Effective selection of public IoT services by learning uncertain environmental factors using fingerprint attention

Abstract The scope of the Internet of Things (IoT) environment has been expanding from private to public spaces, where selecting the most appropriate service by predicting the service quality has become a timely problem. However, IoT services can be physically affected by (1) uncertain environmental factors such as obstacles and (2) interference among services in the same environment while interacting with users. Using the traditional modeling-based approach, analyzing the influence of such factors on the service quality requires modeling efforts and lacks generalizability. In this study, we propose Learning Physical Environment factors based on the Attention mechanism to Select Services for UsERs (PLEASSURE) , a novel framework that selects IoT services by learning the uncertain influence and predicting the long-term quality from the users’ feedback without additional modeling. Furthermore, we propose fingerprint attention that extends the attention mechanism to capture the physical interference among services. We evaluate PLEASSURE by simulating various IoT environments with mobile users and IoT services. The results show that PLEASSURE outperforms the baseline algorithms in rewards consisting of users’ feedback on satisfaction and interference.