## 6 Conclusions and Future Challenges  
The vision outlined by Industry 4.0 for the future of production systems is driven by the aspiration to achieve a more personalized production process. This necessitates flexible and modular systems, with the fundamental components being Cyber-Physical Systems (CPSs). The concept of these systems, originating in the field of embedded systems, has been tailored to fit the framework of Industry 4.0. From an Industry 4.0 overview, CPSs can be seen as the convergence between the physical and virtual worlds. Therefore, CPSs can be characterized as systems reliant on embedded software with the capacity to access physical data and influence physical processes through sensors and actuators. Data acquired by the CPS devices, installed in the vehicles used in the case study, were processed and stored independently of their source (device type, used protocol, data format, etc.). This case study was used as a proof of concept of the presented framework for the integration of CPS devices and logistics applications. However, although this framework has been mainly tested in mobility-related applications, its scope can be extended beyond mobility- and logistics-related applications. To analyze the proposed Framework for CPS devices, applied to logistics applications, the following two metrics were analyzed. * Adaptation to several use cases: the platform that was implemented around the proposed framework has as an objective the tracking of vehicles (including logistics vehicles); however, the architecture allows other applications to be added (as Data Sinks) to add extra features. For example, several types of product scanners can be added to the architecture, each one connected to a specific ecosystem, which sends information to the Mediator. This information is then sent to the Data Processing applications and to the Data Storage to track products. * Easy integration of data sources and applications: the architecture was developed having in mind the easy integration of any data source to any data sink. Besides the use case presented in this paper, this architecture is being used as an integration component in applications related to mobility and sustainability. Figure 5: Detail of a track recorded by the platform. If we look from the perspective of performance in terms of time, the maximum delay that is expected in the data transmission is around 15 s, for those nodes using NBIoT, which is a delay that is acceptable for asset tracking in logistics applications. This time is not dependent on the architecture, but on the network technology. A preliminary measurement to the platform delay was made, using an ecosystem flooding the Mediator with GPS coordinates messages and a Data Consumer to receive these data. This test had as a result a delay in the order of 200 microseconds in a computer with an 11 th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00 GHz PC, with 16 GBytes of RAM, running Ubuntu Linux 22.04 and Java 11. The major achievements of this work were the development of a framework, and a reference implementation, that allows for both speeding up the process of building logistics-related platforms or speeding up the process of integrating new features into an existing platform. This framework also allows for the integration of new data sources into already existing applications, without the need to refactor these applications. Also, the overhead added by the framework is minimal and does not impact the real-time needs of the logistics platforms. Conceptualization, C.S., P.M. and F.B. ; methodology, C.S. and P.M.; investigation: C.S., P.M., J.C. and M.G. ; writing--original draft, C.S., P.M. and F.B. ; writing--review and editing, C.S., P.M., J.C. and F.B. ; supervision, C.S. and J.C.; funding acquisition, C.S. All authors have read and agreed to the published version of the manuscript.