Collision chain mitigation and hidden device-aware grouping in large-scale IEEE 802.11ah networks

Damayanti, Wayan; Kim, Sang-Hyun; Yun, Ji-Hoon

doi:10.1016/j.comnet.2016.09.006

Cited by 29 publications

(10 citation statements)

References 13 publications

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“…Recently, some more interesting approaches have popped up. Several algorithms focus on mitigating hidden node collisions by splitting mutually hidden nodes into orthogonal groups [ 18 , 19 , 20 ]. Additionally, Chang et al proposed a set partitioning algorithm that assumes the (static) traffic demand of each station is known by the AP and load balances them across groups [ 21 ].…”

Section: Related Workmentioning

confidence: 99%

“…Additionally, Chang et al proposed a set partitioning algorithm that assumes the (static) traffic demand of each station is known by the AP and load balances them across groups [ 21 ]. Most of these recent algorithms still assume simplified homogeneous traffic [ 18 , 20 ]. However, two algorithms [ 19 , 21 ] focus on more realistic traffic, where stations have heterogeneous, static and non-saturated traffic.…”

Section: Related Workmentioning

confidence: 99%

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Real-Time Station Grouping under Dynamic Traffic for IEEE 802.11ah

Tian

Khorov

Latré

et al. 2017

Sensors

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IEEE 802.11ah, marketed as Wi-Fi HaLow, extends Wi-Fi to the sub-1 GHz spectrum. Through a number of physical layer (PHY) and media access control (MAC) optimizations, it aims to bring greatly increased range, energy-efficiency, and scalability. This makes 802.11ah the perfect candidate for providing connectivity to Internet of Things (IoT) devices. One of these new features, referred to as the Restricted Access Window (RAW), focuses on improving scalability in highly dense deployments. RAW divides stations into groups and reduces contention and collisions by only allowing channel access to one group at a time. However, the standard does not dictate how to determine the optimal RAW grouping parameters. The optimal parameters depend on the current network conditions, and it has been shown that incorrect configuration severely impacts throughput, latency and energy efficiency. In this paper, we propose a traffic-adaptive RAW optimization algorithm (TAROA) to adapt the RAW parameters in real time based on the current traffic conditions, optimized for sensor networks in which each sensor transmits packets with a certain (predictable) frequency and may change the transmission frequency over time. The TAROA algorithm is executed at each target beacon transmission time (TBTT), and it first estimates the packet transmission interval of each station only based on packet transmission information obtained by access point (AP) during the last beacon interval. Then, TAROA determines the RAW parameters and assigns stations to RAW slots based on this estimated transmission frequency. The simulation results show that, compared to enhanced distributed channel access/distributed coordination function (EDCA/DCF), the TAROA algorithm can highly improve the performance of IEEE 802.11ah dense networks in terms of throughput, especially when hidden nodes exist, although it does not always achieve better latency performance. This paper contributes with a practical approach to optimizing RAW grouping under dynamic traffic in real time, which is a major leap towards applying RAW mechanism in real-life IoT networks.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Real-Time Station Grouping under Dynamic Traffic for IEEE 802.11ah

Tian

Khorov

Latré

et al. 2017

Sensors

View full text Add to dashboard Cite

show abstract

“…Their simplicity makes it computationally feasible to deploy them in real networks. Several algorithms utilize RAW to mitigate hidden node collisions by splitting mutually hidden nodes into orthogonal groups [13], [14], [15]. Chang et al proposed a set partitioning algorithm that assumes the (static) traffic demand of each station is known by the AP and load balances them across groups [16].…”

Section: Related Workmentioning

confidence: 99%

“…15 Mbps represents a near-saturated state, T = 0.11 Mbps represents a medium traffic load and T = 0.095 Mbps results in low traffic load. As E-TAROA was already shown to outperform…”

mentioning

confidence: 99%

IEEE 802.11ah Restricted Access Window Surrogate Model for Real-Time Station Grouping

Tian

Mehari

Santi

et al. 2018

2018 IEEE 19th International Symposium on "A World of Wireless, Mobile and Multimedia Networks" (WoWMoM)

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The Restricted Access Window (RAW) mechanism proposed by IEEE 802.11ah promises to address one of the major problems of the Internet of Things (IoT): high channel contention in large-scale densely deployed sensor networks. The RAW feature allows the Access Point (AP) to divide stations into different groups, with only the stations in the same group being allowed to access the channel simultaneously. Existing station grouping strategies only support homogeneous scenarios, where all sensor stations have the same fixed data transmission interval, modulation and coding scheme (MCS) and packet size. In this paper, we present two contributions to address this issue. First, a surrogate model that predicts RAW performance given specific network conditions and RAW configuration parameters. It is fast to train and can be solved in real-time. Second, the Model-Based RAW Optimization Algorithm (MoROA), which uses the surrogate model to determine the optimal RAW configuration in real-time, for heterogeneous stations and dynamic traffic. We compare the accuracy of our surrogate model to simulation results. Performance of MoROA is compared to existing RAW optimization algorithms and traditional 802.11 channel access methods. The results shows that the trained surrogate model can accurately predict RAW performance with a relative error less than 7% and 10% for 95% and 98% of the RAW configurations respectively. MoROA achieves a throughput up to twice as high as traditional 802.11 channel access functions in dense heterogeneous networks.

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“…One approach for mitigating the hidden node problem of IEEE 802.11 wireless LANs is to divide nodes into multiple clusters so that no hidden node problem exists in each cluster and assign distinct time slots or orthogonal frequency slots to clusters [4,5]. However, the exponential performance degradation of the DCF due to the huge number of active nodes in IoT (internet of things) traffic environment was not considered in [4,5]. In [6], combining the DCF and the PCF, the clustering method for resolving the performance degradation problem due to the huge number of active nodes was proposed without the consideration of the hidden node problem.…”

Section: Introductionmentioning

confidence: 99%