Keywords

1 Introduction

The Internet of Things (IoT) is a revolutionary change for many sectors: Fitness trackers measure our movements, smart fire extinguishers monitor their own readiness for action, and cars turned out to become fully connected vehicles. The availability of the collected data goes hand in hand with the development of Artificial Intelligence (AI) and Machine Learning (ML) algorithms to process them. Despite numerous benefits, the vulnerability of these devices in terms of security remains an issue. Hacks of webcams, printers, children’s toys, and even vacuum cleaners as well as Distributed Denial-of-service (DDoS) attacks reduce confidence in this technology. Users are also challenged to understand and trust their increasingly complex and smart devices, sometimes resulting in mistrust, usage hesitation and even rejection.

These developments mostly cover processing of data in centralized Cloud locations and hence cannot be used for applications where milliseconds matter or for safety–critical applications. 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 an Internet connection [1]. The technological race to bring AI to the Edge can also be seen by very recent developments of hardware manufacturers. For example, Google released Edge TPU [2], a custom processor to run the specific TensorFlow Lite models on Edge devices. Many other, Asian companies like EdgeCortix, or US companies like Quadric are also developing custom silicon for the Edge.

This is where InSecTTFootnote 1 weighs in. The pan-European project InSecTT (Intelligent Secure Trustable Things) provides intelligent, secure, and trustworthy systems for industrial applications as well as comprehensive, cost-efficient solutions of intelligent, end-to-end secure, trustworthy connectivity and interoperability. The project with more than 50 partners, coordinated by VIRTUAL VEHICLE, aims at creating trust in AI-based intelligent systems and solutions as a major part of the Artificial Intelligence of Things (AIoT).

The InSecTT partners believe that AIoT is the natural evolution for both AI and IoT because they are mutually beneficial [3]. AI increases the value of the IoT through Machine Learning by transforming the data into useful information knowledge, while the IoT increases the value of AI through connectivity and data exchange:

$$\text{AI}+ {\rm IoT} = \text{AIoT}. $$

2 Objectives

The overall objectives of InSecTT are to develop solutions for (1) Intelligent, (2) Secure, (3) Trustable (4) Things applied in (5) industrial solutions for European industry throughout the whole Supply Chain (6). More precisely:

  1. 1.

    Providing intelligent processing of data applications and communication characteristics locally at the Edge to enable real-time and safety–critical industrial applications.

  2. 2.

    Developing industrial-grade secure, safe and reliable solutions that can cope with cyberattacks and difficult network conditions.

  3. 3.

    Providing measures to increase trust for user acceptance, make AI/ML explainable and give the user control over AI functionality.

  4. 4.

    Developing solutions for the Internet of Things, i.e., mostly wireless devices with energy- and processing-constraints, in heterogeneous and also hostile/harsh environments.

  5. 5.

    Providing re-usable solutions across industrial domains.

  6. 6.

    Creating a methodological approach with the Integral Supply Chain, from academic, to system designers and integrators, to component providers, applications and services developers and providers and end users.

3 Trustworthiness

The issues of ethics and public trust in deployed AI systems are now receiving significant international interest. In InSecTT, we focus on robustness and ethics, ensuring our developed systems are resilient, secure, and reliable, while prioritizing the principles of explainability and privacy. In InSecTT and its predecessor projects (DEWI, SCOTT) we have investigated this problem. As part of this book, in Chap. “The Development of Ethical and Trustworthy AI Systems Requires Appropriate Human-Systems Integration: A White Paper” we summarize the lessons learned from several years of working with industrial and research partners on developing trustworthy technologies. As result, we propose an approach for research and development of trustworthy AI systems that is based on current EU guidelines of developing ethical AI as well as the proposed EU AI act. That approach puts the human concerns and needs at the center of the development process and consists sets of concrete recommendations for how to develop trustworthy intelligent systems.

4 Building on a Sound Basis

The InSecTT project is built on the basis of the predecessor projects DEWIFootnote 2 and SCOTT.Footnote 3 They, among others, reuse and extend the well-established DEWI Bubble concept and the related, ISO 29182-compliant multi-domain High- Level Architecture [4]. Within the DEWI project key solutions for wireless seamless connectivity and interoperability in smart cities and infrastructures were developed. DEWI was started in March 2014 as part of the ARTEMIS Joint Undertaking and ended in April 2017. The DEWI Bubble concept, the defined DEWI High-Level Architecture, as well as the DEWI technology items have been used as starting point for systems development within SCOTT and can be seen as the continuation of DEWI technology solutions.

Complementary to DEWI, the SCOTT project put additional focus on the following aspects:

  • Extending and connecting Bubbles and integrating distributed Bubbles into the Cloud.

  • Extending the High-Level Architecture concerning security, trustability and Cloud integration.

  • The development of safe and secure solutions for wireless distributed systems: implementing a layer where multiple Bubbles need to cooperate in deterministic (real-time) and secure way to establish systems in distributed locations.

  • Elaboration of new approaches for secure distributed Cloud integration—extending DEWI High-Level Architecture.

  • Developing secure and trustable applications coming from new domains such as Health and Home (besides commercial/public buildings).

InSecTT now goes a significant step further and brings Internet of Things and Artificial Intelligence together. InSecTT builds on the results of DEWI and SCOTT with the goal to:

  • Bring Internet of Things and Artificial Intelligence together (“Artificial Intelligence of Things”, AIoT)

  • Move AI to the edge, i.e. provide intelligent processing of data applications and communication characteristics locally at the edge to enable real-time and safety–critical industrial applications

  • Develop industrial-grade secure and reliable solutions that can cope with cyberattacks and difficult network conditions

  • Enable AI-enhanced wireless transmission

  • Provide measures for trust for user acceptance, make AI/ML explainable and not just a black box that cannot be understood

  • Provide re-usable solutions across industrial domains

5 Driven Through Industrial Applications

InSecTT utilizes a clearly use-case driven approach with use cases from different areas of high relevance to European society and industry; all these use cases are designed for a cross-domain use. InSecTT provides, implemented in 16 different AIoT use cases, cross-domain solutions for 9 industrial domains (see Fig. 1):

Fig. 1
A radial diagram of Intelligent Secure Trustable Things. The domains are smart infrastructure, aeronautics, health, manufacturing, building, maritime, railway, automotive, and public transport.

Overview about the InSecTT industrial domains

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  • Health

  • Smart Infrastructure

  • Urban Public Transport

  • Aeronautics

  • Automotive

  • Railway

  • Manufacturing

  • Maritime

  • and Building.

The cross-domain aspect is not only realized by bringing in components to different domains, but also by interconnecting the domains in a truly cross-domain communication. This can be seen e.g., in use cases on airports or harbours, where information from buildings, vehicles and infrastructure needs to be exchanged with each other. A selection of InSecTT use cases is detailly described in Part 3 “Industrial Applications” of this book.

6 Building Technology for Intelligent, Secure, Trustworthy Things

Based on the unique user-driven approach, the InSecTT project puts focus on:

  • A representative set of Use Cases (UC) in the different domains and Technical Building Blocks (BB) jointly enabling the demonstration of business objectives in all industrial application domains.

  • BBs derived from UCs (as methodologies, SW or HW components to build a SW-tool, a system, or a product, as services; as profiles; as tool or tool chains; as interfaces as well as processes). The BBs are the elements in the project, where most technical work is foreseen.

  • Demonstrators: Every UC driven task and every BB must contribute to a demonstrator. This approach ensures to reach the targeted Technology Readiness Level (TRL) of InSecTT (TRL 7–8).

Figure 2 gives an example of how the use cases and building blocks are related. Each use case consists of a composition of selected BBs. In addition, each UC may have an additional UC-specific adaptation block, i.e., the necessary adjustments to form the UC. To achieve a high degree on interoperability, the definition and the requirements of the BBs are also derived from the targeted UCs.

Fig. 2
A block diagram. In Sec T T building blocks B B 2.1 to 3.5 lead to U C 5.1 with B B 3.3, 2.6, 2.3 and 2.4. U C 5.2 with B B 2.5, 3.1, 2.3, and 2.2, U C 5.3 with B B 3.4, 3.1 and 2.1, and U C 5 dot x with B B 2.2, 2.3, 3.2 and 3.5 that have U C-specific adaptations.

Relation of Building Blocks (BB) and Use Cases (UC) (Exemplary Illustration)

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More information on this topic is presented in Chap. “Structuring the Technology Landscape for Successful Innovation in AIoT”.

7 Reference Architecture for Trustworthy AIoT

The InSecTT Reference Architecture (RA) is the set of guidelines for infrastructure organization of IoT use cases targeting industrial-grade connectivity, security, dependability, interoperability and trustworthiness with the help of AI. It provides the high-level view of building blocks, interfaces, vulnerabilities, security solutions, protocols, and in general the detailed information/control flow of InSecTT use cases in different industrial domains (aeronautics, automotive, railway, building, healthcare, maritime, etc.). This provides us with a tool to analyse reusability, standardization, certification, and verification issues across domains. The InSecTT RA hosts a set of best practices collected across three EU projects: DEWI, SCOTT and InSecTT. The DEWI RA focused on dependability, using IoT protocols as a method to provide interoperability using the concept of DEWI Bubble as the encapsulation of legacy infrastructure. The DEWI RA was built on top of the ISO SNRA (Sensor Network Reference Architecture) [4]. The SCOTT project saw the extension towards a full IoT architecture with high level aspects such as Edge/Fog processing, security, privacy, safety and trustworthiness combining multiple standard architectures. The InSecTT RA re-takes the DEWI/SCOTT frameworks and the Bubble to investigate the impact of AI on IoT architectures, particularly on the standard views or perspectives of the system. The result is an extended 3D functionality model that captures the ability of AIoT systems to host functionalities related to AI such as learning, adaptation, feature extraction, detection, etc. The 3D extension also contains the type of application and the sub-building structure studied in the project. The RA also defines the relations of this new AI perspective with the rest of the views, particularly the physical entity model and the domain model, where we identified the need for an overall approach to organize, manage and schedule distributed leaning resources in a secure and trustworthy manner. The mapping of all sub-building blocks revealed important aspects such as the stress in some interfaces that need to consider the future growth and the demand of learning layers for lower layer information.

More information on this topic is presented in Chap. “The InSecTT Reference Architecture”.

8 Summary

In this article, the importance of bringing together Artificial Intelligence and the Internet of Things was highlighted. This so-called Artificial Intelligence of Things is their natural evaluation, enabling key developments based on constant interplay and integration between AI and IoT. The European project InSecTT was described as a key enabler for the AIoT. After a motivation and analysis of the initial situation, the overall objectives and goals of the project were discussed in detail. It develops intelligent, secure and trustworthy systems for industrial applications to provide comprehensive cost-efficient solutions of intelligent, end-to-end secure, trustworthy connectivity and interoperability for the AIoT. The Reference Architecture allows to deliver a more secure AIoT solution with reduced design effort, decreased costs, and increased quality. In the following chapters, details about the results generated through both technology development as well as industrial applications are shown. In addition, as part of the section “Introduction”, deep insight into the topics of development of trustworthy AI systems, the InSecTT reference architecture and how to structure the technology landscape for successful innovation in AIoT is provided.