Advancement of mobile phone based new sensing paradigm like opportunistic and participatory crowd sensing has lead to increase in both multimedia and scalar sensor data traffic over wireless networks. In opportunistic crowd sensing, there are more chances of missing required mobile phones sensor data due to unpredictable mobility nature of users. Analysis of scheduling schemes under realistic human mobility models has become an important issue for pervasive sensing applications specifically for mobile phone based crowd sensing. This paper refers to a weighted scheduling framework for collecting mobile phone sensor data under Levy Walk mobility model. The proposed method uses duration of stay of mobile phone users as one of the important parameter to improve the scheduling performance in terms of reducing the rate of missing of mobile nodes and thereby increasing the rate of collection of required sensor data. The simulation results show that for improving the scheduling performance in opportunistic crowd sensing, high weight value should be given for duration of stay parameter, when mobile nodes stay within the data collection region for more than schedule iteration time with high probability. © 2015, Springer Science+Business Media New York.