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.

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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.

In the electricity market, wholesale energy prices depend on the balance between energy production and load demand. In the last few years, electricity market has become more and more flexible as many utilities have started to replace the fixed retail prices schemes with prices changing during the day. Dynamic pricing, also known as Real-Time Pricing (RTP), reflects the trend of the wholesale market and allows to reduce the volatility of the wholesale prices, also contributing to a reduction of demand peaks. Electricity customers take advantage of dynamic pricing by shifting their consumption according to the real-time prices or by using Battery Energy Storage Systems (BESS) to shift electricity consumption. As a result, storing electricity in off-peak periods allows customers to lower electricity rates during on-peak periods.\ud This paper describes the application to a medium-scale public facility of a simple BESS operating strategy which aims to maximize the saving for the end-user. The operating strategy is able to identify, for each daily period, the charging and discharging hours, relying only on the hourly spot market price profile (day-ahead electricity prices) and may be applied to all kinds of BESS. The experimental evaluation uses a Lithium-ion (Li-ion) storage system and results highlight how the power profile of the public facility changes as a result of the proposed charging strateg

Nowadays, several network applications require that\ud consumer nodes acquire distributed services from unknown service providers on the Internet. The main goal of consumer nodes is the selection of the best services among the huge multitude provided\ud by the network. As basic criteria for this choice, service cost and Quality-of-Service (QoS) can be considered, provided that the underlying Service-Oriented Architecture (SOA) be augmented in\ud order to support the declaration of this information. The correct behavior of such new SOA platforms, however, will depend on the presence of some mechanisms that allow consumer nodes to\ud evaluate trustworthiness of service providers. This work proposes a new methodology for discouraging antisocial behaviors of malicious service providers that declare QoS higher than the real one. The architecture is fully distributed over the network and\ud emulates a decentralized hierarchical trusting authority capable of managing reputation values and of providing correct QoS assessments

Reliable random number generation is crucial for many available security algorithms, and some of the methods presented in literature proposed to generate them based on measurements collected from the physical environment, in order to ensure true randomness. However the effectiveness of such methods can be compromised if an attacker is able to gain access to the measurements thus inferring the generated random number. In our paper, we present an algorithm that guarantees security for the generation process, in a real world scenario using wireless sensor nodes as the sources of the physical measurements. The proposed method uses distributed leader election for selecting a random source of data. We prove the robustness of the algorithm by discussing common security attacks, and we present theoretical and experimental evaluation regarding its complexity in terms of time and exchanged messages