@inbook{92665dda8fb64d8297db97eaade3ca52,
title = "Impact of Machine-to-Machine Traffic on LTE Data Traffic Performance",
abstract = "Machine-to-machine (M2M) communication is an emerging paradigm in which trillions of intelligent devices are expected to communicate without or with small human intervention. The increasing M2M devices have severe impact on long-term evolution (LTE) data traffics. Moreover, the behavior of M2M traffic also differs from traditional mobile traffic. In future, logistics and transportations are considered to be the main M2M application areas. These applications disparately demand more efficient M2M traffic modeling to reduce end-to-end (E2E) delay between various interconnected machines. This paper investigates several traffic models and highlights the impact of M2M traffic in logistics and transportation on LTE data traffic. We evaluate the overall LTE network performance in terms of E2E delays for file transfer, voice, and video users.",
keywords = "LTE, LTE data traffic, M2M, Traffic modeling",
author = "Yasir Mehmood and Thomas P{\"o}tsch and Marwat, {Safdar Nawaz Khan} and Farhan Ahmad and Carmelita G{\"o}rg and Imran Rashid",
note = "Funding Information: Acknowledgments We are thankful to International Graduate School for Dynamics in Logistics, University of Bremen, Germany and University of Engineering and Technology, Peshawar, Pakistan for supporting this work financially. Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.",
year = "2016",
doi = "10.1007/978-3-319-23512-7_25",
language = "English (US)",
series = "Lecture Notes in Logistics",
publisher = "Springer Science and Business Media B.V.",
pages = "259--269",
booktitle = "Lecture Notes in Logistics",
}