Task Recommendation for Group Users in Public IoT Environments

Abstract

There are an increasing number of public Internet of Things (IoT) devices installed in urban environments, with which users can perform a wide variety of tasks. Owing to the nature of public spaces, such IoT devices must support groups of users rather than just individuals. However, because the type and quality of IoT devices in public environments varies, it may be difficult for groups of users to recognize the opportunities to perform tasks. Moreover, group users are often new to a certain public place, and have not previously performed tasks in IoT-enriched public spaces. In this paper, we propose a two-phase task recommendation approach for groups of IoT users in public environments. In the first phase, we employ a random walk with restart (RWR) algorithm to overcome the problem of sparse historical data for the performance of user tasks in public IoT environments. The second phase predicts a set of operations (IoT device functionalities) that are most appropriate for each candidate task. In this phase, to more effectively predict IoT operations for a user task we consider the contextual semantics of users via a classification model. We evaluate our approach using real-world datasets collected from practical IoT testbed environments. In addition, we show that an appropriate set of task operations can be predicted effectively by considering task types and contextual semantics.