Insights into Cellular Machine-to-Machine Traffic: Large-Scale Analysis

1. A First Look at Cellular Machine-to-Machine Traffic Large Scale Measurement and Characterization M. ZubairShafiq (Michigan State University) LushengJi (AT&T Labs -- Research) Alex X. Liu (Michigan State University) Jeffrey Pang (AT&T Labs -- Research) Jia Wang (AT&T Labs -- Research) 06/12/2012

2. What is Machine-to-Machine (M2M)? • Communications to or from smart devices that function without direct human intervention

3. Motivation • M2M presents a new growth opportunity for carriers: • “Internet of things”, billions of connected devices • Cellular M2M devices and human-operated devices share the same infrastructure • Little is known about M2M cellular traffic • What are the characteristics of M2M traffic? • How different are they from human-operated cellular devices? • Will the differences impact network performance and operations? • Opportunities for improving how M2M and human-operated devices share the network? • We share our initial insights by comparing cellular M2M traffic to smartphone traffic

4. Data

5. Data • IP traffic records from the core network • Gn links between SGSNs and GGSNs • Data covers all states in the United States over the period of one week in August 2010 • Timestamp, Traffic volume, Device type, Application type, Round Trip Time (RTT), Packet loss ratio • Device type (make and model) identified using TAC information in IMEI • Application identification using port information, HTTP host, user-agent, and other heuristics • All traffic records are anonymized and aggregated, no personally identifiable information

6. M2M Device Categorization • Identified millions of active M2M devices belonging to 150 hardware models • Carefully studied all models and categorized them into 6 categories • Fleet management (51%) • Asset tracking (18%) • Building security (14%) • Modems (9%) • Metering (6%) • Telehealth (2%) • Baseline comparison with smartphone traffic: • IP traffic records from hundreds of thousands of smartphones

7. Measurement Analysis

8. Metrics • We study and compare M2M traffic with smartphone traffic in terms of the following metrics: • Data usage  Aggregate traffic volume • Temporal Dynamics  Traffic volume time series and session analysis • Geographical distribution  Mobility • Applications  Distribution • Network Performance  Round trip time and packet loss ratio

9. Aggregate Traffic Volume • Do M2M devices generate as much traffic volume as smartphones? • Is M2M traffic also downlink heavy? • Implications: Spectrum and resource allocation

10. Aggregate Traffic Volume • Smartphone traffic volume is order of magnitude more than M2M traffic volume • Strong diversity across M2M categories • M2M devices have more uplink than downlink • 80% smartphones have more downlink traffic • 80% M2M devices have more uplink traffic

11. Traffic Volume Time series • Does M2M traffic peak at the same time as smartphone traffic? • Does any M2M device category exhibit unusual temporal dynamics? • Can we group M2M device timeseries that can be used to develop billing strategies? • Implications: Capacity planning and billing strategies based on peak usage

12. Time series • Smartphone traffic volume corresponds “human waking” hours • More downlink traffic volume than uplink

13. Time series • Aggregate M2M traffic • Daily volume corresponds “human working hours” • Almost equal uplink and downlink traffic volumes

14. Time series • Modems • Peaks at the start of every hour in uplink and downlink traffic volume • Further confirmed using spectral analysis and drill-down analysis

15. Time series clustering • Distance matrix of model time series • Hierarchical clustering • 4 clusters • High volume high diurnality • High volume low diurnality • Low volume high diurnality • Low volume low diurnality Dendrogram High volume high diurnality High volume low diurnality

16. Session Analysis • Understand the behavior of individual devices • Implications: Billing strategies, radio network parameter optimization, and battery management

17. Active Time • Active time is an important network “usage” metric • It corresponds more closely to radio resource usage than traffic volume; not related to users’ interaction time • Smartphones have the largest average active time • Among M2M device categories, asset tracking devices have the largest active time

18. Session Arrivals and Lengths • Average session inter-arrival time • Smartphones have the smallest average session inter-arrivals • 50% telehealth and metering devices have session inter-arrivals > 12 hours • Average session length • Half M2M categories have about 80% of the devices with average session time lasting less than 5 minute (more radio overheads) • Smartphones have small average session lengths, similar to telehealth and building security devices

19. Mobility • Understand the movement of M2M devices and geographical distribution M2M traffic, and its comparison with smartphone traffic • Implications: Handover management, geographical resource allocation

20. Device Mobility • Metric: Unique cell sector count • Overall M2M devices have lower mobility compared to smartphones, expect for asset tracking devices • As expected, metering and building security have the lowest mobility

21. Geographical Distribution • Study co-location between high volume M2M and smartphone locations using “Cross-L" • Attraction, Repulsion, Independence • M2M and smartphone traffic compete with each other, which may result in congestion

22. Application Usage • Does M2M traffic consist of well-known/standard protocols? • Implications: Protocol standardization

23. Application Usage • M2M and smartphone traffic is mostly TCP, up to 95% • Smartphone traffic belongs to web browsing, audio and video streaming, and email applications (not shown here) • M2M traffic belongs to unknown or miscellaneous realms • Difficult for network operators to diagnose/mitigate • Need for better standardization of M2M protocols misc. unknown web ftp

24. Network Performance • How does M2M traffic compare to smartphone traffic in terms of network performance? • Implications: Device hardware specifications, support for legacy networks

25. Round Trip Time (RTT) • Typically low volume implies high RTT • Impact of communication technology • Majority M2M devices are GPRS and EDGE devices • Telehealth devices have better RTT due to widespread use of 3G technology

26. Packet Loss Ratio • Building security devices have much higher third and fourth quartile packet loss ratios than other M2M devices • M2M traffic generally has higher packet loss ratios • Due to poor deployment location choices • application specific location requirements • lack of user interface that clearly displays cellular signal strength

27. Conclusions • M2M traffic exhibits significantly different traffic patterns as compared to smartphone traffic • Implications • Billing schemes • Diversity; not “one size fits all” like smartphones • Control plane overheads; not just traffic volume • Mitigation of timer-driven coordinated behaviors • M2M protocol standardization and optimization at transport and application layers • Radio technology upgrades

28. Questions?