On building human-aware opportunistic communication strategies for cost-effective content delivery

Abstract

Mobile networks face challenges such as data traffic growth, increasing connected devices, spectrum limitations, and costly infrastructure updates. In this context, opportunistic communication strategies appear to assist in scenarios such as data offloading, expanding the capillarity given by users’ mobility, delivering content in challenging situations (e.g., emergency, rural areas, or crowded places), and fostering innovative applications. State-of-art opportunistic strategies extract human mobility characteristics (e.g., social and spatial) to improve content delivery’s cost-effectiveness. Nevertheless, due to constraints, such as the limited availability of real-world datasets or the lack of a human-centered vision in networking, most previous works relied on traditional or simplified human-aware metrics to reach their purposes. Next-generation proposals need a more in-depth vision of peculiar aspects hidden into mobility datasets while working with more realistic scenarios. While building TOOTS, a novel human-aware opportunistic communication strategy for improved content delivery cost-effectiveness, we learned that several phases play a significant role in reaching strategy’s superior performance. This work guides the reader through the whole process necessary for building the Tactful Opportunistic Communication Strategy (TOOTS). The proposal consists of the following steps: learning human-aspect best practices from state-of-art; characterizing the traces while obtaining strategy’s insights targeting superior performance; using, proposing, and analyzing human-aware metrics; combining such metrics and insights into a complete strategy for cost-effective content delivery in a more realistic scenario, and finally, evaluating the strategy through discussions of its best practices and shortcomings for future work. This work shows that TOOTS can improve the performance of an opportunistic scenario in terms of overhead, delivery rate, and delivery latency by following this proposed process.