Prediction of Domain Behavior through Dynamic Well-Being Domain Model Analysis

As the concept of context-awareness is becoming more popular the demand for improved quality of context-aware systems increases too. Due to the inherent challenges posed by context-awareness, it is harder to predict what the behavior of the systems and their context will be once provided to the end-user than is the case for non-context-aware systems. A domain where such upfront knowledge is highly important is that of well-being. In this paper, we introduce a method to model the well-being domain and to predict the effects the system will have on its context when implemented. This analysis can be performed at design time. Using these predictions, the design can be fine-tuned to increase the chance that systems will have the desired effect. The method has been tested using three existing well-being applications. For these applications, domain models were created in the Dynamic Well-being Domain Model language. This language allows for causal reasoning over the application domain. The models created were used to perform the analysis and behavior prediction. The analysis results were compared to existing application end-user evaluation studies. Results showed that our analysis could accurately predict success and possible problems in the focus of the systems, although certain limitation regarding the predictions should be kept into consideration

Context-aware multi-factor authentication

Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia InformáticaAuthentication systems, as available today, are inappropriate for the requirements of ubiquitous, heterogeneous and large scale distributed systems. Some important limitations are: (i) the use of weak or rigid authentication factors as principal’s identity proofs, (ii) non flexibility to combine different authentication modes for dynamic and context-aware interaction criteria, (iii) not being extensible models to integrate new or emergent pervasive authentication factors and (iv) difficulty to manage the coexistence of multi-factor authentication proofs in a unified single sign-on solution. The objective of this dissertation is the design, implementation and experimental evaluation of a platform supporting multi-factor authentication services, as a contribution to overcome the above limitations. The devised platform will provide a uniform and flexible authentication base for multi-factor authentication requirements and context-aware authentication modes for ubiquitous applications and services. The main contribution is focused on the design and implementation of an extensible authentication framework model, integrating classic as well as new pervasive authentication factors that can be composed for different context-aware dynamic requirements. Flexibility criteria are addressed by the establishment of a unified authentication back-end, supporting authentication modes as defined processes and rules expressed in a SAML based declarative markup language. The authentication base supports an extended single sign-on system that can be dynamically tailored for multi-factor authentication policies, considering large scale distributed applications and according with ubiquitous interaction needs

Efficient prediction model management in mobile systems

With the advent of affordable mobile devices such as smartphones and tablets, the vision of Pervasive Computing has made a big step closer to becoming reality. In order to become truly ubiquitous and seamlessly integrate into everyday life, the design of context-aware applications is essential. Using contextual information obtained for example from the device's sensors such as motion sensors and gps receiver, context-aware applications can adapt their behavior depending on the environment the user is in. In some scenarios, context aware applications can also benefit from knowledge about future contexts. This necessitates the use of a context prediction model. We examine a social network scenario where in addition, the context in question is originally being acquired on another user's device. In this scenario, the prediction model could for example be used to predict the next location or activity of a friend. Prior to that, the prediction model needs to be distributed to and stored on the mobile device running the application. Both high transfer cost and limited space make it imperative to produce small prediction models which still predict the context considerably well. In this thesis, we examined methods to compress Markov-based prediction models of higher order in a lossless and lossy fashion and evaluated these methods on real world and generated data. Our evaluation showed clearly that the compression mechanisms introduced can be successfully applied to significantly reduce the size of the prediction models with only a minor impact on prediction performance

Privacy-preserving human mobility and activity modelling

The exponential proliferation of digital trends and worldwide responses to the COVID-19 pandemic thrust the world into digitalization and interconnectedness, pushing increasingly new technologies/devices/applications into the market. More and more intimate data of users are collected for positive analysis purposes of improving living well-being but shared with/without the user's consent, emphasizing the importance of making human mobility and activity models inclusive, private, and fair. In this thesis, I develop and implement advanced methods/algorithms to model human mobility and activity in terms of temporal-context dynamics, multi-occupancy impacts, privacy protection, and fair analysis. The following research questions have been thoroughly investigated: i) whether the temporal information integrated into the deep learning networks can improve the prediction accuracy in both predicting the next activity and its timing; ii) how is the trade-off between cost and performance when optimizing the sensor network for multiple-occupancy smart homes; iii) whether the malicious purposes such as user re-identification in human mobility modelling could be mitigated by adversarial learning; iv) whether the fairness implications of mobility models and whether privacy-preserving techniques perform equally for different groups of users. To answer these research questions, I develop different architectures to model human activity and mobility. I first clarify the temporal-context dynamics in human activity modelling and achieve better prediction accuracy by appropriately using the temporal information. I then design a framework MoSen to simulate the interaction dynamics among residents and intelligent environments and generate an effective sensor network strategy. To relieve users' privacy concerns, I design Mo-PAE and show that the privacy of mobility traces attains decent protection at the marginal utility cost. Last but not least, I investigate the relations between fairness and privacy and conclude that while the privacy-aware model guarantees group fairness, it violates the individual fairness criteria.Open Acces

Context-awareness for mobile sensing: a survey and future directions

The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions

Towards a multidisciplinary user-centric design framework for context-aware applications

The primary aim of this article is to review and merge theories of context within linguistics, computer science, and psychology, to propose a multidisciplinary model of context that would facilitate application developers in developing richer descriptions or scenarios of how a context-aware device may be used in various dynamic mobile settings. More specifically, the aim is to:1. Investigate different viewpoints of context within linguistics, computer science, and psychology, to develop summary condensed models for each discipline. 2. Investigate the impact of contrasting viewpoints on the usability of context-aware applications. 3. Investigate the extent to which single-discipline models can be merged and the benefits and insightfulness of a merged model for designing mobile computers. 4. Investigate the extent to which a proposed multidisciplinary modelcan be applied to specific applications of context-aware computing

Toward a multidisciplinary model of context to support context-aware computing

Capturing, defining, and modeling the essence of context are challenging, compelling, and prominent issues for interdisciplinary research and discussion. The roots of its emergence lie in the inconsistencies and ambivalent definitions across and within different research specializations (e.g., philosophy, psychology, pragmatics, linguistics, computer science, and artificial intelligence). Within the area of computer science, the advent of mobile context-aware computing has stimulated broad and contrasting interpretations due to the shift from traditional static desktop computing to heterogeneous mobile environments. This transition poses many challenging, complex, and largely unanswered research issues relating to contextual interactions and usability. To address those issues, many researchers strongly encourage a multidisciplinary approach. The primary aim of this article is to review and unify theories of context within linguistics, computer science, and psychology. Summary models within each discipline are used to propose an outline and detailed multidisciplinary model of context involving (a) the differentiation of focal and contextual aspects of the user and application's world, (b) the separation of meaningful and incidental dimensions, and (c) important user and application processes. The models provide an important foundation in which complex mobile scenarios can be conceptualized and key human and social issues can be identified. The models were then applied to different applications of context-aware computing involving user communities and mobile tourist guides. The authors' future work involves developing a user-centered multidisciplinary design framework (based on their proposed models). This will be used to design a large-scale user study investigating the usability issues of a context-aware mobile computing navigation aid for visually impaired people