1 / 17

Speed Dating despite Jammers Dominik Meier Yvonne-Anne Pignolet Stefan Schmid Roger Wattenhofer

Speed Dating despite Jammers Dominik Meier Yvonne-Anne Pignolet Stefan Schmid Roger Wattenhofer. Wireless Networks. Radio Communication Find communication partner (device discovery) Concurrent transmissions disturb each other (Interference). Device discovery under jamming attacks.

arawn
Download Presentation

Speed Dating despite Jammers Dominik Meier Yvonne-Anne Pignolet Stefan Schmid Roger Wattenhofer

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Speed Dating despite Jammers Dominik Meier Yvonne-Anne Pignolet Stefan Schmid Roger Wattenhofer

  2. Yvonne Anne Pignolet @ DCOSS 2009 Wireless Networks • Radio Communication • Find communication partner (device discovery) • Concurrent transmissions disturb each other (Interference) Device discovery under jamming attacks

  3. Yvonne Anne Pignolet @ DCOSS 2009 Adversarial Interference: Jamming Device Discovery No discovery! ID2428 ID5872 channels channels

  4. Yvonne Anne Pignolet @ DCOSS 2009 Adversarial Interference: Jamming ID5872 Device Discovery Discovery! ID2428 ID2428 ID5872 channels channels

  5. Yvonne Anne Pignolet @ DCOSS 2009 Adversarial Interference: Jamming Device Discovery No discovery! ID2428 ID5872 channels channels

  6. E[algo discovery time | t unknown ] Quality: ρ := max t E[best discovery time | t known ] Model: Device Discovery Problem • 2 devices • Want to get to know each other • m channels • Listen/send on 1 channel in each time slot m • Adversary • Always blocks t channels • t < m • Worst case t Quickly? Graceful degradation Goal: Algorithm that lets the devices find each other quickly, regardless of t

  7. Algorithms • Randomized Algorithms • Represented by probability distribution over channels: • choose channel i with probability pi p2 p4 ... pm-1 Perfect for sensor nodes because we are rather stupid.... p1 p3 ... pm • Advantages • Simple • Independent of starting time • Stateless • Robust against adaptive adversaries

  8. E[best discovery time | t known ] • Best Algorithm • In each time slot • if t = 0 choose channel 1 • if t < m/2 choose random channel in [1,2t] • else choose random channel 2t 1 if t=0 4t if t < m/2 m2/(m-t) else E[discovery time knowing t] = t> 0 : Why uniform distribution? Easy! What if we don’t know t? Why channel in [1,2t] ? 2t minimizes discovery time

  9. E[algo discovery time | t unknown ] ρ := max t E[best discovery time | t known ] E[discovery time NOT knowing t] • Example AlgoRandom • In each time slot • choose channel uniformly at random E[time AlgoRandom ] = m2/(m-t) choose t=0 ρRandom = m • Example Algo3 • In each time slot • with prob 1/3 choose channel 1 • with prob 1/3 choose randomly in [1,√m] • with prob 1/3 choose randomly in [1,m] ≈ estimate t = 0 ≈ estimate t = √m/2 ≈ estimate t = m/2 choose t= √m ρ3 = O(√ m)

  10. E[algo discovery time | t unknown ] ρ := max t E[best discovery time | t known ] E[discovery time NOT knowing t] • Example Algolog m • In each time slot • with prob 1/log m choose channel 1 • with prob 1/log m choose randomly in [1,2] • ... • with prob 1/log m choose randomly in [1,2^i] • ... • with prob 1/log m choose randomly in [1,m] ≈ estimate t = 0 ≈ estimate t = 1 ≈ estimate t = 2^(i-1) ≈ estimate t = m/2 choose t= m ρlogm= O(log^2 m)

  11. E[algo discovery time | t unknown ] ρ := max t E[best discovery time | t known ] Optimal Algorithm? • General algorithm • Given probability distribution p, where p1 ≥ p2 ≥ …≥ pm ≥ 0 • In each time slot • choose channel i with probability pi m i=t+1 E[algo discovery time | t] = 1/∑ pi2 m i=1 1/ ∑ pi2 if t=0 1/(4t∑ pi2) if t < m/2 (m-t)/(m2∑ pi2) else E[algo discovery time | t unknown ] m i=t+1 = E[best discovery time | t known ] m i=t+1

  12. Optimal Algorithm? • General algorithm • Given probability distribution p, where p1 ≥ p2 ≥ …≥ pm ≥ 0 • In each time slot • choose channel i with probability pi can choose any t Optimization problem min ρ* s.t. t = 0 1/ ρ* = ∑ pi2 1 ≤ t ≤ m/2 1/ ρ* = 2 t ∑ pi2 t > m/2 1/ ρ* = m2 ∑ pi2 /(m-t) m i = 1 m ρ* = Θ (log2 m) i = t+1 m i = t+1

  13. Simulations: Worst Case Jammer

  14. Simulations: Random Jammer

  15. Case Study: Bluetooth vs Microwave Power (dBm) Bluetooth Channel OPT much better than Bluetooth

  16. Yvonne Anne Pignolet @ IMAGINE 2009 ID2428 ID5872 channels channels Lessons • Interference can • prevent discovery • uniformly random • algorithm not always • best solution • best expected discovery time • price for NOT knowing t: ρ* = Θ (log2 m) O(log2 m) if t=0 O(t log2 m) if t < m/2 O(m2 log2 m /(m-t)) else E[Algoopt] =

  17. Yvonne Anne Pignolet @ DCOSS 2009 That’s it… THANKYOU!

More Related