Networked industrial systems capitalize on recent advancements in sensing, communications, computing and storage to improve productivity, operational and cost efficiency. The proliferation of effective techniques for knowledge extraction drive a paradigm shift in industrial environments and provide a fertile ground for enhanced process monitoring and control capabilities. In an effort to shed light on industrial data management operations, this paper presents two different approaches for dealing with information processing tasks of aggregated sensor measurements. Such tasks constitute part of an end-toend process monitoring solution which is implemented in an open-source platform following a modular, scalable and interpretable procedure. A mapping of the industrial data processing components to the operational principles and architecture of a cyber-physical system reveals useful insights for an automated supervision of critical processes and workflows.

Internet access is a special resource of which needs has become universal across the public whereas the service is operated in the private sector. Mobile Network Operators (MNOs) put efforts for management, planning, and optimization; however, they do not link such activities to socioeconomic fairness. In this paper, we make a first step towards understanding the relation between socioeconomic status of customers and network performance, and investigate potential discrimination in network deployment and management. The scope of our study spans various aspects, including urban geography, network resource deployment, data consumption, and device distribution. A novel methodology that enables a geosocioeconomic perspective to mobile network is developed for the study. The results are based on an actual infrastructure in multiple cities, covering millions of users densely covering the socioeconomic scale. We report a thorough examination of the fairness status, its relationship with various structural factors, and potential class specific solutions.

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