A Distributed Compressive Data Gathering Framework For Mobile Crowdsensing

Abstract

Existing work on data gathering in mobile crowdsensing (MCS) usually assumes that the motion of the participants is either known or predictable via a mobility model. As such, the organizers can tell them when/where to sense, ensuring the data can be gathered from the target area with a minimized number of participants. Knowing exactly where the participants go, however, is not trivial, since the movements of the participants are in fact autonomous and random. In this paper, we investigate a common compressive data gathering framework for MCS, without trying to predict how a specific participant moves. We notice a key observation: while the participants move autonomously in the target sensing area, each trajectory itself provides a random, and thus valuable coverage for a sensing task. With compressive sensing (CS), these random trajectories can be utilized to exploit the spatio-temporal data correlation. Given this, we first introduce a novel idea of virtual sensor network to define the target sensing area, and then propose a distributed CS-based encoding algorithm to compress the data in the target area such that the number of the participants involved in data gathering can be reduced. We further propose a round-based CS reconstruction algorithm to exploit the inter-round data correlation and improve the accuracy of the data reconstruction. Experimental results using real sensor readings show that the proposed scheme achieves better performance than existing ones with dedicated sensor networks.