1,720,981 research outputs found
A gesture recognition framework for exploring museum exhibitions
No full textIn this paper we present a gesture recognition framework for providing the visitors of a museum exhibition with a non intrusive interface for the multimedia enjoyment of digital contents. Early experiments were carried out at the Computer History Museum Exhibition of the University of Palermo
WiP: Smart services for an augmented campus
No full textTechnological progress in recent years has allowed the design of new intelligent learning systems in smart environments aiming to facilitate users' lives. As a consequence, besides making use of traditional sensors for monitoring the quantities of interest, such systems can also benefit from information obtained from the users' smart devices, which can now be considered as additional sensing tools. In this article, we present the design of a novel system based on the fog computing paradigm that can improve the services offered to users on a smart campus by using different smart devices, i.e., smartphones, smartwatches, tablets, smartcameras and so on. In particular, we will describe a system in which several smart devices will collect sensory and context information, whilst the cloud will aggregate and analyze this data to extract information of particular interest. The main challenge of this project is to create an intelligent platform that allows new software modules to be added without having to re-design the entire architecture, and that can provide new services to campus users or improve existing ones
A cognitive architecture for ambient intelligence systems
Full text linkNowadays, the use of intelligent systems in homes and workplaces is a well-established reality. Research efforts are moving towards increasingly complex Ambient Intelligence (AmI) systems that exploit a wide variety of sensors, software modules and stand-alone systems. Unfortunately, using more data often comes at a cost, both in energy and computational terms. Finding the right trade-off between energy savings, information costs and accuracy of results is a major challenge, especially when trying to integrate many heterogeneous modules. Our approach fits into this scenario by proposing an ontology-based AmI system with a cognitive architecture, able to perceive the state of the surrounding environment, to reason on the current situation and act accordingly to modify the state of the environment based on the user’s preferences
A Resilient Smart Architecture for Road Surface Condition Monitoring
No full textNowadays, road surface condition monitoring is a challenging problem that cannot be addressed with traditional techniques. In this paper we propose an architecture for monitoring the condition of road surfaces based on the paradigm of Mobile Crowdsensing. First, a surface detection module extracts high level features from raw data, indicating the presence of hazards. Then, in order to make the system resilient to attacks, the system exploits a reputation module to identify malicious users and filter out unreliable data. Finally, a truth discovery module aggregates the resulting information to obtain the desired truth values. Experiments carried out on a real world dataset prove the resilience of the proposed system to different attacks and the accuracy achieved
A Privacy-Preserving System for Enhancing the QoI of Collected Data in a Smart Connected Community
No full textThe Smart Connected Communities paradigm, which synergistically integrates smart technologies with the surrounding environment, has paved the way for a new generation of applications that provide increasingly intelligent services by leveraging information coming from users, and the IoT. While user collaboration is essential to improve the quality of information (QoI), the interest of providers in data can jeopardize the right to privacy by revealing details that users are not willing to share (e.g., habits, health status). In addition, not all involved users consistently exhibit cooperative behavior, and the presence of attackers often undermines the quality of the collected information. In this paper, we propose a system for aggregating and analyzing user data without ever compromising their privacy, whilst improving QoI. The system uses Privacy Preserving Computation techniques, clustering, and an outlier removal step to improve the quality of information. Utilizing a real-world dataset, we tested our system, demonstrating its resilience in a scenario with potential attackers and its superior performance compared to other state-of-the-art systems
Anomaly Detection for Reoccurring Concept Drift in Smart Environments
No full textMany crowdsensing applications today rely on learning algorithms applied to data streams to accurately classify information and events of interest in smart environments. Unfor-tunately, the statistical properties of the input data may change in unexpected ways. As a result, the definition of anomalous and normal data can vary over time and machine learning models may need to be re-trained incrementally. This problem is known as concept drift, and it has often been ignored by anomaly detection systems, resulting in significant performance degradation. In addition, the statistical distribution of past data often tends to repeat itself, and thus old learning models could be reused, avoiding costly retraining phases on new data, which would waste computational and energy resources. In this paper, we propose a hybrid anomaly detection system for streaming data in smart environments that accounts for concept drift and minimize the number of machine learning models that need to be retrained when shifts in incoming data distribution are detected. The system is multi-tier and relies on two different concept drift detection modules and an ensemble of anomaly detection models. An extensive experimental evaluation has been carried out, using two real datasets and a synthetic one; results show the high performance achieved by the system using common metrics such as F1-score and accuracy
Enabling peer-to-peer User-Preference-Aware Energy Sharing Through Reinforcement Learning
No full textRenewable, heterogeneous and distributed energy resources are the future of power systems, as envisioned by the recent paradigm of Virtual Power Plants (VPPs). Residential electricity generation, e.g., through photovoltaic panels, plays a fundamental role in this paradigm, where users are able to participate in an energy sharing system and exchange energy resources among each other. In this work, we study energy sharing systems and, differently from previous approaches, we consider realistic user behaviors by taking into account the user preferences and level of engagement in the energy trades. We formulate the problem of matching energy resources while contemplating the user behavior as a Mixed Integer Linear Programming (MILP) problem, and show that the problem is NPHard. Since the solution of such problem requires the knowledge of the user behavioral model, we propose an heuristic based on reinforcement learning with bounded regret to learn such model while optimizing the system performance. Comparison with the state-of-the-art approaches using realistic simulations based on real traces shows that our method outperforms existing schemes in several efficiency metrics. Besides, the results reveal that increasing the amount of produced energy improves the learning ability of the system even in a short period. It gives practical insights for implementation of energy sharing systems
Reputation-Based Dissemination of Trustworthy Information in VANETs
No full textWith the emergence of new vehicle communication paradigms such as Vehicle-to-Everything, the possibility of providing advanced services to drivers is becoming a reality. The immediate and targeted warning of dangers offers the opportunity to increase driving safety and make optimal use of the road infrastructure. However, communication reliability between vehicles, or worse, passenger safety, may be compromised by vehicles modified to spread false information or create disorder under coordinated malicious groups. Solutions currently adopted in similar scenarios include the use of Reputation Management Systems (RMS), which allow the reliability of received information to be estimated. However, classic centralized RMSs do not fit the distributed and dynamic nature of vehicular networks. In this paper, a step is taken towards the design of a fully distributed event detection and dissemination system for VANETs, based on vehicle and data reputation, which does not rely on any fixed communication infrastructure. A new reputation model is proposed to reliably detect events and a new communication protocol is defined to disseminate information among vehicles, based on the population protocol model. The experimental evaluation performed on realistic vehicle routes demonstrates the feasibility of the proposed system and its ability to withstand orchestrated attacks, with a significant performance improvement over other state-of-the-art solutions
Reliable Reputation-Based Event Detection in V2V Networks
No full textTechnological advances in automotive and vehicle-to-vehicle communication paradigms promise the implementation of increasingly advanced services to make driving safer and more aware of events such as traffic congestion and road hazards. The detection and dissemination of reliable information about road events is of paramount importance to avoid unpleasant and potentially dangerous situations caused by the dissemination of false messages from unreliable or intentionally tampered vehicles. This paper proposes an event detection system based on reliable data dissemination, exploiting a fully distributed reputation and trust mechanism. Experiments conducted on a dataset containing realistic vehicle tracks on real-world maps demonstrate the system’s ability to withstand the presence of up to 30% of attackers orchestrated to propagate false events without significant performance degradation
A Reinforcement Learning Approach for User Preference-aware Energy Sharing Systems
Full text linkEnergy Sharing Systems (ESS) are envisioned to be the future of power systems. In these systems, consumers equipped with renewable energy generation capabilities are able to participate in an energy market to sell their energy. This paper proposes an ESS that, differently from previous works, takes into account the consumers’ preference, engagement, and bounded rationality. The problem of maximizing the energy exchange while considering such user modeling is formulated and shown to be NP-Hard. To learn the user behavior, two heuristics are proposed: a Reinforcement Learning-based algorithm, which provides a bounded regret, and a more computationally efficient heuristic, named BPT-K, with guaranteed termination and correctness. A comprehensive experimental analysis is conducted against state-of-the-art solutions using realistic datasets. Results show that including user modeling and learning provides significant performance improvements compared to state-of-the-art approaches. Specifically, the proposed algorithms result in 25% higher efficiency and 27% more transferred energy. Furthermore, the learning algorithms converge to a value less than 5% of the optimal solution in less than 3 months of learning
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