Understanding and Partitioning Mobile Traffic using Internet Activity Records Data -- A Spatiotemporal Approach

TL;DR

This paper analyzes internet activity records to uncover spatiotemporal patterns in mobile network traffic, aiding network management and optimization through a novel partitioning scheme.

Contribution

It introduces a new method for extracting and partitioning mobile traffic patterns from IAR data, enhancing network planning and resource management.

Findings

High predictability of spatiotemporal patterns in IAR data

Effective traffic partitioning improves network modeling

Experimental validation on Telecom Italia data

Abstract

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.