Covert evidence gathering has not seen major changes in decades. Law enforcement Agencies (LEAs) are still using conventional, manpower based techniques to gather forensic evidence. Concealed surveillance devices can provide irrefutable evidences, but current video surveillance systems are usually bulky and complicated, are often used as simple video recorders, and require complex, expensive infrastructure to supply power, bandwidth, storage and illumination. Recent years have seen significant advances in the surveillance industry, but these were rarely targeted to forensic applications. The imaging community is fixated on cameras for mobile phones, where the figures of merit are resolution, image quality, and low profile. A mobile phone with its camera on would consume its battery in under two hours. Industrial surveillance cameras are even more power hungry, while intelligent algorithms such as face detection often require extremely high processing power, such as backend server farms, and are not available in conventional surveillance systems. Here we propose to develop and validate a novel, ultra-low-power, intelligent, miniaturised, low-cost, wireless, autonomous sensor (“FORENSOR”) for evidence gathering. Its ultra-sensitive camera and built-in intelligence will allow it to operate at remote locations, automatically identify pre-defined criminal events, and alert LEAs in real time while providing and storing the relevant video, location and timing evidence. FORENSOR will be able to operate for up to two months with no additional infrastructure. It will be manageable remotely, preserve the availability and the integrity of the collected evidence, and comply with all legal and ethical standards, in particular those related to privacy and personal data protection. The combination of built-in intelligence with ultra-low power consumption could help LEAs take the next step in fighting severe crimes.

SCENE is building an integrated security system for vehicular network, for both IoT based data collection and multimedia secured content delivery. The content delivery platform is intelligent enough to be deployed in both IoT mode and in the stand alone mode to deliver contents to subscribed smart application. SCENE adopts a radically different view on the type of infrastructure needed for mid-size municipalities : SCENE is an open mobile platform for smart city applications, supporting different IoT wireless access networks (while providing an excellent level of infrastructure cybersecurity. By using the public transport network, municipalities will achieve complete coverage with less investment in infrastructure. SCENE is based on six pillars:, security, system integrity, scalability, elasticity, visualization, interoperability with external services, and easy management. In order to achieve these six pillars, four modules will be introduced. A security module for both data and communication which will be provided by CEA. Second, a smart gateway for mobile IoT/Content delivery control, aggregation and transmission provided by JCPC. The proposed gateway ensures both scalability and elasticity. Third, a service platform that proposes a powerful IoT oriented architecture to provide interoperability and added value services for integration and data analytics, ensuring scalability and elasticity of the system as well as system integrity by ALMAVIVA. The proposed service platform supports integrated data analytics services among other services. Aa dashboard module is introduced to ensure the ease of management of all system components as well as data visualization. The overall system integration will be implemented by VISIONWARE together with the different municipalities involved. The bus network of Catania in the Italian Sicily Region will be the first SCENE field trial, coupled with a field trial in Rennes city in France, and in Portugal.

Overirrigation and excessive use of synthetic fertilizers and pesticides increase yield cost, contaminate the aquifer and destroy the biodiversity, while suboptimal livestock production increases GhG emissions, contributing to the global warming. The solution on the triangle a) global food needs, b) competitiveness/farmers’ fair income and c) sustainable farming/protection of the environment lies in knowledge. Modern farms create a huge amount of data based on IoT sensors and drones, while vast EO data become available via Copernicus Hubs. AgriDataSpace aims to establish itself as the “Game Changer” in Smart Farming and agri-environmental monitoring, and strengthen the smart-farming capacities, competitiveness and fair income by introducing an innovative, intelligent and multi-technology, fully distributed platform of platforms. To achieve technological maturity and massive acceptance, AgriDataSpace adopts and adapts a multidimensional approach that combines state of the art big data and data-spaces’ technologies (BDVA/IDSA/GAIA-X) with agricultural knowledge, new business models and agri-environment policies, leverages on existing platforms and edge computing, and introduces novel concepts, methods, tools, pilots and engagement campaigns to go beyond today’s state of the art, perform breakthrough research and create sustainable innovation in upscaling (real-time) sensor data, already evident within the project lifetime. AgriDataSpace will be validated via 24 Use cases in 23 pilots in 9 countries, representing more than 181,000ha with 25 types of crops that span from southwest to northeast Europe, outdoor and greenhouse crops, organic and non-organic production, and more than 2,000 animals of 5 types. More than 4,200 farmers will provide insights and more than 89,000 will be directly informed. More than 1,600 sensors will be utilized and more than 4,500 additional sensors will be installed to measure (real-time) data, including more than 2,500 RFID tags.

Data analytics is one of the main cornerstones in many enterprise architectures and the data lake paradigm is more and more adopted to assist organizations in taking reliable, accurate, and fast decisions. Although the initial approaches to address these issues saw the data lakes as the evolution of data warehouses to be implemented on-premises, cloud providers are nowadays including in their offerings platforms able to setup and run them. Nevertheless, the increasing amount of data generated at the edge and the need to enable the data sharing among organizations are posing new challenges in terms of performances, energy efficiency, and privacy/confidentiality which can be properly addressed with data lakes which are deployed along the whole computing continuum as well as building a federation of such data lakes. The ambition of TEADAL is to provide key cornerstone technologies to create stretched data lakes spanning the cloud-edge continuum and multi-cloud, providing privacy, confidentiality, and energy-efficient data management. The TEADAL data lake technologies will enable trusted, verifiable and energy efficient data flows, both in a stretched data lake and across a trustworthy mediatorless federation of them, based on a shared approach for defining, enforcing, and tracking privacy/confidentiality requirements balanced with the need for energy reduction.