The Routing Algorithm

Inthefollowing,weintroduceadistributedroutingsolutionforunderwaterapplications. The

algorithmisenergy-awareandbasedonlyonlocalinformationasend-to-endroute-information

might not be available.

Theobjectiveofthedistributedsolutionistoecientlyexploitthechannelandtominimize

theenergy consumption [J-3]. It triestoexploit thoselinksthatguaranteea lowpacketerror

rate(PER), inorder to maximize theprobabilitythat thepacketis correctlydecoded at the

receiver. Wenowcast thedistributedrouting solution.

Algorithm 5.1UWRP: UnderWater Routing Protocol

Given : i, N i , P i

N i

^istheneighborhood setof

i,

^and

P i

^{is the positive advance set, composed ofnodescloser}

to sinkthan node

i,

^i.e.,

j ∈ P i

ⁱ

d _j,dest < d _i,dest

^.

where

α ∈ [0, 1]

^{is asmoothingparameter applied to thechoice of next-hopfor routing.}

P ER _i,j = 1 − (1 − BER _i,j ) ^s

^{is the} Packet-Error-Rate on thelink

(i, j)

^{for a packetsize}

s

^.

Also,

P j

^{is the remaining energyof sensor}

j

^.

Note that the selection of

j ^∗

^{is subject to two objective functions which need only local}

neighborhoodknowledgeandcanbereadilyavailablewithoutanyprotocoloverhead. Byusing

P ER _i,j

^{with somepositivevalueof}

α

^{,we wantto usealink}

(i, j)

^{withtheminimum valueof}

P ER

^{onthatlinkinordertominimize thenumberof}retransmissionattempts. Andusing

P _j

withits given weight

(1 − α)

^{,wewant tousealink}

(i, j)

^{withmaximumremaining energyin}

order to prolong thenetwork-lifetime of battery-operated UASNs. A weighted sum of these

two quantities results in the selection of next-hop for routing. Note that this information is

availableto the nodeswithout any extrasignaling overhead. Generally,ad hocprotocols rely

on acknowledgments for reliable data transfer. When a node

j

^{receives a packet from}

i

^{, it}

sendsanACKpacketto

j

^{tonotifysuccessfulpacketreception. ThisACKpacketismodied}

to indicate the valuesof certainparameters like

P _j

^and

P ER _i,j

^.

5.5.4 Simulation Results

The PPC-PPM multi-hop performance is tested using Matlab-Simulink. The block diagram

of physical layer system model under consideration is shown in Figure 5.3. A set of Gold

sequence will be generated and pulse positioned inorder to encode the bit information. We

usetheBPSKcarrier at

3.5

^{kHzto modulatethecodewordsandthensendthemthroughthe}

multipath fading channel. We consider

N = 50

^{underwater acoustic sensor nodes that are}

randomly distributedinthe network withsinklocated roughlyinthecenter ofthetopology.

The transmission range of sensors is such that there are no isolated sensors in the network.

The sensornodeslearn thenetwork topology during the initial network deployment to form

anaggregationtreetowardsthesink. Allsensorsarethesourcesofdataandgenerate apacket

every

5mins

^{. Thevalueof}

α

^{for our} simulations issetto

α = (0.65, 0.8]

^{. Multihop routing is}

usedandthenext-hopisdecidedastheoutcomeofthedistributedroutingalgorithmpresented

inSection 5.5.3. Allother detailsof thesimulation modelcan be foundinSection 5.5.2.

Figure 5.10 shows the PER, when 1-hop, traditional routing, between link

(i, j)

^{) versus}

2-hoproutingisused. Wewouldliketomentionherethat2-hopcasedoesnotmeanstatethat

allthesensornodesaretwohopsawayfromthesink,itmerelysaysthatinsteadofaone-hop

routingbetween nodes

i

^and

j

^{,we usean}intermediate sensornode

k

^{androute dataonlinks}

(i, k)

^and

(k, j)

^{instead of link}

(i, j)

^{. This result shows that instead of performing a 1-hop}

routing onlink

(i, j)

^{,a2-hoprouting(on links}

(i, k)

^and

(k, j)

^{)results inadramaticdecrease}

in PEReven when

i

^and

j

^{are inthe}transmission rangeof each other. An interesting factor hereisthe choiceofoptimal

k

^{for eachlink}

(i, j)

^{. Fromthe}neighborhood information

N i

^,we

choose

k(k 6 = i, j)

^{inasimilarfashionas}

j ^∗

^{inAlgo. 5.1. Thisparticularchoiceofroutingalso}

minimizestheaveragenumberoftransmissionattemptsofapacketonagivenlink. Thisresult

isquite evident from Figure5.11. It shows the average number of transmissionattempts for

a packetunderdierent valuesof PER.Thenetwork-lifetime of batteryoperated underwater

acousticsensornodescan be three timeslonger using our routingapproach compared to any

other multi-hop energy-aware routing protocoland is clear from Figure 5.11. Although, this

is achieved at the cost of slightly higher network-layer delay . We use

N ˆ _i,j ^tx

^{to represent the}

average numberof retransmitattempts on thelink

(i, j)

^{and iscalculated asfollows:}

N ˆ _i,j ^tx =

1 1 − P ER i,j

.

^(5.23)

Equation (5.23) assumes independent errors among adjacent packets, which holds when the

channel coherence time is shorter than the average retransmission delay, i.e., the average

time that a sender needs to retransmit an unacknowledged packet. Therefore, given the

harshcharacteristics of theunderwater channel, ourrouting scheme (basedon TRapproach)

performsbetterthanthetraditionalmulti-hoproutingapproachesinUASNs. ThevalueofTR

(PC) isits abilityto focus soundbackat theoriginal source without anya priori knowledge

oftheunderwaterchannel. TheTRfocusenables theunderwatersensornetworktominimize

the eect of multipath and channel fading thus improving the SNR and reducing the BER

(PER for multihop case) and ISI.

−4 −3 −2 −1 0 1 2 3 4 5

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08

SNR (dB)

Packet Error Rate

1 Hop Tx 2 Hops Tx

Figure 5.10: Packet-Error-Rate Vs. SNR

PER=0.08 PER=0.07 PER=0.06 0

1 2 3 4 5 6 7 8 9 10

No. of Transmission Attempts

1 Hop Tx 2 Hops Tx

Figure5.11: Average numberof packet transmissionattempts

5.6 Conclusions and Future Work

In this chapter, weconsidered UASNs thataredeployed toperform collaborative monitoring

of tasks overa given volume ofwater. We rstuse asingle antennatransmitter and receiver

for spatial focusing. PPC implicitly equalizes the channel by refocusing channel spread. We

have quantied the gainof PPC temporal compression, in a single-hop point-to-point setup,

using Gold sequences to implement receivers based on PPM modulation schemes. We have

alsoshownthatPPC-PPMcanbeappliedtoanetworktoachievealmostzero BERfor a

two-hopcommunicationmodecompared tosingle-hopcommunication. Thisresultisofsignicant

importance inmaking routing decisions for UASNs. In particular, itallows a node to select

its next hop withthe aim of minimizing the energy consumption. This cross-layering

(PHY-Routing) improves thenetwork lifetime of battery operated UASNs byreducing thenumber

of retransmission-attempts between any given pair of nodes. It also minimizes the energy

consumption persuccessfultransmission.

Infuture,wewillconsidera multihop3Dunderwateracousticsensornetworkwitha

real-life applicationand extendthe resultsofthis chapterto optimize thescheduling layer(MAC)

and routinglayerwith theaim ofminimizing energyconsumption persuccessfultransmission

and maximizingthenetwork lifetime.

Conclusion and Outlook

In this chapter, we rst summarize briey the contributions of this thesis in Section 6.1. In

Section 6.2, we discuss extensions of this work that will provide interesting challenges for

future research inthe eldof cross-layeroptimizations inwireless sensorandsensor-actuator

networks.

6.1 Summary of Contributions

In this thesis, we have considered three dierent types of sensor networks: 1) WSNs, 2)

SANETs, and 3) UASNs. WSNs consist of large number of distributed sensor nodes that

organize themselves into a multihop wireless network. Typically, these nodes coordinate to

perform acommon task. Onthe otherhand, SANETs arecomposed ofpossiblya large

num-berof tiny,autonomous sensordevices andactuators equipped with wireless communication

capabilities. Distributed systems based on networked sensors and actuators with embedded

computationcapabilities enableaninstrumentationofthephysicalworldatanunprecedented

scale and density, thus enabling a new generation of monitoring and control applications.

SANETs is an emerging technology that has a wide range of potential applications

includ-ing environment monitoring, medical systems, robotic exploration, and smart spaces. Such

networksconsist of large number of distributed sensorand few actuator nodes thatorganize

themselvesintoamultihopwirelessnetwork. SANETsarebecomingincreasinglyimportant in

recent years due to their abilityto detect and convey real-time,in-situ information for many

civilian and military applications. In contrast to terrestial WSNs, UASNs consist of sensors

thataredeployed to perform collaborative monitoring of tasksover a given volume ofwater.

Thesenetworkswill nd applications inoceanographic data collection, pollution monitoring,

oshoreexploration, disaster prevention, assisted navigation, tactical surveillance, and mine

reconnaissance. Moreover,UUVsandAUVsequippedwithsensors,willenabletheexploration

ofnaturalundersearesources andgatheringofscientic dataincollaborative monitoring

mis-sions. Underwateracoustic networkingis theenabling technology for these applications.

Throughoutthiswork,wehaveaddressedthedesign,implementation, andthecross-layer

optimization of routing protocols in WSNs,SANETs, and UASNs. Since theintroduction of

the WSNs and the associated routing/aggregation protocols, this topicshasattracted

signif-icant research eorts, with the ultimate hope to set the basic theory that would be able to

model the diversity of constraints and requirements to characterize it. Meanwhile, dierent

aspectsofthe problemareaddressedcontinuouslybydierent approaches,enabling moreand

moreadvances inthe treatment of thesubject.

IndealingwiththeroutingprotocolsforWSNS,SANETs,andUASNs,wehaveconsidered

cross-layering between: application/MAC, routing/MAC, MAC/PHY layers of the protocol

stackfor such networks.

For WSNs withrandom channel access, we proposed a cross-layered (application/MAC)

datasamplingapproachthatguaranteesalongterm datasamplingratewhileminimizing the

end-to-end delays. Simulation results show that performance of this scheme is better than

the traditional layered architecture, where the channel access mechanism is independent of

the data sampling process. We also saw that the proposed scheme does not require tedious

parameter tuning asisthecase forthelayeredarchitecture.

ForgeneralpurposeWSNs,weproposedanothercross-layeredapproachandobtainedsome

important insights into various tradeos that can be achieved by varying certain network

parameters. Some of them include: 1) Routing can be crucial in determining the stability

propertiesofthenetworkedsensors. 2)Whetherornottheforwardingqueuescanbestabilized

(byappropriatechoiceofWFQweights) dependsonlyon routing andchannel access rates 3)

Wehavealsoseenthattheend-to-endthroughputisindependentofthechoiceofWFQweights.

We therefore, proposed a distributed learning algorithm to achieve Wardrop equilibrium for

the end-to-enddelaysincurredondierentroutesfromasensornodetoafusioncenter(sink).

Fromthesimulationresults,wehaveseenaveryhighdelayforasingle-queuesystem(provided

the systemwasstable)compared to two-queues system.

Another objective wasto minimize thetotal delayinthenetwork inlayered architecture,

where the constraints are the arrival-rate and service-rate of a node. Particularly, we have

shown that the objective function is strictly convex for the entire network. We then use

the Lagrangian dual decomposition method to devise a distributed primal-dual algorithm

to minimize the delay in the network. The deterministic distributed primal-dual algorithm

requires no feedback control and therefore converges almost surely to the optimal solution.

The resultsshow thattherequired optimalvalueof service rate isachieved foreverynodein

the networkbythe distributedprimal-dual algorithm. Itis important to payequal attention

to theobserved delay inthenetwork and energy consumption for data transmissions. A fast

convergence only means that a small amount of extra energy is consumed to perform local

calculations to achieve the desired optimizations. Similarly for the stochastic delay control

algorithm,wehaveshownaprobabilityoneconvergenceanditsrateofconvergenceproperties.

We then proposed a learning algorithm, applicable to both the open system

(layered-architecture) as well as the closed system (cross-layered architecture), to achieve Wardrop

equilibrium for the end-to-end delays incurred on dierent routes from sensor nodes to the

fusion center. For theclosed system, this algorithm also adaptedthechannel access rates of

the sensornodes.

For wireless sensor-actuator networkswith random channel access, we propose that each

sensormusttransmit its readingstoward one actuator onlyinorderto take theburdenof

re-laying,towarddierent actuators,awayfromenergy-constrained sensorsinastraight forward

fashion. We thenproposeda datasampling approach thatguarantees a long term data

sam-pling ratewhile minimizing the end-to-enddelays. Simulation results showthatperformance

of this scheme is better than the traditional layered architecture where the channel access

mechanism is independent of the data sampling process. We also saw that the proposed

schemedoesnot require tediousparameter tuning asis thecase for thelayered architecture.

Wealsoproposedanalgorithmforanoptimalactuatorselectionthatprovidesagoodmapping

between any sensor and an actuator in the network. The selection algorithm nds a

delay-optimalactuator foreach sensorinpolynomial time. We thenproposedalearningalgorithm,

applicable to both theopen systemaswell asthe closed system, to achieve Wardrop

equilib-riumfortheend-to-enddelaysincurredondierentroutesfromsensornodestotheirattached

delay-optimalactuators. Fortheclosedsystem,thisalgorithmalsoadaptedthechannelaccess

rates of the sensornodes. We nally propose a distributed actuation control mechanism for

SANETs that isresponsible foran ecient actuation process.

Theactuatorscandynamicallycoordinateandperformpowercontroltomaintainadened

level of connectivity subject to throughput constraints. The control overhead for static and

mobile actuator scenarios is analyzed using ns-2 simulations. The PC heuristic algorithm is

applicable to multihop SANETs to increase throughput,batterylife and connectivity.

Wethenconsideralargescale SANETwithmultipleactuatorsassinksfordatagenerated

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. We proposedand evaluated (using astandard network simulator, ns-2)our

actuator-selection (LEAD-ADP) and routing scheme (LEAD-RP) on top of a TDMA based

MAC (LEAD-MAC) protocol. We then use the Lagrangian dual decomposition method to

devise adistributedprimal-dual algorithm tomaximize network-lifetime inthenetwork. The

deterministic distributed primal-dual algorithm requires no feedback control and therefore

converges almost surely to the optimal solution. The results showthat 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

thatthisapproachhasnear-optimalperformanceandispracticallyimplementableascompared

to earlier analytical studiesbasedonly on numerical evaluations.

First,thischapter addressesthe problemof isolatedregions inthesensingeldbyletting

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

generated data to only one actuator for processing and the fact that the end-to-end delays

in SANETs is a hard constraint, we jointly optimize theactuator selection and optimal ow

routingsubjectto delay-energyconstraints. Thisapproachhasnear-optimalperformance and

is practically implementable.

Weuseasingleantennatransmitterandreceiverforspatialfocusing. PPCimplicitly

equal-izesthe channel byrefocusing channel spread. We have quantied thegainof PPCtemporal

compression, inasingle-hoppoint-to-pointsetup,usingGoldsequencestoimplementreceivers

basedon PPMmodulationschemes. We have alsoshownthatPPC-PPMcan beappliedto a

networkto achievealmost zero BERfor a two-hopcommunicationmode compared to

single-hop communications. Thisresultis ofsignicant importance inmakingrouting decisions for

UASNs. Inparticular, itallowsa node toselect its nexthop withtheaim of minimizing the

energyconsumption. Thiscross-layering(PHY-Routing)improvesthenetworklifetimeof

bat-tery operated UASNs by reducing thenumberof retransmission-attempts between anygiven

pair of nodes. Italso minimizes the average-energy consumption persuccessfultransmission.

Dans le document Cross layer optimization of wireless sensor and sensor-actuator networks (Page 162-170)