Combining Spatial and Social Awareness in D2D Opportunistic Routing

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.