Tactful Networking: Humans in the Communication Loop

International audienceThis survey discusses the human-perspective into networking through the Tactful Networking paradigm, whose goal is to add perceptive senses to the network by assigning it with human-like capabilities of observation, interpretation, and reaction to daily-life features and associated entities. To achieve this, knowledge extracted from inherent human behavior in terms of routines, personality, interactions, and others is leveraged, empowering the learning and prediction of user needs to improve QoE and system performance while respecting privacy and fostering new applications and services. Tactful Networking groups solutions from literature and innovative interdisciplinary human aspects studied in other areas. The paradigm is motivated by mobile devices' pervasiveness and increasing presence as a sensor in our daily social activities. With the human element in the foreground, it is essential: (i) to center big data analytics around individuals; (ii) to create suitable incentive mechanisms for user participation; (iii) to design and evaluate both humanaware and system-aware networking solutions; and (iv) to apply prior and innovative techniques to deal with human-behavior sensing and learning. This survey reviews the human aspect in networking solutions through over a decade, followed by discussing the tactful networking impact through literature in behavior analysis and representative examples. This paper also discusses a framework comprising data management, analytics, and privacy for enhancing human raw-data to assist Tactful Networking solutions. Finally, challenges and opportunities for future research are presented

A deep dive into location-based communities in social discovery networks

Location-based social discovery networks (LBSD) is an emerging category of location-based social networks (LBSN) that are specifically designed to enable users to discover and communicate with nearby people. In this paper, we present the first measurement study of the characteristics and evolution of location-based communities which are based on a social discovery network and geographic proximity. We measure and analyse more than 176K location-based communities with over 1.4 million distinct members of a popular social discovery network and more than 46 million locations. We characterise the evolution of the communities and study the user behaviour in LBSD by analysing the mobility features of users belonging to communities in comparison to non-community members. Using observed spatio-temporal similarity features, we build and evaluate a classifier to predict location-based community membership solely based on user mobility information