The Internet of Things (IoT) has been widely discussed and investigated, not only by academics but also by various segments of industry and commerce. Once established and fully operational, this technology is set to offer considerable benefits. Sensors will send large amounts of data for analysis and interpretation in real-time or near real-time. These data streams will flow at high speed, in large volumes, varying in type, and value. However, it is likely that sensors will face numerous obstacles what can pollute their data with significant amount of noise. This paper investigates sensor data streams and sensor signals in order to identify their specific characteristics and issues, particularly the problem of noise. This paper also investigates discrete wavelet transform (DWT) to reduce noise. Results here show significant improvements in the sensor signal when using DWT. The principal conclusion of our analysis is that, by knowing the main issues of IoT data, it is possible to track problems and reduce data errors. Another practical conclusion is that DWT is a powerful tool for reducing signal noise.

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.