Paper Info
Title
Machine-to-Machine traffic characterization: Models and case study on integration in LTE
Abstract
This paper analyses and compares three different models for Machine-to-Machine (M2M) traffic and investigates the possibility of integrating generated M2M traffic into a Long Term Evolution (LTE) network. According to the authors' knowledge, current M2M traffic models assume that 1) the M2M traffic is aggregated traffic with constant arrival rate and 2) the cellular networks have enough resources to support the generated traffic. However, neither 1) nor 2) are true for M2M traffic in cellular networks. This paper analyses M2M traffic with variable arrival rate under the assumption that the LTE network has limited resources. The results showcase the characteristics of the M2M traffic in a more realistic manner pinpointing the differences from the standard traffic in cellular network. They reveal that the traffic is not self-similar, but only for large number of machines. Also, the results give insight into the design parameters that should be considered for LTE in order to support M2M traffic.
Year
DOI
Venue
2014
10.1109/VITAE.2014.6934482
VITAE
Keywords
Field
DocType
long term evolution,cellular radio,telecommunication traffic,lte network,long term evolution network,m2m traffic models,cellular networks,machine-to-nachine traffic characterization,variable arrival rate,machine-to-machine communications,traffic modeling,correlation,probability density function,uplink,data models
Machine to machine,Call-second,Traffic generation model,Cellular traffic,Chemistry,Computer network,Cellular network,Traffic shaping,Network traffic simulation,Network traffic control
Conference
Citations 
PageRank 
References 
3
0.42
7
Authors
3
Name
Order
Citations
PageRank
Smiljkovic, K.130.42
Atanasovski, V.2382.40
Gavrilovska, L.3423.74