# Cooperative Approaches to Data Sharing and Analysis for Industrial Internet of Things Ecosystems
## 2 Related Work and Background
### Internet of Things and Smart Manufacturing  
Unlike \"the\" Internet, IoT is a concept that is more than a well-defined stack of technologies or protocols. It is usually characterized by its core characteristics [17]: a global digital connectivity of real-world assets via the digital (Internet) sphere, a representation of physical objects as identifiable digital objects, and a capturing of state and possibly location data on the physical objects with (smart) sensors. Sometimes, digital objects can also trigger actions in physical objects.  
In the field of IoT, a digital representation of a physical asset consists of three parts: the physical object in the real world, an associated digital object, and the data flow that connects both [18]. Notably, the relevant data differ from classical master or planning data because these include the current states of the real object (and possibly the history of those states), as well as analytical models that capture, describe, and/or predict the dependencies between various measured values and the object environment. The described schema allows the addition of functions to change the state of the digital object, which can, in turn, be transferred to the real object, thereby moving the control of the physical system into the digital realm [5; 6; 19].  
IoT solutions have been used in a wide variety of application domains. In addition to consumer products, IoT has been applied to business applications across various industry sectors, such as energy, health care, and manufacturing [20]. This aspect of IoT is discussed under Industrial IoT (IIoT), with the Industrial Internet Consortium (IIC) as a leading standardization body. For manufacturing, the subject of IIoT is connected to the concept of Industry 4.0, which envisions IIoT-based decentral manufacturing as a fourth industrial revolution [21]. By focusing on aspects of production and logistics, Industry 4.0 standards complement the broader but are less specialized IIC concepts [7].  
IIoT/I.4.0-based manufacturing solutions are the building blocks of smart factories [22]. This, in turn, is a pillar of the more general concept of smart manufacturing [23], which the NIST defines as a \"fully integrated, collaborative manufacturing system that responds in real time to meet changing demands and conditions in the factory, supply network, and customer needs\" [24]. The common theme across these concepts is the vision of agile, decentral-organized manufacturing systems that are not hampered by the established hierarchical systems of the past.  
For many established SMEs in the manufacturing sector, such far-fetched visions are not easily attained, given their existing \"brownfield\" environments that often mix state-of-the-art technologies with decade-old legacy equipment. However, they can very well insert specific IIoT applications into their shop floor environments either by bringing in selected \"smart\" machinery, buildings, transportation materials, or parts or by retrofitting existing objects [25; 26].  
A concept for a data cooperative for SME ecosystems needs to be able to navigate such environments [27]. It is, therefore, advisable to formulate it separately from a narrowly defined set of standards, an isolated technology stack, or even a selected platform or product. Therefore, we decided to design our concept in this regard and pursued a technology-agnostic and generalizable concept.