showing 15 related works from this author
An Adaptive Bayesian System for Context-Aware Data Fusion in Smart Environments
2017
The adoption of multi-sensor data fusion techniques is essential to effectively merge and analyze heterogeneous data collected by multiple sensors, pervasively deployed in a smart environment. Existing literature leverages contextual information in the fusion process, to increase the accuracy of inference and hence decision making in a dynamically changing environment. In this paper, we propose a context-aware, self-optimizing, adaptive system for sensor data fusion, based on a three-tier architecture. Heterogeneous data collected by sensors at the lowest tier are combined by a dynamic Bayesian network at the intermediate tier, which also integrates contextual information to refine the infe…
Towards a Smart Campus Through Participatory Sensing
2018
In recent years, the percentage of the population owning a smartphone has increased significantly. These devices provide users with more and more functions that make them real sensing platforms. Exploiting the capabilities offered by smartphones, users can collect data from the surrounding environment and share them with other entities in the network thanks to existing communication infrastructures, i.e., 3G/4G/5G or WiFi. In this work, we present a system based on participatory sensing paradigm using smartphones to collect and share local data in order to monitor make a campus 'smart'. In particular, our system infers the activities performed by users (e.g., students) in a campus in order …
An Ambient Intelligence System for Assisted Living
2017
Nowadays, the population's average age is constantly increasing, and thus the need for specialized home assistance is on the rise. Smart homes especially tailored to meet elderly and disabled people's needs can help them maintaining their autonomy, whilst ensuring their safety and well-being. This paper proposes a complete context-aware system for Ambient Assisted Living (AAL), which infers user's actions and context, analyzing its past and current behavior to detect anomalies and prevent possible emergencies. The proposed system exploits Dynamic Bayesian Networks to merge raw data coming from heterogeneous sensors and infer user's behavior and health conditions. A rule-based reasoner is ab…
Context-awareness for multi-sensor data fusion in smart environments
2016
Multi-sensor data fusion is extensively used to merge data collected by heterogeneous sensors deployed in smart environments. However, data coming from sensors are often noisy and inaccurate, and thus probabilistic techniques, such as Dynamic Bayesian Networks, are often adopted to explicitly model the noise and uncertainty of data. This work proposes to improve the accuracy of probabilistic inference systems by including context information, and proves the suitability of such an approach in the application scenario of user activity recognition in a smart home environment. However, the selection of the most convenient set of context information to be considered is not a trivial task. To thi…
Autonomic behaviors in an Ambient Intelligence system
2014
Ambient Intelligence (AmI) systems are constantly evolving and becoming ever more complex, so it is increasingly difficult to design and develop them successfully. Moreover, because of the complexity of an AmI system as a whole, it is not always easy for developers to predict its behavior in the event of unforeseen circumstances. A possible solution to this problem might lie in delegating certain decisions to the machines themselves, making them more autonomous and able to self-configure and self-manage, in line with the paradigm of Autonomic Computing. In this regard, many researchers have emphasized the importance of adaptability in building agents that are suitable to operate in real-wor…
FIRST
2018
Thanks to the collective action of participating smartphone users, mobile crowdsensing allows data collection at a scale and pace that was once impossible. The biggest challenge to overcome in mobile crowdsensing is that participants may exhibit malicious or unreliable behavior, thus compromising the accuracy of the data collection process. Therefore, it becomes imperative to design algorithms to accurately classify between reliable and unreliable sensing reports. To address this crucial issue, we propose a novel Framework for optimizing Information Reliability in Smartphone-based participaTory sensing (FIRST) that leverages mobile trusted participants (MTPs) to securely assess the reliabil…
Secure e-voting in smart communities
2020
Nowadays, digital voting systems are growing in importance. This is an especially sensitive area, because elections can directly affect democratic life of many smart communities. The goal of digital voting systems is to exploit ICT technologies to improve the security and usability of traditional electoral systems. In this work we present a secure electronic voting system that guarantees the secrecy, anonymity, integrity, uniqueness and authenticity of votes, while offering a user-friendly experience to voters, putting them at ease through the use of technologies familiar to them. To ensure these fundamental security requirements, the system fully separates the registration and voting phase…
WiP: Smart services for an augmented campus
2018
Technological 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 syste…
A fog-based hybrid intelligent system for energy saving in smart buildings
2019
In recent years, the widespread diffusion of pervasive sensing devices and the increasing need for reducing energy consumption have encouraged research in the energy-aware management of smart environments. Following this direction, this paper proposes a hybrid intelligent system which exploits a fog-based architecture to achieve energy efficiency in smart buildings. Our proposal combines reactive intelligence, for quick adaptation to the ever-changing environment, and deliberative intelligence, for performing complex learning and optimization. Such hybrid nature allows our system to be adaptive, by reacting in real time to relevant events occurring in the environment and, at the same time, …
A Context-Aware System for Ambient Assisted Living
2017
In the near future, the world's population will be characterized by an increasing average age, and consequently, the number of people requiring for a special household assistance will dramatically rise. In this scenario, smart homes will significantly help users to increase their quality of life, while maintaining a great level of autonomy. This paper presents a system for Ambient Assisted Living (AAL) capable of understanding context and user's behavior by exploiting data gathered by a pervasive sensor network. The knowledge inferred by adopting a Bayesian knowledge extraction approach is exploited to disambiguate the collected observations, making the AAL system able to detect and predict…
Designing Ontology-Driven Recommender Systems for Tourism
2014
Nowadays, Internet users may experience some difficulty in finding the information they need from the huge multitude of existing web pages. A possible solution to this problem might lie in delegating some of the search tasks to machines, or in other words, in building a Semantic Web in which information could be processed automatically by intelligent software agents. Given the constantly increasing growth of the tourism industry, it might be particularly helpful to develop virtual assistants capable of planning trips on the basis of a user’s interests. If so, adopting Semantic Web technologies would make it possible to provide a more customized service to each user and thus satisfy their re…
Bayesian Modeling for Differential Cryptanalysis of Block Ciphers: A DES Instance
2023
Encryption algorithms based on block ciphers are among the most widely adopted solutions for providing information security. Over the years, a variety of methods have been proposed to evaluate the robustness of these algorithms to different types of security attacks. One of the most effective analysis techniques is differential cryptanalysis, whose aim is to study how variations in the input propagate on the output. In this work we address the modeling of differential attacks to block cipher algorithms by defining a Bayesian framework that allows a probabilistic estimation of the secret key. In order to prove the validity of the proposed approach, we present as case study a differential att…
SecureBallot: A secure open source e-Voting system
2021
Abstract Voting is one of the most important acts through which a community can make a collective decision. In recent years, many works have focused on improving traditional voting mechanisms and, as a result, a wide range of electronic voting (e-Voting) systems have been proposed. Even though some approaches have achieved a proper level of usability, the main challenges of e-Voting are essentially still open: protect the privacy of participants, guarantee secrecy, anonymity, integrity, uniqueness, and authenticity of votes, while making e-Voting as trustful as voting. In order to address this issue, we present SecureBallot, a secure open-source e-Voting system that completely decouples the…
A Resilient Smart Architecture for Road Surface Condition Monitoring
2022
Nowadays, 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 resilien…
IncentMe: Effective Mechanism Design to Stimulate Crowdsensing Participants with Uncertain Mobility
2018
Mobile crowdsensing harnesses the sensing power of modern smartphones to collect and analyze data beyond the scale of what was previously possible with traditional sensor networks. Given the participatory nature of mobile crowdsensing, it is imperative to incentivize mobile users to provide sensing services in a timely and reliable manner. Most importantly, given sensed information is often valid for a limited period of time, the capability of smartphone users to execute sensing tasks largely depends on their mobility pattern, which is often uncertain. For this reason, in this paper, we propose IncentMe, a framework that solves this core issue by leveraging game-theoretical reverse auction …