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Data Collection and Dissemination

Data Collection and Dissemination. Learning Objectives. Understand Trickle – an data dissemination protocol for WSNs Understand data collection protocols in low-duty-cycled WSNs with unreliable links. Prerequisites. Basic concepts of Computer Network protocols

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Data Collection and Dissemination

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  1. Data Collection and Dissemination

  2. Learning Objectives • Understand Trickle – an data dissemination protocol for WSNs • Understand data collection protocols in low-duty-cycled WSNs with unreliable links

  3. Prerequisites • Basic concepts of Computer Network protocols • Basic concepts of protocol optimization

  4. Outline • Data Dissemination • Estrablishing eventual consistency on a shared variable • Trickle – Address single packet • Data Collection • DSF

  5. Data Dissemination - Trickle [Dissemination_1]

  6. Simple Broadcast Retransmission • Broadcast Storm Problem • Redundant rebroadcasts • Severe contention • Collision

  7. Trickle • Motivation • WSNs require network code propagation • Challenges • WSNs exhibit highly transient loss patterns, susceptible to environmental changes • WSNs network membership is not static • Motes must periodically communicate to learn when there is new code • Periodical metadata exchange is costly

  8. Trickle Requirement • Low Maintenance • Rapid Propagation • Scalability

  9. Trickle • An algorithm for code propagation and maintenance in WSNs • Based on “Polite Gossip” • Each node only gossip about new things that it has heard from its neighbors, but it won’t repeat gossip it has already heard, as that would be rude • Code updates “trickle” through the network

  10. Trickle • Within a node time period • If a node hears older metadata, it broadcasts the new data • If a node hears newer metadata, it broadcasts its own metadata (which will cause other nodes to send the new code) • If a node hears the same metadata, it increases a counter • If a threshold is reached, the node does not transmit its metadata • Otherwise, it transmits its metadata

  11. Trickle – Main Parameters • Counter c: Count how many times identical metadata has been heard • k: threshold to determine how many times identical metadata must be heard before suppressing transmission of a node’s metadata • t: the time at which a node will transmit its metadata. t is in the range of [0, τ]

  12. Trickle Maintenance – One Example • Assume • No packet loss • Perfect interval synchronization • How to relax these assumptions? [Dissemination_1]: Figure 3

  13. Trickle – Impact of Packet Loss Rates

  14. Trickle Maintenance without Synchronization – Short Listen Problem • Mote B selects a small t on each of its three intervals • Although other motes transmit, mote B’s transmissions are never suppressed • The number of transmissions per intervals increases significantly [Dissemination_1]: Figure 5

  15. Trickle – Impact of Short Listen Problem

  16. Solution to Short Listen Problem • Instead of picking a t in the range [0, τ], t is selected in the range [τ/2, τ]

  17. Propagation • Tradeoff between different values of τ • A large τ • Low communication overhead • Slowly propagates information • A small τ • High communication overhead • Propagate more quickly • How to improve? • Dynamically adjust τ • Lower Boundτl • Upper Boundτh [Dissemination_1]: Section 5

  18. Trickle Complete Algorithm

  19. Data Collection

  20. Data Collection • Link-Quality based Data Forwarding • Wireless communication links are extremely unreliable • ETX: to find high-throughput paths on multiple • Sleep-Latency Based Forwarding • Duty Cycling: sensor nodes turn off their radios when not needed • Idle listening waste much energy [Collection_2]

  21. Sleep Latency in Low Duty-Cycle Sensor Networks Sleep now. Wake up in 57seconds Sleep now. Wake up in 35 seconds D B 57s latency 35s latency A 13s latency 4s latency E C Sleep now. Wake up in 4 seconds Sleep now. Wake up in 13 seconds [Collection_2]

  22. Unreliable Radio Links D B 70% 90% A 50% 95% C E

  23. State-of-the-art Solutions: ETX ETX only considers link quality ETX = 1/0.5 + 1/0.5 = 4 B 50%, 100s 50%, 100s Expected E2E delay is 400s Sole link quality based solutions cannot help reduce E2E delay in extremely low-duty cycle sensor networks! A D Expected E2E delay is 50s 40%, 10s 40%, 10s C ETX = 1/0.4 + 1/0.4 = 5

  24. State-of-the-art Solutions: DESS DESS = 10 + 10 = 20s DESS only considers sleep latency B 10%, 10s 10%, 10s Expected E2E delay is 200s Sole sleep latency based solutions cannot help reduce E2E delay in extremely low-duty cycle sensor networks! D A Expected E2E delay is 40s 100%, 20s 100%, 20s C DESS = 20 + 20 = 40s

  25. End-to-End Delay vs. Duty Cycle • Suppose one fixed forwarding node • Suffer excessive delivery delays when waiting for the fixed receiver to wake up again if the ongoing packet transmission fails

  26. End-To-End Delay vs. Average Link Quality • Given bad link quality, the end to end delay increases dramatically

  27. Sensor States Representation 1 0 1 1 0 1 0 1 Off On • Scheduling Bits • (10110101)* • Switching Rate • 0.5HZ 16s round time

  28. Data Delivery Process ( 1 0 0 0 0 0 0 0 0 0 )* ( 0 1 0 0 0 0 0 0 0 0 )* ( 0 0 0 1 0 0 0 0 0 0 )* ( 0 0 0 0 0 0 1 0 0 0 )* 1 2 3 4 Sleep latency is 1 Sleep latency is 2 Sleep latency is 3 End to End (E2E) Delay is 6

  29. Main Idea Sleep latency is 1 1st attempt: Sleep latency is 1 We should try a sequence of forwarding nodes instead of a fixed forwarding node! ( 1 0 0 0 0 0 0 0 0 0 )* ( 0 1 0 0 0 0 0 0 0 0 )* ( 0 0 0 1 0 0 0 0 0 0 )* ( 0 0 0 0 0 0 1 0 0 0 )* 1 2 3 4 ( 0 0 1 0 0 0 0 0 0 0 )* 5 Dynamic Switching-based Forwarding (DSF) is important in extremely low duty-cycle sensor networks. ith attempt: Sleep latency is 1 + 10 * (i-1) 2nd attempt: Sleep latency is 1 + 10 =11 2nd attempt: Sleep latency is 1 + 1 =2

  30. Optimization Objectives • EDR: Expected Delivery Ratio • EED: Expected End-to-End Delay • EEC: Expected Energy Consumption

  31. Optimization Objectives(1) : EDR Forwarding Sequence EDR: Expected Delivery Ratio. 2 (0100)* EDR = 70% (1000)* 60% 1 3 EDR for node 1 is (EDR1): (0010)* EDR = 80% 50% 0.6*0.7 + (1-0.6)*0.5*0.8 40% + (1-0.6)*(1-0.5)*0.4*0.9=0.652 4 (0001)* EDR = 90% See Equation (3)

  32. Optimization Objectives(2) : EED Forwarding Sequence EED: Expected E2E Delay. 2 (001000)* EDR = 70%, EED = 10 (100000)* 60%,2 1 3 EED for node 1 is (EED): (000010)* EDR = 80%, EED = 12 50%, 3 40%,5 0.6*1/0.7 * (2 + 10) + (1-0.6) * 0.5 * 1 /0.8 * (3 + 12) + (1-0.6)*(1-0.5)*0.4*1/0.9 * (5 + 9) 4 (000001)* EDR = 90%, EED = 9 See Equation (4)

  33. Optimizing EDR Shall we try all available neighbors? If both node 2 and node 3 are selected as forwarding nodes: EDR1 = 1 * 0.7 = 0.7 2 (010)* EDR = 70% (100)* 100% We should only choose a subset of neighboring nodes as forwarding nodes! 1 100% If only node 3 is selected as forwarding node: EDR1 = 1 * 0.8 = 0.8 3 (001)* EDR = 80%

  34. Optimizing EDR with dynamic programming Try or skip 2 Select only a subset of neighbors as forwarders (010)* EDR = 70% (100)* 60% Try or skip Node 4 has to be selected 1 3 (001)* EDR = 80% 50% Then we attempt to add more nodes into the forwarding sequence backwardly. 40% Try or drop 4 (100)* EDR = 90%

  35. Distributed Implementation • EDRb(Ø) = 1 • The sink node has no packet loss • EEDb(Ø) = 0 • The sink node has no delay • EECb(Ø) = 0 • The sink node has no energy consumption

  36. Distributed Implementation • EDR = 99%, EED = 15, EEC = 2 • EDR = 98%, EED = 2, EEC = 1 1 3 • EDR = 100%, EED = 0, EEC = 0 sink 2 4 • EDR = 97%, EED = 20, EEC = 5 • EDR = 90%, EED = 90, EEC = 12

  37. Complete Protocol Implementation at Node e

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