Duration of stay based weighted scheduling framework for mobile phone sensor data collection in opportunistic crowd sensing

Abstract

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 Thejaswini M cs10p003@iith.ac.in P. Rajalakshmi raji@iith.ac.in the data collection region for more than schedule iteration time with high probability.

Weighted Adaptive Opportunistic Scheduling Framework for Smartphone Sensor Data Collection in IoT

“…The authors of those studies considered such mobility parameters as flight length and distance and mobile node velocity to be important parameters in addition to the residual energy limit of mobile nodes. A scheduling algorithm based on the duration of stay was also proposed for mobile crowd sensed data collection [27]; those authors gave higher priority to mobile nodes that stayed within range of the base station for a short period to reduce data loss. However, when collecting sensor data from smartphones to support IoT applications, it is necessary to combine adaptive and application-oriented scheduling approaches to cope with the unpredictable, heterogeneous, and opportunistic nature of user mobility and to minimize the loss of required sensor data.…”