Heterogeneous networks offer a wide range of multimedia services, such as entertainment, advertising, and video conferences. In this multimedia scenario, users can access video content via heterogeneous wireless networks, such as LTE macro and small cells. Users also expect to receive real-time videos with Quality of Experience (QoE) support, which is a challenging task due to the great diversity of radio base stations in such heterogeneous environments. In this article, we introduce a Quality of Service (QoS-)/QoE-and Radio-aware (SER) handover management algorithm for heterogeneous networks to provide video dissemination with QoS/QoE support. SER algorithm considers the analytic hierarchy process (AHP) to adjust the degree of importance of each criterion in order to select the appropriate radio base station that the mobile node must connect, allowing efficient handover decision making for video transmission with high user experience. Simulation results show that the SER algorithm delivered videos with significant improvement on QoE than existing handover algorithms.

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Among the main problems faced in the context of electric mobility today, the management and monitoring of electric vehicle charging stations, the integration between the diverse types of technologies that make up its architecture, and its low scalability stand out. Therefore, we will present the implementation and complete integration of an electric vehicle charging system in an electric mobility pilot project being executed in the Amazon region in Brazil. Therefore, a literature review of related works will be presented, and its entire implementation will be addressed, from the charging infrastructure, through its back-end system and its Internet of things platform, to its front-end web system for monitoring charging stations. In addition, a complete prototype is created with a real testbed to verify the scalability of the implemented physical system. Based on the testbed evaluations performed, we observe that the implemented system performs well in receiving and sending data from up to 160 electric vehicle charging stations, achieving an average consumption of 26% for CPU and 95% for memory. In addition, it is important to mention that the deployed system supports horizontal scalability, enabling the connection of more charging stations and making it ideal for other integrated systems similar to ours. Based upon the main results obtained with the implemented system, the possibility of carrying out the management and monitoring of charging stations stands out; the integration of different technologies, from the back end and IoT middleware to its front end; a system that supports scalability, enabling the connection of more charging stations; and a reference architecture for charging station management and monitoring systems for the Amazon region.

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