The complexity of large-scale, software-intensive systems demands collaborative efforts across engineering disciplines to assure that all system properties are correctly implemented. Effective communication and knowledge sharing among the involved engineering are critical for fostering this collaboration. As a result, engineering artifacts, such as reference architectures, play a key role as boundary objects—shared objects used by different communities to reach a mutual understanding. This study aimed to understand how engineer should utilize reference architectures as boundary objects, and explored the challenges in using them in the co-engineering of system properties. Semi-structured interviews were conducted with 10 experts from two engineering disciplines focusing on system properties of particular importance to contemporary large-scale software-intensive systems development. To analyze the data, an abductive thematic analysis approach was used. Reference architectures can be used as promoters of synergy, change, and learning by the engineers involved in the co-engineering of system properties. However, related challenges with regard to organizational contexts were identified. These findings underscore the need for further investigations into the relationship between the role of reference architectures, power, and organizational theory. • The complexity of large-scale software systems development forces engineers to collaborate more than ever before. • Engineering artifacts such as reference architectures become critical to accommodate collaboration. • Investigates reference architecture as a boundary object to support co-engineering practices in addressing system properties.

Scenario-based approaches have been receiving a huge amount of attention in research and engineering of automated driving systems. Due to the complexity and uncertainty of the driving environment, and the complexity of the driving task itself, the number of possible driving scenarios that an Automated Driving System or Advanced Driving-Assistance System may encounter is virtually infinite. Therefore it is essential to be able to reason about the identification of scenarios and in particular critical ones that may impose unacceptable risk if not considered. Critical scenarios are particularly important to support design, verification and validation efforts, and as a basis for a safety case. In this paper, we present the results of a systematic mapping study in the context of autonomous driving. The main contributions are: (i) introducing a comprehensive taxonomy for critical scenario identification methods; (ii) giving an overview of the state-of-the-art research based on the taxonomy encompassing 86 papers between 2017 and 2020; and (iii) identifying open issues and directions for further research. The provided taxonomy comprises three main perspectives encompassing the problem definition (the why), the solution (the methods to derive scenarios), and the assessment of the established scenarios. In addition, we discuss open research issues considering the perspectives of coverage, practicability, and scenario space explosion.

Edge computing is projected to have profound implications in the coming decades, proposed to provide solutions for applications such as augmented reality, predictive functionalities, and collaborative Cyber-Physical Systems (CPS). For such applications, edge computing addresses the new computational needs, as well as privacy, availability, and real-time constraints, by providing local high-performance computing capabilities to deal with the limitations and constraints of cloud and embedded systems. Edge computing is today driven by strong market forces stemming from IT/cloud, telecom, and networking—with corresponding multiple interpretations of “edge computing” (e.g., device edge, network edge, distributed cloud). Considering the strong drivers for edge computing and the relative novelty of the field, it becomes important to understand the specific requirements and characteristics of edge-based CPS, and to ensure that research is guided adequately, e.g., avoiding specific gaps. Our interests lie in the applications of edge computing as part of CPS, where several properties (or attributes) of trustworthiness, including safety, security, and predictability/availability, are of particular concern, each facing challenges for the introduction of edge-based CPS. We present the results of a systematic mapping study, a kind of systematic literature survey, investigating the use of edge computing for CPS with a special emphasis on trustworthiness. The main contributions of this study are a detailed description of the current research efforts in edge-based CPS and the identification and discussion of trends and research gaps. The results show that the main body of research in edge-based CPS only to a very limited extent consider key attributes of system trustworthiness, despite many efforts referring to critical CPS and applications like intelligent transportation. More research and industrial efforts will be needed on aspects of trustworthiness of future edge-based CPS including their experimental evaluation. Such research needs to consider the multiple interrelated attributes of trustworthiness including safety, security, and predictability, and new methodologies and architectures to address them. It is further important to provide bridges and collaboration between edge computing and CPS disciplines.

This paper proposes a bio-inspired service provisioning scheme for fog-based industrial Internet of Things (IIoT). We use wild-goats algorithm to address the adaptive fog service provisioning problem. Specifically, the master node is configured to monitor the network condition of all fog nodes inside of its domain. Then, the master node will distribute a service to the most suitable fog node based on network latency, computation cost, and storage cost. Therefore, the service can be accomplished in a real-time. The proposed method also considers a probability of node failure during the service provisioning process due to the harsh condition of IIoT environment. The results show that the proposed scheme can achieve low latency compared to the traditional architectures. The results also show that the node failure can escalate the latency of fog-based IIoT network. Thus, the adaptive service provisioning scheme is essential to maintain the network performance of fog-based IIoT.

This work proposes a framework for distributed data analytic for precision agriculture based on seamless computing paradigm named SeamFarm. Generally, heterogeneous nodes deployed for precision agriculture where these nodes generated an extensive amount of data. Then machine learning can be used to analyze this data for precision agriculture. However, most of the IoT devices are resource-constrained devices, which results in poor performance while conducting a machine learning task. Thus, in SeamFarm, we consider distributing the data as well as the task to all available nodes. The results show that SeamFarm can meet all of the functional and non-functional requirements of distributed data analytic for precision agriculture. Moreover, it can obtain faster data analytic results.