Performance of LTE uplink for IoT backhaul

Piri, Esa; Pinola, Jarno

doi:10.1109/ccnc.2016.7444723

Cited by 15 publications

(4 citation statements)

References 19 publications

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“…In the case of C-ITS, the backhaul network uses ns-3's point-to-point abstraction to connect the eNodeB LTE base station and the controller. We set the backhaul network delay to 100 milliseconds which is the upper bound delay expected for LTE mobile backhaul [14]. For a fair comparison, we use the same parameters for the D-ITS backhaul network, which connects the RSUs.…”

Section: A Experimental Methodologymentioning

confidence: 99%

Dynamically distributed network control for message dissemination in ITS

Kaul

Santos

Rothenberg

et al. 2017

2017 IEEE/ACM 21st International Symposium on Distributed Simulation and Real Time Applications (DS-RT)

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We propose D2-ITS, a flexible and extensible framework to dynamically distribute network control to enable message dissemination in Intelligent Transport Systems (ITS). By decoupling the control-from the data plane, D2-ITS leverages network programmability to address ITS scalability, delay intolerance and decentralization. It uses a distributed control plane based on a hierarchy of controllers that can dynamically adjust to environment-and network conditions in order to satisfy ITS application requirements. We demonstrate the benefits of D2-ITS through a proof-of-concept prototype using the ns-3 simulation platform. Results indicate lower message delivery latency with minimal additional overhead.

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Section: A Experimental Methodologymentioning

confidence: 99%

Dynamically distributed network control for message dissemination in ITS

Kaul

Santos

Rothenberg

et al. 2017

2017 IEEE/ACM 21st International Symposium on Distributed Simulation and Real Time Applications (DS-RT)

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“…Many research studies evaluated the performance of cellular networks in supporting M2M communications. A performance evaluation of LTE QoS has been studied in [5] by varying payload sizes and a solution is proposed to increase throughput in uplink with packet aggregation performed on the IoT gateway. However, M2M low-throughput in the order of Kbps was not considered in this simulation.…”

Section: Related Workmentioning

confidence: 99%

In depth performance evaluation of LTE-M for M2M communications

Dawaliby

Bradai

Pousset

2016

2016 IEEE 12th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)

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The Internet of Things (IoT) represents the next wave in networking and communication which will bring by 2020 tens of billions of Machine-to-Machine (M2M) devices connected through the internet. Hence, this rapid increase in Machine Type Communications (MTC) poses a challenge on cellular operators to support M2M communications without hindering the existing Quality of Service for already established Human-to-Human (H2H) communications. LTE-M is one of the candidates to support M2M communications in Long Term Evolution (LTE) cellular networks. In this paper, we appraise and present an in depth performance evaluation of LTE-M based on cross-layer network metrics. Compared with LTE Category 0 previously released by 3GPP for MTC, simulation results show that LTE-M offers additional advantages to meet M2M communication needs in terms of wider coverage, lower throughput, and a larger number of machines connected through LTE network. However, we show that LTE-M is not yet up to the level to meet future applications requirements regarding a near-zero latency and an advanced Quality of Service (QoS) for this massive number of connected Machine Type devices (MTDs).

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“…Although a few studies have been performed, they mainly focus on traffic shaping [9] or traffic policing [10] by controlling the outbound gateway [11,12]. …”

Section: Introductionmentioning

confidence: 99%

A Scheme to Smooth Aggregated Traffic from Sensors with Periodic Reports

Jang

2017

Sensors

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The possibility of smoothing aggregated traffic from sensors with varying reporting periods and frame sizes to be carried on an access link is investigated. A straightforward optimization would take O(pn) time, whereas our heuristic scheme takes O(np) time where n, p denote the number of sensors and size of periods, respectively. Our heuristic scheme performs local optimization sensor by sensor, starting with the smallest to largest periods. This is based on an observation that sensors with large offsets have more choices in offsets to avoid traffic peaks than the sensors with smaller periods. A MATLAB simulation shows that our scheme excels the known scheme by M. Grenier et al. in a similar situation (aggregating periodic traffic in a controller area network) for almost all possible permutations. The performance of our scheme is very close to the straightforward optimization, which compares all possible permutations. We expect that our scheme would greatly contribute in smoothing the traffic from an ever-increasing number of IoT sensors to the gateway, reducing the burden on the access link to the Internet.

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Performance of LTE uplink for IoT backhaul

Cited by 15 publications

References 19 publications

Dynamically distributed network control for message dissemination in ITS

Dynamically distributed network control for message dissemination in ITS

In depth performance evaluation of LTE-M for M2M communications

A Scheme to Smooth Aggregated Traffic from Sensors with Periodic Reports

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