Self-Management in Chaotic Wireless

DeploymentsA. Akella, G. Judd, S. Seshan, P.

Steenkiste

Presentation by: Zhichun Li

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

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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

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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)

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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

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WifiMaps.com

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

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Simulation

GloMoSim Topology

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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)

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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

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Stretching the distance: D=1

Little impact of interference between nodes on user performance

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Stretching the distance: D=3

The performance of both protocols suffers density

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Stretching the distance: increased load

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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

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Non-overlapping channel assignment Using channel 1, 6, 11 from map 2a we

move to map 2b

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Non-overlapping channel assignment

Three non-overlapping channels Only channel 6

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Transmit power control

Transmit power reduced to 3dBm

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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

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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

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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

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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

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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

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PERF evaluation

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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

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Questions?