Abstract—For large-scale mobile data collection, e.g environmental monitoring, mobile sensing nodes upload sensory data which they have collected via WiFi. Within Internet access point’s (AP) communication range that they can access Internet by AP to do it. However, unfortunately communication resources of APs are quite scarce to the huge amount of sensory data that have to be uploaded. To address these problems in existing research on access request scheduling, this paper builds a dynamic model for quantifying uncertain space-time features of network and mobile access requests, then predict the future revenues of them. Based on it, reverse affects the optimization of the current strategy. This paper proposes an approximation algorithm to solve the optimization of the current strategy based on dynamic models of mobile access requests. Simulation results verify that SOA consistently outperforms the existing algorithm.
Index Terms—Mobile sensing nodes (MSN), data collection, mobile crowd sensing, time to live (TTL).
The authors are with the College of Mathematics Physics and Information Engineering, Jiaxing University, Jiaxing 314001, China (e-mail: chenli20040415@163.com).
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Cite:Li Chen, Kun Deng, Jun Yang, Panpan Li, Tao Jiang, and Tao He, "Mobile Access Control Based on Trajectory Analysis and Prediction for Uploading Data," International Journal of Computer Theory and Engineering vol. 11, no. 5, pp. 84-88, 2019.
