Understanding and Partitioning Mobile Traffic using Internet Activity Records Data -- A Spatiotemporal Approach
This work addresses network management challenges for mobile operators by providing a partitioning scheme based on spatiotemporal analysis, though it appears incremental in applying existing methods to new data.
The authors tackled the problem of understanding and partitioning mobile network traffic by analyzing Internet Activity Records (IARs) from Telecom Italia, identifying predictable spatio-temporal patterns to aid network operators in resource optimization.
The internet activity records (IARs) of a mobile cellular network posses significant information which can be exploited to identify the network's efficacy and the mobile users' behavior. In this work, we extract useful information from the IAR data and identify a healthy predictability of spatio-temporal pattern within the network traffic. The information extracted is helpful for network operators to plan effective network configuration and perform management and optimization of network's resources. We report experimentation on spatiotemporal analysis of IAR data of the Telecom Italia. Based on this, we present mobile traffic partitioning scheme. Experimental results of the proposed model is helpful in modelling and partitioning of network traffic patterns.