MobiSensing: Exploiting Human Mobility for Multi-application Mobile Data Sensing with Low User Intervention

Abstract

The explosive growth of personal mobile devices (e.g., smartphones and pads) has brought about significant potential distributed sensing resources. However, such resources have not been fully utilized due to two problems: i) mobile device mobility usually is not dedicated to data sensing, and ii) users may not be willing to participate in the data sensing proactively, i.e., move to or wait in a specific area. To address these problems, we propose a sensing system, namely MobiSensing, with a low intervention to device owners. It uses the semi-Markov process to model node mobility for future mobility prediction. While moving around, mobile devices connect to the central task assignment server opportunistically through their owners' daily usage. In each connection, the server predicts the connected device's next connection and its mobility between current and the next connection. Then, the server assigns sensing tasks in this period of time that the node is likely to complete to the node. As a result, no proactive operations or movements are required for device owners, and sensing tasks can be completed passively and efficiently. Trace-driven experiments demonstrate the high successful rate of MobiSensing.