Among the several emerging dimensioning, control and deployment of future communication network paradigms stands out the human-centric characteristic that creates an intricate relationship between telematics and human activities. The hard to model dynamics of user behavior introduces new uncertainties into these systems that give rise to difficult network resource management challenges. According to this context, this work reviews several decision-making computational methods under the influence of uncertainties. This work, by means of a systematic literature review, focuses on sensor-based Internet of Things scenarios such as Smart Spaces and Industry 4.0. According to our conclusions, it is mandatory to establish a means for modeling the human behavior context in order to improve resource assignment and management.

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.

This paper presents a comprehensive evaluation of the previously introduced multidisciplinary model to quantify human uncertainty (MMtQHU) within a realistic 5G-enabled cyber–physical–social systems (CPSS) environment. The MMtQHU, which integrates human, social, and environmental factors into CPSS modeling, is applied to the Ingolstadt traffic scenario (InTAS), a detailed urban simulation reflecting high-traffic conditions. By modeling unpredictable driver behaviors, such as deviations from optimal routes, the study assesses the model’s effectiveness in managing human-induced uncertainties in vehicle-for-hire (VFH) applications. The evaluation shows that human uncertainty significantly impacts 5G network resource allocation and traffic dynamics. A comparative analysis of traditional resource allocation methods reveals their limitations in handling the dynamic nature of human behavior. These findings underscore the necessity for advanced, adaptive strategies, potentially leveraging artificial intelligence and machine learning to enhance the resilience and efficiency of 5G networks in CPSS environments. The study offers valuable insights for future advancements in robust and adaptive 5G infrastructure by highlighting the critical role of integrating human behavior into CPSS models.

The intensive use of new technologies that cause more interactions between systems and the daily activities of human users is changing the focus on how network re- sources should be managed. However, these changes can create challenges related to the level of uncertainty that people introduce to the system. In this context, this research study seeks to determine whether people’s uncertainty influences network performance and how significant its impact is. For these purposes, a simulated case study of a Vehicle for Hire application designed to run over a network slicing of a fifth-generation (5G) network. The simulations compared call drop rates in several settings configured to represent different levels of uncertainty, introducing random alterations to free channel planning reserved for the handover process. The simulation results reveal that the uncertainty specifically introduced by people exerts a high negative impact on network performance, evidencing the need to develop an algorithm that considers this uncertainty when managing resources within the 5G network core.