Title: InterCONnected NEXt-Generation Immersive IoT Platform of Crime and Terrorism DetectiON, PredictiON, InvestigatiON, and PreventiON Services
Grant agreement no: 786731
Duration: 1 September 2018 - 31 August 2021
CONNEXIONs aims to develop and demonstrate next-generation detection, prediction, prevention, and investigation services. These services will be based on multidimensional integration and correlation of heterogeneous multimodal data, and delivery of pertinent information to various stakeholders in an interactive manner tailored to their needs, through augmented and virtual reality environments. The CONNEXIONs solution encompasses the entire lifecycle of law enforcement operations including:
- Pre-occurrence crime prediction and prevention
- During-occurrence LEA operations
- Post-occurrence investigation, and crime-scene simulation and 3D reconstruction.
CONNEXIONs will meaningfully enhance operational and (near) real-time situational awareness, through automated identification, interpretation, fusion and correlation of multiple heterogeneous big data sources, as well as their delivery via immersive solutions. Such multimodal data include Surface/Deep/Dark Web and social media content, data acquired by Internet of Things (IoT) devices, and digital evidence. CONNEXIONs will also provide chain-of-custody and path-to-court for digital evidence. The Project will adopt ethics and privacy by-design principles and will be customisable to the legislation of each member state. CONNEXIONs will be validated in field tests and demonstrations in 3 operational use cases:
- Counter-terrorism security in large scale public events
- Human trafficking investigations and mitigation
- Crime investigation and training through 3D scene reconstruction.
Extensive training of LEAs' personnel, hands-on experience, joint exercises, and training material will boost the uptake of CONNEXIONs tools and technologies. CONNEXIONs will adopt a user driven approach with the strong involvement of end users in the design and development cycles.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 786731