The paradigm of pervasive computing is gaining more and more attention nowadays, thanks to the possibility of obtaining precise and continuous monitoring. Ease of deployment and adaptivity are typically implemented by adopting autonomous and cooperative sensory devices; however, for such systems to be of any practical use, reliability and fault tolerance must be guaranteed, for instance by detecting corrupted readings amidst the huge amount of gathered sensory data. This paper proposes an adaptive distributed Bayesian approach for detecting outliers in data collected by a wireless sensor network; our algorithm aims at optimizing classification accuracy, time complexity and communication complexity, and also considering externally imposed constraints on such conflicting goals. The performed experimental evaluation showed that our approach is able to improve the considered metrics for latency and energy consumption, with limited impact on classification accuracy.
In recent years, touchless-enabling technologies have been more and more adopted for providing public displays with gestural interactivity. This has led to the need for novel visual interfaces aimed at solving issues such as communicating interactivity to users, as well as supporting immediate usability and "natural" interactions. In this paper, we focus our investigation on a visual interface based only on the use of in-air direct manipulations. Our study aims at evaluating whether and how the presence of an Avatar that replays user's movements may decrease the perceived cognitive workload during interactions. Moreover, we conducted a brief evaluation of the relationship between the presence of the Avatar and the use of one or two hands during the interactions. To this end, we compared two versions of the same interface, differing only for the presence/absence of the user's Avatar. Our results showed that the Avatar contributes to lower the perceived cognitive workload during the interactions.
the conference committee reflected on SIGUCCS conferences, what they mean to us individually, and what we hope to achieve this year. Our conference theme Connect | Discover was developed as a result of our discussions. We are confident that over the next several days, you will connect with amazing colleagues from other institutions across the US and the world, and discover new ideas and solutions to bring back to your home institution.
Wireless sensor networks (WSNs) are a fundamental building block of many pervasive applications. Nevertheless the use of such technology raises new challenges regarding the development of reliable and fault-tolerant systems. One of the most critical issues is the detection of corrupted readings amidst the huge amount of gathered sensory data. Indeed, such readings could significantly affect the quality of service (QoS) of the WSN, and thus it is highly desirable to automatically discard them. This issue is usually addressed through "fault detection" algorithms that classify readings by exploiting temporal and spatial correlations. Generally, these algorithms do not take into account QoS requirements other than the classification accuracy. This paper proposes a fully distributed algorithm for detecting data faults, taking into account the response time besides the classification accuracy. We adopt the Bayesian networks to perform classification of readings and the Pareto optimization to allow QoS requirements to be simultaneously satisfied. Our approach has been tested on a synthetic dataset in order to evaluate its behavior with respect to different values of QoS constraints. The experimental evaluation produced good results, showing that our algorithm is able to greatly reduce the response time at the cost of a small reduction in classification accuracy.
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