SELECT: Self-Learning Collision Avoidance for Wireless Networks

Chun-Cheng Chen, Eunsoo, Seo,Hwangnam Kim, and Haiyun Luo

Department of Computer Science,University of Illinois, Urbana-Champaign

IEEE Transactions on Mobile Computing,Vol. 7, No.3, 2008

Outline

Introduction Hidden/exposed terminal problem

in 802.11 networks Motivation SELECT

a self-learning collision avoidance mechanism

Performance evaluation Conclusion

Introduction

Limited number of orthogonal channels restricts the deployment of 802.11 APs.– 3 channels for 802.11b/g, 12 for

802.11a– Interference range is long compared

with communication range

Introduction

Recent published data shows 40% of 802.11 APs are operating on channel 6

In Boston, a max number of 85 APs are detected in the interference range– At least 30 APs are directly

interfering with each other

Hidden/exposed terminal problem

Restrain by RTS

Restrain by CTS

Restrain by B’s CTS,Cannot reply E’s RTS

C’s RTS collidewith A->B

Drawbacks of hidden/ exposed receiver problem1. Sender drops the head-of-line data

packet– Resulting in a contention-induced

packet loss

2. Unsuccessful RTS transmission, misled the sender to conclude– Receiver is unavailable

(false link breakage is triggered)– Channel quality at the receiver side is

low (Using low data transfer rate)

Drawbacks of hidden/ exposed receiver problem3. Unsuccessful RTS attempts

inflate sender’s contention window

4. Repeated RTS attempts prevent the sender’s neighbor from transmitting– Low channel utilization

5. Hidden/exposed terminal problem will persists until the clients move and contention relation changes

Motivation

Use MICA2 CC1000 to simulate the operation of 802.11 devices

Exposedreceiver

Potentialsender

RSS at motes C and D while A is transmitting to B

RSS vs. SR (successful ratio) C→D, G →H are active E →F serves as an additional

interference A →B, A records the RTS

successful ratio

RSS vs. RTS SR at mote A

RSS vs. RTS SR over time

Summary of RSS vs. RS

The RSS at the sender and the receiver has strong correlation

To estimate the RSS at the receiver from the sender is complex

The sender can use its RSS as an indicator of the status at receiver

Overview of SELECT

Sender uses the detected RSS to map the receiver’s condition (successful ratio)

RSS is divided into several intervals, each interval has a corresponding SR

RSS ≧ CSthred → channel busy SR ≧ threshold → transmit the data SR ＜ threshold → pretend the

transmission is failed

SELECT: self-learning collision avoidance RSS-SR mapping maintenance RSS-SR mapping lookup Integration with 802.11 DCF Intelligent SR threshold setup

RSS-SR mapping maintenance To update the SR within an interval

Twin Using a variable α (from 0 to 1) to

indicate the weight of old data– α~1: the stored data is very new– α~0: the stored data is almost useless

Current time

Last update time

RSS-SR mapping algorithm

Calculate α

Set updatevariable

Update variable & timestamp

RSS-SR mapping lookup When a sender wants to send

data to a receiver, the sender lookup the corresponding SR under current RSS– Remove out-of-date data first

RSS-SR mapping lookup

Channel Busy

Return SR

Integration with 802.11 DCF

When MAC module access the channel and the result is determined– Udp_RSS_SR

RSS_SR_Look-UP

Integration with 802.11 DCF: when backoff expired

RSS ≧ CSthred → channel busy– Performs random backoff

RSS < CSthred → channel idle– SR ≧ threshold → transmit the data– SR ＜ threshold → pretend the

transmission is failed, also performs random Backoff

Intelligent SR threshold setup (1) The authors assume the

successful ratio (SR) of each RSS is distributed according to the measured RSS distribution

When can a station measure RSS?– During random backoff

Intelligent SR threshold setup (2)

Crssi = number of measured signal strength falls within interval RSSi

T=update interval Trssi= the time that channel

quality falls within interval RSSi

Intelligent SR threshold setup (3) If SRi < threshold, station won’t

transmit during period T The lose of throughput

– △rssj= time spend to transmit a packet within interval RSSj interval

Intelligent SR threshold setup (4) Try to maximize the expected

throughput Total spend time

Time saved by a node at the low-SR

rssi

Available throughput

Simulation setup

Ns-2 2.28 Two-Ray Ground model Communication range: 115m RSSmin=-100dBm RSS validation windows= 2

second CBR/UDP traffic

Exposed receiver

Station 3 is an exposed receiver

Result of exposed receiver(w/o RTS/CTS)

# of drop packets at Node 2Throughput gain at Node 2

Result of exposed receiver (w/o RTS/CTS)

Successful ratio at Node 2 Throughput profile

Result of exposed receiver(with RTS/CTS)

# of drop packets at Node 2Throughput gain at Node 2

Result of exposed receiver (with RTS/CTS)

Successful ratio at Node 2 Throughput profile

Hidden receiver

Station 0 and 3 are hidden receivers to each other

Normalized throughputx: with △: w/o RTS/CTS 802.11 DCF SELECT

Normalized instantaneous throughput: 0->1

Random topology

# of drop packets Throughput gain

Real experiment results by using MICA2

Throughput (pkt/second)

RTS successful ratio

Conclusion

The paper proposes SELECT– An effective and efficient self-

learning collision mechanism SELECT improves throughput by

up to 140 % and the successful ratio by 302 percent

Thank you!!