Collaborative opportunistic mobile crowdsensing – Our work aims to raise opportunistic mobile crowdsensing to a reliable means of observing phenomena, focusing on urban environmental monitoring. To this end, we have developed a set of protocols that together support ”context-aware collaborative mobile crowdsensing at the edge”: (i) CalibrateNoiseTogether implements multi-hop, multi- party calibration; (ii) ContextSense infers the crowdsensors’ physical context using machine learning; (iii) BeTogether implements context-aware grouping of crowdsensors to share the workload and filter out low quality data; and (iv) IAM implements mobile collaborative data analysis at the edge.

Web site: https://github.com/sensetogether