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Routing Protocols for Sensor Networks. Agenda n General Properties n Architectures and Requirements n Routing Protocols Classification n 10 Suggested.

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Presentation on theme: "Routing Protocols for Sensor Networks. Agenda n General Properties n Architectures and Requirements n Routing Protocols Classification n 10 Suggested."— Presentation transcript:

1 Routing Protocols for Sensor Networks

2 Agenda n General Properties n Architectures and Requirements n Routing Protocols Classification n 10 Suggested Routing Protocols: n LEACH n PEGASIS n TEEN n APTEEN n SPIN n DD n MCF n TTDD n RW n RR

3 Acknowledgements n E. Magistretti (U. Bologna Italy) J. Kulik (MIT; BBN Co.) J. Kulik (MIT; BBN Co.) n R. R. Choudhury, P. Kyasanur & N. Vaidya (UIUC) n P. Desai (UFL) n D. Braginsky and D. Estrin (UCLA) n S. Hazarika, W. Chen, Y. Gong & X. Liu (UMASS) n T. Kwon & Mjnam (SNU Korea) n R. Peterson & D. Rus (Dartmouth C.) n H.C. Chung, K. Ghoshal & J. Krishna (TAMU) n C. Tavoularis (Cornell ) n G. Dong (Virginia U.)

4 WSN Dartmouth College

5 Concepts

6 Application: Military From UMASS

7 Environmental

8 Future Health Circulatory Net

9 Agenda n General Properties n Architectures and Requirements n Routing Protocols Classification n 10 Suggested Routing Protocols: n LEACH n PEGASIS n TEEN n APTEEN n SPIN n DD n MCF n TTDD n RW n RR

10 General Properties (1) n Mainly for Information Collection n Single Owner n Up to Hundreds of Thousands of Nodes n Disposable Nodes n Cheap Nodes n Security Concerns

11 General Properties (2) n Bounded Directed Stream (from/to Sink) n Somewhat Limited Computation Capability n Limited Communication Capability n Limited Power Resources n Node may not have Unique ID n Common case - Stationary Nodes

12 Agenda n General Properties n Architectures and Requirements n Routing Protocols Classification n 10 Suggested Routing Protocols: n LEACH n PEGASIS n TEEN n APTEEN n SPIN n DD n MCF n TTDD n RW n RR

13 General Architecture (1) n Sensor Unit n ADC – Analog Digital Converter n CPU – Central Processing Unit n Power Unit n Communication Unit Sensor Network Node Main Components

14 General Architecture (2)

15 General Requirements (1) n Varying Network Size n Inexpensive Nodes Equipment Long Lifetime (Power)  Load-Balancing Long Lifetime (Power)  Load-Balancing n Self-Organization n Re-tasking and Querying Capability

16 General Requirements (2) n Sensible Data Aggregation n Consolidation of Redundant Data n Application Awareness  Tradeoff Communication for Computation n Possible Mobility

17 Agenda n General Properties n Architectures and Requirements n Routing Protocols Classification n 10 Suggested Routing Protocols: n LEACH n PEGASIS n TEEN n APTEEN n SPIN n DD n MCF n TTDD n RW n RR

18 Protocol Classification (1) n Proactive – First Compute all Routes; Then Route n Reactive – Compute Routes On-Demand n Hybrid – First Compute all Routes; Then Improve While Routing

19 Protocol Classification (2) n Direct – Node and Sink Communicate Directly (Fast Drainage; Small Scale) n Flat (Equal) – Random Indirect Route (Fast Drainage Around Sink; Medium Scale) n Clustering (Hierarchical) – Route Thru Distinguished Nodes

20 Protocol Classification (3) n Location Aware – Nodes knows where they are n Location-Less – Nodes location is unimportant n Mobility Aware – Nodes may move – Sources; Sinks; All

21 Protocol Classification (4) n Unicast – One-to-One Message Passing n Multicast (actually Local Broadcast) – Node-to-Neighbors Message Passing n Broadcast – Full-Mesh – Source to Everyone

22 Protocol Classification (5) n Historical Queries: Analysis of historical data “What was the watermark 2h ago in the southeast?” n One-time Queries: Snapshot view “What is the watermark in the southeast?” n Persistent Queries: Monitoring over time “Report the watermark in the southeast for the next 4h” Query Models:

23 Protocol Classification (6)

24 Agenda n General Properties n Architectures and Requirements n Routing Protocols Classification n 10 Suggested Routing Protocols: n LEACH n PEGASIS n TEEN n APTEEN n SPIN n DD n MCF n TTDD n RW n RR

25 1 - LEACH – Discussed … n Self-Organizing – Adaptive Clustering n Cluster-Heads elect themselves – Now – “Random Round-Robin” Future – Power-Based Probability n Nodes die in random n Stationary Sink n Localized Coordination n Data Fusion Low Energy Adaptive Clustering Hierarchy Protocol Highlights

26 1 - LEACH (2) n “Hot Spot” Problem (Nodes on a path from an event-congested area to the sink may drain) n I nadequate for Time-Critical Applications n Stationary Sink – Maybe Unpractical n Basic Algorithm assumes any node can communicate with sink – limited scale Low Energy Adaptive Clustering Hierarchy Main Drawbacks

27 1 - LEACH (3) n Works in Rounds, each with Set-Up (Short) and Steady-State (Long) n Set-Up Phase - subdivided: –Advertisement (I am a Cluster-Head) –Cluster Set-Up (I am in your Cluster) –Schedule Creation (This is your slot) n Steady-State Phase: –Data Transmission using TDMA Low Energy Adaptive Clustering Hierarchy Main Procedures

28 1 - LEACH (4) n Everyone uses the same channel n Different clusters use different CDMA codes n Code chosen in random n Cluster-Head communicate with Sink n Can be extended to Hierarchical Clustering Low Energy Adaptive Clustering Hierarchy Main Procedures

29 1 - LEACH (5) Low Energy Adaptive Clustering Hierarchy Illustrations

30 1 - LEACH (6) Low Energy Adaptive Clustering Hierarchy Illustrations

31 2 - PEGASIS (1) n Token-Passing Chain-Based n Considered Near-Optimal (in a sense) n Nodes die in random n Stationary Nodes and Sink n Every node have a global network map n Data Fusion n Greedy chain construction Protocol Highlights Power-Efficient Gathering in Sensor Information Systems

32 2 - PEGASIS (2) n Stationary Nodes n Global Information Limited Scale: n Information travels many nodes n Assumes any node can communicate with sink Main Drawbacks Power-Efficient Gathering in Sensor Information Systems

33 2 - PEGASIS (3) n Greedy Algorithm Construct Chain – Start at a node far from sink and gather everyone neighbor by neighbor Node i (mod N) is the leader in round i Node i (mod N) is the leader in round i n Nodes passes token thru the chain to leader from both sides n Each node fuse its data with the rest n Leader transmit to sink Main Procedures Power-Efficient Gathering in Sensor Information Systems

34 2 - PEGASIS (4) Illustrations Power-Efficient Gathering in Sensor Information Systems

35 2 - PEGASIS (5) Power-Efficient Gathering in Sensor Information Systems Illustrations Rounds Until Death

36 3 - TEEN (1) n LEACH based Clustering n Smart data transmission (Saves Power) n Nodes dynamic reconfiguration ability n Suits for Time-Critical applications Threshold sensitive Energy Efficient Sensor Network Protocol Highlights

37 3 - TEEN (2) n “Hot Spot” Problem n Cluster-Heads need to listen constantly n Wasted time-slots n Can’t distinguish dead nodes n Other LEACH problems… Main Drawbacks Threshold sensitive Energy Efficient Sensor Network

38 3 - TEEN (3) n LEACH Proactive Clustering n Node transmit in timeslot only if both: –Value greater then a Hard Threshold (H T ) –Value differs from last transmitted value (SV ) by more then a Soft Threshold (S T ) n After transmission SV is reset Main Procedures Threshold sensitive Energy Efficient Sensor Network

39 3 - TEEN (4) Illustrations Threshold sensitive Energy Efficient Sensor Network

40 4 - APTEEN (1) n Improved (Adaptive - Hybrid) TEEN n All TEEN Features n More flexible logic and timeslots n Multi-type Queries: –Historical (What was the temp. then?) –One-time (What’s the temp. now?) –Persistent (Tell me the temp for 2 hours) n Can distinguish dead nodes Adaptive Periodic Threshold-sensitive Energy Efficient Sensor Network Protocol Highlights

41 4 - APTEEN (2) n LEACH problems… n Complex logic Main Drawbacks Adaptive Periodic Threshold-sensitive Energy Efficient Sensor Network

42 4 - APTEEN (3) n LEACH Proactive Clustering n Node transmit in timeslot only if both: –Value greater then a Hard Threshold (H T ) –Value differs from last transmitted value (SV ) by more then a Soft Threshold (S T ) Or If did not transmit for a max time (T C ) Or if queried by some sink n After transmission SV is reset Main Procedures Adaptive Periodic Threshold-sensitive Energy Efficient Sensor Network

43 4 - APTEEN (4) Illustrations Adaptive Periodic Threshold-sensitive Energy Efficient Sensor Network Power Consumption: n As could be expected – APTEEN is better the LEACH but not as good as TEEN

44 5 - SPIN (1) n Network-wide Broadcast Limited by Negotiation and using Local Communication n Flooding problems solved: Implosion – same data from many neighbors Implosion – same data from many neighbors Detection of overlapping regions Detection of overlapping regions Excessive resources consumption (Blindness) Excessive resources consumption (Blindness) n Needs only Localized Information n Data Fusion n Two main protocols SPIN-PP & SPIN-BC Sensor Protocol for Information via Negotiation Protocol Highlights

45 5 - SPIN (2) n Broadcast - Limited Scale – every node handles O(n) messages n Data is updated throughout network – unnecessary in many cases n Network lifetime - not clear n High degree nodes = High power needs Main Drawbacks Sensor Protocol for Information via Negotiation

46 5 - SPIN (3) SPIN-PP (Point-to-Point Communication) n Data is described by meta-data ADV msg. Node has data  sends ADV to neighbors Node has data  sends ADV to neighbors If neighbor do not have data  sends REQ If neighbor do not have data  sends REQ n Node responds by sending the DATA n This process continues around the network n Nodes may aggregate their data to ADV n In a Lossy Network ADV may be repeated periodically and REQ if not answered Main Procedures Sensor Protocol for Information via Negotiation

47 5 - SPIN (4) SPIN-BC (Local Broadcast Communication) n ADV and DATA sending like PP (but in B.C.) n Since only one REQ answer is needed, any node waits a random interval and B.C. REQ only if none was received yet. n The rest – like SPIN-PP Main Procedures Sensor Protocol for Information via Negotiation

48 ADV Node with data Node with data advertises to all its neighbors 5 - SPIN (5) Illustrations Sensor Protocol for Information via Negotiation SPIN-PP

49 REQ Node with data Neighbor requests for data and it is sent Illustrations Sensor Protocol for Information via Negotiation 5 - SPIN (5) SPIN-PP

50 DATA Node with data Node with data advertises to all its neighbors 5 - SPIN (5) Illustrations Sensor Protocol for Information via Negotiation SPIN-PP

51 Node with data ADV Receiving node sends ADV to neighbors Illustrations Sensor Protocol for Information via Negotiation 5 - SPIN (5) SPIN-PP

52 Node with data Receiving neighbors requests for data. REQ Illustrations Sensor Protocol for Information via Negotiation 5 - SPIN (5) Already has data (or dead) SPIN-PP

53 Node with data DATA Receiving node sends ADV to neighbors Illustrations Sensor Protocol for Information via Negotiation 5 - SPIN (5) SPIN-PP

54 6 - DD (1) n Hybrid Data Centric Routing – Looking for Named Data n Query–Response Model n Performs Better than Flooding n Robust and Fault Tolerant (bypass faults) n Localized n Localized Interactions n Data Fusion - n Data Fusion - Application Specific Filters Directed Diffusion Protocol Highlights

55 6 - DD (2) n “Hot Spot” Problem near sink n Periodic Broadcasts of “Interest” Reduces Network Lifetime n Trade-off: Energy Efficiency vs. Robustness and Scalability n Complex Data Aggregation - may Lead to Expensive Node Directed Diffusion Main Drawbacks

56 6 - DD (3) n A Query (Interest) is Broadcasted by a node (sink) n Query Reaches Relevant Sensor Sources n This Sets-Up Exploratory Gradients n Once Data is Available in a Source it is Sent Back via Reinforced Path n Failing Links / Nodes are being Gradually Bypassed Directed Diffusion Main Procedures

57 Source Sink Interest = Interrogation Gradient = Who is interested CLASS_KEY IS INTEREST_CLASS LONGITUDE_KEY GE 10 LONGITUDE_KEY LE 50 LATITUDE_KEY GE 100 LATITUDE_KEY LE 120 SENSOR EQ MOVEMENT INTENSITY GE 0.6 CONFIDENCE GE 0.7 INTERVAL IS 10 EXPIRE_TIME IS 100 6 - DD (4) Directed Diffusion Illustrations

58 Source Sink Interest = Interrogation Gradient = Who is interested 2. subscribe (AttrVec, ApplCallback) 1. subscribe (InterestAttrVec, Callback) InterestAttrVec CLASS_KEY EQ INTEREST_CLASS LONGITUDE_KEY IS 35 LATITUDE_KEY IS 110 SENSOR IS MOVEMENT 3. addFilter (FilAttrVec, FilterCallback) FilterAttrVec CLASS_KEY EQ DATA_CLASS SENSOR EQ MOVEMENT INTENSITY GE 0.7 6 - DD (4) Directed Diffusion Illustrations

59 Interests Setting up gradients Source Sink Interest = Interrogation Gradient = Who is interested 6 - DD (4) Directed Diffusion Illustrations

60 Source Sink 4. h = publish (SensedAttrVec) 5. send (h, SensedAttrVec) SensedAttrVec CLASS_KEY IS DATA_CLASS LONGITUDE_KEY IS 35 LATITUDE_KEY IS 110 SENSOR IS MOVEMENT INTENSITY IS 0.8 CONFIDENCE IS 0.7 Low rate event Sending data … 6 - DD (4) Directed Diffusion Illustrations

61 Source Low rate event 6. FilterCallback.recv (Message m1) m2 CLASS_KEY IS DATA_CLASS LONGITUDE_KEY IS 35 LATITUDE_KEY IS 110 SENSOR IS MOVEMENT INTENSITY IS 0.8 CONFIDENCE IS 0.8 7. sendMessage (Message new) m1a m1b m2 6 - DD (4) Directed Diffusion Illustrations

62 Source Sink Low rate event 8. ApplCallback.recv (NRAttrVec) 6 - DD (4) Directed Diffusion Illustrations

63 Source Sink … and Reinforcing the best path Low rate eventReinforcement = Increased interest CLASS_KEY IS INTEREST_CLASS LONGITUDE_KEY GE 10 LONGITUDE_KEY LE 50 LATITUDE_KEY GE 100 LATITUDE_KEY LE 120 SENSOR EQ MOVEMENT INTENSITY GE 0.6 CONFIDENCE GE 0.7 INTERVAL IS 1 EXPIRE_TIME IS 90 6 - DD (4) Directed Diffusion Illustrations

64 Recovering from node failure Source Sink Low rate event High rate event Reinforcement 6 - DD (5) Directed Diffusion Illustrations

65 Source Sink Stable path Low rate event High rate event 6 - DD (5) Directed Diffusion Illustrations

66 Recovering from link failure Source Sink Low rate event High rate event Reinforcement 6 - DD (6) Directed Diffusion Illustrations

67 Stable path Source Sink Low rate event High rate event Reinforcement Use: “Interests set up gradients drawing down data” 6 - DD (6) Directed Diffusion Illustrations

68 7 - MCF (1) n Cost-Field min Cost from Node to Sink on Optimal Path n Slop-Down the Cost-Fields to Get to Sink n Minimize Multiple Transmissions using Back-Off Algorithm Based on Node Cost n Localized Communication Minimum Cost Forwarding Protocol Highlights

69 7 - MCF (2) n High Time Complexity (due to back-off) n Many Sinks – Large Cost Tables n Cost Field Set-Up Time O(N) n No Load-Balancing Main Drawbacks Minimum Cost Forwarding

70 7 - MCF (3) Broadcast ADV msg. and get Answers from all Sinks  Create Cost-Fields Broadcast ADV msg. and get Answers from all Sinks  Create Cost-Fields n Calculate Back-Off Timer Proportional to Cost per each Sink n Needed Information Sent thru Slop n If no ACK until Timer Expires – Resend ADV Main Procedures Minimum Cost Forwarding

71 7 - MCF (4) Minimum Cost Forwarding IllustrationsCost Timeline A B C

72 7 - MCF (5) Minimum Cost Forwarding Illustrations S = 200 B = 120 C = 90 A=150 Sink = 0 130 100 110 50 60 90

73 8 - TTDD (1) n Grid Structure Clustering n Stationary Location-Aware Nodes n Mission Aware – Infrequent Changes n Greedy Geographical Forwarding – Building Grid n Localized Communication Two-Tier Data Dissemination Protocol Highlights

74 8 - TTDD (2) n No Mobile Sensors n Requires Location Information n Grid Nodes may Drain Main Drawbacks Two-Tier Data Dissemination

75 8 - TTDD (3) n Grid Build using Greedy Algorithm and Location Awerness n Node Floods Messages to Dissemination Nodes n Dissemination Nodes Forward to Sink n If a Node Fails – Grid is Fixed Main Procedures Two-Tier Data Dissemination

76 Source Dissemination Node Sink Data Announcement Query Data Immediate Dissemination Node 8 - TTDD (4) Illustrations Two-Tier Data Dissemination TTDD Basics

77 Source Dissemination Node Sink Data Announcement Data Immediate Dissemination Node Immediate Dissemination Node Trajectory Forwarding Trajectory Forwarding 8 - TTDD (5) Illustrations Two-Tier Data Dissemination TTDD Mobile Sinks

78 TTDD Multiple Mobile Sinks Source Dissemination Node Data Announcement Data Immediate Dissemination Node Trajectory Forwarding 8 - TTDD (6) Illustrations Two-Tier Data Dissemination

79 9 - RW (1) n Finding a Random Walk over a Grid n Multi-path Routing n Load Balancing n Large Scale Networks n Nodes Assumed to be Mostly Stationary n No Location Information Needed n Little State Information n Localized Communication Random Walks Protocol Highlights Different Routes at Different Times

80 9 - RW (2) n Topology may not be Practical (Nodes are Assumed to be Located at Cubic Grid Junctions) Main Drawbacks Random Walks

81 9 - RW (3) - RSG Regular Static Graphs Find coordinates differences (  x,  y) using Distributed Bellman Ford (local comm.) Find coordinates differences (  x,  y) using Distributed Bellman Ford (local comm.) n For every node compute probability of moving on X and Y (By the diagonal to the destination) n On each node move to a adjacent one on X or Y using that probability. Adjust near end. All Paths together draws a straight “Banana” Main Procedures Random Walks

82 9 - RW (4) - ISG Irregular Static Graphs (Some dead nodes) n Same as RSG but… n If one adjacent node is missing – go to the other (with p=1). n If both are missing – go to a neighbor whose B-F distance to the destination is strictly smaller than the current node (This will create a detour). n (Could optimize by not getting to that node…). Main Procedures Random Walks

83 9 - RW (5) - DG Dynamic Graphs (Nodes may sleep and wake) n Same as ISG but… n When a node changes state: the one-hop neighbors change B-F labels and possibly trigger further label (distances) changes n Concerns: –Delays in propagating updates –Sensitivity to inaccuracies in labels Main Procedures Random Walks

84 9 - RW (6) - RSG Illustrations Random Walks

85 9 - RW (7) – RSG vs. ISG Illustrations Random Walks ISG

86 9 - RW (8) – RSG vs. ISG Illustrations Random Walks RSG (DG Similar) ISG A Random walk by flipping a fair coin Load Distribution - Narrow

87 9 - RW (9) – RSG vs. ISG Illustrations Random Walks RSG (DG Similar) ISG A Random walk by RSG/ISG algorithms Load Distribution - Flat

88 10 - RR (1) n Observation: for many application any arbitrary path will do – No Need for a Shortest Path n Nodes are Densely Distributed n Bidirectional Links n Localized Communication n Stationary Nodes n Meet Trails of Queries and Events Rumor Routing Protocol Highlights

89 10 - RR (2) n Attractive only when the ratio between events and queries is inside a threshold where it is not attractive to flood neither. n Optimal parameters depend heavily on topology (but can be adaptively tuned) n Does not guarantee delivery Main Drawbacks Rumor Routing

90 10 - RR (3) n Movement on the net is done by several agents, trying (randomly) to walk straight. n Every node maintains lists of neighbors and events (how to get to the reporting node). n An agent coming from and event is updating nodes it visits. n An agent coming from a query is searching for ways to the reporting nodes. n High probability the lines will intersect. Main Procedures Rumor Routing

91 10 - RR (4) Rumor Routing Illustrations Event 1 Event 2 Knows Event 1 Knows Event 2Knows Both Event Agent

92 10 - RR (5) Rumor Routing Illustrations Event Source Query Source VeryTheoreticalExecution

93 Agenda n General Properties n Architectures and Requirements n Routing Protocols Classification n 10 Suggested Routing Protocols: n LEACH n PEGASIS n TEEN n APTEEN n SPIN n DD n MCF n TTDD n RW n RR Done!!!

94 Conclusions n WSN will spread to many applications n Properties and Requirements are both Unique and Diversified n Routing Protocol choice is and probably will continue to be Application Driven n More Analysis, Simulations and new Ideas are needed for every category

95 References (1) n Q. Jiang, D. Manivannan, Routing Protocols for Sensor Networks, IEEE Consumer Communications and Networking Conference (CCNC'04), 2004. n R. Jurdak, C. V. Lopes, P. Baldiy, A Framework for Modeling Sensor Networks, 19th Annual ACM Conference on Object-Oriented Programming, Systems, Languages, and Applications (OOPSLA'04), 2004. n W. Heinzelman, A. Chandrakasan, and H. Balakrishnan, Energy- Efficient Communication Protocol for Wireless Microsensor Networks, IEEE Proceedings of the IEEE International Conference on System Sciences, 2000. n S. Lindsey, C. S. Raghavendra, PEGASIS: Power Efficient GAthering in Sensor Information Systems, IEEE Aerospace Conference, 2002.

96 References (2) n A. Manjeshwar and D. P. Agrawal, TEEN: A Protocol for Enhanced Efficiency in Wireless Sensor Networks, Proceedings of the 1st International Workshop on Parallel and Distributed Computing Issues in Wireless Networks and Mobile Computing (with IPDPS'01), 2001. n A. Manjeshwar and D. P. Agrawal, APTEEN: a hybrid protocol for efficient routing and comprehensive information retrieval in wireless sensor networks, Proceedings of the International Parallel and Distributed Processing Symposium (IPDPS'02), 2002. n J. Kulik, W. Heinzelman, and H. Balakrishnan, Negotiation-Based Protocols for Disseminating Information in Wireless Sensor Networks, Wireless Networks, Vol. 8, pp. 169-185, 2002. n C. Intanagonwiwat, R. Govindan, D. Estrin, J. S. Heidemann, and F. Silva, Directed Diffusion for Wireless Sensor Networking, IEEE/ACM Transactions on Networking, vol. 11, no. 1, pp. 2-16, 2003.

97 References (3) n F. Ye, A. Chen, S. Lu, L. Zhang, A Scalable Solution to Minimum Cost Forwarding in Large Sensor Networks, Proceedings of the 10th IEEE International Conference on Computer Communications and Networks (ICCCN'01), 2001. n F. Ye, H. Luo, J. Cheng, S. Lu, and L. Zhang, A Two-Tier Data Dissemination Model for Large-scale Wireless Sensor Networks, ACM International Conference on Mobile Computing and Networking (MOBICOM'02), 2002. n S. D. Servetto, G. Barrenechea, Constrained Random Walks on Random Graphs: Routing Algorithms for Large Scale Wireless Sensor Networks, In the Proceedings of the 1st ACM International Workshop on Wireless Sensor Networks and Applications (WSNA'02), 2002. n D. Braginsky, D. Estrin, Rumor Routing Algorithm For Sensor Networks, In the Proceedings of the 1st ACM International Workshop on Wireless Sensor Networks and Applications (WSNA'02), 2002.

98 Karl Friedrich Hieronymus Baron of Munchausen (1720-1797) SenseYour Network Dude


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