Techniques originating from the Internet of Things (IoT) and Cyber-Physical Systems (CPS) areas have extensively been applied to develop intelligent and pervasive systems such as assistive monitoring, feedback in telerehabilitation, energy management, and negotiation. Those application domains particularly include three major characteristics: intelligence, autonomy and real-time behavior. Multi-Agent Systems (MAS) are one of the major technological paradigms that are used to implement such systems. However, they mainly address the first two characteristics, but miss to comply with strict timing constraints. The timing compliance is crucial for safety-critical applications operating in domains such as healthcare and automotive. The main reasons for this lack of real-time satisfiability in MAS originate from current theories, standards, and technological implementations. In particular, internal agent schedulers, communication middlewares, and negotiation protocols have been identified as co-factors inhibiting the real-time compliance. This paper provides an analysis of such MAS components and pave the road for achieving the MAS compliance with strict timing constraints, thus fostering reliability and predictability.

UML has been largely adopted as a standard modeling language. The emergence of UML from different modeling languages that refer to various system aspects causes a wide variety of completeness and correctness problems in UML models. Several methods have been proposed for dealing with correctness issues, mainly providing internal consistency rules but ignoring correctness and completeness with respect to the system requirements and the domain constraints. In this article, we propose addressing both completeness and correctness problems of UML models by adopting a domain analysis approach called application-based domain modeling (ADOM). We present experimental results from our study which checks the quality of application models when utilizing ADOM on UML. The results advocate that the availability of the domain model helps achieve more complete models without reducing the comprehension of these models.