Energy-aware and quality-driven sensor management for green mobile crowd sensing

Mobile Crowd Sensing (MCS) is a novel class of Internet of Things applications which exploits the inherent mobility of wearable sensors and mobile devices to observe phenomena of common interest, typically over large geographical areas (e.g. traffic conditions, air pollution, noise in urban areas). Since MCS applications generate large amounts of sensed data which is collected and preprocessed by devices with limited energy supply, challenges arise with respect to sensor management to ensure an energy-aware and quality-driven data acquisition process. In this paper we present a framework for Green Mobile Crowd Sensing (G-MCS) which utilizes a quality-driven sensor management function to continuously select the k-best sensors for a predefined sensing task. Our G-MCS solution utilizes a cloud-based architecture centered around a publish/subscribe communication model to enable the interaction of mobile devices with the cloud for energy-aware MCS. In particular, it obviates redundant sensor activity while satisfying sensing coverage requirements and sensing quality, and consequently reduces the overall energy consumption of an MCS application. We present a model for G-MCS and evaluate its energy savings for different application requirements and geographical sensor distribution scenarios. Furthermore, our model evaluation on a real data set shows that in certain identified cases, significant energy consumption reductions can be achieved by utilizing the proposed framework, which opens the door for green solutions within the area of MCS applications.