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Data caps and service degradation are techniques used to control subscribers' data consumption. These techniques have emerged mainly due to the growing demands placed on the networking stack created by the continuous increase in the number of connected users and their feature-rich, bandwidth-heavy Over-the-Top (OTT) applications. In the mobile network's scope, where traditional operators offer user data plans with limited resources, service degradation is a standard mechanism used to throttle consumption. Limiting user data usage helps to utilize resources better and to ensure the network's reliable performance. Nevertheless, this degradation is applied in a generalized way, affecting all user applications without considering behavior. In this paper, we propose a reference model aiming to address this constraint. Specifically, we attempt to personalize service degradation policies by providing a guideline for users' OTT consumption behavior classification based on Incremental Learning (IL). We evaluated our model's viability in a case study by investigating the efficacy of several IL algorithms on a dataset containing realworld users' OTT application consumption behavior. The algorithms include Naive Bayes (NB), K-Nearest Neighbor (KNN), Adaptive Random Forest (ARF), Leverage Bagging (LB), Oza Bagging (OB), Learn++, and Multilayer Perceptron (MLP). The obtained results show that ARF and a composition between LB and ARF achieve the best performance yielding a classification precision and recall of over 90%. Based on the obtained results, we propose service degradation policies to support decision making in missioncritical systems. We argue the strong applicability of our model in real-world scenarios, especially in user consumption profiling. Our reference model offers a conceptual basis for the tasks that need to be performed when defining personalized service degradation policies in current and future networks like 5G. To the best of our knowledge, this work is the first effort in this matter.

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The development of telemedicine programs for the public health network of the Department of Cauca, Colombia, (Department is the major political and territorial division of the country. The Department of Cauca is located on the Pacific coast in the southwest of the country.) would make it possible to satisfy many identified needs such as medical coordination, continuing education, epidemiologic surveillance, patient referral and counterreferral, and an end to the feeling of isolation among professionals who work in rural health centers. Nevertheless, geographic, economic, and social difficulties, and the lack of a telecommunication infrastructure in areas with these characteristics present a challenge of such magnitude that the majority of existing telemedicine projects in Colombia have been centered in urban or other areas which present fewer difficulties. In the municipality of Silvia, the University of Cauca has established a prototype network using the "Hispano-American Health Link" (EHAS in Spanish) program technologies, which uses very high frequency (VHF) and wireless fidelity, (WiFi, a set of standards for wireless local area networks) radio systems for the deployment of low-cost voice and data networks. Over this network information access and exchange services have been developed, in order to meet the needs identified above. The objectives were to obtain information about the development of the project's activities and their possible impact. Project telecommunication network and information services are described, and the results and conclusions of the first evaluation are presented.

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