System Design in the Era of IoT — Meeting the Autonomy Challenge

Sifakis, Joseph

doi:10.4204/eptcs.272.1

Cited by 16 publications

(16 citation statements)

References 23 publications

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“…In contrast to the previous examples, the environment involves a dynamically and unpredictably changing number of agents and objects in particular due to agent mobility. While for chess or soccer agents the gaming rules are static and well-understood, traffic rules are dynamic and hard to formalize [2]. Rigorous definition of a coherent set of individual goals for an ensemble of robocars is a non-trivial problem.…”

Section: Agent Goals and Plan Generationmentioning

confidence: 99%

“…Sources of uncertainty are multiple, including time-varying load, dynamic change due to mobility, bursty events, and most critical events such as failures and attacks. The Self-adaptation module generates objectives to cope with such situations involving imperfect knowledge and lack of predictability [2,5]. This can be achieved to some extent, using knowledge, e.g.…”

Section: Autonomic Complexitymentioning

confidence: 99%

“…Providing guarantees for complex autonomous systems faces several limitations [2]. One is the decomposition and formalization of high-level goals in terms of concrete requirements verifiable on the system behavioral model.…”

Section: Trustworthy Autonomous Systems -From Correctness At Design Tmentioning

confidence: 99%

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Autonomous Systems – An Architectural Characterization

Sifakis

2019

Lecture Notes in Computer Science

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The concept of autonomy is key to the IoT vision promising increasing integration of smart services and systems minimizing human intervention. This vision challenges our capability to build complex open trustworthy autonomous systems. We lack a rigorous common semantic framework for autonomous systems. It is remarkable that the debate about autonomous vehicles focuses almost exclusively on AI and learning techniques while it ignores many other equally important autonomous system design issues.Autonomous systems involve agents and objects coordinated in some common environment so that their collective behavior meets a set of global goals. We propose a general computational model combining a system architecture model and an agent model. The architecture model allows expression of dynamic reconfigurable multi-mode coordination between components. The agent model consists of five interacting modules implementing each one a characteristic function: Perception, Reflection, Goal management, Planning and Self-adaptation. It determines a concept of autonomic complexity accounting for the specific difficulty to build autonomous systems.We emphasize that the main characteristic of autonomous systems is their ability to handle knowledge and adaptively respond to environment changes. We advocate that autonomy should be associated with functionality and not with specific techniques. Machine learning is essential for autonomy although it can meet only a small portion of the needs implied by autonomous system design. We conclude that autonomy is a kind of broad intelligence. Building trustworthy and optimal autonomous systems goes far beyond the AI challenge.

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Section: Agent Goals and Plan Generationmentioning

confidence: 99%

Section: Autonomic Complexitymentioning

confidence: 99%

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Autonomous Systems – An Architectural Characterization

Sifakis

2019

Lecture Notes in Computer Science

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“…Engineering security is a multidisciplinary endeavour [14]. Designing ICT architectures, and IoT systems, in particular, is typically a rigorous and disciplined system engineering task [15]. However, it is also acknowledged in [15] that there is an increasing disagreement between theory and practice in system design.…”

Section: A Design Conceptmentioning

confidence: 99%

“…Designing ICT architectures, and IoT systems, in particular, is typically a rigorous and disciplined system engineering task [15]. However, it is also acknowledged in [15] that there is an increasing disagreement between theory and practice in system design. This gap is mainly accounted to the huge push for fast system integration, e. g. , by market needs.…”

Section: A Design Conceptmentioning

confidence: 99%

Designing a Secure IoT System Architecture from a Virtual Premise for a Collaborative AI Lab

Mehri¹,

Ilie²,

Tutschku³

2019

Proceedings 2019 Workshop on Decentralized IoT Systems and Security

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IoT systems are increasingly composed out of flexible, programmable, virtualised, and arbitrarily chained IoT elements and services using portable code. Moreover, they might be sliced, i.e. allowing multiple logical IoT systems (network + application) to run on top of a shared physical network and compute infrastructure. However, implementing and designing particularly security mechanisms for such IoT systems is challenging since a) promising technologies are still maturing, and b) the relationships among the many requirements, technologies and components are difficult to model a-priori. The aim of the paper is to define design cues for the security architecture and mechanisms of future, virtualised, arbitrarily chained, and eventually sliced IoT systems. Hereby, our focus is laid on the authorisation and authentication of user and host, as well as on code integrity in these virtualised systems. The design cues are derived from the design and implementation of a secure virtual environment for distributed and collaborative AI system engineering using so called AI pipelines. The pipelines apply chained virtual elements and services and facilitate the slicing of the system. The virtual environment is denoted for short as the virtual premise (VP). The use-case of the VP for AI design provides insight into the complex interactions in the architecture, leading us to believe that the VP concept can be generalised to the IoT systems mentioned above. In addition, the use-case permits to derive, implement, and test solutions. This paper describes the flexible architecture of the VP and the design and implementation of access and execution control in virtual and containerised environments.

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Reflections on Bernhard Steffen’s Physics of Software Tools

Garavel

Mateescu

2019

Lecture Notes in Computer Science

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Many software tools have been developed to implement the concepts of formal methods, sometimes with great success, but also with an impressive tool mortality and an apparent dispersion of efforts. There has been little analysis so far of such tool development as a whole, in order to make it more coherent, efficient, and useful to the society. Recently, however, Bernhard Steffen published a paper entitled "The Physics of Software Tools: SWOT Analysis and Vision" that precisely proposes such a global vision. We highlight the key ideas of this paper and review them in light of our own experience in designing and implementing the CADP toolbox for the specification and analysis of concurrent systems.

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System Design in the Era of IoT — Meeting the Autonomy Challenge

Cited by 16 publications

References 23 publications

Autonomous Systems – An Architectural Characterization

Autonomous Systems – An Architectural Characterization

Designing a Secure IoT System Architecture from a Virtual Premise for a Collaborative AI Lab

Reflections on Bernhard Steffen’s Physics of Software Tools

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