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Idle Communication Power

Exploiting. Idle Communication Power. to Improve Wireless Network Performance and Energy Efficiency. Lei Guo , Xiaoning Ding , Haining Wang, Qun Li, Songqing Chen , and Xiaodong Zhang. Challenges in Wireless System Design. Energy saving is not easy

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Idle Communication Power

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  1. Exploiting Idle Communication Power to Improve Wireless Network Performance and Energy Efficiency Lei Guo, Xiaoning Ding, Haining Wang, Qun Li, Songqing Chen, and Xiaodong Zhang

  2. Challenges in Wireless System Design • Energy saving is not easy • Limited battery capacity in wireless devices • High power consumption in wireless communication • High performance costs energy and fairness • Wireless users demand high throughput, but … • A high throughput device needs less sleep. • A channel allocation mechanism can favor some but degrade performance of others. • Can we win both instead of addressing the trade-off?

  3. Power Consumption for Wireless Communication • Energy consumption % • A standard way to save energy • Put the WNI into sleep when idle (for a 5 V device) up to 10% total energy > 50% total energy high power mode 450 mA low power mode 15 mA

  4. Access point Buffer data for sleeping stations Broadcast beacon with TIM periodically (100 ms) Sleeping station Wake up periodically to receive beacon Poll access point to receive data Sleep again Traffic Indication Map (TIM) wake up poll receive data 802.11 Power Saving Plan in its Basic Infrastructure Mode Internet AccessPoint sleeping station

  5. Observations of IEEE 802.11 Protocol • A client/server model • Each station independently communicates with AP • AP serves a station one at a time via the channel. • The saving mode affects TCP traffic • Increasing RTT and decreasing throughput. • Performance anomaly (Infocom’03) • Non-uniform transfer rates between different stations to AP due to distance and obstacle condition differences. • A low speed station has low channel utilization rate. • Waste energy while a station is waiting for its turn. • Idle communication power due to strong dependency

  6. Existing Solutions to address the Limits • Reducing idle communication power by • Traffic prediction: bounded slowdown (MOBICOM’02) • Self-tuning with application hints (MOBICOM’03) • Limits: case by case, and accuracy can vary. • Address the performance anomaly • Time-based fairness scheduling: a constant time unit is given to each station (USENIX 04) • Limits: poorly conditioned stations suffer: fast is faster, and slow is slower. (energy: 1 bit = CPU 10,000 cycles) • Our work: to winboth performance and energy subject to fair scheduling.

  7. While the channel is used by one station, idle communication power is wasted in many other stations AP Source of Idle Communication Power Wireless performance anomaly makes this power waste worse, but also with an opportunity.

  8. Outline • Motivation and rationale • System model and algorithms • System design and implementation • Performance evaluation • Conclusion

  9. To help low channel rate stations to Increase throughput and extend network coverage AP X Restructure a Wireless Network to P2P model to Enable Multi-hop Relays

  10. Effectiveness of Relays is from Strong Dependency • Slow stations become faster • Completing the data transfer ahead of the unit time. • Equivalent to move the station closer to AP or improve the station’s communication condition. • Fast stations serve as proxies for slow stations • Performance improvement of slow stations reduced the waste of idle communication powers of fast stations --- shortening the waiting time. • Effective P2P coordination among stations is the key.

  11. Incentive and Fairness to Fast Stations • Why not sleep or wait, but proxy/relay for others? • Sleep lowers throughput, and wait wastes energy. • Idle communication energy can be used • The saved time in slow stations should be contributed. • How do we handle fairness? • A proxy should be given incentive for its service • For either proxy or client, the throughput and energy efficiency should be improved after relays.

  12. Rationale • Energy efficiency: • effective number of bits delivered per energy unit • Self-incentive multi-hop relay • Use channel time to pay the relay service • A win-win solution

  13. ti=Dt = 1/n Sp S0 Sq S1 S2 … Si … Sn AP Proxy Client 1 round System Model • Time based fairness in the shared radio channel • Consequence of multi-hop relays • Proxy: throughput ­ Þ idle time ¯ Þ energy efficiency­ • Client: channel rate ­ Þ throughput ­ idle idle

  14. Token-based Channel Scheduling • A token is a ticket for a data transfer (RX/TX) in one time unit • AP initially distributes an equal amount of tokens to each station (fairness). • A pair of RX & TX consumes one token. • A token bucket is used in channel scheduling. • Multi-hop relays are operated under token exchanges. • The token mechanism provides incentive to fast stations: receive more time units than relays needed.

  15. A Token Bucket Associated with Each Station tokens from AP Overflow! Re-allocate to other stations by AP Token Bucket Transmitter 1 token per packet packets Packet Queue

  16. Maintaining a Routing Table in AP S1 AP S2 S4 S3

  17. Put Them Together: Selfish Forwarding - SFW • Proxy discovery and selection • A poorly conditioned client broadcasts a relay request • One or more stations bet to relay with a price (tokens). AP assigns a relaying station for clients based on the second price auction in game theory. • Channel scheduling • AP distributes tokens without any enforcement. • The relaying actions are determined by token exchanges among stations. • Multi-hop routing is done by a routing table in AP

  18. SFW System Implementation • Access Point • NetGear MA311 802.11b PCI wireless adaptor • HostAP linux driver version 0.1.3 • Wireless Stations • NetGear MA401 802.11b PCMCIA wireless adaptor • ORiNOCO linux driver version 0.15rc2

  19. Protocols Compared in Experiments • DCF • Most widely used protocol in 802.11b network • Distributed Coordination Function • TBF • Time-based Fairness (USENIX 2004) • SFW • Selfish Forwarding (our own)

  20. AP Single Client Experiment 11Mbps 11Mbps 1Mbps slow link!

  21. Channel allocation scheme Channel allocation scheme Performance Evaluation 1 proxy (P), 1 client (Q) 16% 170% throughput (Mbps) 266% energy efficiency (Mb/J)

  22. AP Multi-clients Experiment 11Mbps 1Mbps

  23. Performance Evaluation 1 proxy, multiple clients Proxy throughput gain

  24. Conclusion • Exclusive communications between AP and stations create idle communication power. • Wireless performance anomaly is an opportunity. • P2P based Selfish Forwarding Protocol • Improve performance and energy efficiency for everyone • Make the channel sharing and scheduling more fair. • It is easy to implement for practical usage.

  25. Thank you!

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