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T he evolution of mobile computing improves communication and collaboration among people. The main pillars of this transformation are: the development and production of mobile devices with multimedia capabilities and equipped with two or more network interfaces, the availability of ubiquitous wireless connectivity and the popularity of online social applications. Online social networks noteworthy features that allow for the creation and sharing of digital content within social circles, also called Social Media. Web Services attach the geographic location of the device to the digital content, creating the so-called location-based social media. Equipped with their phones and tablets, people are creating and consuming social media anywhere. However, it is a challenge to keep such mobile devices connected in current and next generation wireless network environments e.g., multiple ISPs (Internet Service Provider) and multiple communication technologies. Recent researches proposes components for managing wireless connectivity that make simultaneous use of the current and past connectivity context data. Such components are mobility predictors, handovers mechanisms or mobility managers that use connectivity context data in a particular way to achieve its purposes. In this research, we orchestrated a set of methods for connectivity management in a feedback loop. We argue that the process of gathering connectivity context data can be designed as a sensing system, whose sensors are wireless network interfaces. As part of this sensing system, the social circles may assist the management of connectivity by sharing connectivity context data. The main idea is to use locationbased services to share connectivity context data within social circles. Thus, online social networks add scale to the system and enables collaboration around recent, local, and social context data. The goal is to enhance wireless connectivity experiences in terms of QoS (Quality of Service) metrics e.g., throughput, latency and signal quality. We report how this data is handled using complex networks metrics e.g., vertex’s strength and centrality degree, to identify high density handover areas, define the mobile users’ reputation and to reveal the networks coverage. Real experiments showed that collaboration can improve QoS metrics from ∼18 to ∼30% if compared to just use a mobility predictor or a modern operational system, respectively. The discussion unfolds with focus on the collaboration’s efficiency as function of

Uma abordagem ciente de contexto e embasada por feedbacks para o gerenciamento de handovers em ambientes NGN