Licia Capra

CARISMA: context-aware reflective middleware system for mobile applications

Mobile devices, such as mobile phones and personal digital assistants, have gained wide-spread popularity. These devices will increasingly be networked, thus enabling the construction of distributed applications that have to adapt to changes in context, such as variations in network bandwidth, battery power, connectivity, reachability of services and hosts, etc. In this paper, we describe CARISMA, a mobile computing middleware which exploits the principle of reflection to enhance the construction of adaptive and context-aware mobile applications. The middleware provides software engineers with primitives to describe how context changes should be handled using policies. These policies may conflict. We classify the different types of conflicts that may arise in mobile computing and argue that conflicts cannot be resolved statically at the time applications are designed, but, rather, need to be resolved at execution time. We demonstrate a method by which policy conflicts can be handled; this method uses a microeconomic approach that relies on a particular type of sealed-bid auction. We describe how this method is implemented in the CARISMA middleware architecture and sketch a distributed context-aware application for mobile devices to illustrate how the method works in practice. We show, by way of a systematic performance evaluation, that conflict resolution does not imply undue overheads, before comparing our research to related work and concluding the paper.

Urban Computing: Concepts, Methodologies, and Applications

Urbanizations rapid progress has modernized many peoples lives but also engendered big issues, such as traffic congestion, energy consumption, and pollution. Urban computing aims to tackle these issues by using the data that has been generated in cities (e.g., traffic flow, human mobility, and geographical data). Urban computing connects urban sensing, data management, data analytics, and service providing into a recurrent process for an unobtrusive and continuous improvement of peoples lives, city operation systems, and the environment. Urban computing is an interdisciplinary field where computer sciences meet conventional city-related fields, like transportation, civil engineering, environment, economy, ecology, and sociology in the context of urban spaces. This article first introduces the concept of urban computing, discussing its general framework and key challenges from the perspective of computer sciences. Second, we classify the applications of urban computing into seven categories, consisting of urban planning, transportation, the environment, energy, social, economy, and public safety and security, presenting representative scenarios in each category. Third, we summarize the typical technologies that are needed in urban computing into four folds, which are about urban sensing, urban data management, knowledge fusion across heterogeneous data, and urban data visualization. Finally, we give an outlook on the future of urban computing, suggesting a few research topics that are somehow missing in the community.

xlinkit: a consistency checking and smart link generation service

xlinkit is a lightweight application service that provides rule-based link generation and checks the consistency of distributed Web content. It leverages standard Internet technologies, notably XML, XPath, and XLink. xlinkit can be used as part of a consistency management scheme or in applications that require smart link generation, including portal construction and management of large document repositories. In this article we show how consistency constraints can be expressed and checked. We describe a novel semantics for first-order logic that produces links instead of truth values and give an account of our content management strategy. We present the architecture of our service and the results of two substantial case studies that use xlinkit for checking course syllabus information and for validating UML models supplied by industrial partners.

XMIDDLE: A Data-Sharing Middleware for Mobile Computing

An increasing number of distributed applications will be written for mobilehosts, such as laptop computers, third generation mobile phones, personaldigital assistants, watches and the like. Application engineers have to dealwith a new set of problems caused by mobility, such as low bandwidth, contextchanges or loss of connectivity. During disconnection, users will typicallyupdate local replicas of shared data independently from each other. Theresulting inconsistent replicas need to be reconciled upon re-connection. Tosupport building mobile applications that use both replication andreconciliation over ad-hoc networks, we have designed xmiddle, a mobilecomputing middleware. In this paper we describe xmiddle and show how it usesreflection capabilities to allow application engineers to influencereplication and reconciliation techniques. xmiddle enables the transparentsharing of XML documents across heterogeneous mobile hosts, allowing on-lineand off-line access to data. We describe xmiddle using a collaborativee-shopping case study on mobile clients.

Temporal diversity in recommender systems

Collaborative Filtering (CF) algorithms, used to build web-based recommender systems, are often evaluated in terms of how accurately they predict user ratings. However, current evaluation techniques disregard the fact that users continue to rate items over time: the temporal characteristics of the systems top-N recommendations are not investigated. In particular, there is no means of measuring the extent that the same items are being recommended to users over and over again. In this work, we show that temporal diversity is an important facet of recommender systems, by showing how CF data changes over time and performing a user survey. We then evaluate three CF algorithms from the point of view of the diversity in the sequence of recommendation lists they produce over time. We examine how a number of characteristics of user rating patterns (including profile size and time between rating) affect diversity. We then propose and evaluate set methods that maximise temporal recommendation diversity without extensively penalising accuracy.

Media sharing based on colocation prediction in urban transport

People living in urban areas spend a considerable amount of time on public transport, for example, commuting to/from work. During these periods, opportunities for inter-personal networking present themselves, as many members of the public now carry electronic devices equipped with Bluetooth or other wireless technology. Using these devices, individuals can share content (e.g., music, news and video clips) with fellow travellers that are on the same train or bus. Transferring media content takes time; in order to maximise the chances of successful downloads, users should identify neighbours that possess desirable content and who will travel with them for long-enough periods. In this paper, we propose a user-centric prediction scheme that collects historical colocation information to determine the best content sources. The scheme works on the assumption that people have a high degree of regularity in their movements. We first validate this assumption on a real dataset, that consists of traces of people moving in a large citys mass transit system. We then demonstrate experimentally on these traces that our prediction scheme significantly improves communication efficiency, when compared to a memory(history)-less source selection scheme.

Mobile Computing Middleware

Recent advances in wireless networking technologies and the growing success of mobile computing devices, such as laptop computers, third generation mobile phones, personal digital assistants, watches and the like, are enabling new classes of applications that present challenging problems to designers. Mobile devices face temporary loss of network connectivity when they move; they are likely to have scarce resources, such as low battery power, slow CPU speed and little memory; they are required to react to frequent and unannounced changes in the environment, such as high variability of network bandwidth, and in the resources availability. To support designers building mobile applications, research in the field of middleware systems has proliferated. Middleware aims at facilitating communication and coordination of distributed components, concealing complexity raised by mobility from application engineers as much as possible. In this survey, we examine characteristics of mobile distributed systems and distinguish them from their fixed counterpart. We introduce a framework and a categorisation of the various middleware systems designed to support mobility, and we present a detailed and comparative review of the major results reached in this field. An analysis of current trends inside the mobile middleware community and a discussion of further directions of research conclude the survey.

Reflective Middleware Solutions for Context-Aware Applications

In this paper, we argue that middleware for wired distributed systems cannot be used in a mobile setting, as the principle of transparency that has driven their design runs counter to the new degrees of awareness imposed by mobility. We propose the marriage of reflection and metadata as a means for middleware to give applications dynamic access to information about their execution context. Finally, we describe a conceptual model that provides the basis of our reflective middleware.

B-Trust: bayesian trust framework for pervasive computing

Without trust, pervasive devices cannot collaborate effectively, and without collaboration, the pervasive computing vision cannot be made a reality. Distributed trust frameworks may support trust and thus foster collaboration in an hostile pervasive computing environment. Existing frameworks deal with foundational properties of computational trust. We here propose a distributed trust framework that satisfies a broader range of properties. Our framework: (i) evolves trust based on a Bayesian formalization, whose trust metric is expressive, yet tractable; (ii) is lightweight; (iii) protects user anonymity, whilst being resistant to “Sybil attacks” (and enhancing detection of two collusion attacks); (iv) integrates a risk-aware decision module. We evaluate the framework through four experiments.

Trust based collaborative filtering

k-nearest neighbour (kNN) collaborative filtering (CF), the widely successful algorithm supporting recommender systems, attempts to relieve the problem of information overload by generating predicted ratings for items users have not expressed their opinions about; to do so, each predicted rating is computed based on ratings given by like-minded individuals. Like-mindedness, or similarity-based recommendation, is the cause of a variety of problems that plague recommender systems. An alternative view of the problem, based on trust, offers the potential to address many of the previous limiations in CF. In this work we present a varation of kNN, the trusted k-nearest recommenders (or kNR) algorithm, which allows users to learn who and how much to trust one another by evaluating the utility of the rating information they receive. This method redefines the way CF is performed, and while avoiding some of the pitfalls that similarity-based CF is prone to, outperforms the basic similarity-based methods in terms of prediction accuracy.