INFOCOM 2025 Papers

Affiliate marketing is a revenue-sharing marketing scheme by which an affiliate, such as a blogger or YouTuber, garners commissions for promoting a merchant's goods or services, thereby aiming to foster a mutually beneficial relationship between affiliates and merchants. Despite being a multi-billion-dollar global industry, affiliate marketing remains inadequately explored, and the research community lacks a comprehensive understanding of its intricate ecosystem. In this paper, we present the first comprehensive empirical study of the affiliate marketing ecosystem. We conduct thorough measurements to assess the prevalence of affiliate marketing, estimate the market size, and elucidate the characteristics of affiliates, merchants, and intermediary affiliate networks. Over a continuous span of 13 months, we monitored four of the most prominent affiliate aggregation platforms, yielding a substantial dataset. We observed 467,219 unique offers - tasks to be undertaken by affiliates - involving 37,109 merchants and 556 affiliate networks across the four platforms. Notably, these offers would cost the merchants more than 19 million USD for the completion of all the actions pre-defined in these offers, such as signing up or making a transaction. Additionally, we compiled a large-scale dataset comprising 124,462 affiliate links, enabling us to conduct a comprehensive investigation. Finally, we propose machine learning models incorporating the characteristics of affiliate links to detect real-world affiliate marketing campaigns.

In indoor fire rescue, swift and precise fire source localization and fire severity assessment are pivotal for firefighting strategic planning and casualty evacuation. However, existing solutions primarily focus on detecting fire presence, which do not offer insights into fire's localization and severity. In this paper, we propose UltraFlame, an accurate, user-friendly, and timely system for pinpointing fire sources and assessing fire severity based on acoustic sensing, which bridges significant gaps in fire safety and response. UltraFlame consists of a collocated commodity speaker and microphone pair, sensing fire by emitting inaudible sound waves. We conduct an in-depth investigation of sound propagation impacted by fire combustion, providing physically interpretable data for deep learning framework and enabling fire source localization even without any sound reflection by fire. We dedicatedly establish a correlation between fire severity and sound propagation delays, which serves as an effective indicator for estimating the heated region. Finally, an appropriate deep learning framework is employed to effectively extract temporal and spatial features from channel measurement. Extensive experiments demonstrate that 94% of the localization results have an error of less than 0.8m. Additionally, UltraFlame achieves an accuracy of 96.9% in fire severity assessment across diverse setups, providing real-time and reliable monitoring.