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Self-Management in Chaotic Wireless Deployments

This presentation discusses the challenges of managing wireless networks in chaotic and unplanned environments. It explores related work, analyzes performance, proposes new algorithms for power control and rate adaptation, and concludes with potential solutions to improve network performance in chaotic deployments.

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Self-Management in Chaotic Wireless Deployments

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  1. Self-Management in Chaotic Wireless Deployments A. Akella, G. Judd, S. Seshan, P. Steenkiste Presentation by: Zhichun Li

  2. Overview • Chaotic Wireless Networks • Related Work • Analysis of performance • Proposed algorithms • Conclusion

  3. Chaotic Wireless Networks • Unplanned networks deriving from individual deployments • Unmanaged networks often using the same channel and not taking care of power control Self-Management as automatic configuration of key access point properties

  4. Related work • Some existing software for network management, but designed for large scale networks • Rate control existing algorithms but not in conjunction with power control • Some algorithms reduce power usage to extend battery life • Chaotic network is different from ad hoc networks (limited mobility, sufficient power, competition for bandwidth and spectrum)

  5. Data sets used • Place Lab: 802.11b APs located in various US Cities, allows devices location by using radio beacons • Pittsburgh Wardrive: based on a few densely populated residential areas, it provides Geographic coordinates, ESSID, MAC address, Channel Used • WifiMaps: provides Geographic Information Systems maps, for each AP it has info about Geo coordinates, zip code, ESSID, Channel employed, MAC address

  6. WifiMaps.com

  7. Some observations: APs’ density, channels, 802.11b vs. 802.11g

  8. Simulation GloMoSim Topology

  9. Simulation assumptions • Each node on the map is an AP • Each AP has D clients with 1 ≤ D ≤3 • Clients are within 1 meter from their AP and they don’t move • All APs transmit on channel 6 • All APs use fixed power level of 15dBm • All APs transmit at fixed rate 2Mbps • RTC/CTS is turned off (default settings)

  10. Simulation runs • http with thinking time by Poisson distribution with mean equal to 5s or 20s • Comb-ftpi, i clients run FTP transmission • Results: • 83.3 Kbps average load for Http • 0.89 Mbps for FTP

  11. Stretching the distance: D=1 Little impact of interference between nodes on user performance

  12. Stretching the distance: D=3 The performance of both protocols suffers density

  13. Stretching the distance: increased load

  14. Two proposed solutions • To limit the impact of interference between nodes we can: • Use an optimal static allocation of non-overlapping channels • Reduce the transmit power levels

  15. Non-overlapping channel assignment • Using channel 1, 6, 11 from map 2a we move to map 2b

  16. Non-overlapping channel assignment Three non-overlapping channels Only channel 6

  17. Transmit power control Transmit power reduced to 3dBm

  18. So… • End-user performance can suffer significantly in chaotic deployments, especially when there is aggressive use of network • Managing power control and using static allocation of non-overlapping channels can reduce the impact of interference on performance

  19. Problems need to solve • By reducing the transmission power, we face a tradeoff between interference and throughput of the channel, since the transmitter is forced to use a lower rate to deal with the reduced signal-to-noise ratio • Chaotic networks: independent users or organizations (often 1 AP) that want to transmit always at highest power with suboptimal results in terms of performance

  20. Ideal solution • Algorithms “socially responsible” that act for the good of the entire area and reduce their power appropriately • Different from other algorithms that require global coordination between multiple APs • New power control management could be quickly spread due to the high rate of deployments of 802.11g

  21. Proposed algorithms • PARF: Power-controlled Auto Rate Fallback • Based on ARF • It Attempts to elect the best transmission rate • If a certain number (6) of consecutive packets are sent successfully, the node selects the next higher transmission rate • If a certain number (4) of consecutive packets are dropped, the node decrements the transmission rate • Extension of ARF by adding low power states above the highest rate state. Power is repeatedly reduced until either the lowest level is or the transmission failed threshold is reached

  22. Proposed algorithms • PERF: Power-controlled Estimated Rate Fallback • Based on ERF: • It uses path loss information to estimate the SNR with which each transmission will be received • It tries the rate immediately above the estimated transmission rate after a consecutive successful send • If the estimated SNR is above a certain amount the decision threshold for the highest transmit rate, the transmission power is reduced to estimatedSNR = decisionThreshold + powerMargin

  23. PERF evaluation

  24. Conclusion • Power control and rate adaptation can reduce interference between nodes in a dense wireless network • Implementing those management algorithms in commercial APs it is possible and it would spread quickly

  25. Questions?

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