Analysis and Prediction of Mobile Application Usage Based on location In case of ethiotelecom

No Thumbnail Available



Journal Title

Journal ISSN

Volume Title


Addis Ababa University


The explosive growth of smart devices, network access points, and new mobile application development drives users to use more and more mobile applications and, this has lead to the explosive growth of mobile data traffic. It has a high impact on mobile service providers to manage network data traffic because application usage is different from one location to other with time. Understanding the application-level traffic patterns from a completely different location angle is effective for operators and content providers to create technical and business plans. In this paper, we have established several typical traffic patterns and predict application category traffic demand per clustered location in a mobile cellular network. We explore mobile traffic patterns by clustering each application category into five clusters based on traffic volume and location. Then, we implement a random forest model to predict the traffic demand of three of the most highly utilized applications per cluster location.This outcome could be useful in relevant future applications, with the prospect to achieve average 96% predictive accuracy per application category per cluster. Understanding popular application at the clustered locations and predicting the traffic demand of a popular application could significantly improve user experience, average latency, energy consumption, spectral efficiency, back-haul traffic, and network capacity. Those outcomes are possible via designing and implementing a cache server or planning and optimizing the network resources based on predicted traffic demand.



Mobile application usage, Traffic demand, Popular application