[Back]

M. Laner, N. Nikaein, P. Svoboda, D. Drajic, M. Popovic:
"M2M traffic modeling: methodology, strategies, and analysis";
in: "Machine-to-machine communications, architecture, performance and applications", issued by: M. Dolher and C. Anton; Woodhead Publishing Series in Electronic and Optical Materials, London, 2014.

Abstract: Machine-to-machine (M2M) or Machine-type Communication (MTC) is expected contribute a significant traffic share in future wireless networks. It exhibits considerably different traffic patterns than human-type communication; thus, claims for new traffic models and simulation scenarios. Such models should (i) accurately capture the behaviour of single MTC device as well as (ii) enable the concurrent simulation of massive numbers of devices (e.g., up to 30 000 devices per cell) with their potential synchronous reactions to an event. Source traffic models (i.e., each device is modelled as autonomous entity) are generally desirable for their precision and flexibility. However, their complexity is in general growing quadratically with the number of devices. Aggregated traffic models (i.e., all device are summarized to one stream) are far less precise but their complexity is mainly independent of the number of devices.
In this chapter, we present several modelling strategies known from literature; namely, (i) a typical strategy for aggregated M2M traffic, (ii) a model for source traffic and (iii) a hybrid approach, combining advantaged from both worlds. The three models are explained and compared through a common use-case, which consists of recorded network traffic produced by a specific M2M application. It allows both to illustrate the trade-off between accuracy and complexity and to guarantee comparability of future studies by the deployment of common models.
Keyword: M2M, MTC, Markov Chain, traffic modelling, traffic volume.

Abstract: Machine-to-machine (M2M) or Machine-type Communication (MTC) is expected contribute a significant traffic share in future wireless networks. It exhibits considerably different traffic patterns than human-type communication; thus, claims for new traffic models and simulation scenarios. Such models should (i) accurately capture the behaviour of single MTC device as well as (ii) enable the concurrent simulation of massive numbers of devices (e.g., up to 30 000 devices per cell) with their potential synchronous reactions to an event. Source traffic models (i.e., each device is modelled as autonomous entity) are generally desirable for their precision and flexibility. However, their complexity is in general growing quadratically with the number of devices. Aggregated traffic models (i.e., all device are summarized to one stream) are far less precise but their complexity is mainly independent of the number of devices.
In this chapter, we present several modelling strategies known from literature; namely, (i) a typical strategy for aggregated M2M traffic, (ii) a model for source traffic and (iii) a hybrid approach, combining advantaged from both worlds. The three models are explained and compared through a common use-case, which consists of recorded network traffic produced by a specific M2M application. It allows both to illustrate the trade-off between accuracy and complexity and to guarantee comparability of future studies by the deployment of common models.
Keyword: M2M, MTC, Markov Chain, traffic modelling, traffic volume.