localized algorithm for aggregate fairness in wireless sensor networks authors : shigang chen, zhan...

Localized Algorithm for Localized Algorithm for Aggregate Fairness in Aggregate Fairness in

Wireless Sensor Wireless Sensor NetworksNetworks

Authors : Shigang Chen, Zhan ZhangAuthors : Shigang Chen, Zhan Zhang

CISE university of FloridaCISE university of Florida

Published : MobiCom 2006Published : MobiCom 2006

OutlineOutline

IntroductionIntroduction Aggregate FairnessAggregate Fairness Distributed Computation of Aggregate Flow Distributed Computation of Aggregate Flow

WeightsWeights AFAAFA SimulationSimulation ConclusionsConclusions

IntroductionIntroduction

Congestion control is great importance in sensor networks.

When a sensor network scales up with more sensors deployed in a larger area, the traffic volume increases but the channel capacity around the bottlenecks cannot be increased easily.

IntroductionIntroduction

IntroductionIntroduction

Aggregate FairnessAggregate Fairness

Every source nodes generate the same packets Every source nodes generate the same packets in weightless environment.in weightless environment.

Source nodes generate packets proportional to Source nodes generate packets proportional to its weight.its weight.

All packets send to sink node (s) success.All packets send to sink node (s) success.

Distributed Computation of Distributed Computation of Aggregate Flow WeightsAggregate Flow Weights

a, b, c a, b, c U Uii

(a, i) : upstream l(a, i) : upstream link of iink of i

w, x, y, z w, x, y, z D Dii

(w, i) : downstrea(w, i) : downstream link of Im link of I

N : set of sensorsN : set of sensors E = {(k, i) | i E = {(k, i) | i N, N,

k k U Uii}}

i

a

b

c

x

w

y

z

Base station 1Base station 2

Distributed Computation of AggregaDistributed Computation of Aggregate Flow Weightste Flow Weights

A flow s is the sequence of data packets generated from a data source s in N.

The data rate of flow s is denoted as d(s) the weight is denoted as w(s) ri(s) be the rate at which the packets of flow s p

ass through sensor i rs(s) = d(s) rk,i(s) be the rate at which the packets of flow s p

ass through link (k, i)

Distributed Computation of AggregaDistributed Computation of Aggregate Flow Weightste Flow Weights

Distributed Computation of AggregaDistributed Computation of Aggregate Flow Weightste Flow Weights

A rate assignment {ri(s), i, s N∀ ∈ ; rk,i(s), s ∀ N, ∈ ∀(k, i) E} ∈ is feasible if the following

constraints are satisfied.

Distributed Computation of AggregaDistributed Computation of Aggregate Flow Weightste Flow Weights

Distributed Computation of AggregaDistributed Computation of Aggregate Flow Weightste Flow Weights

Distributed Computation of AggregaDistributed Computation of Aggregate Flow Weightste Flow Weights

Distributed Computation of AggregaDistributed Computation of Aggregate Flow Weightste Flow Weights

Distributed Computation of AggregaDistributed Computation of Aggregate Flow Weightste Flow Weights

AFAAFA

make sure that a sensor k sends a packet to a sensor i only when i has the buffer space to hold the packet

i sends out a packet (RTS/CTS/DATA/ACK), it piggybacks its current buffer state in the frame header

When sensor i is congested, it computes a rate limit for each upstream neighbor k as follows

SimulationSimulation

SimulationSimulation

SimulationSimulation

SimulationSimulation

SimulationSimulation

SimulationSimulation

SimulationSimulation

SimulationSimulation

SimulationSimulation

SimulationSimulation

SimulationSimulation

SimulationSimulation

SimulationSimulation

SimulationSimulation

ConclusionsConclusions

This paper studies the end-to-end fairness problem in data-collection sensor networks.

We formally define a new aggregate fairness model, prove its properties, and propose a distributed algorithm that implements the model.

The simulation results confirm the effectiveness of the algorithm in achieving (weighted) fairness

among competing data flows.