 dBm resp. A node spends 0.5 unit of battery energy for reception An active coordinator spends 0.5 unit of battery energy if it is within the radio range of transmitting coordinator 24

27. 10/30/2015 Simulation Topology 24

28. 10/30/2015 Simulation Parameter GUI 26

29. 10/30/2015 Simulation metrics Metrics and terms: Normalized energy Event Network partition Metrics and terms: Normalized energy Event Network partition 27

30. 10/30/2015 Simulation varying the transmit power Transmit power = -2 dBm, 1 dBm, 4 dBm Sensitivity = -90 dBm Node energy = 250 units Transmit power = -2 dBm, 1 dBm, 4 dBm Sensitivity = -90 dBm Node energy = 250 units 28

31. 10/30/2015 Simulation Results varying the transmit power 29

32. 10/30/2015 Simulation varying the transmit power Transmit power = -2 dBm, 1 dBm, 4 dBm Sensitivity = -90 dBm Node energy = 100 units Transmit power = -2 dBm, 1 dBm, 4 dBm Sensitivity = -90 dBm Node energy = 100 units 30

33. 10/30/2015 Simulation Results varying the transmit power 31

34. 10/30/2015 Simulation varying the transmit power Transmit power = -2 dBm, 1 dBm, 4 dBm Sensitivity = -90 dBm Node energy = 500 units Transmit power = -2 dBm, 1 dBm, 4 dBm Sensitivity = -90 dBm Node energy = 500 units 32

35. 10/30/2015 Simulation Results varying the transmit power 33

36. 10/30/2015 Simulation Results varying the transmit power Network partition is extended with increase in transmit power A grid width of 200 m provides longest network partition All the grid width networks perform better than traditional flooding Network partition is extended with increase in transmit power A grid width of 200 m provides longest network partition All the grid width networks perform better than traditional flooding 34

37. 10/30/2015 Simulation varying the sensitivity Transmit power = 1 dBm Sensitivity = -87 dBm, -90 dBm, -93 dBm Node energy = 250 units Transmit power = 1 dBm Sensitivity = -87 dBm, -90 dBm, -93 dBm Node energy = 250 units 35

38. 10/30/2015 Simulation Results varying the sensitivity 36

39. 10/30/2015 Simulation varying the sensitivity Transmit power = 1 dBm Sensitivity = -87 dBm, -90 dBm, -93 dBm Node energy = 100 units Transmit power = 1 dBm Sensitivity = -87 dBm, -90 dBm, -93 dBm Node energy = 100 units 37

40. 10/30/2015 Simulation varying the sensitivity 38

41. 10/30/2015 Simulation varying the sensitivity Transmit power = 1 dBm Sensitivity = -87 dBm, -90 dBm, -93 dBm Node energy = 500 units Transmit power = 1 dBm Sensitivity = -87 dBm, -90 dBm, -93 dBm Node energy = 500 units 39

42. 10/30/2015 Simulation varying the sensitivity 40

43. 10/30/2015 Simulation Results varying the sensitivity Network partition is extended as sensitivity is increased Network partition is extended by a factor of 4 when S=-93 dBm and by a factor of 3 when S=-90 dBm compared to when S=-87 dBm A grid width of 200 m provides longest network partition All grid widths perform better than traditional flooding Network partition is extended as sensitivity is increased Network partition is extended by a factor of 4 when S=-93 dBm and by a factor of 3 when S=-90 dBm compared to when S=-87 dBm A grid width of 200 m provides longest network partition All grid widths perform better than traditional flooding 41

44. 10/30/2015 Simulation Results scalability Parameters: Number of nodes = 100, 250, 500, 750, 1000, 1250, 1500 Sensor field = 1000 m X 1000 m Battery life per node = 250 units Transmit power = 1 dBm Sensitivity = -90 dBm Transition region width = 60 m Path loss exponent = 3.5 Grid width = 200 m Parameters: Number of nodes = 100, 250, 500, 750, 1000, 1250, 1500 Sensor field = 1000 m X 1000 m Battery life per node = 250 units Transmit power = 1 dBm Sensitivity = -90 dBm Transition region width = 60 m Path loss exponent = 3.5 Grid width = 200 m 42

45. 10/30/2015 Simulation Results scalability 1

46. 10/30/2015 Simulation Results scalability Node redundancy increases, partition is extended Partition for 1500 nodes is extended by a factor of 2 compared to 1000 nodes Partition for 1500 nodes is extended by a factor of 17 compared to 100 nodes Linear increase in network partition Node redundancy increases, partition is extended Partition for 1500 nodes is extended by a factor of 2 compared to 1000 nodes Partition for 1500 nodes is extended by a factor of 17 compared to 100 nodes Linear increase in network partition 44

47. 10/30/2015 Conclusions Network partition is prolonged as transmit power increases Network partition is prolonged as sensitivity increases Grid width of 200 m show consistently better performance in extending network partition Network partition for 1500 nodes is extended by a factor of 17 compared to 100 nodes Comparison with traditional flooding algorithm Network partition is prolonged as transmit power increases Network partition is prolonged as sensitivity increases Grid width of 200 m show consistently better performance in extending network partition Network partition for 1500 nodes is extended by a factor of 17 compared to 100 nodes Comparison with traditional flooding algorithm 45

48. 10/30/2015 Future work Physical implementation Localized reflooding Node mobility Physical implementation Localized reflooding Node mobility 46

49. 10/30/2015 Thank You Questions?