Current active projects
Advanced Analytics And Machine Learning Algorithms for Predictive Maintenance on Medium Voltage Distribution Networks
The failure of elements in modern energy grid systems causes service disruption and is one of the main cause of maintenance costs. To avoid failures, periodic maintenance operations are typically executed so to identify possibly failing elements and replace them before they fail.
The key idea is to adopt machine learning techniques to extract models from data, with the goal to predict feeder failures and to address predictive maintenance in the most effective way.
Statistical Models, Data Analytics and Machine Learning – Fog Computing and Opportunistic Networking
The project covers two main research areas: “Data analytics” and “Fog computing and opportunistic network”.
The aim of “Data analytics” is to analyze historical data provided by Tierra Telematics about on-road and off-road vehicle usage by means of data mining and machine learning techniques.
The aim of “Fog computing and opportunistic network” is to distribute resources and services of calculation, storage of data, control and network functionality on the infrastructure that connects the Cloud to the Internet of Things (IoT).
Big Data And Machine Learning Algorithms for Predictive Maintenance on Combustion Engine cars
The aim on this project is to analyse actual data collected via onboard car monitoring systems to understand which are the most important signals to predict engine faults, and so model them. The key idea is to apply big data and machine learning techniques to select the most important signals, and to extract models using supervised learning approaches, with the goal of predicting the targeted failures.
Car sharing and electric charging station placement from data
The Free Floating Car Sharing system refear to a model of car rental where users rent for a short period of time, usually a few hour or less a car. The peculiarity of this system is that users can rent and drop a car wherever in a geo-fence area. As this transportation means saw an important growh in the last years, it is important to study it. For this reason this project has two main goals.
The first goal is to study the feasibility of creating a platform that harvests data offered by the platforms of different FFCS companies, and design big data analytics to extract higher level information such as mobility patterns, customers’ habits, gas consumption, typical areas of usage, and more.
The second goal is demonstrate the potentials of these Big Data analytics by designing an electric based FFCS systems (e-FFCS) leveraging actual data coming from current gas based FFCS platforms.
Data-driven urban systems modelling and analysis
Nowadays the huge amount of data available on mobility infrastructures and urban mobility calls for new models and analysis able to discover users’ habits and patterns.
The SmartData@PoliTO center use the expertise accumulated on big data analysis and machine learning to analyse such data to discover new users’ habits as well as make quantitative measure on different aspect on mobility and efficiency of city processes.
The development of Interactive data visualization platform allows to disseminate the scientific results to a large audience, from stakeholder to private citizens. The interactive platform devised by the SmartData@PoliTO center can be used also for testing new urban scenario and new mobility solutions.
Recurrent Neural Networks applied to network and service data
Analysis, using data mining techniques (non necessarily only neural networks) of the alarm logs generated in the 3G/4G mobile network. The number of alarms generated in such a system is very high, and not all of them can and should be shown to the operator to decide a possible human intervention for their solution. The system currently used is based on a fixed set of rules to limit the number of raised events. The aim of the project is to discover if data mining techniques can be used to discover relations among the alarms to define new filtering rules, both to reduce the number of raised alarms, and to possibly adapt to different network configurations that might not have been considered in the design of the original rule set.
SHIELD – Securing Against Intruders and Other Threats through a NFV-Enabled Environment
SHIELD is a EU H2020 project proposing a universal solution for dynamically establishing and deploying virtual security infrastructures into ISP and corporate networks. SHIELD builds on the huge momentum of Network Functions Virtualization (NFV) to deploy security appliances into virtual Network Security Functions (vNSFs), instantiated within the network infrastructure, effectively monitoring and filtering network traffic in a distributed manner. Logs and metrics from vNSFs are aggregated into an information-driven Data Analysis and Remediation Engine (DARE), which leverages state-of-the-art big data storage and analytics to predict specific vulnerabilities and attacks.
The SmartData@PoliTO center contributes with expertise on network monitoring, attack simulation, big data processing and preparation, as well as machine learning algorithms for the identification of threats and anomalies.
The project aims to create a HPC, Big Data competence center for open and scalable Artificial Intelligence with applications centred in health, food processing, mechatronics, automotive, aerospace. The infrastructure of the center will be easily accessible through cloud services. To maximize technology transfer, the center will operate by co-designing applications and technological solutions. The center will offer highly specialized support to foster innovation and develop skills in local companies, thus stimulating the expansion of market opportunities.
Research project with EDISON SpA “City building mapping: using public data to find energy efficiency of buildings”, that focuses on the harvesting of open data for the characterization of the energy efficiency and consumption of both residential and commercial buildings. The project first step focuses on the design and implementation of a big data approach to collect data publicly available about the energy efficiency of buildings, as for example reported in contracts, or in surveys. This would produce a sampled map considering a region, in which only some buildings would have information while for the majority of other buildings no information would be available. In a second step, we will design machine learning approaches to extend the coverage map to those buildings with the same characteristics as those found in the data.