Intelligent Secure Trustable Things

In the Artificial Intelligence of Things (AIoT), Artificial Intelligence (AI) and Internet of Things (IoT) are mutually beneficial. AI increases the value of the IoT through machine learning by transforming the data into useful information, while the IoT increases the value of AI through connectivity and data exchange. Therefore, InSecTT – Intelligent Secure Trustable Things, a pan-European effort with 52 key partners from 12 countries, will provide intelligent, secure and trustworthy systems for industrial applications to provide comprehensive cost-efficient solutions of intelligent, end-to-end secure, trustworthy connectivity and interoperability to bring the Internet of Things and Artificial Intelligence together. InSecTT aims at creating trust in AI-based intelligent systems and solutions as a major part of the AIoT, i.e. moving AI to the edge and making AI and ML based systems trustable, explainable and not just a black box. 
InSecTT will foster cooperation between big industrial players from various domains, a number of highly innovative SMEs distributed all over Europe and cutting-edge research organisations and university. The project features a big variety of industry-driven use cases embedded into various application domains, i.e. smart infrastructure, building, manufacturing, automotive, aeronautics, railway, urban public transport, maritime as well as health. The demonstration of InSecTT solutions in well-known real-world environments like trains, ports, airports and the health sector will generate huge impact on both high and broad level, going from citizens up to European stakeholders. It will establish the EU as a center of intelligent, secure and trustworthy systems for industrial applications enabled by a strong industry with a strong reputation and an informed society, in order to enable products and services based on AI compliant to European values and “Made in Europe".  

The EU-funded InSecTT project will provide comprehensive cost-efficient solutions with intelligent, end-to-end secure, trustworthy connectivity and interoperability. Applying the project’s solutions to real-world settings will help to establish the EU as a hub of intelligent, secure and trustworthy systems for industrial applications. 

In recent years, technological development in consumer electronics and industrial applications has developed rapidly. More and smaller, networked devices are able to collect and process data anywhere. The Internet of Things (IoT) is a revolutionary change for many sectors like healthcare, building, automotive, railway, etc. 

Going to the edge - Bringing Internet of Things and Artificial Intelligence together. By moving AI to the edge, i.e. processing data locally on a hardware device, real-time applications for self-driving cars, robots and many other areas in industry can be enabled. The push of AI towards the edge can also be seen by recent announcements in consumer electronics. Google has reduced the size of the cloud-based AI voice recognition model from 2 GB to only 80 MB, so that it can also be used on embedded devices and does not need Internet connection. The technological race to bringing AI to the edge can also be seen by very recent developments of hardware manufacturers. In October 2018, Google released Edge TPU, a custom processor to run the specific TensorFlow Lite models on edge devices. Many other, mostly US companies like Gyrfalcon, Mythic and Syntiant are also developing custom silicon for the edge. 

AI + IoT = AIoT

The InSecTT partners believe that Artificial Intelligence of Things (AIoT) is the natural evolution for both AI and IoT because they are mutually beneficial. AI increases the value of the IoT through machine learning by transforming the data into useful information, while the IoT increases the value of AI through connectivity and data exchange. The overall objectives of InSecTT are to develop solutions for Intelligent, Secure, Trusted Things applied in industrial solutions for European industry. 

More precisely:

- Providing intelligent processing of data applications and communication characteristics locally at the edge to enable real-time and safety-critical industrial applications;
- Developing industrial-grade secure and reliable solutions that can cope with cyberattacks and difficult network conditions;
- Providing measures for trust for user acceptance, make AI/ML explainable and not just a black box that cannot be understood;
- Developing / Testing distributed AI applications for safety critical systems and conditions;
- Developing solutions for Internet of Things, i.e. mostly wireless devices with energy- and processing-constraints, in heterogeneous and also hostile environments;
- Providing re-usable solutions across industrial domains;
- Methodological approach with the Integral Supply Chain, from academic, to system designers and integrators, to component providers, applications and services developers & providers and end users. 

CINI Units involved:

- Università degli Studi di Modena e Reggio Emilia;
- Università degli Studi di Parma;
- Università Roma Tre;
- Università degli Studi di Firenze;
- Sapienza Università di Roma;
- Università della Calabria. 

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Publications

1	Boschini, Matteo; Bonicelli, Lorenzo; Buzzega, Pietro; Porrello, Angelo; Calderara, Simone "Class-Incremental Continual Learning into the eXtended DER-verse" IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, vol. 45, pp. 5497 -5512 , 2023 | DOI: 10.1109/TPAMI.2022.3206549 Journal
2	Panariello, A.; Porrello, A.; Calderara, S.; Cucchiara, R. "Consistency-Based Self-supervised Learning for Temporal Anomaly Localization" Proceedings of the European Conference on Computer Vision Workshops, 2022, vol. 13805, Tel Aviv, Israel, pp. 338 -349 , 23/10/2022, 2023 | DOI: 10.1007/978-3-031-25072-9_22 Conference
3	Fabbri, Matteo; Braso, Guillem; Maugeri, Gianluca; Cetintas, Orcun; Gasparini, Riccardo; Osep, Aljosa; Calderara, Simone; Leal-Taixe, Laura; Cucchiara, Rita "MOTSynth: How Can Synthetic Data Help Pedestrian Detection and Tracking?" Proceedings of the IEEE/CVF International Conference on Computer Vision, Virtual, pp. 10829 -10839 , Ottobre 11-17 2021, 2021 | DOI: 10.1109/ICCV48922.2021.01067 Conference
4	Buzzega, Pietro; Boschini, Matteo; Porrello, Angelo; Abati, Davide; Calderara, Simone "Dark Experience for General Continual Learning: a Strong, Simple Baseline" Advances in Neural Information Processing Systems 33 (NIPS 2020), vol. 2020-, Vancouver, Canada, 6-12 December 2020, 2020 Conference
5	Buzzega, Pietro; Boschini, Matteo; Porrello, Angelo; Calderara, Simone "Rethinking Experience Replay: a Bag of Tricks for Continual Learning" Proceedings of the 25th International Conference on Pattern Recognition, Milan, Italy, pp. 2180 -2187 , 10-15 January 2021, 2020 | DOI: 10.1109/ICPR48806.2021.9412614 Conference