CTMC modelling for H2H/M2M coexistence in LTE‐A/LTE‐M networks

Fawal, Ahmad Hani El; Najem, Mohamad; Mansour, Ali; Roy, Frédéric Le; Jeune, Denis Le

doi:10.1049/joe.2018.5042

Cited by 8 publications

(3 citation statements)

References 14 publications

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“…In our previous study, we initially received one request per time slot for each type of traffic (M2M and H2H) and observed no limitation problems, as expected. We then began to increase the number of requests (El Fawal et al, 2018). After simultaneously connecting 52,000 devices, the server stopped accepting requests to avoid overload.…”

Section: Proposed Enhanced Queue Modeling For Healthcare Trafficmentioning

confidence: 99%

The Impact of Healthcare Traffic Over H2H and M2M Networks During the Spread Phase of Pandemic Diseases

Samhan

Fawal²,

Uddin³

et al. 2021

JER is an international, peer-reviewed journal that publishes f

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Recently, the coronavirus pandemic has caused widespread panic around the world. Modern technologies can be used to monitor and control this highly contagious disease. A plausible solution is to equip each patient who is diagnosed with or suspected of having COVID-19 with sensors that can monitor various healthcare and location parameters and report them to the desired facility to control the spread of the disease. However, the simultaneous communication of numerous sensors installed in the majority of an area’s population results in a huge burden on existing Long-Term Evolution (LTE) networks. The existing network becomes oversaturated because it has to manage two kinds of traffic in addition to normal traffic (text, voice, and video): healthcare traffic generated by a large number of sensors deployed over a huge population, and extra traffic generated by people contacting their family members via video or voice calls. In pandemics, e-healthcare traffic is critical and should not suffer packet loss or latency due to network overload. In this research, we studied the performance of existing networks under various conditions and predicted the severity of network degradation in an emergency. We proposed and evaluated three schemes (doubling bandwidth, combining LTE-A and LTE-M networks, and request queuing) for ensuring quality of service (QoS) of healthcare sensor (HCS) network traffic without perturbation from routine human-to-human or machine-to-machine communications. Finally, we simulated all proposed schemes and compared them with existing network scenarios. The results have showed that when we have doubled the bandwidth the SCR of all traffic was 100% as same as the Queue strategy. However, when we prioritized the HCS traffic the SCR has recorded 100%, while H2H and M2M traffic has recorded 73%. When we used hybrid network LTE-A and LTE-M network, the HCS and H2H traffic has recorded 100% and M2M traffic has recorded 70%. After analyzing the results, we conclude that our proposed queuing schemes performed well in all conditions and provide the best QoS for HCS traffic.

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Section: Proposed Enhanced Queue Modeling For Healthcare Trafficmentioning

confidence: 99%

The Impact of Healthcare Traffic Over H2H and M2M Networks During the Spread Phase of Pandemic Diseases

Samhan

Fawal²,

Uddin³

et al. 2021

JER is an international, peer-reviewed journal that publishes f

View full text Add to dashboard Cite

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“…The service separation assists in providing guaranteed class service to massive critical MTC communication with an acceptable QoS. A mathematical analysis is offered in the study of El Fawal et al (2018) with a deduction that M2M/H2H traffic in emergency scenarios should not have a user's priority settings. In another research piloted in Akpakwu et al (2017), the focus is given to the congestion-control mechanism.…”

Section: System Descriptionmentioning

confidence: 99%

Machine-to-machine and cell-to-cell traffic handling using relay and carrier aggregation prioritize on LTE-A PRO network

Pana¹,

Balyan²,

Groenewald³

2020

International Journal on Smart Sensing and Intelligent Systems

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With the advent of artificial intelligence (AI) and smart cities, the amount of connected devices is on the rise. The communication medium perfectly suited for interconnecting all the massive machinetype communications (mMTC) is long-term evolution advanced (LTE-A). The design in this paper consists of relay stations (RSs) that are connected to the base stations (BSs). The radio access network (RAN) will consist of a base station/eNodeB (eNB) with one or two hops of RS. The machine-to-machine (M2M) communication considered here is of the type of smart meters and sensors data calls. These calls are handled according to their delay-tolerant capability. The proposed (PR-M2M) scheme assigns resources with priority without reserving the resources. The Vienna LTE Downlink level simulator is used to investigate the operation of the proposed scheme. The packet-dropping probability and average throughput of the system are simulated with other existing schemes in the studied literature. The results obtained from the simulation indicate that the PR-M2M outperformed existing schemes.

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“…In literature, various contributions have studied the throughput, range, and power of unlicensed (e.g., [1]- [3]) and licensed IoT (e.g., [4]- [6]). Furthermore, a monitoring system was tested by combining NB-IoT and LoRa [7], and a hybrid 3G-LoRa-Sigfox for power grid monitoring was verified with legacy technologies [8].…”

Section: Introductionmentioning

confidence: 99%

Latency of Concatenating Unlicensed LPWAN with Cellular IoT: An Experimental QoE Study

Alvin¹,

Motamedi²,

Abdulla³

2021

Preprint

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Developing low-power wide-area network (LPWAN) solutions that are efficient to adopt, deploy and maintain are vital for smart cities. The poor quality-of-service of unlicensed LPWAN, and the high service cost of LTE-M/NB-IoT are key disadvantages of these technologies. Concatenating unlicensed with licensed LPWANs can overcome these limitations and harness their benefits. However, a concatenated LPWAN architecture will inevitably result in excess latency which may impact users' quality-of-experience (QoE). To evaluate the real-life feasibility of this system, we first propose a concatenated LPWAN architecture and experimentally measure the statistics of end-to-end (E2E) latencies. The concatenated delay margin is determined by benchmarking the latencies with different LPWAN architecture schemes, namely with unlicensed IoT (standalone LoRa), cellular IoT (standalone LTE-M), and concatenated IoT (LoRa interfaced with LTE-M). Through extensive experimental measurement campaigns of 30, 000 data points of E2E latencies, we show that the excess delay due to LPWAN interfacing introduces on average less than 300 milliseconds. The proof-of-concept results suggest that the latency for concatenating unlicensed LPWAN with cellular IoT is negligible for smart city use cases where human perception and decision making is in the loop.

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CTMC modelling for H2H/M2M coexistence in LTE‐A/LTE‐M networks

Cited by 8 publications

References 14 publications

The Impact of Healthcare Traffic Over H2H and M2M Networks During the Spread Phase of Pandemic Diseases

The Impact of Healthcare Traffic Over H2H and M2M Networks During the Spread Phase of Pandemic Diseases

Machine-to-machine and cell-to-cell traffic handling using relay and carrier aggregation prioritize on LTE-A PRO network

Latency of Concatenating Unlicensed LPWAN with Cellular IoT: An Experimental QoE Study

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