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
– 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?
Power Consumption for Wireless Communication
• Energy consumption %
up to 10%
total energy
> 50%
total energy
• A standard way to save energy
– Put the WNI into sleep when idle (for a 5 V device)
high power mode
450 mA
low power mode
15 mA
802.11 Power Saving Plan in its Basic
Infrastructure Mode
• 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
Internet
Access Point
receive data
sleeping station
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
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 win both performance and energy
subject to fair scheduling.
Source of Idle Communication Power
Wireless
performance
anomaly
While the
channel
is used by one
makes
this
power
waste
worse,
but
station, idle communication power
also with an opportunity.
is wasted in many other stations
AP
Outline
• Motivation and rationale
• System model and algorithms
• System design and implementation
• Performance evaluation
• Conclusion
Restructure a Wireless Network to P2P model to
Enable Multi-hop Relays
To help low channel rate stations
to Increase throughput and extend
network coverage
AP
X
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.
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.
Rationale
• Energy efficiency:
–
effective number of bits delivered per energy unit
• Self-incentive multi-hop relay
– Use channel time to pay the relay service
Proxy
Client
Throughput
Increase
Increase
A win-win solution
Energy efficiency
Increase
Increase
System Model
• Time based fairness in the shared radio channel
ti = Dt = 1/n
S1
idle
S2
…
Si
… idle Sn
1 round
• Consequence of multi-hop relays
S0
Sp
Sq
AP
Proxy
Client
– Proxy: throughput  idle time  energy efficiency
– Client: channel rate  throughput
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.
A Token Bucket Associated with Each Station
tokens from AP
Token
Bucket
packets
Packet Queue
Overflow!
Re-allocate to other
stations by AP
Transmitter
1 token per packet
Maintaining a Routing Table in AP
S1
AP
Hop Station
1
Self
2
S3
S4
Hop Station
1
S2
2
Self
STA
S1
S2
S3
S4
Route
R(0,2)
R(2,3)
R(2,4)
Route
R(0,2)
R(2,3)
Proxy
----S2
S2
S2
S4
S3
Route
R(0,1)
R(0,2)
R(0,3)
R(0,4)
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
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
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)
Single Client Experiment
11Mbps
AP
11Mbps
1Mbps
slow link!
Performance Evaluation
throughput (Mbps)
1 proxy (P), 1 client (Q)
16%
170%
energy efficiency (Mb/J)
Channel allocation scheme
266%
Channel allocation scheme
Multi-clients Experiment
11Mbps
1Mbps
AP
Performance Evaluation
1 proxy, multiple clients
Proxy throughput gain
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.
Thank you!