Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment

By Huandong Wang, Fengli Xu, Yong Li, Pengyu Zhang, Depeng Jin. ACM/SIGCOMM Internet Measurement Conference, 2015

364 citations recorded by the source corpus.

Understanding mobile traffic patterns of large scale cellular towers in urban environment is extremely valuable for Internet service providers, mobile users, and government managers of modern metropolis. This paper aims at extracting and modeling the traffic patterns of large scale towers deployed in a metropolitan city. To achieve this goal, we need to address several challenges, including lack of appropriate tools for processing large scale traffic measurement data, unknown traffic patterns, as well as handling complicated factors of urban ecology and human behaviors that affect traffic patterns. Our core contribution is a powerful model which combines three dimensional information (time, locations of towers, and traffic frequency spectrum) to extract and model the traffic patterns of thousands of cellular towers. Our empirical analysis reveals the following important observations. First, only five basic time-domain traffic patterns exist among the 9,600 cellular towers. Second, each of the extracted traffic pattern maps to one type of geographical locations related to urban ecology, including residential area, business district, transport, entertainment, and comprehensive area. Third, our frequency domain traffic spectrum analysis suggests that the traffic of any tower among the 9,600 can be constructed using a linear combination of four primary components corresponding to human activity behaviors. We believe that the proposed traffic patterns extraction and modeling methodology, combined with the empirical analysis on the mobile traffic, pave the way toward a deep understanding of the traffic patterns of large scale cellular towers in modern metropolis.

Subfields: Computer Vision and Pattern Recognition, Ocean Engineering, Transportation

View Paper: https://www.semanticscholar.org/paper/0f0caece2fed6fc689b01f6d4521dca9f44c5dde

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Min Chen
2. Paul Patras
3. Hamed Haddadi
4. Xiaoming Fu
5. Chaoyun Zhang
6. Yong Li
7. Depeng Jin

In Ocean Engineering

1. Paul Patras
2. Hamed Haddadi
3. Chaoyun Zhang

In Transportation

1. Fengli Xu
2. Pengyu Zhang
3. Yong Li
4. Depeng Jin

Citations in the corpus

• Trajectory Recovery From Ash: User Privacy Is NOT Preserved in Aggregated Mobility Data (2017, background): “Keywords Trajectory privacy; aggregated mobility data; statistic data privacy”
• Deep Transfer Learning for Intelligent Cellular Traffic Prediction Based on Cross-Domain Big Data (2019, background): “However, it is well understood that these influential factors are directly correlated to the generation of cellular traffic [5], [6].”
• Narrow Band Internet of Things (2017, background): “From the perspective of transmission distance, IoT communication technologies can be categorized into short-distance communication technologies andWAN communication technologies [5], [6].”
• Task Offloading for Mobile Edge Computing in Software Defined Ultra-Dense Network (2018, background): “Therefore, future communication network not only needs to support wireless content access, but also offers the provisioning of computational offloading for UEs [6], [7], especially for mobile healthcare [8], smart grid [9], Internet of vehicles [10], intelligent services [11], etc.”
• Deep Learning in Mobile and Wireless Networking: A Survey (2018, background): “traffic [456] and application popularity [457] are difficult to understand (see the example city-scale traffic snapshot in Fig.”