Free Access Distributed Queue Protocol for Massive Cellular-Based M2M Communications with Bursty Traffic

Bui, Anh-Tuan H.; Nguyen, Chuyen T.; Thang, Truong Cong; Pham, Anh T.

doi:10.1109/vtcfall.2018.8690880

Cited by 7 publications

(8 citation statements)

References 6 publications

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“…Several works evaluated the DQ algorithm in LTE networks [5], [9], [10], [29]- [32]. In [5], the authors proposed the DQ algorithm as a replacement to the Aloha-based protocols used in LTE networks for the support of the massive number of devices in the network.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of the Distributed Queueing Algorithm in a Multichannel Network with Mixed-Type Traffic

Nibitanga¹,

Mwangi²,

Ndungu³

2021

EAI Endorsed Trans IoT

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Networks such as LTE-M, NB-IoT, LoRa and SigFox are being deployed for massive IoT communications. However, the Aloha protocols used for the media access are inefficient when the network size is high. As a result, a Distributed Queuing (DQ) algorithm is being proposed to replace the conventional access schemes because of its superior network performance. In this paper, we study the algorithm with no prior consideration of any underlying technology at the physical layer. We perform a steady-state analysis of the algorithm in a network where the traffic is composed of periodic and urgent packets. It was found that for high traffic from the network all the nodes were taking part in each contention. Nevertheless, for better performance in terms of access delay and packet drop rate, it was preferable to operate the algorithm in the small traffic interval. Moreover, the downlink traffic has been observed to have a significant impact on the stability of the algorithm.

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Section: Related Workmentioning

confidence: 99%

“…Otherwise, they are sent in a Data Transmission Queue (DTQ) when they have been granted access to the channel and they are waiting for data transmission. Several works [5]- [7], [9]- [11] analyzed the DQ algorithm and have found that it outperforms Aloha-based algorithms in terms of several network performance metrics.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Distributed Queueing Algorithm in a Multichannel Network with Mixed-Type Traffic

Nibitanga¹,

Mwangi²,

Ndungu³

2021

EAI Endorsed Trans IoT

View full text Add to dashboard Cite

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“…The authors in [85] considered the use of DQ approach with a proposed modification to eliminate the need for employing class barring in 5G and LTE-A networks for M2M devices. Their objective was to deal with massive and bursty M2M device access that may be delayed during the barring process.…”

Section: ) Collision Resolution Using Grouped Virtual Dqmentioning

confidence: 99%

Media Access Control in Large-Scale Internet of Things: A Review

2020

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The Media Access Control (MAC) serves as an imperative part of wireless communication networks which enables efficient provisioning of communication resources for device interconnectivity and ensures Quality of Service (QoS). The emergence of Large-Scale Internet of Things (LS-IoT) networks is characterised by a multi-domain distributed wireless communication network that provides end-to-end connectivity for a multitude of active heterogeneous Machine-to-Machine (M2M) devices. The nature of LS-IoT networks requires robust and scalable MAC protocols to manage concurrent, dynamic and massive media access and resource allocation. Several reviews have been conducted on MAC protocols with a handful of them focused on LS-IoT networks or massive M2M networks. In this paper, the characterisation of LS-IoT networks and the MAC component are extensively discussed delineating the impact of LS-IoT on the MAC. Emerging research issues on the MAC for LS-IoT networks including high collision probability, high control overheads, spectrum constraints, timing constraints, hardware constraints, energy consumption and hidden terminals are discussed. Some recently proposed solutions in literature for enhancing the MAC in LS-IoT networks are discussed under LS-IoT specific MAC protocol enhancement classifications. The contributions and drawbacks of the recent solutions in literature are discussed and summarised. The Future direction that is oriented towards the use of virtualisation within the framework of Network Function Virtualisation (NFV) and Software-Defined Networking (SDN) approaches is proposed. This is aimed at providing a dynamic distributed multidimensional MAC resource scaling approach for LS-IoT access network devices to ensure a robust and effective MAC for massive M2M devices in LS-IoT networks. INDEX TERMS Large-scale Internet of Things (LS-IoT), machine-to-machine (M2M) communication, media access control (MAC), wireless networks.

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“…Bui et al proposed the Free Access Distributed Protocol (FADQ) in [12]. First, the algorithm divides the number of colliding devices into a subset of smaller groups and then applies the conventional DQ algorithm to each group.…”

Section: Dq In Lte Networkmentioning

confidence: 99%

“…Most of the studies reviewed have been found to put an emphasis on the evaluation of these four performance metrics: the access delay [3]- [5], [9], [10], [12]- [16], the throughput [2]- [6], [15], [16], the energy consumption [2], [3], [5], [8], [14], and the number of attempts per node [3], [4], [10], [11]. Besides, it is a fact that the algorithm outperforms the Aloha-based schemes in terms of those performance metrics.…”

Section: Introductionmentioning

confidence: 99%

Statistical Analysis of a Distributed Queuing Random Access Protocol in a Massive Communication Environment

Nibitanga¹,

Mwangi²,

Ndungu³

2020

EAI Endorsed Trans IoT

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Most of the networks deployed for massive IoT communications use Aloha-based algorithms for channel access. However, those algorithms are known to be unstable and inefficient when the network size is high. Since recently, a Distributed Queuing (DQ) algorithm is being proposed as a solution to mitigate several of the Aloha issues in IoT networks. In this paper, a statistical performance analysis of the DQ algorithm without any prior consideration of any physical layer is presented. We evaluate the DQ algorithm in a massive communication environment and give the average values for these performance metrics: collision resolution time, access delay per sensor, channel throughput, number of attempts required by a sensor to complete the contention process, number of nodes contending per frame and the distribution of contention slots into idle, successful, and collided. The goal of this paper is to provide a statistical baseline performance evaluation of the DQ algorithm in general.

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Free Access Distributed Queue Protocol for Massive Cellular-Based M2M Communications with Bursty Traffic

Cited by 7 publications

References 6 publications

Analysis of the Distributed Queueing Algorithm in a Multichannel Network with Mixed-Type Traffic

Analysis of the Distributed Queueing Algorithm in a Multichannel Network with Mixed-Type Traffic

Media Access Control in Large-Scale Internet of Things: A Review

Statistical Analysis of a Distributed Queuing Random Access Protocol in a Massive Communication Environment

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