2018 IEEE 34th International Conference on Data Engineering (ICDE) (2018)
Apr 16, 2018 to Apr 19, 2018
Continuous proximity detection monitors the real-time positions of a large set of moving users and sends an alert as long as the distance of any matching pair is smaller than the threshold. Existing solutions construct either a static safe region with maximized area or a mobile safe region with constant speed and direction, which cannot not capture real motion patterns. In this paper, we propose a new type of safe region that relies on trajectory prediction techniques to significantly reduce the communication I/O. It takes into account the complex non-linear motion patterns and constructs a stripe to enclose the sequence of future locations as a predictive safe region. The stripe construction is guided by a holistic cost model with the objective of maximizing the expected time for the next communication. We conduct experiments on four real datasets with four types of prediction models and our method reduces the communication I/O by more than 30% in the default parameter settings.
image matching, image motion analysis, image representation, mobile computing, object tracking, target tracking
Y. Xu, D. Zhang, M. Zhang, D. Li, X. Wang and H. T. Shen, "Continuous Proximity Detection via Predictive Safe Region Construction," 2018 IEEE 34th International Conference on Data Engineering (ICDE), Paris, France, 2018, pp. 629-640.