contextualised service delivery in internet of things, smart parking for smart cities

ContextualisedServiceDeliveryinInternetofThingsSmartParkingforSmartCi<es

AliYavari*,PremPrakashJayaraman†,DimitriosGeorgakopoulos†*RMITUniversity,Melbourne,Australia†SwinburneUniversityofTechnology,Melbourne,Australiamail@aliyavari.com

RMIT University - July 2015 2

Inthelate1960s,communicaEonbetweentwocomputerswasmadepossiblethroughacomputernetworkIntheearly1980stheTCP/IPstackwasintroduced.CommercialuseoftheInternetstartedinthelate1980WorldWideWeb(WWW)becameavailablein1991InternetofThingstermbyKevinAshton1998(“TheInternetofThingshasthepoten0altochangetheworld,justastheInternetdid.Maybeevenmoreso”)WebofThings(WoT)in2000MITAuto-IDcentrepresentedtheirIoTvisionin2001IoTwasformallyintroducedbyInternaEonalTelecommunicaEonUnion(ITU)in2005More“thingsorobjects”wereconnectedtotheInternetthanpeople.2008-2009[Cisco]

19601980199620002001

CiscoIBSGprojecEons,UNEconomic&SocialAffairsh`p://www.un.org/esa/populaEon/publicaEons/longrange2/WorldPop2300final.pdf

6.307

6.721 6.894 7.347 7.83

0

10

20

30

40

50

2003 2008 2010 2015 2020

Billion

sofD

evices

WorldPopulaEon

50 Billion

SmartObjects RapidadopEonrateofdigitalinfrastructure

5 x faster than electricity & telephony

“Things”perperson

InflecEonPoint

1.1 Billion Data points generated by sensors daily 500 Gigabytes

Data generated by an offshore oil rig weekly

1000 Gigabytes Data generated by an oil refinery daily

10,000 Gigabytes Data generated by a jet engine every 30 minutes

2.5 Billion Gigabytes Data generated worldwide daily

90% of the world’s data Has been created in the last 2 years!

•  CiscoIBSGprojecEons,UNEconomic&SocialAffairsh`p://www.un.org/esa/populaEon/publicaEons/longrange2/WorldPop2300final.pdf

Sensors and other Internet-connected devices that are all connected to theinternetandtheyinteractintelligentlytomakethedevelopmentanddeliveryofnewservicesandproducts

Anewparadigmwhichconnectsavarietyofthings-Allthethingsthathavetheabilitytocommunicate

IdenEfy

Communicate

Sense

Control

“Thepriceoflightislessthanthecostofdarkness.”–ArthurCNielsen

Wisdom

Knowledge

Informa<on

Data

Process

ParkingSpaceinaSmartCity

•  Directdriverstoemptyparkingspaces–  R.E.Barone,T.Giuffrè,S.M.Siniscalchi,M.A.Morgano,andG.Tesoriere,“Architectureforparkingmanagementin

smartciEes,”IETIntell.Transp.Syst.,vol.8,no.5,pp.445–452,2014.–  R.Lu,X.Lin,H.Zhu,andX.Shen,“SPARK:anewVANET-basedsmartparkingschemeforlargeparkinglots,”in

INFOCOM2009,IEEE,2009,pp.1413–1421.–  R.Grodi,D.B.Rawat,andF.Rios-GuEerrez,“Smartparking:ParkingoccupancymonitoringandvisualizaEonsystem

forsmartciEes,”inSoutheastCon2016,2016,pp.1–5.–  Y.Zheng,S.Rajasegarar,andC.Leckie,“ParkingavailabilitypredicEonforsensor-enabledcarparksinsmartciEes,”in

IntelligentSensors,SensorNetworksandInforma0onProcessing(ISSNIP),2015IEEETenthInterna0onalConferenceon,2015,pp.1–6.

–  J.Cherian,J.Luo,H.Guo,S.-S.Ho,andR.Wisbrun,“Poster:ParkGauge:GaugingtheCongesEonLevelofParkingGarageswithCrowdsensedParkingCharacterisEcs,”inProceedingsofthe13thACMConferenceonEmbeddedNetworkedSensorSystems,2015,pp.395–396.

–  Z.Ji,I.Ganchev,M.O’Droma,L.Zhao,andX.Zhang,“Acloud-basedcarparkingmiddlewareforIoT-basedsmartciEes:designandimplementaEon,”Sensors,vol.14,no.12,pp.22372–22393,2014.

•  ProvideanesEmateofaveragewaiEngEmetopark–  P.R.deAlmeida,L.S.Oliveira,A.S.Bri`o,E.J.Silva,andA.L.Koerich,“PKLot–Arobustdatasetfor–  A.Koster,A.Oliveira,O.Volpato,V.Delvequio,andF.Koch,“RecogniEonandrecommendaEonofparkingplaces,”in

Ibero-AmericanConferenceonAr0ficialIntelligence,2014,pp.675–685.–  J.Rico,J.Sancho,B.Cendon,andM.Camus,“ParkingeasierbyusingcontextinformaEonofasmartcity:Enablingfast

searchandmanagementofparkingresources,”inAdvancedInforma0onNetworkingandApplica0onsWorkshops(WAINA),201327thInterna0onalConferenceon,2013,pp.1380–1385.

–  E.Akhavan-Rezai,M.F.Shaaban,E.El-Saadany,andF.Karray,“Onlineintelligentdemandmanagementofplug-inelectric

–  Y.GengandC.G.Cassandras,“New‘SmartParking’systembasedonresourceallocaEonandreservaEons,”IEEETrans.Intell.Transp.Syst.,vol.14,no.3,pp.1129–1139,2013.

ParkingSpaceinaSmartCity

•  Wetakeintoaccounteachdriver'scontextthatmayinclude:–  driver’spreferences(e.g.,parkinacoveredparkingspace), –  drivingexperience(e.g.,avoidnarrowparkingspaces),–  car’slocaEon(e.g.,collectedfromthedriver’ssmartphone),–  vehicle’sproperEes(e.g.,vehicletype,length,height,etc.),–  otherparkinginformaEonprovided(e.g.,theproperEesofparkingspaces,

suchasshaded,covered,etc.).

Observe

Orient

Decision

AcEon

Internet-connectedDevices

Connec<vity

Internet-scaledData

DataReduc<on

IoTServicesandApplica<on

Presenta<on

Contextualisa<on

Context

ContextorcontextualinformaEonisanyinformaEonaboutanyenEtythatcanbeused to effecEvely reduce the amountof reasoning required (viafiltering,aggregaEon,andinference)fordecisionmakingwithinthescopeofspecificapplicaEons.

ContextualisaEon

•  ContextualisaEon excludes irrelevant data fromconsideraEonandhas thepotenEal to reducedatafromseveralaspectsincludingvolume,velocity,andvarietyinIoTapplicaEons

•  ContextualisaEonof IoTdatacanhelp improve thevalueofinformaEonextractedfromIoT

•  ContextualisaEon improve the data processing andknowledgeextracEoninIoTapplicaEons

ContextCollecEon

ContextualisaEon

DisseminaEonofthecontextualised

data

•  AnapproachtorepresentandcontextualiseddataoriginaEngfromIoTdevices.•  AmechanismtoefficientlyquerythecontextualisedIoTdata•  AnexampleofasmartparkingspacerecommenderapplicaEon•  AnexperimentalevaluaEonoftheproposedcontextualisedIoTdataquerying

approachusingsyntheEcdatageneratedfromMelbournecitydatasetsh`ps://data.melbourne.vic.gov.au/

ContextualisedServiceDeliveryintheInternetofThings

A.Yavari,P.P.Jayaraman,D.Georgakopoulos,andS.Nepal,‘‘ContaaS:Anapproachtointernet-scalecontextualisaEonfordevelopingefficientinternetofthingsapplicaEons,’’inHawaiiInternaEonalConferenceonSystemSciencesHICSS

Conclusion

•  Scalable and real-Eme contextualisaEonof IoT data has thepotenEal tosignificantly improve data processing for large scale IoT applicaEons inSmartCiEes

•  Weproposedanapproach to contextualise andquery Internet scale IoTdata and we exemplify the approach via a smart parking spacerecommenderapplicaEonforSmartCiEes.

•  Theexperimental scenario in thispaper illustrates that contextualisaEonof IoTdatareducesqueryEmesfor IoTservices(suchasasmartparkingspacerecommender)bymorethan3EmesincomparisonwithasituaEonwherethequeryiscontextualisaEonagnosEc.

ThankYou!

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