Telecom Italia is an Italian telecommunications company which provides landline services, mobile services, and DSL data services. It was founded in 1994 in a merger of several state-owned telecommunications companies, the most important of which was Società Italiana per L'Esercizio Telefonico p.A., known as S.I.P., from the initials of the earlier Società Idroelettrica Piemontese, the former state monopoly telephone operator in Italy. The companies stock is traded in the Borsa Italiana.Telecom Italia, TIM, TIM Brasil, Olivetti, Telecom Italia Sparkle are the main brands of Telecom Italia Group. Wikipedia.
Telecom Italia | Date: 2017-02-22
A method for identifying and locating at least one relevant location visited by at least one individual within a geographical area served by a wireless telecommunication network is proposed. The method comprising the following steps: selecting (203) a predetermined time period (T) over which the identifying and locating of the at least one relevant location have to be performed, selecting (206,209) a typology of relevant location to be identified and located, retrieving (218) recorded time and position data recorded by the telecommunication network and regarding events (ei) in which a user equipment carried by the at least one individual interacted with the telecommunication network, computing (236) a probability that each event for which recorded time and position data have been retrieved occurred in the relevant location of the selected type based on the recorded time data, identifying and clustering (239) events occurred within a predefined distance from each other and having a similar probability, computing (242) a weight value for each cluster of events identified in order to take into account statistical aspects that affect the identification and the positioning of the selected relevant location, comparing (251) the weight value with a threshold weight value, if the weight value is equal to, or greater than, the threshold weight value, identifying (254) the relevant location as belonging to the selected typology of relevant location, and providing (230) an indication of the position of the at least one relevant location based on the recorded position data of the events of the cluster, or if the weight value is lower than the threshold weight value, identifying (224) the relevant location as not belonging to the selected typology of relevant location.
Telecom Italia | Date: 2017-03-08
A method (200) is proposed for allocating, in a wireless communications network (100), radio resources (PRBk) for uplink transmissions. The method comprises: selecting (205), among said radio resources (PRBk), an allocation group (AG(i)) comprising an ordered succession of radio resources available for allocation from a first radio resource (PRBF) to a last radio resource (PRBL(i)), iterating the following operations: selecting (225) the last radio resource (PRBL(i)) of the allocation group (AG(i)), each last radio resource (PRBL(i)) taking, at each iteration, a position (PL(O)) in the ordered succession lower than the position taken in the ordered succession by the last radio resource (PRBL(i)) at the respective previous iteration, estimating (210,215) a signal to interference-plus-noise ratio per radio resource (SINRPRB(i)) of the allocation group (AG(i)) according to a number of radio resources (Q(ij) of the allocation group (AG(ij), from the first (PRBF) to the last (PRBL(i)) radio resources of the allocation group (AG(i)), and according to history transmissions information, and until (220) the signal to interference-plus-noise ratio per radio resource (SINRPRB(i)) is higher than a predetermined signal to interference-plus-noise ratio (SINRTH), allocating (240,255) the radio resources from said first (PRBF) to said last (PRBL(i)) radio resources of the allocation group (AG(i)).
Telecom Italia | Date: 2017-04-05
It is disclosed a method to perform a mobile payment by a mobile user device. The method comprises, at the mobile user device: retrieving information indicative of a payment account of a recipient of the payment; acquiring an image of a displayed amount to pay and retrieving the amount to pay on the basis of the acquired image; and on the basis of the retrieved information, performing the payment of the retrieved amount to pay to the recipients payment account.
Telecom Italia | Date: 2017-04-05
A method for managing personal data of a user of a user device is disclosed. Said user device is adapted to have installed thereon an APP. Said APP is configured to require access to said personal data when running on said user device. The method comprises: - creating a certification for said APP, said certification being based on a corresponding statement providing information regarding the relationship between said APP and personal data of the user; - associating said certification to said APP for certifying said APP; - allowing the user to provide user-defined policies about exploiting the user personal data; - checking whether the user-defined policies provided by the user are compatible with requirements of said APP defined in the corresponding statement; - if the user-defined policies are compatible with said requirements of said APP defined in the statement, carrying out the following operations when the APP running on the user device requires to access personal data: - enforcing rules defined in the APP statement to guarantee that the APP accesses PD according to the requirements of said APP defined in the corresponding statement; - if, as a result of said enforcing rules,the requirements of said APP defined in the statement are not fulfilled, denying access to the personal data; -if, as a result of said enforcing rules,the requirements of said APP defined in the statement are fulfilled, carrying out the following operations: -if the APP requests to access real time personal data, collecting personal data from the user device according to the user-defined policies, and, if said personal data is to be collected, storing the accessed personal data in a secured digital space; -if the APP requests to access personal data already stored in the secured digital space,retrieving said personal data from the secured digital space according to the user-defined policies.
Telecom Italia | Date: 2017-01-04
A method (200) for managing a cellular network (100) is proposed. The cellular network (100) comprises a plurality of macro nodes (105i) defining respective macro cells (105ci) and a plurality of small nodes (110i,j) within said macro cells (105ci). The method (200) comprises, at each current time snapshot of a plurality of time snapshots and for each macro cell (105ci), the following steps: providing (205), according to a history traffic load (Hi,k) of the cellular network (100), an overload probability (i,k) in a first configuration of the cellular network (100) with only macro nodes (105i) activated; identifying (305-310), among said plurality of time snapshots, first candidate time snapshots (LMi) for small nodes (110i,j) deactivation, in each first candidate time snapshot (LMi) the overload probability (Oi,k) being lower than a threshold overload probability (OTHj); and, if (435) the current time snapshot is one among the first candidate time snapshots (LM), deactivating (440) each small node (110i,j) having (430) a current number (NPRBi,j) of allocated radio resources lower than a threshold number (NPRB,THi,j) and being (420) within a macro cell (105ci) currently having no macro (105i) or small (110i,j) nodes in overload condition.
Agency: European Commission | Branch: H2020 | Program: IA | Phase: IoT-01-2016 | Award Amount: 25.43M | Year: 2017
Automated driving is expected to increase safety, provide more comfort and create many new business opportunities for mobility services. The market size is expected to grow gradually reaching 50% of the market in 2035. The IoT is about enabling connections between objects or things; its about connecting anything, anytime, anyplace, using any service over any network. There is little doubt that these vehicles will be part of the IoT revolution. Indeed, connectivity and IoT have the capacity for disruptive impacts on highly and fully automated driving along all value chains towards a global vision of Smart Anything Everywhere. In order to stay competitive, the European automotive industry is investing in connected and automated driving with cars becoming moving objects in an IoT ecosystem eventually participating in BigData for Mobility. AUTOPILOT brings IoT into the automotive world to transform connected vehicles into highly and fully automated vehicle. The well-balanced AUTOPILOT consortium represents all relevant areas of the IoT eco-system. IoT open vehicle platform and an IoT architecture will be developed based on the existing and forthcoming standards as well as open source and vendor solutions. Thanks to AUTOPILOT, the IoT eco-system will involve vehicles, road infrastructure and surrounding objects in the IoT, with a particular attention to safety critical aspects of automated driving. AUTOPILOT will develop new services on top of IoT to involve autonomous driving vehicles, like autonomous car sharing, automated parking, or enhanced digital dynamic maps to allow fully autonomous driving. AUTOPILOT IoT enabled autonomous driving cars will be tested, in real conditions, at four permanent large scale pilot sites in Finland, France, Netherlands and Italy, whose test results will allow multi-criteria evaluations (Technical, user, business, legal) of the IoT impact on pushing the level of autonomous driving.
Agency: European Commission | Branch: H2020 | Program: IA | Phase: IoT-01-2016 | Award Amount: 17.60M | Year: 2017
The SoundCity Project MONICA aims to provide a very large scale demonstration of multiple existing and new Internet of Things technologies for Smarter Living. The solution will be deployed in 6 major cities in Europe. MONICA demonstrates a large scale IoT ecosystem that uses innovative wearable and portable IoT sensors and actuators with closed-loop back-end services integrated into an interoperable, cloud-based platform capable of offering a multitude of simultaneous, targeted applications. All ecosystems will be demonstrated in the scope of large scale city events, but have general applicability for dynamically deploying Smart City applications in many fixed locations such as airports, main traffic arterials, and construction sites. Moreover, it is inherent in the MONICA approach to identify the official standardisation potential areas in all stages of the project. MONICA will demonstrate an IoT platform in massive scale operating conditions; capable of handling at least 10.000 simultaneous real end-users with wearable and portable sensors using existing and emerging technologies (TRL 5-6) and based upon open standards and architectures. It will design, develop and deploy a platform capable of integrating large amounts of heterogeneous, interoperable IoT enabled sensors with different data capabilities (video, audio, data), resource constraints (wearables, Smartphones, Smartwatches), bandwidth (UWB, M2M), costs (professional, consumer), and deployment (wearable, mobile, fixed, airborne) as well as actuators (lights, LED, cameras, alarms, drones, loudspeakers). It will demo end-to-end, closed loop solutions covering everything from devices and middleware with semantic annotations through a multitude of wireless communication channels to cloud based applications and back to actuation networks. Humans-in-the-Loop is demonstrated through integrating Situational Awareness and Decision Support tools for organisers, security staff and sound engineers situation rooms.
Agency: European Commission | Branch: H2020 | Program: IA | Phase: ICT-14-2016-2017 | Award Amount: 7.07M | Year: 2017
Information technology has driven, directly or indirectly, much of Europes economic growth during the last decades as the role of data transitioned from the support of business decisions to becoming a good in itself. An open approach towards data value creation has become critical in the new networked economy, with Europe well placed to nurture this new revolution. However, to date Europes data economy has yet to achieve the same levels of growth as those in the US and Asia. Data Pitch will seek to address this critical gap by creating a transnational, Europe-wide data innovation ecosystem that will bring together data owners and Big Data technology providers, with startups and SMEs with fresh ideas for data-driven products and services. Our project will: - explore the critical factors that impact the way organisations create value from sharing data; - organise a competition addressing economic, societal, and environmental challenges, present and future, to identify promising digital innovators and data-empowered solutions; - create a cross-sectoral, secure data experimentation facility which will offer the winners of this competition a purposeful environment to nurture their ideas; and - support them by solving common concerns through funding, technical, legal, marketing, and commercial assistance. Drawing on the experience from key players in the consortium, we will establish a European Data Innovation Lab (DIL), guided and promoted by the hugely visible engagement channels and commentators at the Guardian and an international network of hundreds of organisations that have already confirmed their intention to join forces with and support Data Pitch. Together with them we will make the European data economy stronger and help the region re-gain leadership in innovation through digital transformation.
Agency: European Commission | Branch: H2020 | Program: RIA | Phase: ICT-10-2016 | Award Amount: 5.03M | Year: 2017
The demand for larger and more interconnected software systems is constantly increasing, but the ability of developers to satisfy it is not evolving accordingly. The most limiting factor is software validation, which typically requires very costly and complex testing processes. This project aims at significantly improving the efficiency and effectiveness of the testing process and, with it, the overall quality of large software systems. For this, we propose to apply the divide-and-conquer principle, which is commonly used for architecting complex software, to testing by developing a novel test orchestration theory and toolbox enabling the creation of complex test suites as the composition of simple testing units. This test orchestration mechanism is complemented with a number of tools that include: (1) Capabilities for the instrumentation of the Software under Test enabling to reproduce real-world operational conditions thanks to features such as Packet Loss as a Service, Network Latency as a Service, Failure as a Service, etc. (2) Reusable testing services solving common testing problems including Browser Automation as a Service, Sensor Emulator as a Service, Monitoring as a Service, Security Check as a Service, Log Ingestion and Analysis as a Service, Cost Modeling as a Service, etc. (3) Cognitive computing and machine learning mechanisms suitable for ingesting large amounts of knowledge (e.g. specifications, logs, software engineering documents, etc.) and capable of using it for generating testing recommendations and answering natural language questions about the testing process. The ElasTest platform thus created shall be released basing on a flexible Free Open Source Software and a community of users, stakeholders and contributors shall be grown around it with the objective of transforming ElasTest into a worldwide reference in the area of large software systems testing and of guaranteeing the long term sustainability of the project generated results.
Agency: European Commission | Branch: H2020 | Program: RIA | Phase: ICT-01-2016 | Award Amount: 4.91M | Year: 2017
Cyber-Physical Systems (CPS) find applications in a number of large-scale, safety-critical domains e.g. transportation, smart cities, etc. While the increased CPS adoption has resulted in the maturation of solutions for CPS development, a single consistent science of system integration for CPS has not yet been consolidated. Therefore CPS development remains a complex and error-prone task, often requiring a collection of separate tools. Moreover, interactions amongst CPS might lead to new behaviors and emerging properties, often with unpredictable results. Rather than being an unwanted byproduct, these interactions can become an advantage if explicitly managed since early design stages. CPSwarm tackles this challenge by proposing a new science of system integration and tools to support engineering of CPS swarms. CPSwarm tools will ease development and integration of complex herds of heterogeneous CPS that collaborate based on local policies and that exhibit a collective behavior capable of solving complex, industrial-driven, real-world problems. The project defines a complete toolchain that enables the designer to: (a) set-up collaborative autonomous CPSs; (b) test the swarm performance with respect to the design goal; and (c) massively deploy solutions towards reconfigurable CPS devices. Model-centric design and predictive engineering are the pillars of the project, enabling definition, composition, verification and simulation of collaborative, autonomous CPS while accounting for various dynamics, constraints and for safety, performance and cost efficiency issues. CPSwarm pushes forward CPS engineering at a larger scale, with an expected significant reduction of development time and costs. Project results will be tested in real-world use cases in 3 different domains: swarms of Unmanned Aerial Vehicles and Rovers for safety and security purposes; autonomous driving for freight vehicles; and swarm of opportunistically collaborating smart bikes.