A Multidisciplinary Model to Quantify Human Uncertainty in Human-Centric Cyber-Physical-Social Systems: A 5G Application Use Case

The emerging Human-Centric Networks (HCN) paradigm shifts the passive role of individuals to an active one, intertwining the uncertainty of network resource usage with human dynamics, which are difficult to analyze and predict. This phenomenon implies an increase in reciprocal interactions between Cyber-Physical-Social Systems (CPSS) and human activities, presenting the challenge of efficiently allocating network resources while taking into account qualitative human uncertainty. In this study, we propose a conceptual model that addresses and quantifies such uncertainties. The proposed model is characterized by its adaptability to various CPSS applications, facilitating its integration into existing applications and future innovations. The adaptability of the model is based on the application of the sociological concept of Boundary Objects (BO), which allows for the structuring of system components and the generation of a reference architecture that facilitates systematic problem solving. To evaluate the model, we propose a use case related to a Vehicle for Hire (VFH) application operating within a 5G network slice. The integration of the proposed model with the OMNET++ simulation framework has allowed to demonstrate the effectiveness of the model in intricate computational environments and have shown its capacity to incorporate previously overlooked elements that are essential for the optimal allocation of resources in CPSS. This study proposes a methodology for comprehending and mitigating the consequences of human uncertainty, emphasizing the significance of a multidisciplinary approach to resource allocation in sophisticated technological systems.