Camille Salinesi¹, Clotilde Rohleder², Asmaa Achtaich^{1, 3} and Indra Kusumah^{1, 2}, ¹CRI -Paris 1 Sorbonne University, France, ²University of Applied Science HTWG Constanz, Germany and ³Siweb – Université Mohammed 5, Maroc

System engineering focuses on the realization of complex systems, from design all the way to management. Meanwhile, in the era of Industry 4.0 and Internet of Things, systems are getting more and more complex. This complexity comes from the usage of smart sub systems (e.g. smart objects, new communication protocols, etc.) and new engineering product development processes (e.g. through Open Innovation). These two aspects namely the IoTrelated sub system and product development process are our main discussion topics in our research work. The creation of smart objects such as innovative fleets of connected devices is a compelling case. Fleets of devices in smart buildings, smart cars or smart consumer products (e.g. cameras, sensors, etc.) are confronted with complex, dynamic, rapidly changing and resource-constrained environments. In order to align with these context fluctuations, we develop a framework representing the dimensions for building Self-adaptive fleets for IoT applications. The emerging product development process Open Innovation is proven to be three time faster and ten times cheaper than conventional ones. However, it is relatively new to the industry, and therefore, many aspects are not clearly known, starting from the specific product requirements definition, design and engineering process (task assignment), until quality assurance, time and cost. Therefore, acceptance of this new approach in the industry is still limited. Research activities are mainly dealing with high and qualitative levels. Whereas methods that supply more transparent numbers remain unlikely. The project-related risks are therefore unclear, consequently, the Go / noGo decisions become difficult. This paper contributes ideas to handle issues mentioned above by proposing a new integrated method, we call it InnoCrowd. This approach, from the perspective of IoT, can be used as a base for the establishment of a related decision support system.

Industry 4.0, Internet of Thing, Crowdsourcing, Neural Network, Decision Support System