ILR: Improving Location Reliability in Mobile Crowd Sensing

People-centric sensing with smart phones can be used for large scale sensing of the physical world at low cost by leveraging the available sensors on the phones. However, the sensed data submitted by participants is not always reliable as they can submit false data to earn money without executing the actual task at the desired location. To address this problem, the authors propose ILR, a scheme which Improves the Location Reliability of mobile crowd sensed data with minimal human efforts. In this scheme, the authors bootstrap the trust in the system by first manually validating a small number of photos submitted by participants. Based on these validations, the location of these photos is assumed to be trusted. Second, the authors extend this location trust to co-located sensed data points found in the Bluetooth range of the devices that provided the validated photos. In addition, the scheme also helps to detect false location claims associated with sensed data. The authors applied ILR on data collected from their McSense prototype deployed on Android phones used by students on their campus and detected a significant percentage of the malicious users.