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Cyber-Physical Systems (CPS) integrate computation, networking and physical processes to produce products that are autonomous, intelligent, connected and collaborative. Resulting Cyber-Physical Systems of Systems (CPSoS) have unprecedented capabilities but also unprecedented corresponding technological complexity. This paper aims to improve understanding, awareness and methods to deal with the increasing complexity by calling for the establishment of new foundations, knowledge and methodologies. We describe causes and effects of complexity, both in general and specific to CPS, consider the evolution of complexity, and identify limitations of current methodologies and organizations for dealing with future CPS. The lack of a systematic treatment of uncertain complex environments and “composability”, i.e., to integrate components of a CPS without negative side effects, represent overarching limitations of existing methodologies. Dealing with future CPSoS requires: (i) increased awareness of complexity, its impact and best practices for how to deal with it, (ii) research to establish new knowledge, methods and tools for CPS engineering, and (iii) research into organizational approaches and processes to adopt new methodologies and permit efficient collaboration within and across large teams of humans supported by increasingly automated computer aided engineering systems.

Autonomous agents rely on sensor data to construct representations of their environments, essential for predicting future events and planning their actions. However, sensor measurements suffer from limited range, occlusions, and sensor noise. These challenges become more evident in highly dynamic environments. This work proposes a probabilistic framework to jointly infer which parts of an environment are statically and which parts are dynamically occupied. We formulate the problem as a Bayesian network and introduce minimal assumptions that significantly reduce the complexity of the problem. Based on those, we derive Transitional Grid Maps (TGMs), an efficient analytical solution. Using real data, we demonstrate how this approach produces better maps than the state-of-the-art by keeping track of both static and dynamic elements and, as a side effect, can help improve existing SLAM algorithms.

With the increasing spread and adoption of electronics and software as integral parts of all kinds of physical devices, such devices are becoming controlled by their embedded software. Correspondingly, the manufacturing business has started the transition from selling hardware to selling features (e.g. “insane mode” and “ludicrous mode” in Tesla Model S). Consequently, a trustworthy system to automate such a process becomes essential. This article introduces a permissioned blockchain-based feature management system for assembly devices. Firstly, it leverages software licensing technology to control assembly devices’ features. Secondly, by recording the license ownership transaction data in a permissioned blockchain, the approach (1) takes advantage of blockchain’s trust mechanism and its distributed nature to improve the trustworthiness of the feature management system, and (2) adopts the permissioned blockchain technology to ensure that the license transactions are only visible and applicable to authenticated actors. We further describe an implementation, a proof-of-concept evaluation focusing on functionality and performance, as well as a security analysis.

This paper proposes a service-oriented tool-chain with an emphasis on domain-specific views, simulation automation, and process management to support model-based system engineering of aero-engines. In the tool-chain, a domain-specific modeling approach is adopted to facilitate the descriptions of co-design workflows and the related development information. The relevant domain-specific models are the basis for automated creation of a Web-based process management system consolidating and controlling service-oriented technical resources (models, data, and tools). In particular, the system also provides support for automated orchestration of tool operations, model, and simulation configurations. In order to promote the model and tool interoperability, the tool-chain adopts open standards for integrations, including open services for lifecycle collaboration and functional mock-up interface. Finally, through a case study of simulation-based aero-engine performance analysis, we evaluate the flexibility and efficiency of this tool chain by comparing it with a traditional simulation process both qualitatively and quantitatively.