Conclusions and F uture work

ThischapterconsidersalargescaleSANETwithmultipleactuatorsassinksfordatagenerated

bythe sensors. Since many applications require to have each source node send all its locally

generated data to only one actuator for processing, it is necessary to optimally map each

sensorto itsactuator. Also consideringthe factthattheend-to-enddelaysinwireless

sensor-actuator networksisahardconstraint, wejointly optimizetheactuatorselection andoptimal

ow routing subject to energy and delay constraints with the global aim of maximizing the

networklifetime. Weproposedandevaluated(usingns-2)ouractuator-selection(LEAD-ADP)

and routing scheme (LEAD-RP) on top of a TDMA based MAC (LEAD-MAC) protocol.

We thenuse the Lagrangian dual decomposition method to devise a distributed primal-dual

algorithm tomaximizenetwork-lifetime inthenetwork. Thedeterministicdistributed

primal-dual algorithm requires no feedback control and therefore converges almost surely to the

optimal solution. The results show that the required optimal value of lifetime is achieved

for every node in the network by the distributed primal-dual algorithm. We also provide

a comparison to the analytical bound. Simulation results show that this approach has

near-optimalperformanceandispracticallyimplementableascomparedtoearlieranalytical studies

based onlyon numerical evaluations.

Thischapter also addresses the problem of isolated regionsin thesensingeld byletting

actuatorsexchangetheirCSITandjointlyperformbeamforminginordertodeliverscheduling

information to sensor nodes. The gains of cooperation were shown by simulating the

aver-age number of isolated sensors for the case of single actuator transmission and cooperative

transmission.

Inthenear-future,wewillconsiderareal-lifeSANETapplicationandsimulateitsbehavior

with the LEAD self-organizing framework to observe its performance. We will take into

consideration a dynamic actuator-assignment scenario to timely transport data in a mobile

wirelesssensor-actuator network.

Table 4.1: Notations

Symbols Denitions

N

^{The total numberofSensorsinthe network}

M

^{The total numberofActuators inthenetwork}

B

^{The total numberof}BaseStations inthenetwork

e _i

^{Initial energy ofa sensornode}

i E i

^{Initial energy ofan actuator node}

i

g i

^{The locallygenerated data rateat sensor}

i P _rx

^Power consumption coecient for receivingdata

c _i,j

^Power consumption coecient for transmitting datafromsensor

i

^{to sensor}

j α, β

^{Two constants termsinpower}consumption for transmitting data

d _i,j

^{The geographicdistance between two nodes}

i

^and

j f s ^s i ^k ,s ^,A j ^l

or f _s ^s _i ^k _,A ^{,A l}

l

The ow ratefrom sensor

i

^{to sensor}

j

^(oractuator

l

^{) withsource and}destination beingsensor

k

^andactuator

l

F _s,s (or F _s,A )

^{The setof ows from onesensorto another (orActuator node)}

F _{s,s i}

^{The setof ows coming into sensor}

i

The datavolume (inbits)transferred from sensor

i

^tosensor

j

^(orActuator

l

^{) with}

source and destination being sensor

k

^{and Actuator}

l

υ _s,s (υ _s,A )

^{The setof volume froma sensorto anothersensor(oran actuator)}

υ _{s,s i}

^{The setof incomingvolume into sensor}

i

υ _{s i ,s} (υ _{s i ,A} )

^{The setof outgoing volume froma sensor}

i

^{to anothersensor(or anactuator)}

The ow ratefrom actuator

i

^{to actuator}

j

^(orBaseStation

l

^)withsource

and destination beingactuator

k

^andBaseStation

l

F _A,A (or F _A,B )

^{The setof ows from oneactuator to another (or}BaseStation)

F _{A,A i}

^{The setof ows coming into an actuator}

i

F _{A i ,A} (F _{A i ,B} )

^{The setof ows goingout of actuator}

i

^{to other actuators(or}BaseStation)

G i

^{The locallygathered dataat actuator}

i

^,

G i = P

i g i , (1 ≤ i ≤ N ) λ ^{A i ,B l}

^{If thedatagathered at actuator}

i

^willbe transmittedto BaseStation

l

^,

then

λ ^{A i ,B l} = 1;

^otherwise

λ ^{A i ,B l} = 0

The datavolume (inbits)transferred from actuator

i

^{to actuator}

j

(or BaseStation

l

^{) withsource and}destination beingactuator

k

and BaseStation

l

υ A,A (υ A,B )

^{The setof volumesfrom aactuator to another actuator (ora} BaseStation)

υ A,A i

^{The setof incomingvolume into actuator}

i

υ _{A i ,A} (υ _{A i ,B} )

^{The setof outgoing volumesfroma actuator}

i

^{to another}

actuator (or aBaseStation)

µ ^{A i ,B l} = λ ^{A i ,B l} T

^inMILP-relax

Table 4.2: Useful states for the sensor node with associated power consumption and delay

(time to reach

S ₄

^{from anygiven state)}

OperatingState StrongARM Memory ADC Radio PowerConsumption Delay(ms) NotationUsed

S ₀

^{Sleep sleep O O 50(}

^µ

^{W) 50}

^{E s} node ⁰

S ₁

^{Sleep Sleep On O 5(mW) 20}

^{E s} node ¹

S ₂

^{Sleep Sleep On}

^Rx

^{10(mW) 15}

^{E s} node ²

S ₃

^{Idle Sleep On}

^Rx

^{100(mW) 5}

^{E s} node ³

S ₄

^{Active Active On}

^{T x, Rx}

^{400(mW) NA}

^{E s} node ⁴

Table 4.3: The simulation area is such that there are atleast two sensors in each others

transmissionrange

Sensors Area (

m ²

^{) Actuators}

100 500*500 2

150 600*600 3

.

.

.

.

.

.

.

.

.

400 970*970 8

Cross-Layer Routing in UASNs

UASNs consist of sensors that are deployed to perform collaborative monitoring of tasks

over a given volume of water. These networks will nd applications in oceanographic data

collection,pollutionmonitoring,oshoreexploration,disasterprevention,assistednavigation,

tacticalsurveillance,and mine reconnaissance. The qualityof theunderwater acousticlinkis

highly unpredictable, since it mainly depends on fading and multipath, which are not easily

modeledphenomena. This inreturn severelydegrades theperformance at higher layers such

as extremely long and variable propagation delays. In addition, this variation is generally

larger inhorizontal links than invertical ones.

Inthis chapter, we rst analyzea modulation schemeand associatedreceiver algorithms.

Thisreceiver design take advantage of the TR and properties of spread spectrum sequences

known as Gold sequences. Furthermore, they are much less complex than receivers using

adaptive equalizers. This technique improves the signal-to-noise ratio (SNR) at the receiver

and reduces the biterror rate (BER).We then applied PC to the caseof network

communi-cation. We show thatthis approach can give almost zero BERfor a two-hop communication

mode compared to the traditional direct communication. This linklayerinformation isused

at thenetworklayerto optimize routingdecisions. Weshowtheseimprovementsbymeans of

analyticalanalysisand simulations.

5.1 Introduction

Acoustic signaling for wireless digital communications inthe sea environment can be a very

attractivealternativetobothradiotelemetryandcabled systems. However,time-varying

mul-tipath andoftenharshambientnoiseconditionscharacterizetheunderwateracousticchannel,

oftenmakingacoustic communications challenging. Thesensors mustbeorganized inan

au-tonomous network that self-congures according to the varying characteristics of the ocean

environment. Major challenges inthedesign ofUASNsare:

•

^{The channel isseverelyimpaired,mainly due to multipath.}

•

^{Temporary lossof} connectivity mainly dueto shadowing.

•

^{Thepropagation delay isve ordersofmagnitude higherthaninradiofrequency}

terres-trial channels andis usuallyvariable[4 ].

•

^{Extremely low availablebandwidth.}

•

^{Limited battery energy at disposal.}

In this chapter, we present our analysis of a modulation scheme and associated receiver

al-gorithms. We also present the quantication of SNR and BER gains using PC in a single

transmitter-receiver setting. We then applied PC technique to a multi-hop communication

systesm. Thislinklayerinformationisusedatthenetworklayertooptimizerouting decisions.

Thiscross-layering improvesthenetworklifetimeofbatteryoperatedUASNsbyreducingthe

number ofretransmission attempts.

The organization of this chapter is as follows. Section 5.2details some of theinteresting

related work. In Section 5.3, we discuss the basic building blocks that contributed to the

proposed solution. We present the receiver algorithms that take advantage of TR and low

cross-correlation of Gold sequences for single-hop point-to-point communication and its

per-formance analysis in Section 5.4. We apply the idea of PC on a linear network to improve

someperformancemetricsinSection5.5. Wealsopresentadistributedrouting algorithm and

its performance analysis for a larger network size. In Section 5.6, we conclude the chapter

andoutlinethe future directions.

Dans le document Cross layer optimization of wireless sensor and sensor-actuator networks (Page 143-148)