Traffic Modeling and Analysis in the Performance of Parking Sensor Networks

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Parking Sensor Networks

Trista Lin, Hervé Rivano, Frédéric Le Mouël

To cite this version:

Trista Lin, Hervé Rivano, Frédéric Le Mouël. Traffic Modeling and Analysis in the Performance of

Parking Sensor Networks. [Research Report] RR-8480, INRIA. 2014. �hal-00948120�

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RESEARCH

REPORT

N° 8480

December 2013

Traffic Modeling and

Analysis in the

Performance of Parking

Sensor Networks

RESEARCH CENTRE

GRENOBLE – RHÔNE-ALPES

Inovallée

Trista Lin

∗

,HervéRivano

∗

, Frédéri Le Mouël

∗†

Proje t-TeamURBANET

Resear hReport n°8480De ember201321pages

Abstra t: Networktra modelisa riti alproblemforurbanappli ation,mainlybe auseofits diversityandnode density. Aswireless sensornetworkis highly on ernedwith thedevelopment ofsmart ities, areful onsiderationtotra modelhelps hooseappropriateproto olsandadapt network parameters to rea h best performan es on energy-laten y tradeos. In this paper, we ompare the performan e of two o-the-shelf medium a ess ontrol proto ols on two dierent kinds of tra models, and then evaluate their appli ation-end information delay and energy onsumption whilevarying tra parametersand nodedensity. Fromthe simulationresults, we highlightsomelimitsindu edby node density, o urren efrequen yand non-uniform hara ters ofevent-drivenappli ations. Whenit omestoreal-timeurbanservi es,aproto olsele tionshall reallybetakenintoa ount-evendynami ally-withaspe ialattentiontoenergy-delaytradeo. Tothisend,weprovideseveralinsightsonparkingsensornetworks.

Key-words: parkingsensornetwork,networktra modeling,informationdelay

∗

INRIA,UniversitédeLyon,INSA-Lyon,CITI-INRIA,F-69621,Villeurbanne,Fran e

†

Résumé: Lesréseauxde apteurssansl sontessentielsaudéveloppementdes villes intelli-gentes. Pour lesétudier, lesmodèlesde tra employéssont ru iauxpourprendre en ompte lesspé i ités des appli ations urbaines, ainsi que la diversité et la densité desnoeuds. Dans etravail,nous omparonslesperforman esdedeuxproto oles lassiquesde ontrled'a èsau médium(MAC)surdeuxmodèlesdetra diérents. Nousnousintéressonsàleurperforman es en termes d'e a ité énergétique et de délai d'a heminement de l'information en fon tion de l'intensitédel'a tivitémesuréeet deladensitéduréseau. Nousmettonsenéviden eleslimites de pertinen e de haque appro he et en dérivons des onseils sur les paramètres àutiliser en fon tiondelasituationainsiquedesperspe tivesversdesproto oless'adaptantaux onditions réellesdel'a tivitémesurée.

Mots- lés : réseau de apteursde stationnement, la modélisation du tra de réseau, délai desinformations

1 Introdu tion

As the urban population is in reasing it brings the e onomi growth and the denser urban mobility[22℄. Thersttobeae tedisthetra . Thankstothesmartphonete hnology,drivers angetdiverseurbaninformationsimplyfrommobileapporinternet. Thus,theavailabilityand qualityof urban informationbe ome themostimportant riteriafor ities. Twogeneral types of information are real-time and non real-time. The former is time-sensitiveand tells urrent statusorup omingevents,forexample,tra ,publi transit,surveillan eandsoon. Thelatter is time-insensitiveand tellsthetimelessinformation,history, fore astors hedule,forexample, environmentalmonitoring,weatherfore ast,lo altravelinformationandsoon. The ontentof information anbegeneratedandmaintainedbygovernmentinstitutions,rms,usersorwireless networkedsensors. User-generated ontentis rowdsour eddatawhi henri hestheinformation sour esatdierentprospe ts. Plentyofinterestinginformation analsobeshareda ordingto users' sudden or periodi urban mobility. The published ontent ould, however, be outdated or false be ause of insu ient parti ipants or mali ious users, as well as limited to human's observation. In viewofthis fa t, wirelessnetworkedsensors help obtainmorevarious typesof information and assure of thea urate measurement. A ording to information types, sensors sendupdatedinformationperiodi ally,ondemandorburstingly. Thatis,anetworkpa ketwhi h onsists of ertaininformation, shallbetreatedwithits orrespondingprioritysoasto respe t ana eptableinformationdelay[26℄. Here,weareinterestedinthepossibleservi eswhi h anbe arriedoutbynetworkedsensorsdueto thein reasingmobilityneed. Amongwhi h,thetra ongestion is the greater thought at present and a huge per entof tra jams are aused by the vehi les looking forparkingspa es. Sofar asurban driversare on erned, smarton-street parkingsystemassistedbynetworkedsensors isneededtoshorten theparkingsear htimeand theparkingdistan e fromdestinations.

Parking sensornetwork isformed by dierenttypesof networkedsensors, whi h andete t vehi le'spresen e. Sensorswhi h olle tdata24hoursaday,allowustofollowupthe ongestion problem anytime. Two ategories of sensing methods are stationary and mobile. Stationary sensorsnormallyxedonthepavement, urb,parkingmeter,oraboveparkingspa es andete t parkingo upan ywithinthesensingrange. Mobilesensorsmountedonvehi les andolikewise in movement,a ordinglythedete tion rangeislarger yet mu hlessrea tive. Forthisreason, muni ipalities tends toinstall thousandsof on-site parkingsensors in the ity entre, and, our studiesalsofo usonthistypeofsensornetworks.

Parking sensornetworksasthegeneralurban sensornetwork hasthefollowingproblems to ta kle:

Link quality Parking sensorsareinstalledalongthe urbsothatthewirelesssignaliseasily ae tedbythe hangingurbanenvironment. Bynow,linkqualityisnormallyassessedbyseveral indi ators in aeld test. In viewofsto hasti ally varyinglink status,periodi tra modelis oftenappliedin urbanappli ationtoinformgatewaysofsensors'existen es.

Nodedensity Sensordeploymentae tsthemediuma essmethodandnetworkload. Owing tothela kofmultipledete tion,parkingsensorsaremerelygottendonein demar atedspa es, to wit,onevehi ledete tionsensor perspa e. Byassuming that

N

parkingsensorsareserved by one gateway, the distan e between any two parking sensors

s

i

and

s

j

where

1 ≤ i, j ≤ N

willsatisfy

ks

i

− s

j

k ≥ l

whenever

i 6= j

. However,the ommuni ationrangeofea hgatewayis boundedsothat

N

will notbearbitrarilylarge.

Tra variation Thedesign of network proto ols shall betailor-made for the need of net-worked sensors whi h is appli ation-oriented and an be des ribed by tra models. If the sele tedappli ation demands areal-time servi e from thenetwork, proto ols must berea tive enoughto respe ttheminimumlaten y. Insmartparkingappli ation,theinformation delayis quitestri t. Threemainstreamtra modelsarerequest,eventandtime-driven. Request-driven is irrelevantforsmart parkingappli ation sin e ontinuous re ordingis required. Time-driven appli ationwhi h generatesperiodi tra isoften usedin testingtheperforman eof network proto olsbe auseofitsweakdependen eontheenvironment. Event-drivenappli ationisknotty duetoitsvarietyondierenttypesofobservedevents.

Our network stru ture omprises three omponents: parking sensor, gateway and mobile vehi le. Parkingsensoristhemeasurementpoint,towitthesour eofinformation,andstationary just like the parking spa e it is wat hing. Gateway plays two roles whi h are to aggregate the information form parking sensors and disseminate the aggregated information to vehi les a ordingtotheirrespe tiveinterests. Mobilevehi leisthenetworkparti ipantwithaninterest of parkingspa esin thevi inity of agiven destination. To arry outasmart parkingservi e, diverseappli ation models betweenthe three omponentsshall bedis ussedin orderto design adaptednetworkproto ols. Inthisreport,theappli ation modelsbetweenparkingsensorsand gatewaysarestudied be ausethedeploymentofsensornodesand thedesignofnetworkwillbe riti alforthesubsequentinformationdissemination. Ourbodyofworkistosimulatethetra inuen e on stationary WSN with the aim of ameliorating the design of network ar hite ture in orderto a hieveitsbest performan ein anurbanenvironmentandredu e theurbantra . The problem we are addressing is the impa t of urban parking density, event frequen y and non-uniform tra parameters to the real-time servi e onstraints. Weveried the impa t of tra variation on event and time driven appli ations through xed and dynami bandwidth allo ationsorthroughdierentnumberofsensors. Our ontributionsaresummarizedasfollows: 1. Modelingofevent-andtime-drivenurbansmartparkingappli ationsbyobservingvehi le's

arrivalanddeparture.

2. Energy and delayperforman e evaluation of event- andtime-driven appli ationsthrough extensiveexperimentsonurbans enarios,helps to ndoutthenetworklimitwith tra variationintwotypi albandwidthallo ationproto ols.

3. EngineeringinsightstostreamlinetheWSN onstru tionofurbansmartparking appli a-tions,helpnetworkdesigners sele tappli ableproto olsandoptimizethenetwork perfor-man e.

2 Ba kground and related works

Smart on-street parking appli ation has re eived a lot of attention in re ent years. Its main missions are to olle t the real-time parking o upan y information and to disseminate these informationto driverssimplythroughsmartparkingapp. Twotypesof olle tionmethods are mobileandstationary. Theformeristo takeadvantageofvehi le'smobilityto olle t informa-tion along the route. In whi h, the most e onomi alis rowd-basedmobile appli ation. But the rowdsour edinformationisfrequentlyunavailable,outdatedorfalsebe auseofinsu ient parti ipants,freeridersandmali ioususers[6,2,10,13℄. Therefore,itisobviousthat rowdsour -ing parkingassistan e system an notreally provide a reliablereal-time time servi e required bymuni ipalities[19℄. Alternatively, an ultrasoni sensor an be side-mounted ona taxi abor busso as to dete t an on-street parkingspot map. Forexample, the ParkNet system in San

Fran is o[17℄, olle tsdatawiththelo ationinformationfromGPSre eiverandthentransmitit overa ellularuplinkevery60se ondstothe entralserver.Su hamobileparkingsensorsystem requiresmu hlessinstallation,yetneedsalongeraverageinter-pollingtime,towit,25minutes for80

%

ofthe ellsinbusierdowntownareawithonly300 abs. Stationary olle tionmethodis to installon-site vehi ledete tionsensors[20℄ soasto monitorthe o upan y statusof parking spa es. Based on this, large-s ale road-side parkingsensor network has been implemented in many ities.

SFparkproje t[23℄istheearliestmuni ipalsmartparkingproje twhi hadopted8200 station-ary in-ground parkingsensorsand deployedalarge-s aleof multi-hop parkingsensornetworks in the downtown area of San Fran is o. Ea h parking sensor, in ommuni ation with nearby relays,re ordswhenvehi les omeandleave. Theinformationdelayis al ulatedbyhowlongit takesthesensornetworktopro essandsendsoutaevent.

85%

ofevents anbere eivedwithin 60se onds. Ea hsensorsendsamessageeverydayeveniftheo upan ystatusdoesn't hange. Also SFpark applies a dynami pri ing poli y in order to keep a 75

%

o upan y rate in any parkingblo ks. LAExpresspark[14℄adoptsamulti-hopparkingsensornetworkinLosAngeles. Inaddition, the ommuni ationmoduleusesDust Networks'TSMPproto ol[18, 24℄,designed to operateonmultiple hannels. Physi ally thewireless hannel is dividedupin time and fre-quen y, and ea h resulting unit of the hannel is assigned to satisfy data ow requirements, mainly event-driven. Similarly, Fastprk[8℄ proje t also installed stationary in-ground parking sensorsin Bar elonaandfollowstheZigbee erti ation. Parking sensorssendamessagewhen theo upan ystatus hangesandevery20minutestoinformthegatewaytheirexisten es. Ni e parkisoneappli ationofConne tedBoulevardproje tinNi e. Parkingsensorssendamessage with update information every 60 se onds or while the o upan y status hanges. Thus the parkingsystemisupdatedevery10se ondsanddriverspaystheirparkingfeebyse ond. Ea h sensor anworkupto 8years. Beijing ityalsoimplementedasmartparkingsystem. Parking sensors dete tthe vehi le'spresen e every8se onds and thentransmitthe informationto the entral server. The in-ground parkingsensor an work for 5years without repla ing battery. Thedisadvantageofin-groundparkingsensorsistheinstallationandthela kofmultiple dete -tion,northedimensionof parkedvehi le. Thatis, onlydemar atedparkingareais supported. ConverselyParkNet anwork onthedete tionofdemar atedand un-demar atedparkingarea iftheparkingmapisknownin advan ed.

Parking sensor network is a spe ialized form of WSN and ertainly inherits its problems, viz energy-e ien y, laten yand throughput. These indi atorsare all relatedto the designof networkproto ols[3,29,11,1,15,21℄. Thetasksofparkingsensornetworksaretogetreal-time informationofparkingspa eavailabilityandtohavegoodresilien etoadaptthetra variation. Thus, the laten ytakesan utmostimportant roleamong all thefa tors. Tooptimizenetwork parameters for the best performan e of sensor networks, the network tra and phenomenon are relevant. The measurement of vehi le arrival and departure, the key fa tor of generating network tra , is one body twosides, either vehi les equipped with GPS re eiverknowtheir own lo ations for mobile dete tion, orinstall external on-site sensors for stationary dete tion. Vehi learrivaland departure havebeenstudied in several kindsof real-time urban events,for example,publi transitvehi lereal-timeposition,tra signal ontrola ordingtra owand some losed arparks[12,30,28℄. Aspreviouslymentioned,amongthreetypesoftra models, request-drivendoesnotx theneedofmuni ipalitiesnorthequality ofservi e[16℄requiredfor real-timeappli ation.Hen e,a a hingplatformispreferredtobuildonthegatewaysoastostore the urrentparkingo upan yinformationandresponserapidlytoparkingqueriesfromnearby drivers[5℄. Also, it helps redu e the systemdelaytime if applying entralized orde entralized dynami parking resour eallo ation[9℄. Sin e the arrival and departure are assumed Poisson distributed,theo upan y rateoftheparkingsystem anbeanalyzedbyMarkovpro ess[4℄.

3 Network tra model

Theunderlyingparametersforsimulatingthe network tra are starttime,duration, interval, pa ketsize andthepro edure. Inurbansensornetwork,sensorsareoftenstationarytomonitor ertainevents,andthen ethepa ket transmissionshallalwayshappenandneverend. Inother words, the key point of modeling an event-driven appli ation will be to nd an appropriate distributiontodenethetra interval,vizinter-eventinterval. Next,wepresentthemodeling of the event o urren e and the denition of information delay with respe t to urban smart parkingappli ations.

3.1 Vehi le's arrival and departure

Inparkingsensornetworks,themainobservedeventsarevehi les'arrivalsanddepartures. Also, ea hvehi learrivala ompaniesexa tonedeparturepriortonextarrival. Tomodelit,werst look at theevento urren esequen eon oneparkingsensor. We supposeea h parkingsensor ispre ise enoughand providesmerelytwostatus, namelyo upied andva ant(gure 1). The intervalfromvehi le'sarrivaltodepartureisso- alledo upan y time

T

p,i

during whi h sensor

i

dete tsvehi le'spresen e. Likewise,theintervalfrom one'sdeparturetonextarrivalisva ant time

T

v,i

duringwhi hsensor

i

dete tsnothing. Both

T

p,i

and

T

v,i

propershallbedes ribedby attingdistributioninordertoapproximatetheirrandomness. Byassumingthat

T

p,i

and

T

v,i

arebothexponentiallydistributed withrateparameters

λ

i

and

µ

i

,wehave:

Figure1: O upan ystatusofoneparkingsensorovertime

ˆ The probability of hoosing a o upan y time

X

anbe al ulated by

P r(T

p,i

= X) =

λ

i

e

−λ

i

X

andthemeanis

E[T

p,i

] = λ

−1

i

.

ˆ Theprobabilityof hoosingava anttime

Y

anbe al ulatedby

P r(T

v,i

= Y ) = µ

i

e

−µ

i

Y

andthemeanis

E[T

v,i

] = µ

−1

i

.

Afterapplyingtheexponentialdistribution,thetimelineofsensor

i

'sparkingstatusisshown in gure 2. The o upied period presents

T

p,i

and the rest

T

v,i

. The average interval of ve-hi le's arrival will be

T

p,i

+ T

v,i

and the average o upan y rate will be

T

p,i

/(T

p,i

+ T

v,i

) =

λ

−1

i

/(λ

−1

i

+ µ

−1

i

)

. If

λ

−1

i

> µ

−1

i

, theaverageo upan y rate willbe greaterthan50%. Expo-nentialdistributionis one shape of Weibull sothat the tra interval analso bereshaped if needed. Besides,burstytra onsideredpartofevent-drivenwithParetodistribution[25℄isnot dis ussedinourstudiesasoneevent anonlybedete tedbyoneparkingsensoratatime. Ina businessarea,theparkingstatusvaries fastbe auseof theparkingtimelimit(

λ

i

is small)and thehigherhotspot parkingdemand(

µ

i

issmall).

Suppose that N networked parkingsensors are installedin adistri t and form onesubnet. These parking sensors an be installed along a street or in a rossroads. Ea h parking spa e

i

hasaverageo upied andva antperiods

λ

−1

i

and

µ

−1

i

. Thus, the newparametersfor global o upied andva ant time will be

λ =

P

N

i=1

λ

i

and

µ =

P

N

i=1

µ

i

su h that theglobal parking o upan yratewillbe

λ

−1

_/(λ

−1

_{+ µ}

−1

₎

. Ingure3,itshowsthetime-varyingo upan yrate's timelineof24parkingsensorsin adaywhile

λ = µ

.

0

10000

20000

30000

40000

50000

60000

70000

Timeline

occupied

Figure 2: Sensor's o - upan ystatus

0

0.2

0.4

0.6

0.8

1

0

10000 20000 30000 40000 50000 60000 70000 80000 90000

Timeline

occupancy rate

Figure 3: O upan y ratein adistri t

0

0.005

0.01

0.015

0.02

0.025

0.03

0

200 400 600 800 1000 1200

packets/second/sensor

(

λ

+

µ

)

-1

Figure 4: Generated pa kets by event-driven tra model

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

0.22

0

200

400

600

800

1000

packets/second/sensor

ω

Figure 5: Generated pa kets by periodi tra model 3.2 Event-driven tra

The event o urren e frequen y has a great impa t on event-driven appli ations. Sin e ea h sensornodesendsapa ketwhileonevehi learrivesandanotherwhen itleaves,assumevehi le arrivalsanddeparturesarePossiondistributed withanaveragearrivalanddepartureratesof

α

and

β

vehi lesperse ond. Thus,thegeneratedtra willbe

α + β

perse ond. Foroneparking spa e(

N = 1

),therelationbetween(

α + β

)and

(λ + µ)

−1

isshown ingure4. Inwhi h, itis obviousthat theprodu tof

(α + β)

and

(λ + µ)

−1

isa onstant.

(α + β)(λ + µ)

−1

_{= k}

(1) Sin e there are

N

limited parkingspa es, it means that the number of parkedvehi les

n

p

in this areais between0 and

N

. Assume the queueingmodel in this network with

N

parking sensors an be des ribed by the birth and death pro ess, one ase of ontinuous-time Markov pro ess,withaM/M/1/Kqueue,i.e.,inniteinputandoutput,1systemandbuersizeK.The probabilityof

j

parkedvehi lesisdenedas

P

j

(t)

. Whenabirthhappens,thenumberofparked vehi les in reases 1with abirth rate

α

j

from state

j

to

j + 1

where

α

j

=

α

for

0 ≤ j < N

. Whena deathhappens, thenumberof parked vehi les des reases 1with adeathrate

β

j

from state

j

to

j − 1

where

β

j

=

β

for

0 < j ≤ N

. Theprobabilityofzeroparkedvehi le

P

0

(t)

an onlyberea hedbythetransitionfrom stateoneto zerosothat

αP

o

(t) = βP

1

(t)

. Similarly,the probabilityof

i

parkedvehi les

P

i

(t)

inthisarea anberea hedbythetransitionfromthestates

i − 1

and

i + 1

to

i

sothat

αP

i

(t) = βP

i+1

(t)

and

(α + β)P

i

(t) = αP

i−1

(t) + βP

i+1

(t)

. Let

ρ

equalsto

α

β

where

β > α

,then

P

i

(t) = ρP

i−1

(t) = ρ

i

_P

0

(t)

where

P

0

(t) =

1−ρ

1−ρ

N +1

. The average numberofparkedvehi les

n

p

in thisarea anbe al ulatedbyequations2and3.

Thenumberoftotalparkingspa esmultipliedbytheo upan yrateistheaveragenumber ofparkedvehi les:

n

p

= N ∗

λ

−1

λ

−1

_{+ µ}

−1

(2) Theexpe tation value of averageparked vehi les stands for the average number of parked vehi les:

n

p

=

N

X

k=0

kP

k

(t) =

N

X

k=0

kρ

k

P

0

(t) =

1

1 − ρ

−

1 + N ρ

N

+1

1 − ρ

N

+1

(3) A ordingtoLittle'sformula,theaveragenumberofparkedvehi les

n

p

isequaltotheprodu t ofvehi learrivalrate

α

andaverageparkingtimeofN vehi les

λ

−1

,i.e.,

n

p

= αλ

−1

. Hen e,

λ

and

µ

anbe al ulatedfrom thegiven

α

and

β

byequations 4and 5,andvi e versa.

λ

−1

₌

n

p

α

=

1

α

(

1

1 − ρ

−

1 + N ρ

N+1

1 − ρ

N

+1

) =

1

α

(

β

β − α

−

β

N+1

_{+ N α}

N+1

β

N+1

_{− α}

N+1

)

(4)

Figure6: The al ulationofinformation de-layforevent-andtime-drivenappli ations

(a) 12 nodes

(b) 24 nodes

(c) 36 nodes

(d) 48 nodes

(e) 60 nodes

(f) 72 nodes

(g) 84 nodes

(h) 96 nodes

gateway

sensor

Figure 7: Topologies of dierent numbers of sensors. In rease the node density by adding one more line or one more side of urbparking.

µ

−1

_{= N α}

−1

_{− λ}

−1

(5) Here

λ

and

µ

onlystand fortheglobalparametersof alltheparkedvehi les in thesystem. Ifwesuppose alltheparkingspa esare uniform,ea hparkingspa ehasthesame

λ

i

=

λ

N

and

µ

i

=

_N

µ

for

1 ≤ i ≤ N

. Butin real life,ea h parkingspa ehasdierentpreferen esa ording totheir relativepositionsor ommer ialinterests, forexample,sensors haverespe tiveaverage parkingandidletimea ordingtothelo al ommer iala tivities. That istosay,foranygiven

λ

and

µ

, theindividual parameters

λ

i

and

µ

i

ofea h parkingspa e annot be obtained. But theseparameterswill ae ttheperforman ea ordingtheproto ols'properties.

3.3 Periodi /Time-driven tra

On the ontrary, time-driven appli ation is only ae ted by the tra interval

ω

instead of o urren efrequen y. The amountof generated pa kets is inverselyproportionalto the tra interval

ω

,shownin gure5. Whileusingperiodi tra model,theamountofnetwork tra is unae tedby the sensory information. It simplysends out apa ket with the urrent time-stampedstatuswhenthetimeisup.

3.4 Denition of information delay

Themaingoalofparkingsensornetworksistoprovideareal-timeurbanservi etodrivers. The prin ipalperforman eindi atorwhi hwelook at,therefore,istheinformationdelay,denedas therequiredtimeforknowinga hangedo upan ystatusofaparkingsensor. Informationdelay isthesumofsensingduty- y le,appli ationdelay,end-to-enddelayandqueuingdelay. Wehave negle tedthesensingduty y lewhi hisnormallyquiteshort. Inevent-drivenappli ation,ea h sensorsendsoutanupdatedinformationaton ewhendete tinganyevent,namely,appli ation delayisalmost zero. Time-drivenappli ationis subje tto thetra intervalsothat an appli- ationdelay shallbe added up. In gure6, wesee that alonger tra interval de reases the tra intensitybut also ausesalongerinformation delaywhi his notpreferablefor real-time parkingservi es.

Table1: Simulationparameters

Transmitpoweroutput3dBm Re eivesensitivity-110dBm Datarate250kbps

P

tx

65.7mW

P

rx

56.5mW

P

cs

55.8mW

P

of f

30

µ

W

E

radio.switch

0.16425mJ Transmissionrange50m Simulationtime: 86400se onds Sensornodenumber: 12-96 Appli ationparameters: Event-driven-

λ

&

µ

,Time-driven-

ω

Pa ketsize 84bytes

MAC:duty- y ledxed&dynami bandwidthallo ation,slotduration=0.1s,retransmission andpiggyba kenabled.

4 Urban smart parking appli ation experiments with real-time servi e onstraints

Bandwidthallo ation anbexedordynami . Inthisworkweevaluatetwoo-the-shelfmedium a ess ontrol proto ols: duty- y led TDMA for xed bandwidth allo ation and duty- y led CSMA for dynami one. Our simulations, performed with the WSNet simulator[27℄ , usethe topologies depi ted in gure 7 with various node density. We evaluate the required energy onsumptionandtheinformationdelayofourtra modelsindierents enarios. Thedistan e betweentwoadja entsensors onthesameroad-side is5meters. The sensorsin thevi inityof gatewayare10metersaway. Thatistosay,

ks

i

− s

j

k ≥ l = 5

whenever

i 6= j

Somesimulationparametersareindi atedintable1,inwhi h,ea hparkingsensor anrea h the gateway through one hop. Two types of bandwidth allo ations are used to evaluate the impa t of event-driven model ompared with time-driven one. We hoose to use o-the-shelf mediuma ess ontrolproto ols,sothatwe ankeeptheobje tivityinoursimulationresultand analysis withoutany ex eption of aparti ular proto ol. Duty- y led TDMA is used for xed bandwidthallo ation,andduty- y led CSMAfordynami bandwidthallo ation

1 .

4.1 Impa t of node density 4.1.1 Fixedbandwidth allo ation

Consideringthesensornetworkis oftenbandwidth-limited, in single hannels enario,theonly mediumresour eistimedivision. Ea hnodeispre-assignedtoonepartitionofmediumresour e in order to transmit their pa kets. While the network oordinator doesnot know in advan e thetra modelandgeolo ationofea hnode, itpre-assignsanequalpartitionto nodesinthe subnet. Ifanodehasnopa kets tosend initsterm,theothers still annotseizethis o asion tosendtheirpa kets.

s

0

s

1

s

2

...

s

N

ina tive

←−

duty y le

−→

Figure8: Duty y leofxedbandwidthallo ation

Theduty y le omprisesanina tiveperiodand

N + 1

timeslotsfor

N

parkingsensorsand gateway,showningure8. Ea hsensor anonlysenditspa ketonitspre-assignedtimeslot. By assumingtheina tiveperiod=0,themaximum apa itywillbethere ipro alofslot duration

s

i

, viz10pa kets perse ond,andthe durationof duty y le an be al ulatedby

T

duty.cycle

= 1

Threepointsarenot onsideredinoursimulationsthataretheenergy onsumptionofrstsyn hronization, hourly hangingtra parameters,andthetime-varyingnetworkthroughput ausedbytheunstablelinkquality. Thus,alltheretransmissions omemerelyofpa ket ollision. Thehourly hangingtra parameters

λ

i

and

µ

i

anbeobtainedfromtheexistingmuni ipalparkingpaymentinformation.

200

210

220

230

240

250

260

270

280

290

10

20

30

40

50

60

70

80

90

100

Energy consumption of one sensor in a day

Number of sensors within one gateway

ω

= 60s

ω

= 120s

ω

= 300s

no packet

Figure9: Per-node en-ergy onsumption of time-driven appli ation under xed bandwidth allo ation

200

210

220

230

240

250

260

270

280

10

20

30

40

50

60

70

80

90

100

Energy consumption of one sensor in a day

Number of sensors within one gateway

λ

i

+

µ

i

= 40

λ

i

+

µ

i

= 200

λ

i

+

µ

i

= 1200

no packet

Figure 10: Per-node energy onsumption of event-driven ap-pli ation under xed bandwidthallo ation

0

50

100

150

200

250

300

350

400

450

10

20

30

40

50

60

70

80

90

100

Information delay (s)

Number of sensors

ω

=60s, >98% packets arrive

ω

=120s, >98% packets arrive

ω

=300s, >98% packets arrive

ω

=60s, average info delay

ω

=120s, average info delay

ω

=300s, average info delay

Figure 11: Information delay of time-driven appli ation while

N

and tra parameters hange under xed bandwidthallo ation

0

5

10

15

20

25

10

20

30

40

50

60

70

80

90

100

Information delay (s)

Numbor of sensors within one gateway

λ

i

+

µ

i

= 40, >98% packets arrive

λ

i

+

µ

i

= 200, >98% packets arrive

λ

i

+

µ

i

= 1200, >98% packets arrive

λ

i

+

µ

i

= 40, average info delay

λ

i

+

µ

i

= 200, average info delay

λ

i

+

µ

i

= 1200, average info delay

Figure12: Information delay of event-driven appli ation while

N

and tra parameters hange under xed bandwidthallo ation

(N + 1) ∗ s

i

= 0.1(N + 1)

. Ifthepa kettransmissionisfailedonthe urrenttimeslot,thenext onewillbein

0.1(N + 1)

se onds. Tominimizetheidlelisteningperiod,ea htransmittersends averysmallreservationbea onmessagebeforestartingthedatatransmission. Ifthereservation fails (the re eiveris unrea hable), the transmitter will put the pa ket into thequeue, turn o itsradio and wait for thenexttime slot. Instead ofwasting energyto doavain transmission, sensornodesprefertoevaluatethere eiver'savailabilitythroughtheseverysmallbea ons. The advantages of xed bandwidth allo ation are the mu h less pa ket ollision and lower energy onsumption sin e nodes only send bea ons in ertain slots. If sensor nodes' tra model is given and stati , xed bandwidth allo ation anoptimize the resour eassignment in order to rea habetterperforman e. Thedrawba ksarethatmanytimeslotsarewastedsothatalonger delaytimeis aused,alsotheurbantra modelisdynami andtime-variant. Sin eea hnode in luded the gatewayhas its orrespondingtime slot, that is, the gatewayneeds to know the amount of nodes in itssubnet in order to allo ate theresour e. Ifthere is anew node whi h intendsto join this network,the gatewaywill haveto reallo atethe resour ewhilethere is no enoughtimeslots.

Figures 9and10 showstheper-nodeenergy onsumption usingtime-and event-driven net-work tra while the number of sensors varies. It is obvious that the per-node energy on-sumption is elevated while the node density in reases, even in reasingly signi ant when the tra intensity is high(

(λ + µ)

−1

or

ω

is small). If the tra intensity is not that high, the energy onsumption iselevated in thebeginning on groundsof additional ontrol pa kets and thenstabilized. Butwhenit omestoinformationdelay,thesituationisnotthesame. Figure11 showstheinformationdelayoftime-drivenmodelismerely

ω

-related. Figure12showsthatthe information is proportional to thenumber of sensors be ause of theduty y le amplied with thenumberofnodes

N

.

4.1.2 Dynami bandwidth allo ation

s

0

ina tive

ontention datatransmission

←−

duty y le

−→

Figure13: Duty y leofdynami bandwidthallo ation

Dynami bandwidthallo ationuses ontention-basedmediuma ess ontrolduetothe inex-ibleresour eallo ationpreviouslymentioned. Theprin ipleisthatthenodegetsitspartitionof

240

250

260

270

280

290

10

20

30

40

50

60

70

80

90

100

Energy consumption of one sensor in a day

Number of sensors within one gateway

ω

= 60s

ω

= 120s

ω

= 300s

no packet

Figure 14: Per-node energy onsumption of time-driven appli ation under dynami band-widthallo ation

230

240

250

260

270

280

10

20

30

40

50

60

70

80

90

100

Energy consumption of one sensor in a day

Number of sensors within one gateway

λ

i

+

µ

i

= 40

λ

i

+

µ

i

= 200

λ

i

+

µ

i

= 1200

no packet

Figure 15: Per-node energy onsumption of event-driven appli- ation under dynami bandwidthallo ation

0

50

100

150

200

250

300

350

400

450

10

15

20

25

30

35

40

45

50

Information delay (s)

Number of sensors

ω

= 60s, >98% packets arrive

ω

_ω

= 120s, >98% packets arrive

= 300s, > 98% packet arrive

ω

= 60s, average info delay

ω

= 120s, average info delay

ω

= 300s, average info delay

Figure16: Information delay of time-driven appli ation while

N

and tra parameters hange under dynami bandwidthallo ation

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

10

20

30

40

50

60

70

80

Information delay (s)

Number of sensors within one gateway

λ

i

+

µ

i

= 40, >98% packets arrive

λ

i

+

µ

i

= 200, >98% packets arrive

λ

i

+

µ

i

= 1200, >98% packets arrive

λ

i

+

µ

i

= 40, average info delay

λ

i

+

µ

i

= 200, average info delay

λ

i

+

µ

i

= 1200, average info delay

Figure17: Information delay of event-driven appli ation while

N

and tra parameters hange under dynami bandwidthallo ation

networkresour ewhenitasksfor. Ifmorethantwonodesde laretheirdemands,a ompetition willbeheldto hoosewhoisthe urrenttransmitter. Nodeswholosethe ompetitionwillgoto sleepand waitfor nextresour eallo ation. If the ompetition methodis notgoodenough,the pa ket ollisionwill happen frequently and drop thenetwork performan e. To dothat, nodes listen to the hannel to know ifthe hannel is busy before sending a pa ket. Ifthe hannel is busy, nodes will put the pa ket into their queues and wait for next ompetition. Otherwise, nodes will sendouta bea on messagewiththe reservationinformation. If thedesiredre eiver gets this pa ket,it will replyan a knowledgmentand then atransmissionreservationis done. Also onsideringthebandwidthislimited,thenetworkresour eisalsotime-division.

Soasto redu e the idle listening, aduty y le isalso applied and ontainsthe ontention, datatransmissionandina tiveperiods,shwoningure13. Theadvantagesofdynami resour e allo ationare thebetteruseof networkresour eand ashort networkdelay. Thedrawba ksis theinevitablepa ket ollisionwhi h ausesarbitrarilyhigh energy onsumptionand laten yon groundsofendless ontentionstriggeredbyhighnodedensity. Byassumingtheina tiveperiod isequaltozeroandtheslotdurationis0.1se onds,themaximum apa itywillbethere ipro al ofslotduration,viz10pa ketsperse ondandthedurationofduty y leis0.1se onds. Ifthere is anew nodewhi hintends tojoin this network,it willjust join the ompetition and in rease thepa ket ollisionrate.

Figure14 and 15 show theper-nodeenergy onsumption oftime- and event-drivenmodels while

N

varies. Unlikethe previous ase,the elevated energy onsumption is mainly provoked by the in reasing ompetitors during ea h ontention period. While

ω = 60s

and

N = 96

, the energy onsumption de lines, inasmu h as ex essive pa ket ollisions ause no su essful transmissionreservation. Figure16 showstheinformation delayof time-drivenmodel whi h is

ω

-relatedaswell. After

N

is greaterthan60,theinformationdelayisalsoarbitrarilylargedue to theveryhighpa ket ollisionrate. However,in event-drivenmodel, theinformationdelayis alsoproportionaltotheduty y le. Thedieren eisthattheduty y leofdynami bandwidth allo ationdoesnotvary with

N

,thusea hnodetriesto senditspa ketswithin

κ

timesofduty y le where

κ

is a onstant. The moretransmissiondemands, thegreatervalueof

κ

, shown in gure 17. Even the average information delay is just alittle bit higher than thehalf of duty y le,theglobalinformation delay anstillvary to5timesofduty y le. Hen e,0.5se ondsis onsideredastheguaranteedmaximuminformationdelay.

4.2 Impa t of tra intensity

220

240

260

280

300

320

340

360

380

400

0

500

1000

1500

2000

2500

Per-node energy consumption in a day

ω

w/ packets

w/o packets

Figure 18: Per-node energy onsumption of time-driven appli- ation under dynami bandwidth allo ation while

N = 24

200

220

240

260

280

300

0

500

1000

1500

2000

2500

Per-node energy consumption in a day

ω

w/ packets

w/o packets

Figure 19: Per-node energy onsumption of time-driven appli- ation under xed bandwidth allo ation while

N = 24

Figure 20: Information delay - dynami band-widthallo ation- time-drivenwithvaried

λ

,

µ

and

ω

Figure21: Information delay-xedbandwidth allo ation-time-driven withvaried

λ

,

µ

and

ω

Figure 22: Information delay-Fixedbandwidth allo ation - non-uniform -

(λ + µ)

−1

_{= 1.667}

-

ω

=

60s

Figure 23: Information delay -Fixed bandwidth allo ation - non-uniform -

(λ + µ)

−1

_{= 8.33}

-

ω

=

60s

Figure 24: Information delay -Fixedbandwidth allo ation - non-uniform -

(λ + µ)

−1

_{= 1.667}

-

ω

=

300s

Figure 25: Information delay-Fixedbandwidth allo ation - non-uniform -

(λ + µ)

−1

_{= 8.33}

-

ω

=

300s

Figure 26: Information delay - Dynami band-width allo ation - non-uniform -

(λ + µ)

−1

₌

1.667

-

ω

= 60s

Figure 27: Information delay - Dynami band-width allo ation - non-uniform -

(λ + µ)

−1

₌

8.33

-

ω

= 60s

Figure 28: Information delay - Dynami band-width allo ation - non-uniform -

(λ + µ)

−1

₌

1.667

-

ω

= 300s

Figure 29: Information delay - Dynami band-width allo ation - non-uniform -

(λ + µ)

−1

₌

8.33

-

ω

= 300s

4.2.1 Periodi /time-driventra parameter

Sin eperiodi tra islessae tedbythebandwidthallo ationmethodandstronglyrelatedto thetra interval

ω

,gures18and19showtherelationbetweentra interval

ω

andper-node energy onsumption. The periodi tra is equivalent to onstant bit rate so that the tra isknownand uniformamong all sensornodes. Hen e thedeviation of onsumedenergy isnot apparent. However,itisobviousthat the onsumedenergyisextremelylowwhile

ω ≥ 1200

. In otherwords,theinformationdelaywhi hisproportionalto1200willnotbea eptablefor real-timeurbanservi e. Butitisinterestingtoassignaperiodi tra withalongintervalonsensor nodessimplytoinformgatewaysoftheirexisten esand urrentbatterystatus. Figures20and21 showtheinformationdelayinfun tion and

(λ + µ)

−1

and

ω

. Notethatinperiodi /time-driven appli ation,sensor veries theo upan y status every

ω

se ond and then sends apa ket with

220

240

260

280

300

320

0

200

400

600

800

1000

1200

Per-node energy consumption in a day

(

λ

i

+

µ

i

)

-1

w/ packets

w/o packets]

Figure 30: Per-node energy onsumption of event-driven appli- ation under dynami bandwidth allo ation while

N = 24

200

220

240

260

280

300

320

0

200

400

600

800

1000

1200

Per-node energy consumption in a day

(

λ

i

+

µ

i

)

-1

w/ packets

w/o packets

Figure 31: Per-node energy onsumption of event-driven ap-pli ation under xed bandwidth allo ation while

N = 24

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0.09

0.1

0

200

400

600

800

1000

1200

Information delay (s)

(

λ

i

+

µ

i

)

-1

Figure 32: Average in-formation delay while the tra parameters hange and

N = 24

under dynami band-width allo ation

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

0

200

400

600

800

1000

1200

Information delay (s)

(

λ

i

+

µ

i

)

-1

Figure 33: Average in-formation delay while the tra parameters hangeand

N = 24

un-derxedbandwidth al-lo ation

thesensedinformationtogateways. Thus, ifsensor hangesitso upan ymorethantwotimes in

ω

se onds, the updated information will not be re orded anywhere and the urrent status will also be less antique. When

(λ

i

+ µ

i

)

−1

_{> ω}

, the information delay time is proportional to

ω

2

, otherwise,

2(λ

i

+ µ

i

)

−1

. The bandwidth allo ation methods make no dieren e to the informationdelay.

Ingures22and23,weseethatwhen

ω

is

60

se onds,thevariationof

(λ

i

+ µ

i

)

−1

onlyae ts theamountofpa kets,themaximumandaverageinformation,however,arenotae tedatall. Therefore,when

ω

is

300

se onds,

(λ

i

+ µ

i

)

−1

doesae ttheaverageinformation. Ingures24 and25, theee tof

(λ

i

+ µ

i

)

−1

is obvious. Theinformationdelayofpa ketsfrom nodes1-9is mu hshorterthantheothers. Thatisbe ausetheirtra parametersaremorea tivethanthe periodinterval,namely

(λ

i

+µ

i

)

−1

_{≪ ω}

for

1 ≤ i ≤ 9

. Inaddition,ingures23and25,thepa ket numberis redu ed as thenode id in reases. Take dynami bandwidthallo ationto substitute thexedoneandseetheresultsingures26-29. Inthisway,someupdatedinformationwillbe missedifthesensordoesnotstoreitintothebuer. Aspreviouslymentioned,neitherbandwidth allo ationmethodae tstheperforman eoftime-drivenappli ation.

4.2.2 Event-driven tra parameters

Besidesnodedensity,theimpa toftra variationonenergy onsumptionandinformationdelay is also important. Figures 30 and 31showstheper-nodeenergy onsumption whenthe tra parameters vary under xed and dynami bandwidth allo ations. Compared with gure 18 and 19, we see that the onsumed energy deviation is aused by the tra dieren e among sensor nodes,in parti ularin thexed bandwidthallo ation. That isbe ausexedbandwidth allo ation is more sus eptible to network tra . Similarly, the average information delay in gures32and 33alsoshowsahigherdeviation inxedbandwidthallo ation.

Toexplainthisphenomenon,we omparetheinformationdelaywithuniformandnon-uniform parameters. Ingure34and35,

(λ + µ)

−1

_{= 1.667}

. Byassumingallthe Nparkingspa esare uniform,

λ

i

=

λ

N

and

µ

i

=

µ

N

for

1 ≤ i ≤ N

. The uniform simulation result is shown in gure 34. If the N parking spa es are non-uniform, set

λ

i

≤ λ

j

and

µ

i

≤ µ

j

for

i ≤ j

su h that

(

P

N

i=1

λ

i

+

P

N

i=1

µ

i

)

−1

= (λ + µ)

−1

= 1.667

. Then in gure 35, we see that somenodes whi hhaveasmaller

(λ

i

+ µ

i

)

−1

havetotransmitmorepa ketsbut annothavemoreassigned time-slotssothatalongerinformationdelayisprovoked.

On the ontrary, apply the same tra parameters in dynami bandwidth allo ation and omparetheresultsin gures36and 37. Itis obviousthat thetra variationdoesnotae t theinformationlaten yin dynami bandwidthallo ation,aswellas

(λ + µ)

−1

_{= 8.333}

38and39.

Evenenlargethe valueof

(λ + µ)

−1

to 50 withrespe tivedierent

λ

i

and

µ

i

values, it an stillbeseenthat node1and2ingure40haveseveralpa ketswithalongerinformationdelay butnotin grue41. It meanseventhe network resour eis adequateforallgenerated pa kets, aninexibleallo ationmethod anstill ollapsethenetworkperforman e.

Figure34: Information delay-xedbandwidth allo ation - uniform

-(λ + µ)

−1

_{= 1.667}

Figure 35: Informa-tiondelay-xed band-width allo ation- non-uniform -

(λ + µ)

−1

₌

1.667

Figure 36: Information delay - dynami band-width allo ation - uni-form -

(λ + µ)

−1

₌

1.667

Figure37: Information delay - dynami band-width allo ation - non-uniform -

(λ + µ)

−1

₌

1.667

Figure 38: Informa-tiondelay-xed band-widthallo ation- non-uniform -

(λ + µ)

−1

₌

8.333

Figure39: Information delay - dynami band-widthallo ation- non-uniform -

(λ + µ)

−1

₌

8.333

Figure 40: Informa-tiondelay-xed band-widthallo ation- non-uniform -

(λ + µ)

−1

₌

50

Figure41: Information delay - dynami band-widthallo ation- non-uniform -

(λ + µ)

−1

₌

50

4.3 Multiple hop

Table2: Transmissionpowerat2hops[7℄ Transmitpoweroutput: 0dBm

P

T X

: 48mW

Thetransmissionrangeofsensornodesisproportationaltoitstransmitpoweroutput,aswell asthepower onsumptionperunitoftime. Whilethetransmissionrangeisredu ed,sensornode ouldbeoutofthe ommuni ationrangeofthegatewayandthen ouldonlysendoutitspa kets viaitsneighbors. Thesimulationresultsingure42-53usealowertransmissionpower,shown in table 2. Here werst look at the xed bandwidth allo ation. In gures42-44, wesee that informationdelayfornodes,whi h aretwohops faraway,is in reasedby onemoreduty- y le duration. Butifthetra parametersarenon-uniform,theinformationdelay anbeamplied on ertainnodesduetotheunexiblebandwidthallo ation,showningures45-47. Also,ifthe furthernodesandtheirrelays(nodes1-5)bothhavealotofpa ketstosend,thedelaytimewill beevenmoresevere.

Figure 42: Information delay -Fixedbandwidthallo ation- uni-form-

(λ + µ)

−1

_{= 1.667}

-2hops

Figure 43: Information delay -Fixedbandwidthallo ation- uni-form-

(λ + µ)

−1

_{= 8.33}

-2hops

Figure 44: Information delay -Fixedbandwidthallo ation- uni-form-

(λ + µ)

−1

_{= 50}

-2hops

Figure 45: Information delay - Fixed bandwidth allo ation -non-uniform-

(λ + µ)

−1

_{= 1.667}

-2hops-

(λ

i

+µ

i

)

−1

_≤

_(λ

j

+µ

j

)

−1

when

i ≤ j

Figure 46: Information delay - Fixed bandwidth allo ation -non-uniform-

(λ + µ)

−1

_{= 8.33}

-2hops-

(λ

i

+µ

i

)

−1

_≤

_(λ

j

+µ

j

)

−1

when

i ≤ j

Figure 47: Information delay - Fixed bandwidth allo ation -non-uniform-

(λ + µ)

−1

_{= 50}

-2 hops-

(λ

i

+ µ

i

)

−1

_≤

_(λ

j

+ µ

j

)

−1

when

i ≤ j

Figure 48: Information delay -Dynami bandwidth allo ation -uniform -

(λ + µ)

−1

_{= 1.667}

-2 hops

Figure 49: Information delay -Dynami bandwidthallo ation -uniform -

(λ + µ)

−1

_{= 8.33}

- 2 hops

Figure 50: Information delay -Dynami bandwidth allo ation -uniform-

(λ + µ)

−1

_{= 50}

-2hops

Figure 51: Information delay -Dynami bandwidth allo ation -non-uniform-

(λ + µ)

−1

_{= 1.667}

-2hops-

(λ

i

+µ

i

)

−1

_≤

_(λ

j

+µ

j

)

−1

Figure 52: Information delay -Dynami bandwidth allo ation -non-uniform-

(λ + µ)

−1

_{= 8.33}

-2hops-

(λ

i

+µ

i

)

−1

_≤

_(λ

j

+µ

j

)

−1

Figure 53: Information delay -Dynami bandwidth allo ation -non-uniform-

(λ + µ)

−1

_{= 50}

-2 hops-

(λ

i

+ µ

i

)

−1

_≤

_(λ

j

+ µ

j

)

−1

Figure 54: Information delay -Fixed/Dynami bandwidth allo- ation-non-uniform-

(λ+µ)

−1

₌

1.667

-

ω

= 60s

-2hops

Figure 55: Information delay -Fixed/Dynami bandwidth allo- ation-non-uniform-

(λ+µ)

−1

₌

8.33

-

ω

= 60s

-2hops

Figure 56: Information delay -Fixed/Dynami bandwidth allo- ation-non-uniform-

(λ+µ)

−1

₌

50

-

ω

= 60s

-2hops

Figure 57: Information delay -Fixed/Dynami bandwidth allo- ation-non-uniform-

(λ+µ)

−1

₌

1.667

-

ω

= 300s

-2hops

Figure 58: Information delay -Fixed/Dynami bandwidth allo- ation-non-uniform-

(λ+µ)

−1

₌

8.33

-

ω

= 300s

-2hops

Figure 59: Information delay -Fixed/Dynami bandwidth allo- ation-non-uniform-

(λ+µ)

−1

₌

50

-

ω

= 300s

-2hops

Nowweobservethesames enarioin dynami bandwidthallo ation. Ingures48-50,these furthernodesallhavealongerinformationdelayaswellasinpreviousparagraph. While apply-inganon-uniformtra parameters,thedelaytimeingures51-53. Intermsofxedbandwidth allo ation,dynami bandwidthallo ationadaptesmu hbettertothevariationoftra param-eters. But we anstill see somepa kets from nodes1-5 haveaslightlylonger delaywhi h are ausedbythe ompetitiveareaandhiddenterminalproblem.

Multiple hop prolongs the information delay whi hever appli ation model is applied. In gures54-56,asindi atedinprevioussessions,themaximuminformationisonlyrelatedtothe tra interval

ω

plusone-hopdelay,i.e., onetimeof duty- y leduration. Onlywhentheevent o urren efrequen yishighthanthetra interval,theaverageinformationdelaywillbeshorter be auseof somelosingdata,in gure57. As thetra o urren efrequen yredu es,thedata lossis gradually unapparent, in gures 58and 59. In addition, dynami bandwidthallo ation hasmore transmission oni t sin emultiple-hop network bringsmore hidden terminal in the network.

4.4 Duty y le

Fromgures8and13, we anseethat duty y leismainly determinedbyslot durationwhi h is, however,bounded by tra model. Theminimum slot duration

s

min

shall belongenough to omplete areservation and apiggyba ked pa ket transmission so that pa ket size and data rate are the important fa tors. Nevertheless, the maximum slot duration

s

max

is limited by theminimumrequiredthroughput a ordingto appli ation. That isto say,iftheslotduration is equalto

s

i

se onds, themaximumthroughput will notex eed

1

s

i

0

50

100

150

200

250

0

0.2

0.4

0.6

0.8

1

1.2

Per-node energy consumption

Information delay (s)

Event - (

λ

i

+

µ

i

)

-1

= 40

Event - (

λ

i

+

µ

i

)

-1

= 200

Event - (

λ

i

+

µ

i

)

-1

= 1200

Figure 60: Per-node energy-delay tradeo of event-drivenappli ationindynami bandwidth al-lo ationwhile

N

= 24

0

50

100

150

200

250

0

5

10

15

20

25

Per-node energy consumption

Information delay (s)

Event - (

λ

i

+

µ

i

)

-1

= 40

Event - (

λ

i

+

µ

i

)

-1

= 200

Event - (

λ

i

+

µ

i

)

-1

= 1200

Figure 61: Per-node energy-delay tradeo of event-drivenappli ationin xedbandwidth allo- ationwhile

N

= 24

event-drivenappli ation ismainly proportionalto theduty y le,wevariedslot durationfrom 0.1 to 1.2 se onds in the topologyin gure 7(b) with 24 nodes and then got theenergy-delay tradeo.

Figure60showstheenergy-delaytradeoindynami bandwidthallo ation. For

(λ

i

+µ

i

)

−1

₌

40

,

s

max,40

= 1.667

and we an see that the energy deviation is more obvious when the slot

durationapproximatesto

s

max,40

. Alsotheaverageinformationdelayisequalto

κ ∗ T

duty.cycle

=

κs

i

where

κ

varies from

1

2

to approximately1when

s

i

in reases. Figure61 showsthe energy-delay tradeo in xedbandwidth allo ation. Byrepla ing

T

duty.cycle

= (N + 1)s

i

, the average information delay is equal to

κ(N + 1)s

i

where

κ

varies from

1

2

to approximately1 when

s

i

in reases. However,thankstotheextremelylow ollisionrateinxedbandwidthallo ation,the energydeviationisstillloweventhoughwein reasetheslotduration.

A ordingly,whilehavingthesameslotduration,theenergy onsumptionindynami band-width allo ation is a bit higher, yet the information delay is mu h shorter. In other words, subje tto thethroughput onditions,forthesameinformation delay, dynami bandwidth allo- ation onsumessigni antlylessenergythanxedone.

5 Engineering Insights

Inthisse tion,wesummarizeourresultsinse tion4andprovideengineeringinsightsto stream-linetheWSN onstru tionofurbansmartparkingappli ation. Thebandwidthallo ationmethod is the utmost important key point of determining energy onsumption and information delay whentra andnodedensitiesareknownapriori. Weappliedtwofundamentaltypesof band-width allo ations to our simulationsinstead of hoosing parti ular proto ols. In this way, we ansee learlythat howthetra and nodedensities ae t thenetwork performan e andthe results ould serveasguidelines forurban sensor networkdesigners. Sin e thenetwork loadis al ulatedbysumminguptherespe tivetra loadonea hnode,andshallnotbegreaterthan thenetworkthroughput,wedis ussitseparatelyfromthefollowingthreeviewpointsbyreferring togure62.

5.1 Node density

Thenetwork loadelevatesundoubtedlyasthenumberof nodes

N

in reases. Indynami band-widthallo ation,thersttobeae tedistherisingpa ket ollisionratebe auseofmore om-petitors in onetime slot. When

N > 50

, theinformationdelaybe omes arbitrarilylarge. The energy onsumptionrst risesdueto theretransmission,andthen falls, inasmu h asthe han-nel is always busy. However,the ontentionmethod of dynami bandwidth allo ation anbe improved to servemoresensor nodes, likewhat is donein[11, 1, 15℄. Else, in xed bandwidth allo ation,thethroughputisnonethelessonthedownsidesas

N

in reases. This isdueto the extensionofduty y le. Thatistosay,thenetworkloadofea hnodeshallnotbegreaterthan themaximumnetworkthroughput,viz

α

i

+ β

i

≤ (T

duty.cycle

)

−1

_{= s}

i

∗ (N + 1) + T

inactive

−1

,to ensurethenetworkis apabletopro essallthedemands. Hen e,Dynami bandwidthallo ation has ashort information delay and is more adaptive to the non-uniform tra parameters for

N ≤ 50

, i.e, an be improved by agood ontention method. Fixed bandwidth allo ation an avoid the pa ket ollisionproblem while thenetwork density is high but still miss an optimal s hedulingwhi h onsidersroutingandMACproto olsto improveitslaten y.

5.2 tra intensity

The network load of node

i

is equal to

α

i

+ β

i

= k(λ

i

+ µ

i

)

. When the tra intensity is high, event-driven appli ation is suggested on grounds of its mu h shorter information delay. Onthe ontrary, time-drivenappli ation generates ex essivepa kets when

ω

is small, and the informationdelayistoolongwhen

ω

islarge. Aspreviouslymentioned,time-drivenappli ation isoftenusedto informgatewayofsensors'existen es,forexample,toreportthehourlybattery status,alsotoprovidetheinformationoflinkquality. If

(λ

i

+ µ

i

)

−1

islargeenough,towitevent frequen yisverylow,theupdatedpa ket anbemergedwithotherhourlyinformation. Inother words, in time-driven appli ation, the energy onsumption is extremely low for

ω ≫ 1200

. If

(λ

i

+µ

i

)

−1

≫ 2ω

,and

λ

−1

i

and

µ

−1

i

arebothmu hgreaterthan

4ω

,itmeanstheevento urren e rateislowandtime-driven anbe onsidered. Anthorproblemoftime-drivenappli ationistheir starttime. Ifallsensornodeshaveaverysimilarstarttimeandtra interval,aburstytra anbegeneratedand givesanapparentinuen eondynami bandwidthallo ation.

5.3 Duty y le

What will happen if the network tra and node densities are both high? When

N

is large, thethroughputofxedbandwidthallo ationdropsbe auseoftheextensionofduty y le. The maximumnetworkthroughput inxed bandwidthallo ation, al ulatedbythe inverseof duty y le

1

(N +1)∗s

i

+T

inactive

,shallbegreaterthan

k(λ

i

+ µ

i

)

or

1

ω

respe tivelyinevent-ortime-driven appli ations. Be ause

k ≈ 0.48

and

(λ

i

+ µ

i

)

−1

_{≫ 2ω}

to applytime-drivenappli ation,wethen get

k(λ

i

+ µ

i

) ≪

1

4ω

. Certainly

1

ω

ex eeds

1

(N +1)∗s

i

+T

inactive

fasterthan

1

4ω

. Byassuming

N

m

is themaximumnumberofnodesto applytime-drivenappli ation in xedbandwidthallo ation, wehave

1

(N +1)∗s

i

+T

inactive

=

1

ω

. Thus,

N

m

=

ω

s

i

− 1

if

T

inactive

= 0

. 6 Con lusion

Inthisreport,wehavestudiedparkingsensornetworks,espe iallyfo usingondelay onstraints andenergy e ien y issuesfrom aviewpointof tra . Twotypes oftra models, viz event-andtime-driven,areperformedwith dierentrateparameters. Weprovideengineeringinsights for urban sensor network designers, in parti ular the best ombination of tra models and

Figure62: Best ongurationversusvehi lea tivityandnetworkdensity

bandwidthallo ationdependingontheurbana tivityandthenodedensity. Eventhoughthese thresholds anbeslightlyshiftedbyparti ularoptimized proto ols, nodoubt itretains a lear overview to build urban appli ations over WSNs. These insights highlight the importan e to developanadaptiveMACproto ol whi his ableto distributedlydete tthe intensityof tra andswit hbetweenevent-and time-driventra modelwhenrequired.

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