Abstract: A data acquisition algorithm in participatory sensing systems based on the compressed sensing theory was proposed. In this algorithm， the un-transmitted data was estimated by analyzing social relationship between mobile nodes. And then the observation matrices were refreshed using estimated data. Finally the compressed sensing algorithm was exploited to reconstruct original data according to both transmitted and estimated data. The proposed algorithm could greatly reduce the amount of data transmitted in the participatory sensing systems while still achieve good data reconstruction accuracy. The random walk mobility model was exploited in the simulations to validate the feasibility of this algorithm. The simulation results showed that， compared with the traditional compressed sensing algorithm， the amount of data transmitted over the network could be remarkably reduced without losing data fidelity， and hence the network overhead could be decreased.

Key words: participatory sensing, compressed sensing, social relationship, data acquisition, data estimation