This thesis investigates the possibility of connecting an embedded device, STM32 B-L4S5I-IOT01A IoT discovery node, to a Eclipse Arrowhead framework local cloud.This thesis also examines the benefits of using the Eclipse Arrowhead framework compared to its competitors Amazon Web Services and Microsoft Azure. The world is entering a new industrial revolution, often referred to as Industry 4.0, moving towards a more decentralized and software-oriented means of production.This fourth industrial revolution incorporates System of Systems, Cyber-Physical Systems, and embedded software technologies. One of the internet-based industrial solutions is the Eclipse Arrowhead framework. The Eclipse Arrowhead framework contains many examples in various promgramming languages and technologies but lacks an example of a specific piece of hardware connecting to a local Eclipse Arrowhead cloud.Therefore, a project with the clear intent to showcase both the capabilities and possibilities of Cyber-Physical systems and the Eclipse Arrowhead framework is needed. The system this thesis implements consists of three major parts: the stm32 board, a Python flask app, and the Eclipse Arrowhead framework.The main objective of the Eclipse Arrowhead framework is to connect the consumer and the provider in a safe and structured way.The provider is built with C/C++ using ARMs' mbed os.  The response time of the different frameworks, Eclipse Arrowhead framework and Amazon Web Services, was measured.We made a thousand attempts to form an adequate basis for an average response time. In addition to presenting the average response time, we calculated the maximum and minimum response times to understand the different frameworks' performance further.  The thesis shows some benefits in response time when running an Eclipse Arrowhead framework local cloud instead of using a remote service such as Amazon Web Services. Average response time decreased by 17.5 times while running an Eclipse Arrowhead framework local cloud.Maximum and minimum response times decreased by 1.9 and 134 times, respectively.