Mobility prediction in telecom cloud using mobile calls

Abstract

The proliferation of the telecom cloud has fostered increasing attention on location-based applications and services. Due to the randomness and fuzziness of human mobility, it still remains open to predict user mobility. In this article, we investigate the large-scale user mobility traces that are collected by a telecom operator. We find that mobile call patterns are highly correlated with the co-location patterns at the same cell tower at the same time. We extract such social connections from cellular call records stored in the telecom cloud, and further propose a mobility prediction system that can run as an infrastructure-level service in telecom cloud platforms. We implement the mobility pattern discovery into a cloud-based location tracking service that can make online mobility prediction for value-added telecom services. Finally, we conduct a couple of case studies on mobilityaware personalization and predictive resource allocation to elaborate how the proposed system drives a new mode of mobile cloud applications. 2014 IEEE.