Decentralized Reasoning in Ambient Intelligence

100 Pages·2012·1.68 MB·English

by  José Viterbo

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José Viterbo Markus Endler
•
Decentralized Reasoning
in Ambient Intelligence
123
JoséViterbo MarkusEndler
Department of Computer Science Department of Informatics
Universidade FederalFluminense (UFF) PontifíciaUniversidade Católica do
Niterói, RJ Rio deJaneiro (PUC-Rio)
Brazil Rio deJaneiro, RJ
Brazil
ISSN 2191-5768 ISSN 2191-5776 (electronic)
ISBN 978-1-4471-4167-9 ISBN 978-1-4471-4168-6 (eBook)
DOI 10.1007/978-1-4471-4168-6
SpringerLondonHeidelbergNewYorkDordrecht
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Contents
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Problem Setting. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3 Goals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.4 Organization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2 Fundamental Concepts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.1 Scenario. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.2 Ambient Intelligence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.3 Context-Awareness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.4 Ontology-Based Context Model. . . . . . . . . . . . . . . . . . . . . . . . 13
2.5 Context Reasoning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.6 Rule-Based Reasoning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.1 Distributed Reasoning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
3.2 Gaia. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.3 OWL-SF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.4 DRAGO. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.5 P2P-DR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
3.6 P2PIS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
3.7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
v
vi Contents
4 Cooperative Reasoning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
4.1 System Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
4.2 Patterns of Interaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
4.2.1 User Side Reasoning. . . . . . . . . . . . . . . . . . . . . . . . . . 37
4.2.2 Ambient Side Reasoning . . . . . . . . . . . . . . . . . . . . . . . 37
4.2.3 Cooperative Reasoning . . . . . . . . . . . . . . . . . . . . . . . . 38
4.3 Design Strategies for a Reasoning Service . . . . . . . . . . . . . . . . 38
4.3.1 Functional Attributes. . . . . . . . . . . . . . . . . . . . . . . . . . 39
4.3.2 Non-Functional Attributes . . . . . . . . . . . . . . . . . . . . . . 40
4.4 Cooperative Reasoning Formalization . . . . . . . . . . . . . . . . . . . 42
4.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
5 Our Approach for Cooperative Reasoning . . . . . . . . . . . . . . . . . . 47
5.1 Strategy for Rule-Based Context Reasoning . . . . . . . . . . . . . . . 47
5.1.1 Synchronous Interaction. . . . . . . . . . . . . . . . . . . . . . . . 48
5.1.2 Asynchronous Interaction. . . . . . . . . . . . . . . . . . . . . . . 51
5.1.3 Stability of Context Data. . . . . . . . . . . . . . . . . . . . . . . 53
5.2 Algorithm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
5.3 Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
5.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
6 Case Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
6.1 Synchronous Interaction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
6.2 Asynchronous Interaction. . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
6.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
7 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
7.1 Architecture Overview. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
7.2 Ontology Management and Reasoning. . . . . . . . . . . . . . . . . . . 68
7.3 Context Model Service (CMS) . . . . . . . . . . . . . . . . . . . . . . . . 69
7.4 The Decentralized Reasoning Service (DRS) . . . . . . . . . . . . . . 70
7.5 Prototype Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
7.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
8 Evaluation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
8.1 Testbed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
8.2 Functional Test. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
8.3 Performance Test. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
8.3.1 Response Time. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
8.3.2 Communication Traffic and Memory Footprint. . . . . . . . 87
8.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
Reference. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
Contents vii
9 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
9.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
9.2 Why Two-Tier?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
9.3 Future Work. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
Acronyms
ABox Assertional box
AmI Ambient Intelligence
API Application programming interface
CMS Context model service
ConfComp Conference companion application
ConfOrg Conference organizer application
DL Description logics
DRS Decentralized reasoning service
DRS/A Ambient decentralized reasoning service
DRS/D Device decentralized reasoning service
FOL First order logic
HTTP Hypertext transfer protocol
IP Internet protocol
J2ME Java 2 Platform, Micro edition
MoCA Mobile collaboration architecture
OWL Web ontology language
P2P Peer-to-peer
RDF Resource description framework
SWRL Semantic Web Rule Language
TBox Terminological box
ix
Chapter 1
Introduction
Abstract In this chapter we clarify the motivations and purpose of this work.
Initially, in the next section, in order to explain what instigated our research, we
presentabriefoverviewaboutAmbientIntelligence,theresearchareainwhichlies
theobjectofthiswork.InSect.1.2wediscuss,indetail,theproblemaddressedin
this work and its main characteristics, setting the scope of the proposed approach.
InSect.1.3weclearlydefinethegoalwepursuedduringourresearchwork.Finally,
inSect.1.4wedescribethegeneralorganizationofthiswork.
· · ·
Keywords Ambient Intelligence Ubiquitous computing Context-awareness
· ·
Rule-basedreasoning Decentralizedreasoning Cooperativereasoning
1.1 Overview
Ubiquitous computing features the seamless integration of computer systems into
the everyday lives of users to provide information and functionalities anytime and
anywhere[33].Itinvolvestheinteractionofdiversecomputationaldevicesaiming
atprovidingservicestosupportusersinatransparentandcontinuousway.Forthis
purpose,ubiquitouscomputingenvironmentsencompassdifferentkindsofsensors
andmobiledevices(e.g.,PDAs,notebooks,smartphones),interconnectedthrougha
combinationofseveralwirelessnetworktechnologies.
Compared to traditional distributed systems, ubiquitous computing systems are
characterizedbyanincreaseddynamismandheterogeneityofdevices,applications
and services [29]. The underlying ubiquitous computing infrastructures are more
complexandbringintotheforegroundissuessuchasusermobility,devicediscon-
nections,joinandleaveofdevices,heterogeneousnetworks,aswellastheneedto
integrate the physical environment with the computing infrastructure [18]. A soft-
ware infrastructure to support the execution of ubiquitous applications has to be
flexible and reactive to deal with the unpredictable, heterogeneous and dynamic
J.ViterboandM.Endler,DecentralizedReasoninginAmbientIntelligence, 1
SpringerBriefsinComputerScience,DOI:10.1007/978-1-4471-4168-6_1,
©TheAuthor(s)2012
2 1 Introduction
natureofthecomputingenvironmentsencounteredwhenusersmovearounddiffer-
entlocations[21].
Inubiquitouscomputingenvironments,alargenumberofautonomoussoftware
entities,i.e.,ubiquitousapplicationsandservices,worktogethertotransformphysi-
calspacesintosmartandinteractiveenvironments[23].Insuchscenarios,newissues
arebeginningtoarise,suchashowtoenableuserstocontinuouslyperformtheircom-
putationaltaskswhilemovingthroughdifferentlocations[30],howtoenableusers
tocooperatewhilelocatedindifferentplaces[22],orhowtopromotead-hoccoop-
erationinsituationswhenagroupofuserssharingcommoninterestsunexpectedly
meetatthesamelocationandneedtoengageincollectiveactivities[7].
Theapproachesandtechnologiesforsupportingthesenewwaysofworkingare
stillunderinvestigation.Nevertheless,aparticularlyinterestingtrendinubiquitous
computingisexploringtheAmbientIntelligence(AmI)paradigm,amultidisciplinary
approach that aims at the integration of innovative sensing, communication and
actuationtechnologiestocreatecomputer-mediatedenvironmentsthatsupportuser
activities through specific services of the environment, provisioned with minimal
userintervention[31].
AprominentexampleofanenvironmentenrichedwithAmIisa“smarthome”,
a house equipped to bring advanced services to its users [3]. In such an environ-
ment, several domestic artifacts and items can be enriched with sensors to gather
information about their use and in some cases even to act independently without
humanintervention[11].Forinstance,doorlocksinasmarthomecouldbecapable
ofidentifyingpersonsandpermittinghands-freeopening,whichcouldbeauseful
functionality for elderly, children and disabled people, as well as parents entering
thehomewithbothhandsoccupiedwithcarryingshoppingbagsandguidingseveral
childrenalong[15].
Another class of applications for AmI focuses on providing health monitoring
andsupporttoindividualswithcognitiveorphysicalimpairments,aimingathelping
these people to live independently by improving their access to a wide range of
services and facilities [24]. For example, in the case of people at an early stage
of senile dementia—the most frequent case being elderly people suffering from
Alzheimer’sdisease—suchasystemcouldbetailoredtoensureappropriatecareat
criticaltimesbymonitoringactivities,diagnosingrisksituations—suchasastroke
orheartattack—andadvisingacarerordoctorincaseofneed[4].
TherehasbeenalsoalotofsignificantworkontheuseofAmItechnologiesforfa-
cilitatinginteractioninacademicenvironments,suchasuniversities[26,27],research
centers[12],conferencefacilities[25,9],i.e.,environmentswhereresearchers,teach-
ers and students gather to engage in learning activities or participate in technical
meetingsandpresentations.Insuchascenario,forinstance,afterdetectingthatall
listedkeyparticipantsinapre-arrangedmeetinghavealreadyarrivedintherespec-
tiveroom,anAmIservicecoulddimthelights,stopthebackgroundmusicandturn
ontheprojectorinthatroom[10].
Essentially, an AmI system should be aware of the presence of people in the
geographical space, perceive their needs and be able to autonomously personalize
andmakeavailableservicestohelpuserstoperformtheirtasks[14].Forthatreason,
1.1 Overview 3
as a fundamental requirement, AmI applications must be capable of automatically
responding to dynamic changes in the environment—such as a person entering or
leavingaroom,asteepincreaseinthetemperatureindicatedbyasensororanew
deviceconnectingtothelocalnetwork,etc—withnoneorminimalhumaninterfer-
ence.Hence,applicationsexecutinginAmIsystemsareintrinsicallycontext-aware,
i.e.,theyhavetostronglyrelyoncontextdatacollectedfromsensorsembeddedinto
the environment and in the user’s devices to trigger adaptations at different levels,
suchasinthewirelesscommunicationlinks,middlewareandapplicationservices,
ortheuserinterfaces.
AmIrequiresmiddlewaresupportforsoftwaredevelopment—anddeployment—
capableofintegratinglargequantitiesofdifferentdevicesandsensorsandbuilding
a programmable and auto-configurable infrastructure [23]. Several projects, e.g.,
Gaia[25],CoBrA[9],CHIL[28],etc.,havedevelopedprototypesofsuchenviron-
ments, usually focusing on specific use cases, user tasks or application domains.
Ingeneral, these middleware systems provide not only services to store,distribute
andprocesscontextdatacollectedfromdifferentsources,butalsoservicesforrea-
soning about context information, which may be based on logical inference using
some type of derivation rule mechanism, e.g., first-order logic, descriptive logic,
case-basedreasoningorfuzzylogic,orotherspecializedtechniques,suchasneural
networks,Bayesiannetworks,etc[17].
InAmIsystems,asinmostubiquitoussystems,reasoningisnecessaryforseveral
purposes.First,itisrequiredfordealingwiththeintrinsicimperfectionanduncer-
tainty of context data [16]. In this way, reasoning allows the detection of possible
errors,toestimatemissingvalues,todeterminethequality,precisionandvalidityof
thecontextinformation.Itcouldbeused,forinstance,forresolvingconflictswhen
differentpositioningmethodsinformdifferentlocationsforasameentity.Forexam-
ple,whenauserforgetshisGPS-enabledsmartphoneinameetingroomandlogs
intohisoffice’sworkstation[8],bothpiecesofinformationareprovidedtothemid-
dleware. Second, reasoning may also be used for determining higher-level context
information,i.e.,inferringnew,implicitpiecesofcontextinformationderivedfrom
rawcontextdata.Forexample,reasoningcouldbeusedtodeterminethataseminar
hasstartedbasedoninformationaboutthelocationofaspeakerinaconferenceroom
andonthevolumeofnoiseproceedingfromthepulpitarea[29].Third,reasoningis
fundamentalfortriggeringactionsoradaptationsaccordingtospecificsituationsthat
maybemeaningfulandrelevanttosomeapplications[13].Forexample,reasoning
could be used to identify that a user is busy when he is in a meeting room with at
least one other colleague, his coffee cup and at least one of his colleagues’ coffee
cupsareco-locatedinthesameroomandarewarm[19].
In most cases, context reasoning in AmI systems is very complex due to the
dynamic, imprecise and ambiguous nature of context information and the need to
process large volumes of data collected from a large number of context providers.
This complexity is increased by the fact that in some conditions reasoning needs
tobeperformedinadecentralizedwayinvolvingseveralentitiesofthesystem[5].
Decentralization takes the form of physical distribution of computing and sensor