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esources under a middleware based on web services is too high, which in turn supposes the exclusionof limited resource nodes and therefore takes WSN out of a transparent integration.Automatic service adaption is a key requirement when services use different interfaces and protocols.Generally, an adaptation process has to be carried out in order to inter-operate services belongingto different domains, for example, an UPnP and an Oxygen service. This requirement can be effectivelyaddressed by means of proxies between services, or software wrappers for specific domains.One of the least considered issues is dynamic service ranking. There exist several approachesintended to evaluate the service’s suitability for a specific purpose. However, as far as the authors areaware, no attempts have been directly addressed to Ambient Intelligence middlewares. The servicecontinuity requirement remains unconsidered by most of the approaches, basically, due to the factthat they make the simple assumption of having just one type of service in each scenario.Despite the fact that the externalization approach has not been applied to Ambient Intelligence, itis interesting from the middleware point of view. This approach is adopted by the ExORB proposal,presented in [120], mainly aimed at mobile phones. This approach is aimed at constructing configurable,updateable and upgradeable middleware services that do not depend on user interventionwhenever a change is required. Despite the fact that it offers support for dynamism, the reality is thatit does not dynamically respond to changes in the environment, but it rather responds to developer’sactions.Attending to the most challenging features of service composition, Table 2.5 summarizes thecontributions made by some of the most representative middlewares for Ambient Intelligence, asenumerated in the previous section.The lack of a standard API (Application Programming Interface) for Ambient Intelligence services,similar to POSIX (Portable Operating System Interface for Unix) interfaces for operating systems,has delegated the service design task to developers. This is a major drawback since the lack ofagreement supposes that similar services have different interfaces, making it more difficult to complywith interoperability demands and therefore, hindering the composition process.32
MiddlewarePlatformInformation Definition of AutomaticModel Services ServiceCompositionUPnP HTTP/SOAP XML XML templates PossibleOSGi JVM/Java No Developer decisionDynamicbundle invocationAMIGO OSGI/Web Services Ontologies Developer decision PossibleHYDRA Hydra middleware Device OntologySemantic WebServices(SAWSDL)Possible (AutomaticGenerationof SWS proxies)MOREMORE middleware MORE IntegratedPossible (ServiceWeb services(Publish/Subscribe) SOA Model Chain Assembly)MPOWER MPOWER middleware MDA/UML Web services PossibleMEDUSA UbiSOAP and AmIi non-specified XML-basedPossible(Activity-orientedcomputing)GAIA Distributed Objects No Developer Decision PossibleCORTEXOpenCOMSentientObject ModelTask ModelUnspecifiedAURACoda,Activity-basedPossibleSpectra, computing XML-based (Taskand Odyssey paradigm abstraction)SOCAM OSGi Ontologies OSGi compliant Possibles (rules)Table 2.1: Ambient Intelligence middleware features for service composition33
Page 1: DEPARTAMENTO DE TECNOLOGÍAS Y SIST
Page 4 and 5: María José Santofimia RomeroTelé
Page 7: ResumenLa Inteligencia Ambiental, p
Page 11 and 12: ContentsContentsList of TablesList
Page 13: CONTENTSVII7.4.4 The Plan Executor
Page 17: List of Figures4.1 Kripke model for
Page 21: Part IPreliminaries3
Page 24 and 25: This gap poses an urgent need to de
Page 26 and 27: and events, and the mechanisms to r
Page 28 and 29: Maria’s ankle as an impediment to
Page 30 and 31: 1.3 Aims and objectivesGiven that t
Page 32 and 33: The fourth item concerns the way th
Page 34 and 35: of such goals and desires and how t
Page 36 and 37: tion, is addressed by coping with a
Page 38 and 39: networking from being widely spread
Page 40 and 41: The MERL’s Ambient Intelligence f
Page 42 and 43: tions come into play. These scenari
Page 44 and 45: interact with electronic devices, e
Page 46 and 47: tionally, the middleware architectu
Page 48 and 49: The idea behind the work proposed h
Page 53: Part IIUnderstanding35
Page 56 and 57: obvious. In this sense, sociologist
Page 58 and 59: true throughout a time interval, or
Page 60 and 61: application domain is (true, false)
Page 62 and 63: (SubAsbstrac Nathan Nathan2007)(Sub
Page 64 and 65: Moreover, events not only cannot be
Page 66 and 67: epresented by means of the notion o
Page 68 and 69: Context-awareness is one of the mai
Page 70 and 71: In any case, the adopted behavioral
Page 72 and 73: when the water level starts increas
Page 74 and 75: Traditionally, these responses have
Page 76 and 77: true.Additionally, the meaning of t
Page 78 and 79: (6) Statement → Service-ID perfor
Page 80 and 81: the set of possible contexts, and M
Page 82 and 83: in which S is a nonempty set of sta
Page 84 and 85: are considered possible given the p
Page 86 and 87: cope with the demands involved in d
Page 89 and 90: Chapter 5Understanding Context Situ
Page 91 and 92: Figure 5.1: Overall view of the pro
Page 93 and 94: adjusting existing knowledge to sim
Page 95 and 96: of some events involves the stateme
Page 97 and 98: ( new-statement { picker } {is loca
Page 99 and 100: The way of determining which after
Page 101 and 102:
CL-USER > ( the-x-of-y-is-z { enter
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CL-USER > ( the-only-x-of-y-is-z {
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CL-USER > ( get-element-fluent ( lo
Page 107 and 108:
The specificity of the propositiona
Page 109 and 110:
Following the same dynamic, the dif
Page 111:
part is intended to propose a solut
Page 115 and 116:
Chapter 6Behavioral Response Genera
Page 117 and 118:
and action selection by means of a
Page 119 and 120:
wants the room to be at a higher te
Page 121 and 122:
state of the world with those plann
Page 123 and 124:
conflict. The later strategy requir
Page 125 and 126:
Function f returns the actions, fro
Page 127:
next step selected in the plan. The
Page 130 and 131:
of it. It is also possible to try t
Page 132 and 133:
Agent System (MAS), individual agen
Page 134 and 135:
the requirements stated for the BRG
Page 136 and 137:
The action planning algorithmMaking
Page 138 and 139:
The advantages underlying service c
Page 140 and 141:
effects. On the contrary, an approp
Page 143:
Part IVValidation and discussions12
Page 146 and 147:
taking place. The interpretation of
Page 148 and 149:
The key elements of the evaluation
Page 150 and 151:
also been proved to serve as a mean
Page 152 and 153:
Table 8.2: Simulation Configuration
Page 154 and 155:
the knowledge-base, it saves time i
Page 156 and 157:
effects and the sensed ones leads t
Page 158 and 159:
Table 8.3: Personal information of
Page 160 and 161:
Scenario Interpretations Number of
Page 162 and 163:
understand the terms used to descri
Page 164 and 165:
Finally, the causal explanation app
Page 166 and 167:
2. A2: To provide a service composi
Page 168 and 169:
System has to be motivated by goals
Page 170 and 171:
een addressed by this thesis. Howev
Page 173 and 174:
Bibliography[1] Gregory D. Abowd, A
Page 175 and 176:
[25] Diane J. Cook, Juan C. Augusto
Page 177 and 178:
[54] Tao Gu, Hung Keng Pung, and Da
Page 179 and 180:
[83] Clemens Lombriser, Nagendra B.
Page 181 and 182:
[110] Davy Preuveneers, Jan Van den
Page 183 and 184:
[134] John F. Sowa. Conceptual Stru
Page 185:
Part VIAppendix167
Page 188 and 189:
Ambient Intelligence environment, i
Page 190 and 191:
invocation. However, in reality the
Page 192 and 193:
consists in querying the Topic Mana
Page 194 and 195:
Figure A.4: Multi-Agent System over
Page 196 and 197:
The result of the planning algorith
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concepts and relationships are impl
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As listed below, the recognition ac
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184Figure A.8: Sequence diagram for
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}query = " ( b−wire ( car ( list
Page 207:
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