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A measurement-based analysis of machine-to-machine (M2M) communications over a cellular network

A measurement-based analysis of machine-to-machine (M2M) communications over a cellular network. Jussi Marjamaa NomadicLab Ericsson Research Wireless Access Networks OY L M Ericsson AB. OUTLINE. Methodology M2M traffic/subscriber amounts per application Traffic amounts per subscriber

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A measurement-based analysis of machine-to-machine (M2M) communications over a cellular network

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  1. A measurement-based analysis of machine-to-machine (M2M) communications over a cellular network Jussi Marjamaa NomadicLab Ericsson Research Wireless Access Networks OY L M Ericsson AB

  2. OUTLINE • Methodology • M2M traffic/subscriber amounts per application • Traffic amounts per subscriber • Radio performance • Popular devices • Conclusions

  3. CN Control Plane User Plane SGSN RAN GSMA IMEI DB External Networks RNC GGSN DB SGSN Events Probe Events RNC Events TAC data METHODOLOGY:Data sources • Analysis of the current M2M traffic in a cellular network • Data sources: • tcpdump setup in front of GGSN (=Probe events) • RNC events • SGSN events • TAC data from GSMA

  4. METHODOLOGY:M2M identification and grouping • M2M devices are recognized by TAC and APN based filtering • Grouping based on APN names or the type of a device Structure of IMEI [TS 23.003]: IMEI = International Mobile Station Equipment Identity TAC = Type Allocation Code SNR = Serial Number CD/SD = Check Digit / Spare Digit

  5. SHARE OF ACTIVE SUBSCRIBERS BETWEEN THE M2M APPLICATIONS ~5-7% of all active subscribers are M2M Active subscriber = PS data transmissions during the measured week

  6. SHARE OF TRAFFIC AMOUNTS BETWEEN THE M2M APPLICATIONS 0,01% of DL and 0,05% of UL traffic is M2M: 65% GERAN 35% UTRAN (81% of all the network’s traffic in UTRAN) M2M PDP activations: 87% GERAN 13% UTRAN (67% of all the activations in the network from UTRAN) (Total traffic volumes during one week)

  7. TRAFFIC AMOUNTS PER ACTIVE SUBSCRIBER (1/3) Only smart metering and security are having more UL than DL traffic: For comparison:~87 % of all the mobile network traffic is going to DL direction

  8. TRAFFIC AMOUNTS PER ACTIVE SUBSCRIBER (2/3) • Smart metering has the least data received (exponentially distributed) • Taxi and transport has the most (uniformly distributed)

  9. TRAFFIC AMOUNTS PER ACTIVE SUBSCRIBER (3/3) • Small data amounts for all M2M groups compared to the network average

  10. RADIO PERFORMANCE (1/3):RSCP and Ec/N0 (mean values) Values based on UE’s measurement reports on serving cell CPICH (Common Pilot Channel) RSCP = Received Signal Code Power Ec/No = Received Energy Per Chip Divided by the Power Density in the Band = RSCP/RSSI RSSI = Received Signal Strength Indicator

  11. RADIO PERFORMANCE (2/3):RSCP (CDF) • Poor RSCP for tracking, taxi and transport and part of smart metering

  12. RADIO PERFORMANCE (3/3):Ec/N0 (CDF) • Very good Ec/No values for smart metering • Poor Ec/No for applications with high mobility (tracking, taxi and transport)

  13. THE MOST POPULAR DEVICES WCDMA support

  14. CINTERION EU3-E VS. EU3-P (1/2) • Both the devices are mainly used for smart metering • Difference in supported bands (Qualcomm chipsets): • EU3-E (900/2100) • EU3-P (850/900/2100)

  15. CINTERION EU3-E VS. EU3-P (2/2) • EU3-P is experiencing better RSCP and Ec/No values

  16. CONCLUSIONS • M2M utilization is still small in terms of subscriber and traffic amounts • Smart metering is clearly the biggest application area • Payment is the second biggest • Relatively small traffic amounts for all M2M • Traffic is mostly DL oriented • Most of the M2M traffic (~65%) is carried over GERAN, though over a half of the devices are WCDMA capable • ~100 different M2M device models in use but most of the devices are different variations of Cinterion EU3 • Cinterion is the biggest UE device vendor • Big differences in radio performance between the different M2M groups • The worst performance experienced by the applications with: • Higher mobility • Items placed in the edges of coverage areas • Crowded places (hot-spots)

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