The development of Cyber-physical Systems (CPS) draws more interest from both researchers and industrial practitioners considering the opportunities they offer in almost all areas of industry. However, the engineering and management of CPS are challenging tasks due to their inherent heterogeneity and complexity characteristics. Regarding the development of CPS, there currently exists no standard methodology owing to the complexity of the domain. One of the key approaches to reduce the development complexity for CPS is Model-driven Engineering (MDE), which is frequently used in many industrial domains for software development to increase the level of platform abstraction. Nevertheless, it is always almost challenging, especially for the new researchers in this field, to determine the appropriate tools and languages to perform a particular MDE activity during CPS development. To the best of our knowledge, there is no guideline that demonstrates which language(s)/tool(s) to use for the various MDE techniques/phases for the development of CPS. This paper presents a Systematic Literature Review (SLR) study that focuses on identifying and classifying the recent research practices pertaining to CPS development by applying MDE approaches. With the objective of providing a general overview of the field, the study evaluates 140 research papers published during 2010–2018. Accordingly, a precise view of the various MDE tools and languages used in the development life-cycle of CPS, addressed MDE techniques/activities, and targeted CPS components is presented. We believe that the conducted study will guide researchers and practitioners to identify appropriate tools and languages according to the system requirements. It may also help in getting an overall understanding of the research trends for further research and development on the MDE of CPS.

Driving vehicles according to eco-driving principles and techniques have significant impact on decreasing both fuel consumption and carbon dioxide (CO2) emissions. In addition to some kind of technical and/or mechanical features brought by today’s new generation vehicles, driver behavior is also one of the greatest factors affecting the fuel consumption. Many studies show that the effect of eco-driving education on the drivers loses its impact in long term and there needs some sort of continuous monitoring and/or feedback mechanisms. This kind of driver monitoring becomes very critical especially in fleets composed of heavy-duty vehicles, such as municipality buses, truck fleets, etc. Moreover, in order to adapt behavior to drive more economically, information about instant fuel consumption has to be provided to the driver. Hence, in this paper, we introduce an eco-driving system in which data gathered from the controller area network (CANBus) of public transport vehicles are processed for both comparative and fair evaluation of bus drivers’ eco-driving performance. Moreover, in-vehicle components of the system guide the drivers during their trips; provide feedbacks and real-time warnings considering the fuel consumption. Developed system was successfully deployed and evaluated in one of the public metrobus systems used by approximately 250000 passengers every day. Based on the 15-months evaluation period, the results are very promising in the sense that both drivers and operators found the system useful and the system provided fuel saving up to approximately 5% even in the short term of monthly comparisons.

Development of software agents according to belief–desire–intention (BDI) model usually becomes challenging due to autonomy, distributedness, and openness of multi‐agent systems (MAS). Hence, here, a domain‐specific modelling language (DSML), called DSML4BDI, is introduced to support development of BDI agents. The syntax of the language provides the design of agent components required for the construction of the system according to the specifications of BDI architecture. The implementation of designed MAS on Jason BDI platform is also possible via model‐to‐text transformations built in the DSML. The comparative evaluation results showed that a significant amount of artefacts required for the exact MAS implementation can be automatically achieved by employing DSML4BDI. Moreover, time needed for developing a BDI agent system from scratch can be reduced to one‐third in the case of using DSML4BDI. Finally, qualitative assessment, based on the developers’ feedback, exposed how DSML4BDI facilitates development of BDI agents.