LAUSR

Mobile crowdsensing (MCS) is a promising paradigm where sensor-embedded mobile devices are exploited for collecting and sharing environmental data. In MCS, the participating mobile devices sense the environment, collect the data, (pre-)process the data and transmit the data or pre-processing results to the server for further processing. In wireless edge networks, transmission and/or processing of sensed data may be unsuccessful due to the unstable wireless channels, limited bandwidth, energy and computation resources. To optimize the MCS performance, it is imperative to jointly consider the data sensing, processing and transmission for MCS system design. In this paper, we propose a joint sensing, communication and computation (JSCC) framework for multi-dimensional resource constrained MCS systems. We formulate the JSCC design as an optimization problem, by jointly controlling the data sensing, transmission and computation offloading schemes in the system. Simulation results show that the proposed JSCC framework significantly outperforms several baseline solutions without jointly considering data sensing-transmission-computation and/or multi-dimensional resource limitations.