Autonomous and distributed recruitment and data collection framework for opportunistic sensing

Abstract

People-centric sensing is a novel approach that exploits the sensing capabilities offered by smartphones and the mobility of users to sense large scale areas without requiring the deployment of sensors in-situ. Given the ubiquitous nature of smartphones, people-centric sensing is a viable and efficient solution for crowdsourcing data. In this work, we propose a fully distributed, opportunistic sensing framework that involves two main components which both work in an ad hoc fashion: Recruitment and Data Collection. We analyzed the feasibility of our distributed approach for both components through preliminary simulations. The results show that our recruitment method is able to select 66% of the nodes that are appropriate for the sensing activity and 88% of the messages sent by these selected nodes reach the sink by using our data collection method.