Pervasive computing in Daidalos

Service Management

Pervasive Computing in Daidalos Daidalos offers a platform for service and identity management that supports secure context-aware and personalizable delivery of service discovery, composition, and adaptation. It also provides a runtime environment for deployment and execution.

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he Daidalos project (www. ist-daidalos.org) is employing pervasive computing to organize network and communication technologies to enhance the user experience. More specifically, Daidalos seeks to help users access services no matter where they are and to improve the user experience by accounting for contextual information and user preferences. Our motivation to improve Nicholas K. Taylor the user experience comes Heriot-Watt University, Edinburgh from our belief that future users will be bombarded with Patrick Robertson numerous electronic services. German Aerospace Center Everywhere, anytime access Babak A. Farshchian to services, facilitated by everSINTEF Information and increasing coverage via various network technologies, means Communication Technology that users will constantly be Kevin Doolin exposed to services through Waterford Institute of Technology different network-enabled channels. In such a world, Ioanna G. Roussaki service providers will be conNational Technical cerned mainly with getting University of Athens users’ attention, and network Liam Marshall providers will be concerned LAKE Communications mainly with increased network use. Pervasive computing in Robert Mullins Daidalos addresses an imporWaterford Institute of Technology tant piece of this puzzle—supSteffen Drüsedow porting ordinary users.

Deutsche Telekom AG Laboratories Kajetan Dolinar Security Technology Competence Center

74 PER VA SI V E computing

The Pervasive Service Platform Ubiquitous access to thirdparty services and content is becoming the norm in Internet and pervasive computing

(see the “Related Work in Pervasive Computing Platforms” sidebar). Figure 1 illustrates the main functional components in the Daidalos pervasive service platform (PSP) that deliver its pervasive functionality. The service and identity management layer allows ubiquitous access to services. Its functionality includes service discovery and recomposition into value-added services for users. Its service discovery component is necessary to support open markets—service providers can advertise services, and users can find the services they need. Because we anticipate that each provider will offer only a limited number of services, we included a service composition component. The identity management component ensures that a security and privacy infrastructure can be applied to service discovery and composition. The service ontology management component defines interoperability among services and works as a glossary for service providers and consumers. It’s also a key aspect of service composition, validating various services’ semantics. The session and resource management component supports a managed runtime environment for composed services, in which service providers and operators can apply policies in combination with user personalization. The user experience management layer enhances the user experience. Its context management component collects raw context data from various sensors, refines it, and delivers this higher-level context information to the platform or to applications and services requesting it. Similarly, the learning management component learns from users’ historical behavior, such as interactions with services, and uses this knowledge to maintain updated user preferences for various

Published by the IEEE CS n 1536-1268/11/$26.00 © 2011 IEEE

Related Work in Pervasive Computing Platforms

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fundamental challenge in pervasive computing has been to create platforms that scale-up smart-space infrastructures to operator-grade infrastructures that can support many users. The Daidalos platform lies at the boundary between pervasive and ubiquitous computing smart spaces and mobile telecommunications operators’ service-oriented developments.

Operator-Driven Projects Although the Daidalos consortium is unique in incorporating so many European telecommunications operators, other operatordriven projects are noteworthy. Sahara (Service Architecture for Heterogeneous Access, Resources, and Applications) addresses similar service-related concerns, such as multiple providers, security, scalability, and performance.1 However, it doesn’t address smart spaces or distributed devices and appliances. Spice (Service Platform for Innovative Communication Environment) introduced a distributed communication sphere that can include multiple distributed devices.2 However, it doesn’t include embedded and public devices, so security and privacy issues don’t arise. Both Sahara and Spice assume a “walled garden” business model, whereas the Daidalos pervasive computing platform is based on open but controlled Internet models. The MobiLife project has conducted extensive user surveys to specify a reference architecture for ubiquitous applications.3 It has much in common with Daidalos but doesn’t address overall deployment issues.

Smart Spaces and Mobile Users In pervasive and ubiquitous computing, many projects address infrastructures for smart spaces and mobile users. This research focuses largely on specific application areas (emphasizing advanced user interaction) or closed systems limited to laboratory environments. Notable projects include Oxygen (http:// oxygen.csail.mit.edu/index.html), the Intelligent Home,4 Blue Space,5 the Adaptive House,6 Gaia,7 Synapse (www. mlab.t.u-tokyo.ac.jp/research/2005/context-aware_ computing/synapse_e.php), MavHome,8 Ubisec (Ubiquitous Networks with Secure Provision of Services, Access, and Content Delivery),9 and Magnet (My Personal Adaptive Global Net; www.telecom.ece.ntua.gr/magnet). The Aura project contains architectural similarities to Daidalos.10 It’s concerned with network-related issues such as quality of service and service composition based on quality changes. However, it doesn’t consider trust and security issues when users are in foreign domains. Similarly, the Gator Tech Smart House illustrates a general-

January–March 2011

ized architecture for smart homes, which consists of middleware and an application development environment.11 Gator Tech middleware is concerned mainly with closed and trusted home environments, requiring resource and device heterogeneity but not security or privacy. Finally, Celadon proposes a zone-based solution in which users must preregister with the zone owner to use the services.12

References 1. B. Raman et al., “The Sahara Model for Service Composition across Multiple Providers,” Pervasive Computing, LNCS 2414, Springer, 2002, pp. 585–597. 2. S. Tarkoma et al., “Spice: A Service Platform for Future Mobile IMS Services,” Proc. IEEE Int’l Symp. World of Wireless, Mobile, and Multimedia Networks (WoWMoM 07), IEEE CS Press, 2007, pp. 1–8. 3. M. Strutterer et al., “Managing and Delivering Context-Dependent User Preferences in Ubiquitous Computing Environments,” Proc. 2007 Int’l Symp. Applications and the Internet (SAINTW 07), IEEE CS Press, 2007, p. 4; http://doi.ieeecomputersociety.org/10.1109/ SAINT-W.2007.60. 4. V. Lesser et al., “The Intelligent Home Testbed,” Proc. Autonomy Control Software Workshop (Autonomous Agent Workshop), 1999; http:// coitweb.uncc.edu/~anraja/papers/acs-aa-99.pdf. 5. S. Yoshihama, P. Chou, and D. Wong, “Managing Behavior of Intelligent Environments,” Proc. 1st IEEE Int’l Conf. Pervasive Computing and Communications (PerCom 03), IEEE Press, 2003, pp. 330–337. 6. M.C. Mozer, “Lessons from an Adaptive House,” Smart Environments: Technologies, Protocols and Applications, D. Cook and R. Das, eds., John Wiley and Sons, 2004, pp. 273–294. 7. B.D. Ziebart et al., “Learning Automation Policies for Pervasive Computing Environments,” Proc. 2nd Int’l Conf. Autonomic Computing (ICAC 05), IEEE Press, 2005, pp. 193–203. 8. M.G. Youngblood, L.B. Holder, and D.J. Cook, “Managing Adaptive Versatile Environments,” Proc. 3rd IEEE Int’l Conf. Pervasive Computing and Communications (PerCom 05), IEEE Press, 2005, pp. 351–360. 9. J. Groppe and W. Mueller, “Profile Management Technology for Smart Customizations in Private Home Applications,” Proc. 16th Int’l Workshop Database and Expert Systems Applications (DEXA 05), IEEE Press, 2005, pp. 226–230. 10. D. Garlan et al., “Project Aura: Toward Distraction-Free Pervasive Computing,” IEEE Pervasive Computing, vol. 1, no. 2, 2002, pp. 22–31. 11. S. Helal et al., “The Gator Tech Smart House: A Programmable Pervasive Space,” Computer, vol. 38, no. 3, 2005, pp. 50–60. 12. S. McFaddin et al., “Modeling and Managing Mobile Commerce Spaces Using RESTful Data Services,” 9th Int’l Conf. Mobile Data Management (MDM 08), IEEE Press, 2008, pp. 81–89.

PER VA SI V E computing 75

Service Management

Figure 1. The Daidalos pervasive service platform (PSP) architecture. The service and identity management layer provides identity-aware service discovery and composition, which are at the core of all service invocations. The user experience management layer adds functions to improve service adaptation to users and their context while protecting their privacy.

Applications and services API User experience management Context management

Preference management

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API Service and identity management Service ontology management

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Session and resource management Service discovery Web services technologies

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Travel Athens

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Luxor Tr avel Inc. Fr om M unich to Athens: Advanced Date: Fr om MUC To ATH Flight :

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….. …. Selection: ……... Car Rental: Selection: ……... Outwar d

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Service ontology management. Supporting an open market for services requires some form of agreement about service semantics and APIs. Daidalos realizes this agreement in the form of a service ontology, which represents each service type in OWL-S, a semantically rich ontology language.1 The ontology manager provides access to these definitions and verifies services’ APIs for interoperability. It assists our Athens traveler in searching for, discovering, and composing semantically correct and compatible services.

Advanced

Figure 2. The interplay within the service and identity management layer following a user’s free-text service request “Travel Athens.” The numbered boxes show the five main processing stages.

services. Through the preference management component, preferences can be applied to platform services, such as changing network quality-of-service (QoS) parameters, or provided to thirdparty applications and services. The privacy negotiation component builds on identity management to enhance negotiation mechanisms between users and services.

processing) will trigger a search for relevant services in this context (through service discovery). Daidalos composes the services to complete the process (through service composition) and ranks content according to preferences (through the service ranker).

Service and Identity Management Suppose a traveler uses Daidalos to plan a business trip from Munich to Athens. The service and identity management layer orchestrates the complex process of searching for and discovering airline, car rental, and hotel booking services (see Figure 2). In this example, simply typing the keywords “Travel Athens” (as part of query pre-

Service discovery. When our traveler

enters the query “Travel Athens,” Daidalos initiates a service discovery process. This process includes registering, advertising, and finding services.2 Service providers advertise their services in registries; users or applications can search for registered services using various criteria. Daidalos service discovery is context aware and personalizable. Federation mechanisms facilitate communication among service registries belonging to different enterprises, producing a controlled and secure service environment in which microservice providers can also operate. Service discovery also implements an overlay discovery infrastructure that Daidalos

www.computer.org/pervasive

Figure 3. Geographic retrieval using Daidalos context management. The user is in Area A; Daidalos retrieves the context on the basis of this location.

Retrieve entity based on coordinates

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uses to find lower-layer services, such as network services.

Node

Identity management. This part of the

Daidalos PSP is central to users’ acceptance of pervasive services because it provides the means for privacy-aware service access while letting service providers charge for services.4 The identity model is involved at all stages of an account’s life cycle, from its creation, to device login, to service selection and use. Daidalos identity management uses virtual identities, to ensure that our traveler’s identity isn’t revealed to the various service providers involved in the provision of the final composed service. The complete set of identifying information concerning a person or service provider is distributed in various locations run by different authorities, or providers. A virtual identity (VID) scheme ensures coordinated disclosure


Look-up Area A home node

daidalos0.eu

Service composition. Addressing the

user’s “Travel Athens” request might involve pulling together services from different organizations. To provide additional value, discovered services can be composed with other services. Service composition is necessary for even the simplest services because they’ll inevitably need to be composed with I/O services or devices before use. Daidalos service composition prepares a list of the service instances to compose, initiates the composed service, and monitors and recomposes the composed service in case of an environment change.3 An important service composition function is ranking the composed services according to the current context and user preferences. Users can then choose a candidate service instance from the ranked list, or Daidalos can choose the highest-ranking service.

Use case

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of all this data. A VID is a unique pseudonymic identifier that allows access to a subset of a user’s personal data. The VID’s owner decides its scope, which details exactly what can be disclosed. Identity management acts on behalf of users, tracking VIDs and managing their life-cycle states. It assesses the possible dangers associated with information disclosure under a particular VID in a session with a particular peer, and controls the VID transactions’ lifecycle states. So, if the user revokes a VID, identity management must stop all transactions involving that VID.

Context management. When our trav-

This component is responsible for setting up the actual instance of a session that will provide the composed service (in our Athens scenario, the “Travel Athens” service) to the user. Session and resource management ensures that resources are available for running the service, are reserved, are authenticated using the identity management infrastructure, and that the service is initialized.

eler arrives at the departure airport in Munich, Daidalos uses his current location (through the look-up area in Figure 3) to obtain the latest estimate of how long it will take him to pass through security and reach his departure gate. This estimate comes from an airport-operator service reacting to his current location in the airport and his flight details. We designed the Daidalos PSP to effectively employ users’ context. The context management component acquires raw context data from multiple sources, such as users, hardware and software sensors, network elements, Daidalos enabling services, and thirdparty services. It then handles the secure and privacy-aware maintenance, sharing, and manipulation of this data. 5,6 It provides a uniform way to access context information, thus hiding context management’s complexity and offering consumers an integrated context-manipulation interface. Furthermore, context management supports sophisticated contextawareness features, including

User Experience Management As we mentioned before, this layer supports context awareness, personalization, learning, and privacy.

• context inference to derive high-level context information from raw sensor data, • location-based queries based on the

Session and resource management.


Service Management

• context obfuscation preferences.

Context management

Preference management component

Preference ontology manager Preference condition monitor

Preference GUI

Learning management component

Action handler Feedback GUI Figure 4. Daidalos uses the preference management component and preference condition monitor to deliver preferences to services. The feedback GUI and action handler react to and record user actions in services. The preference GUI (with the preference ontology manager) acquires preferences explicitly, whereas the learning management component acquires them implicitly.

Open Geospatial Consortium’s Simple Features SQL standard to access context data, and • quality-of-context modeling and exploitation. The Daidalos PSP’s service and identity management layer includes a distributed database management system that consists of the mandatory context management components residing at any node hosting a Daidalos PSP. The PSP’s user experience management layer handles context management enhancement. This consists of optional components that improve context management’s intelligence, optimize its performance, and offer additional functionality to external actors. Preference management. When our

traveler arrives at the Athens airport, his new context—particularly his new location (through the context manager in Figure 4)—proactively triggers reevaluation of his preferences (through the preference condition monitor). Any services using those preferences are informed of the new preference values (through the preference management


component). For instance, on the basis of his previous visits to Athens, Daidalos might have learned (through the learning management component) that he prefers to use the metro here. Daidalos will automatically change his preference for buses for local travel—currently set for the context of Munich—to a preference for the metro (through the preference condition monitor), now that his location is known to be Athens. It will then update his route-planner service accordingly (through the preference management component). Daidalos achieves personalization via the preference management component, which acts as a gateway to user preferences, storing, retrieving, and evaluating preferences as well as updating users’ preference sets with new learned information.7 The system caters for a range of preference types, including • personalizable parameters for thirdparty services, • filtering and ranking preferences for service discovery and composition, • VID selection preferences, • privacy policy preferences, and

Users can enter preferences into the system manually either through the preference GUI (with the preference ontology manager’s aid) or by selecting predefined stereotypes.8 Daidalos can also learn preferences from implicitly gathered user behavior data based on notifications from services indicating an important user action and associated context snapshots. Services use the preference management component to request user preferences as a function of the user’s context. Upon receiving a request, the preference management component evaluates the preference’s conditions and delivers the corresponding preference outcome to the service. The preference condition monitor can also actively push preference outcomes to a service when a change in the user’s context affects a relevant preference. In some situations, a service might want to inform users that it’s about to perform an action based on a preference outcome. So, the feedback GUI notifies users to expect a change and lets them intervene to prevent it if they wish. The feedback GUI passes the user reaction to the proposing service and to the learning management component (see Figure 4). Learning management. Our traveler regularly makes business trips abroad, and his habits and preferences are reasonably predictable. By monitoring his choices on previous trips (through the action handler in Figure 4), Daidalos has learned (through the learning management component) that he always tries to use the same hotel chain. When he was planning his trip, his booking service used this learned information to select a hotel from that chain in Athens (through the preference management component). Our traveler also has particular habits when he enters his hotel room, which the system has learned over time. One such habit is using any pervasive technologies the hotel provides, such as wall displays.


Daidalos will automatically seek to use these technologies if they’re available in his room (through the preference condition monitor). Daidalos supports many learning algorithms. Preference mining employs C4.5 and Bayesian networks for offline learning; a dynamic incremental associative network supports online learning. In all cases, the system passes new learned preference information to the preference management component to update the user’s preference set. The addition of learning complements the explicit personalization achieved through GUIs and stereotypes, allowing for a more complete and up-to-date preference set. Without such functionality, users would have the burden of having to enter and manage all of their potentially numerous preferences manually. Preferences that arrive at the preference management component through the preference GUI replace the old preferences because the new preferences are explicit statements of users’ wishes. On the other hand, preferences that come from the learning management component merge with older preferences. This merging also involves updating the strength and confidence of a learned preference outcome. Further research into this process’s algorithmic and sociotechnical aspects is necessary. The system’s overall desirability and effectiveness have been the subject of a large user study,9 and experiments into the preference and learning management systems’ usability are ongoing. Privacy negotiation. When seeking a hotel in Athens, the booking service might benefit from knowing whether our traveler prefers a bath or a shower. This is a bit more personal than our traveler is comfortable with; he regards such a request as an intrusion into his privacy. Privacy negotiation can use his privacy preferences to negotiate with the booking service. If the service insists that this information is mandatory, Daidalos won’t use this particular service and can


seek an alternative service. However, if the service doesn’t insist on disclosure of this information, the booking can proceed without it, possibly leading to a less desirable room but preserving our traveler’s privacy. In privacy negotiation, Daidalos tries to obtain the optimum balance between anonymity and the required accuracy level in terms of the private information disclosed to a service. Each VID gives access to a subset of user information. The privacy negotiation subsystem facilitates the selection of this subset by letting users negotiate with services about how much information to reveal. Privacy negotiation uses preferences and context information to make negotiation as simple as possible.10 The process combines a simple credentials system and a semantically richer ontology-based approach to obtain the optimum balance between anonymity and semantic completeness, within the limits of acceptable computation time and storage space.

A Deployment and Runtime Environment for Pervasive Services When our traveler arrives in his Athens hotel room, Daidalos activates the room’s large display—calling into action its deployment and runtime environment—and transfers some of his active services from his PDA to this new display using the session manager. The traveler uses this device to start a city guide application that will help him find a restaurant serving fresh seafood—one of his favorite dishes (through the preference management component). A service session manager uses the selected service composition to create a session plan and passes this on to the deployment manager, which launches the individual services in the plan. Should recomposing a service become necessary, service composition recomposes the composition and passes it to the session manager, which computes any necessary changes. The deployment and runtime envi-

ronment is a supporting layer for managing the pervasive services’ life cycle. Its functionality is available via APIs for use by other enabler components, such as context management and session and resource management, and by third-party service providers and operators. These components allow policy and configuration management for individual service instances and whole classes of services. Because the deployment and runtime environment’s functionality isn’t as focused as other PSP subsystems’ functionality, we adopted a well-defined granularity approach rather than a monolithic design. The result is an architecture based on the Knopflerfish (www.knopflerfish.org) implementation of OSGi (Open Service Gateway Initiative) with several components, each with a specific task and a well-defined interface. It was fully implemented and evaluated in 2007. The deployment manager provides functionality for the deployment, undeployment, and starting and stopping of service instances as part of composed service sessions. It supports native OSGi services and Web services running on remote hosts. In the latter case, the deployment sequence includes proxy generation. The deployment manager also provides a mechanism for local or remote storage of the service state. Included in deployment sequences and combined with monitoring, this feature provides state recovery after a service failure. Daidalos supports A4C (authentication, authorization, accounting, auditing, and charging) mechanisms for mutual authentication and restriction of service access via access control on the basis of VIDs and related certificates. A resource manager controls per-service resources; in particular, it incorporates QoS and a system for monitoring service behavior and health at a serviceinstance level.

Third-Party Services Interface Daidalos offers APIs to services, collectively called PervasiveX APIs.


Service Management

PervasiveX is a set of simple-to-use, high-level Web service interfaces to the Daidalos PSP components, providing a network interface for third-party application developers. Developers with no knowledge of the Daidalos internals can manipulate its pervasive services as if calling any ordinary Web service. A simple function call from a Java program lets users tap into the PSP world in a simple, straightforward way. The third-party services’ designers are responsible for deploying them. If the user terminal has the resources to support the third-party service, then Daidalos can download the service to the terminal, where it can interact with the PSP via a PSP client on the terminal. Alternatively, the service can run on a third-party server. In this case, a service thin client executes on the terminal, and the PSP client can run on the terminal or the third-party server.

System Evaluation To ascertain users’ concerns about Daidalos and how useful they believe it would be, we performed a survey of nearly 100 users.9 In general, 88 percent of the respondents were happy with the system acting autonomously on their behalf (only 11 percent objecting outright), taking account of their preferences, as long as their privacy was respected. However, they expressed particular concern regarding location information disclosure (only 56 percent were comfortable with this). Perhaps the most telling outcome was that more respondents between the ages of 20 and 50 would pay for the conveniences provided (28 percent) than not (23 percent), with acceptability increasing to 77 percent if the cost was subsidized—for instance, by an employer. Interestingly, women were less reluctant to pay (15 percent) than men (24 percent). We also used the MIT Media Lab Reality Mining dataset (http:// reality.media.mit.edu/dataset.php) to test Daidalos’s ability to learn user preferences. This data was collected


from 100 subjects subscribing to different cellular networks. The data contained records of subject profiles, calls, and context spanning a total of 350,000 hours. We randomly selected three subjects from the dataset and achieved maximum tenfold crossvalidation preference prediction accuracies of 98, 96, and 92 percent for each subject, with average accuracies across all cross-validations of 96.2, 84.0, and 75.4 percent, respectively. The last result reflects the third subject’s particularly eclectic behavior.

D

aidalos uniquely addresses pervasive computing at two levels:

• The service and identity management layer allows access to services through multiple channels and on multiple interaction devices to mobile users, while preserving seamless access to resources, guaranteeing QoS, preserving security and privacy, and maintaining service flow. • The user experience management layer collects and refines user context information, learning from users’ past interactions with services and applying this acquired knowledge when adapting and delivering services. These two layers are tightly interconnected. For instance, the service and identity management layer provides user information though collecting context information from sensors, and the user experience management layer is an actuator that applies knowledge about users to services at the other layer. The Daidalos Consortium has implemented and evaluated the Daidalos PSP. This has led to many further research projects, notably the Persist project (www.ict-persist.eu) and the Societies project (www.ict-societies.eu). The Persist project has developed a Personal Smart Space (PSS), which is associated with the portable devices users carry

and moves around with them, providing context-aware pervasiveness to the user at all times and in all places.11 The Societies project aims to develop a Community Smart Space (CSS) that integrates pervasive computing with social computing.12

Acknowledgments We acknowledge the indispensible contributions of the partners involved in this research from NEC, Siemens, Telecom Italia, University of Rome “La Sapientia,” and the University of Stuttgart, as well as the rest of the 37 partner organizations constituting the Daidalos consortium.

References 1. D. Martin et al., “Bringing Semantics to Web Services: The OWL-S Approach,” Semantic Web Services and Web Process Composition, LNCS 3387, Springer, 2004, pp. 26–42. 2. K. Frank, V. Suraci, and J. Mitic, “Personalizable Service Discovery in Pervasive Systems,” Proc. 4th Int’l Conf. Networking and Services (ICNS 08), IEEE Press, 2008, pp. 182–187. 3. K. Doolin et al., “Supporting Ubiquitous IMS-Based Teleconferencing through Discovery and Composition of IMS and Web Components,” J. Network and Systems Management, vol. 16, no. 1, 2008, pp. 92–112. 4. J. Porekar, K. Dolinar, and B. JermanBlazic, “Middleware for Privacy Protection of Ambient Intelligence and Pervasive Systems,” WSEAS Trans. Information Science and Applications, vol. 4, no. 3, 2007, pp. 633–639. 5. C. Pils et al., “Federation and Sharing in the Context Marketplace,” Proc. 3rd Int’l Symp. Location- and ContextAwareness, LNCS 4718, Springer, 2007, pp. 121–138. 6. I. Roussaki, M. Strimpakou, and C. Pils, “Distributed Context Retrieval and Consistency Control in Pervasive Computing,” J. Network and Systems Management, vol. 15, no. 1, 2007, pp. 57–74. 7. S.M. McBurney et al., “Managing User Preferences for Personalization in a Pervasive Service Environment,” Proc. 3rd Advanced Int’l Conf. Telecommunications, IEEE Press, 2007, p. 32. 8. E. Papadopoulou et al., “Adapting Stereotypes to Handle Dynamic User Profiles in


the Authors Nicholas K. Taylor is a professor in and head of the Computer Science Department at HeriotWatt University, Edinburgh, UK. His research interests include pervasive systems, machine learning, and eLife. Taylor has a PhD in expert systems from the University of Nottingham. He’s a Chartered Engineer, a Chartered Information Technology Professional, and a Chartered Mathematician. He’s also a fellow of the British Computer Society and the Higher Education Academy and a member of the Edinburgh Mathematical Society, the Institute of Mathematics and Its Applications, and the Society for the Study of Artificial Intelligence and Simulation Behaviour. Contact him at [email protected]. Patrick Robertson leads the German Aerospace Center (DLR) Broadband Systems research group. His research interests include navigation signals and systems, simultaneous localization and mapping, vehicle-to-vehicle communications networks and driver-assistance services, and Bayesian inference techniques for context awareness in pervasive computing systems. Robertson has a PhD in electronic engineering from the University of the Federal Armed Forces, Munich. He’s a member of IEEE. Contact him at [email protected]. Babak A. Farshchian is a senior research scientist at SINTEF Information and Communication Technology and an associate professor at the Norwegian University of Science and Technology (NTNU). His research interests include pervasive computing platforms, mobile services, collaboration technologies, and ambient assisted living. Farshchian has a PhD in information systems engineering from NTNU. He’s a member of the IEEE Computer Society and the ACM. Contact him at [email protected]. Kevin Doolin is the chief engineer and Scientific and Technology Board chair of the Telecommunications Software and Systems Group at the Waterford Institute of Technology, Ireland. His research interests include future Internet service and pervasive communications. Doolin has a BTech in electronic engineering from the Waterford Institute of Technology. He’s a member of the NEM (Networked and Electronic Media) and eMobility Technical Platform groups, founder of the Irish Future Internet Forum, and coordinator of the FP7 Persist (Personal Self Improving Smart Spaces) and Societies (Self Orchestrating Community Ambient Intelligence Spaces) projects. Contact him at [email protected].

a Pervasive System,” Proc. 4th Int’l Conf. Advances in Computer Science and Technology, ACTA Press, 2008, pp. 7–12. 9. M. Roddy, ed., Daidalos Deliverable D531—First Phase Validation Report, European Commission, 2006. 10. E. Papadopoulou et al., “Using User Pref-


erences to Enhance Privacy in Pervasive Systems,” Proc. 3rd Int’l Conf. Systems, IEEE CS Press, 2008, pp. 271–276; http:// doi.ieeecomputersociety.org/10.1109/ ICONS.2008.46. 11. N.K. Taylor, “Personal eSpace and Personal Smart Spaces,” Proc. 2nd IEEE Int’l Conf. Self-Adaptive and Self-Orga-

Ioanna G. Roussaki is a lecturer at the National Technical University of Athens School of Electrical and Computer Engineering. Her research interests include ubiquitous and pervasive computing, context awareness, Web services, semantics and ontologies, e-negotiations, mobile and personal communications, virtual home entertainment, mobile agent systems, heuristic algorithms, and complexity theory. Roussaki has a PhD in telecommunications from the National Technical University of Athens. She’s a member of IEEE and the Technical Chamber of Greece. Contact her at [email protected]. Liam Marshall is a research software engineer at LAKE Communications in Ireland. His research interests include pervasive computing, Web services, and mobile communications. Marshall has a BEng in digital and software systems engineering from GalwayMayo Institute of Technology. Contact him at liam. [email protected].

Robert Mullins is a researcher at the Waterford Institute of Technology, Ireland. His research interests include pervasive computing and service management. Mullins has an MSc in computer science from University College Cork. Contact him at rmullins@ tssg.org.

Steffen Drüsedow is an architect and developer at Deutsche Telekom AG Laboratories. His research interests include context-aware pervasive service platforms. Drüsedow has a Diploma in communication engineering from the University of Applied Science, Berlin. Contact him at steffen.druesedow@ telekom.de.

Kajetan Dolinar is a researcher at the Security Technology Competence Center (SETCCE) in Slovenia. His research interests include security, privacy, and trust assurance in information systems. Dolinar has a Diploma in mathematics from the University of Ljubljana. Contact him at [email protected].

nizing System Workshops (SASO 08), IEEE CS Press, 2008, pp. 156–161. 12. I.G. Roussaki et al., “Self-Improving Personal Smart Spaces for Pervasive Service Provision,” Towards the Future Internet: Emerging Trends from European Research, G. Tselentis et al., eds., IOS Press, pp. 193–203.
