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Department of Computer Science University of Virginia

Addressing Burstiness for Reliable Communication and Latency Bound Generation in Wireless Sensor Networks. Department of Computer Science University of Virginia. Sirajum Munir , Shan Lin, Enamul Hoque, S. M. Shahriar Nirjon, John A. Stankovic, and Kamin Whitehouse. Problem Definition.

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Department of Computer Science University of Virginia

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  1. Addressing Burstiness for Reliable Communication and Latency Bound Generation in Wireless Sensor Networks Department of Computer Science University of Virginia Sirajum Munir, Shan Lin, Enamul Hoque, S. M. Shahriar Nirjon, John A. Stankovic, and Kamin Whitehouse

  2. Problem Definition • Reliable delivery • Retry on each link until the packet is received Source N1 N2 N3 2 3 3 N4 N6 N5 N7 5 N8 N10 N9 Destination

  3. Problem Definition • Reliable delivery • Retry on each link until the packet is received • Problem: • Unbounded E2E latency • Not acceptable for real-time applications Source N1 N2 N3 N4 N6 N5 N7 N8 N10 N9 Destination

  4. Overview • Basic Approach • Estimate maximum “burst” length for each link • #consecutive failures Source 100 N1 N2 N3 2 3 200 3 5 N4 3 N6 N5 N7 35 2 5 4 5 62 3 N8 N10 N9 Destination

  5. Overview • Basic Approach • Estimate maximum “burst” length for each link • Key insight: • Burstiness caused by physical world dynamics • Some links are relatively insulated from these dynamics Source 100 N1 N2 N3 2 3 200 3 5 N4 3 N6 N5 N7 35 2 5 4 5 62 3 N8 N10 N9 Destination

  6. Overview • Basic Approach • Estimate maximum “burst” length for each link • Choose routes that only use non-bursty links Source 100 N1 N2 N3 2 3 200 3 5 N4 3 N6 N5 N7 35 2 5 4 5 62 3 N8 N10 N9 Destination

  7. Overview • Basic Approach • Estimate maximum “burst” length for each link • Choose routes that only use non-bursty links • Schedule packet transmission to avoid interference between links Source1 Destination2 N1 N2 N3 N4 N6 N5 N7 N8 N10 N9 Source2 Destination1

  8. Outline • Modeling link burstiness • E2E latency bounds • Evaluation

  9. Modeling Burstiness • Modeling link Bursts • Bmax, B’min per link • Bmax = Maximum No. of time slots where transmission can fail • B’min = Minimum No. of time slots available for transmission • W = Bmax + B’min • Example • B’min = 1 • W = 2 1 0 0 1 1 0 1 …

  10. Modeling Burstiness 1 0 0 1 1 0 1 … • Modeling link bursts • Bmax, B’min per link • Bmax = Maximum No. of time slots where transmission can fail • B’min = Minimum No. of time slots available for transmission • W = Bmax + B’min • Example • B’min = 1 • W = 3 • Bmax = 2

  11. Modeling Burstiness • X  X X X  X X XX X … • Different from existing models • The β Factor [Srinivasan et al.] • Models burstiness based on the distribution of burst lengths • Our model only cares about the maximum burst length

  12. Empirical Study • 21 Days-long • Indoor testbed • 48 Tmote Sky nodes • 3.6 M packets/link • 200 packets/sec • Compute Bmax, B’min, PRR of every link

  13. Empirical Study • Verified: • Some links are very bursty • Some links are not bursty • Bmax is not predicted by PRR • Some highly reliable links (PRR>0.99) still have very large bursts

  14. Outline • Modeling link burstiness • E2E latency bounds • Evaluation

  15. E2E Latency Bound Source Source Bmax N1 N1 N2 N2 N3 N3 Bmax Bmax Bmax Bmax Bmax N4 N4 Bmax N6 N6 N5 N7 N5 N7 Bmax Bmax Bmax Bmax Bmax Bmax Bmax N8 N8 N10 N10 N9 N9 Destination Destination • Min Latency Bound: NP-Hard • Greedy solution: Principles • Routing : Least burst routing

  16. E2E Latency Bound Source N1 N2 N3 N4 N6 N5 N7 N8 N10 N9 Destination • Greedy solution : Principles • Routing: Least burst route • Schedule packet transmission • Allocating time slots: • How many time slots to allocate per link? • Allocate Bmaxi+1 contiguous time slots, for i-th link • Can we do even better? • Yes ! Overlap some streams’ time slot allocation

  17. E2E Latency Bound N1 N2 Time Slots Schedule w/ overlapping Schedule w/o overlapping Prioritizing rule • What is overlapping? • Assume Link L(1,2) has Bmax=2, B’min=4 • 2 Streams: S1, S2 • Why do we need overlapping ? • W/O overlapping: Avg LB = (3 + 6)/2 = 4.5 • W/ overlapping: Avg LB = (3 + 4) /2 = 3.5

  18. E2E Latency Bound N1 N2 Time Slots Complete overlapping: Doesn’t work ! Time Slots • How much to overlap? • Assume Link L(1,2) has Bmax=2, B’min=4 • 2 Streams: S1, S2 • Overlap at most B’min number of streams

  19. E2E Latency Bound N1 N2 Time Slots Complete overlapping: Doesn’t work ! Time Slots • How much to overlap? • Assume Link L(1,2) has Bmax=2, B’min=4 • 2 Streams: S1, S2 • Overlap at most B’min number of streams

  20. E2E Latency Bound N1 N2 Time Slots Complete overlapping: Doesn’t work ! Time Slots • How much to overlap? • Assume Link L(1,2) has Bmax=2, B’min=4 • 2 Streams: S1, S2 • Overlap at most B’min number of streams

  21. E2E Latency Bound Summary • Greedy solution : Principles • Routing: Least burst routing • Allocating time slots: • How many time slots to allocate per link? • Bmaxi+1 contiguous time slots • Without complete overlapping • How much to overlap? • Overlap at most B’mini streams’ time slot allocation • How to handle interference? • Use IM to avoid interference

  22. Outline • Modeling link burstiness • E2E latency bounds • Evaluation

  23. Evaluation • Experimental Setup • Same testbed as empirical study • 48 Tmote Sky nodes • Same packet transmission rate • 200 packets/sec • RBS style time-synchronization

  24. Evaluation 12.4% • Effect of Bmax • B’min = 1 • Multiplying factor: K • Allocate Bmaxi*K + 1 time slots, for i-thlink • As K increases • Average LB increase linearly • E2E DMR becomes 0 at K = 0.6 ! • Allocate Bmaxi*0.6 + 1 time slots -> save 12.4% latency • K allows us to control E2E DMR and LB ! • Avg. LB increases linearly

  25. Evaluation • Effect of B’min • As B’min increases • LB decreases • Then starts to increases again ! • Minimize average LB by an intelligent selection of B’min.

  26. Contributions • New model of link burstiness • Estimates maximum consecutive packet loss • Not captured by βfactor or PRR • New scheduling algorithms for E2E latency bounds • Empirical evaluation • 21 day link characterization • Testbed evaluation of LB miss ratio with 10 simultaneous streams

  27. Conclusions • Can provide reasonable estimate of latency bounds • Not a guarantee • The “K” parameter helps control the trade-off between miss ratio and latency • One important step to combine wireless networking with real-time control

  28. Questions?

  29. Backup Slides

  30. One Final Issue… • Change in burst behavior? • Packet Recovery • Each node queues un-transmitted packets. • Transmits later if free slot available. • Link Adaptation • Each node keeps a record when it fails to transmit • Sends this report to B.S. periodically • B.S. reschedules by doubling/ halving the allocated time slots • LB expands/shrinks dynamically

  31. Stationarity • Can we assume that Bmax is stationary? • Can classify links: • Bursty links had highly variable Bmax • Non-bursty links were more consistent • Why? Due to physical dynamics • Must ensure that measurement period captures all physical dynamics • No stronger requirement than any model

  32. IM • Characterizing Interference: • Define an Interference Matrix, IM • Measurement based on PRR

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