Decentralized and Compressed Data Storage for Mobile Crowdsensing.

Sensing data acquired with crowdsensing are generally stored at central cloud servers, since massive data are involved and sensing devices do not have enough space to store them. Although each sensing device only has limited storage capacity, the total size of storage across thousands of devices can be considerable. In view of this, this paper addresses decentralized storage problem in mobile crowdsensing system, providing an alternative to cloud-based data storage. By investigating a virtual sensor model, the movement of a participant in the target sensing area is formulated as a random sampling over the data field related to this area. With a particular encoding algorithm, the data field is compressed into only one measurement along with a random sampling process. Each participant stores its own measurements as if various compressed snapshots of the data field are separately stored by different participants. We further investigate a recovery algorithm, reconstructing the original data field by carefully decoding enough measurements. Extensive experiments validate the proposed storage scheme under various crowdsensing scenarios, and our scheme achieves excellent performance in terms of recruitment overhead, decoding time, and decoding accuracy.