Autonomic communications seek to improve the ability of network and services to cope with unpredicted change, including changes in topology, load, task, the physical and logical characteristics of the networks that can be accessed, and so forth. Broad-ranging autonomic solutions require designers to account for a range of end-to-end issues affecting programming models, network and contextual modeling and reasoning, decentralised algorithms, trust acquisition and maintenance---issues whose solutions may draw on approaches and results from a surprisingly broad range of disciplines. We survey the current state of autonomic communications research and identify significant emerging trends and techniques.

PERVASIVE computing 53Trust is situation-specific; trust in one environment doesn't directly transfer to another environment. So a notion of context is necessary.Authorized licensed use limited to: TRINITY COLLEGE DUBLIN. Downloaded on January 21, 2009 at 05:54 from IEEE Xplore. Restrictions apply.them in a particular way-for example, to update old address book entries with accurate information. However, the principal could deviate from this expected behavior, and the combined likelihood and severity of this is the risk of granting them a privilege. Risk analysisIn SECURE, the risks of a trust-mediated action are decomposed by possible outcomes. Each outcome's risk depends on the other principal's trustworthiness (the likelihood) and the outcome's intrinsic cost. For example, an address update might itself be out-of-date or maliciously misleading. These two outcomes' costs would reflect the user's wasted time, and the likelihoods would depend on trust in the other party.An outcome's costs could span a range of values. For example, a user might have received a correct phone book entry. This third outcome's cost could show a net benefit to the user, as the user might successfully use it later. However, if the number became out-ofdate by the time it was used, that would be a net loss. To reflect this uncertainty, you might represent the distribution of costs as a cost-PDF (probability density function). Figure 1 illustrates a user contemplating a parameterized interaction with principal p. For each possible outcome, the user has a parameterized cost-PDF (a family of cost-PDFs) that represents the range of possible costs and benefits the user might incur should each outcome occur.While the risk evaluator assesses the possible cost-PDFs, the trust calculator provides information t that determines the risk's likelihood based on the principal's identity p and other parameters of the action. The risk evaluator then uses this trust information to select the appropriate cost-PDF.Finally, the request analyzer combines all the outcomes' cost-PDFs to decide if the action should be taken or to arrange further interaction. Because any uncertainty is preserved right up to the decision point, this allows more complex decision making than simple thresholding, allowing responses such as "not sure" if there isn't enough information.In our continuing example, if Liz's PDA received a phone number from Vinny's PDA, she might not think it was maliciously misleading based on her trust in Vinny's honesty. She might think it could be out-of-date, however, if Vinny had given her stale information before, attributing a higher risk to this outcome. Finally, she'd consider the potential benefit of having a correct number, again moderated by Vinny's trustworthiness. Liz's PDA would do all these calculations on her behalf using its model of her trust beliefs, as Figure 2 illustrates. If the benefits outweighed the other outcomes' costs, the PDA would then accept the information.On the other hand, if John-a colleague from a competing research gr...

Pervasive computing is by its nature open and extensible, and must integrate the information from a diverse range of sources. This leads to a problem of information exchange, so sub-systems must agree on shared representations. Ontologies potentially provide a well-founded mechanism for the representation and exchange of such structured information. A number of ontologies have been developed specifically for use in pervasive computing, none of which appears to cover adequately the space of concerns applicable to application designers. We compare and contrast the most popular ontologies, evaluating them against the system challenges generally recognized within the pervasive computing community. We identify a number of deficiencies that must be addressed in order to apply the ontological techniques successfully to next-generation pervasive systems.

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In reengineering legacy code it is frequently useful to introduce a design pattern in order to add clarity to the system and thus facilitate further program evolution. We show that this type of transformation can be automated in a pragmatic manner and present a methodology for the development of design pattern transformations. We address the issues of the definition of a starting point for the transformation, the decomposition of a pattern into minipatterns and the development of corresponding minitransformations that can introduce these minipatterns to a program. We argue that behaviour preservation is a key issue and develop a rigorous argument of this for each minitransformation we discover. The architecture of an existing software prototype is also discussed and the results of applying this methodology to develop a transformation for the Factory Method pattern are presented.

Location-enhanced applications are poised to become the first real-world example of ubiquitous computing [11]. In this paper, we emphasize the practical aspects of getting location-enhanced applications deployed on existing devices, such as laptops, tablets, PDAs, and cell phones, without the need to purchase additional sensors or install special infrastructure. Our goal is to provide readers with an overview of the practical considerations that are currently being faced, and the research challenges that lie ahead. We ground the article with a summary of initial work on two deployments of locationenhanced computing: multi-player location-based games and a guide for the Edinburgh Festival. Ubiquitous Location SystemsA large number of research and commercial location systems have been developed over the past two decades [7]. In general, these systems have one of two goals: providing highly accurate location estimates, on the order of centimetres, within a small area, or providing lower accuracy over a wide coverage area [8]. Systems with a focus on accuracy typically require both extensive infrastructure and relatively expensive sensors. AT&T Cambridge's Active Bats [1] system, for example, employs active ultrasonic badges and requires the installation of ceiling-mounted ultrasound receivers every square meter.

Location is a core concept in most pervasive computing systems. Beyond simple uses such as pinpointing an individual's position or identifying a region's occupants, location is a key index for richer querying of an individual's or environment's context.Although at first glance a simple concept, location information's representation has many forms and subtleties, each suited to particular application classes. 1 To provide application developers with easy access to location information, we must support different positioning systems with varying data formats as well as fusion algorithms to estimate position from multiple readings. We also need a data access approach that hides this complexity and heterogeneity from the developer. This problem has no general solution, necessitating specific frameworks for working with specific kinds of data.To meet the needs of location-based applications, we've developed lightweight space and sensing models and a set of extensible components that support customization and emerging technologies. The space model supports a range of geometric and relative-spatial-positioning descriptions found in the literature. The sensing model abstracts over various types of positioning systems and incorporates the capture of uncertainty, serving as a foundation on which developers can apply sensor-fusion techniques. Our programming framework, LOC8, sits atop the space and sensing models, providing a rich API for querying location data and exploring its many representations. RequirementsA location model should support location data representations from different positioning technologies and extensible metadata descriptions. Many well-known systems can report an entity's coordinate or symbolic position, from GPS and Active Badge to more recent systems such as Ubisense and the fingerprint-based positioning system. 2 Beyond these are less conventional and less expensive methods of reporting an entity's location. For example, a Bluetooth spotter, which can detect the presence of mobile phones, PDAs, and laptops, might position a device within 10 meters of a known point. We can use this information to infer the device owner's position. Using a location model supporting a range of expressive representations for spaces, spatial relationships, and positioning systems, the authors created LOC8, a programming framework for exploring location data's multifaceted representations and uses. Environments frequently contain multiple positioning systems, so translating readings into a common language of location-centric primitives is important. Because no positioning technology claims to provide perfect accuracy, this language must also provide quality measures to support sensor-fusion techniques for uncertain data. Quantifying uncertainty associated with positioning systems has proved a hot topic in recent years. 3,4 A space model provides a set of primitives that allow descriptions of regions of space and the relationships between them. Such primitives must support the mapping of positioning systems' different ...

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