Presented herein is a framework for prioritizing location measurements of multiple client devices. In particular, rather than using a round robin scheduling approach, the techniques presented herein utilize a machine learning block (e.g., random forests) to predict a score for each client device, along with a score-based scheduler.
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Zhang, Xu; Tran, Huy; Silverman, Matt; Mukherji, Abhishek; Raghuram, Vinay; Bhattacharyya, Abhishek; and Pandey, Santosh, "MACHINE LEARNING FRAMEWORK FOR PRIORITIZING LOCATION MEASUREMENTS OF MULTIPLE DEVICES", Technical Disclosure Commons, (December 21, 2018)