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Social-aware algorithms have marked themselves as the most successful strategies for cost-effective content delivery in mobile opportunistic networks. However, these strategies do not consider the importance of users' geographical locations or the spatial properties of human mobility. Given this fact, in this work we propose to combine spatial and social properties to improve the cost effectiveness of content delivery in opportunistic device-to-device networks. We leverage and describe four spatial and social properties and characterize them in a real-world dataset. As a proof of concept of employing those properties, we propose SAMPLER, a forwarding algorithm that combines social awareness, points of interest within a region, and users' individual mobility patterns to cost-effectively deliver messages in opportunistic device-to-device networks. We compare SAMPLER to the state-of-the-art social-aware algorithm Bubble Rap, and with a modified version of Bubble Rap that incorporates static relay nodes. Our experiments, conducted using both real-world (NCCU) and state-of-the-art synthetic (SWIM) traces, confirm that the combination of social and spatial awareness can increase the delivery performance. [ABSTRACT FROM PUBLISHER]

The increasing ubiquity of mobile devices with inter-connection capability has leveraged the possibility of device-to-device communication. In the next generation cellular networks, device-to-device communication is already considered a fundamental feature. A problem in multihop D2D networks is how to define forwarding algorithms that simultaneously achieve high delivery ratio and low network overhead. In the past decade, several algorithms were proposed to achieve those goals in the context of delay-tolerant networks. Among them, probabilistic and socially aware approaches have become the most successful strategies. However, existent socially aware algorithms do not account for the underlying dynamics and evolving nature of social groups, and require the ability to detect communities, which is a complex and expensive task. In this work, we introduce an opportunistic routing protocol that is aware of group meetings and their evolution over time. Our algorithm is parameter-free and does not require the detection of communities. In large-scale scenarios, our algorithm achieves approximately the same delivery ratio of the state-of-the-art solution with 40 percent less network overhead. [ABSTRACT FROM PUBLISHER]