Modern cars consist of a number of complex embedded and networked systems with steadily increasing requirements in terms of processing and communication resources. Novel automotive applications, such as automated driving, rise new needs and novel design challenges that cover a broad range of hardware/software engineering aspects. In this context, this paper provides an overview of the current technological challenges in on-board and networked automotive systems. This paper encompasses both the state-of-the-art design strategies and the upcoming hardware/software solutions for the next generation of automotive systems, with a special focus on embedded and networked technologies. In particular, this paper surveys current solutions and future trends on models and languages for automotive software development, on-board computational platforms, in-car network architectures and communication protocols, and novel design strategies for cybersecurity and functional safety.

This paper investigates the interplay of cloud computing, fog computing, and Internet of Things (IoT) in control applications targeting the automation industry. In this context, a prototype is developed to explore the use of IoT devices that communicate with a cloud-based controller, i.e., the controller is offloaded to cloud or fog. Several experiments are performed to investigate the consequences of having a cloud server between the end device and the controller. The experiments are performed while considering arbitrary jitter and delays, i.e., they can be smaller than, equal to, or greater than the sampling period. This paper also applies mitigation mechanisms to deal with the delays and jitter that are caused by the networks when the controller is offloaded to the fog or cloud.

In this paper we discuss the implementation of the state-of-theart end-to-end response-time and delay analysis as two individual plug-ins for the existing industrial tool suite Rubus-ICE. The tool suite is used for the development of software for vehicular embedded systems by several international companies. We describe and solve the problems encountered and highlight the experiences gained during the process of implementation, integration and evaluation of the analysis plug-ins. Finally, we provide a proof of concept by modeling the automotive-application case study with the existing industrial model (the Rubus Component Model), and analyzing it with the implemented analysis plug-ins.

Cloud computing and Internet of Things (IoT) are computing technologies that provide services to consumers and businesses, allowing organizations to become more agile and flexible. Therefore, ensuring quality of service (QoS) through service-level agreements (SLAs) for such cloud-based services is crucial for both the service providers and service consumers. As SLAs are critical for cloud deployments and wider adoption of cloud services, the management of SLAs in cloud and IoT has thus become an important and essential aspect. This paper investigates the existing research on the management of SLAs in IoT applications that are based on cloud services. For this purpose, a systematic mapping study (a well-defined method) is conducted to identify the published research results that are relevant to SLAs. This paper identifies 328 primary studies and categorizes them into seven main technical classifications: SLA management, SLA definition, SLA modeling, SLA negotiation, SLA monitoring, SLA violation and trustworthiness, and SLA evolution. This paper also summarizes the research types, research contributions, and demographic information in these studies. The evaluation of the results shows that most of the approaches for managing SLAs are applied in academic or controlled experiments with limited industrial settings rather than in real industrial environments. Many studies focus on proposal models and methods to manage SLAs, and there is a lack of focus on the evolution perspective and a lack of adequate tool support to facilitate practitioners in their SLA management activities. Moreover, the scarce number of studies focusing on concrete metrics for qualitative or quantitative assessment of QoS in SLAs urges the need for in-depth research on metrics definition and measurements for SLAs.