Section: New Results

Let Opportunistic Crowdsensors Work Together for Resource-efficient, Quality-aware Observations

Participants: Yifan Du, Valérie Issarny (MiMove), Françoise Sailhan (CNAM)

Opportunistic crowdsensing empowers citizens carrying hand-held devices to sense physical phenomena of common interest at a large and fine-grained scale without requiring the citizens’ active involvement. However, the resulting uncontrolled collection and upload of the massive amount of contributed raw data incur significant resource consumption, from the end device to the server, as well as challenge the quality of the collected observations. Our research tackles both challenges raised by opportunistic crowdsensing, that is, enabling the resource-efficient gathering of relevant observations. To achieve so, we introduce the BeTogether middleware fostering context-aware, collaborative crowdsensing at the edge so that co-located crowdsensors operating in the same context, group together to share the work load in a cost- and quality-effective way. Our implementation-driven evaluation of the proposed solution, which leverages a dataset embedding nearly one million entries contributed by 550 crowdsensors over a year, shows that BeTogether increases the quality of the collected data while reducing the overall resource cost compared to the cloud-centric approach.