Spatiotemporal Scheduling for Crowd Augmented Urban Sensing

In urban environments, mobile crowdsensing can be used to augment in-situ sensing deployments (e.g. for environmental and community monitoring) in a flexible and cost-efficient manner. The additional participation provided by crowdsensing enables improved data collection coverage and enhances timeliness of data delivery. However, as the number of participating devices/users increases, efficient management is required to handle the increased operational cost of the infrastructure and associated cloud services - exploiting spatiotemporal redundancy in sensing can help cost-efficient utilization of resources. In this paper, we develop solutions to exploit the mobility of the crowd and manage the sensing capability of participating devices to effectively meet application/user demands for hybrid urban sensing applications. Specifically, we address the spatiotemporal scheduling problem to create high-resolution maps (e.g. for pollution sensing) by developing a common framework to capture spatiotemporal impact of multiple sensor types that generate heterogeneous data at different levels of granularity. We develop an online scheduling approach that leverages the knowledge of device location and sensing capability to selectively activate nodes and sensors. We build a multi-sensor platform that enables data collection, data exchange, and node management. Prototype deployments in three different campus/community testbeds were instrumented for measurements. Traces collected from the testbeds are used to drive extensive large scale simulations. Results show that our proposed solution achieves improved data coverage and utility under data constraints with lower costs (30% fewer active nodes) than naive approaches.