Service Differentiation in IEEE 802.11ah WLAN under Restricted Access Window based MAC protocol

Sangeetha, U.; Babu, A. V.

doi:10.1016/j.comcom.2021.03.017

Cited by 10 publications

(5 citation statements)

References 17 publications

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“…Related studies concerning RAW optimization are presented here, and details are shown in Table 5 . The current solutions for RAW optimization in the literature primarily focus on enhancing the network performance in terms of throughput [ 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 115 ], channel efficiency/utilization [ 97 , 104 , 116 , 117 , 118 ], and energy consumption [ 59 , 102 , 105 , 111 , 112 , 119 , 120 , 121 , 122 , 123 , 124 ].…”

Section: Mac Features Of the Ieee 80211ah And Current Resource Alloca...mentioning

confidence: 99%

“…However, in a high network density case, the performance is degraded by increasing the number of STAs due to high contention. Recently, the authors in [ 115 ] proposed a grouping method by splitting the RAW into two sub-RAWs. Their proposed RAW method allows the STAs to group in a similar size inside each sub-RAW.…”

Section: Mac Features Of the Ieee 80211ah And Current Resource Alloca...mentioning

confidence: 99%

“…unknown[114] UL/DL throughput the prediction accuracy of the right number of slots in a RAW, contained in BI is still a concern analytical[115] UL/DL throughput multi-class with traffic-aware STAs and heterogeneous traffic requirements are not considered MATLAB…”

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confidence: 99%

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Resource Management for Massive Internet of Things in IEEE 802.11ah WLAN: Potentials, Current Solutions, and Open Challenges

Farhad

Pyun

2022

Sensors

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IEEE 802.11ah, known as Wi-Fi HaLow, is envisioned for long-range and low-power communication. It is sub-1 GHz technology designed for massive Internet of Things (IoT) and machine-to-machine devices. It aims to overcome the IoT challenges, such as providing connectivity to massive power-constrained devices distributed over a large geographical area. To accomplish this objective, IEEE 802.11ah introduces several unique physical and medium access control layer (MAC) features. In recent years, the MAC features of IEEE 802.11ah, including restricted access window, authentication (e.g., centralized and distributed) and association, relay and sectorization, target wake-up time, and traffic indication map, have been intensively investigated from various aspects to improve resource allocation and enhance the network performance in terms of device association time, throughput, delay, and energy consumption. This survey paper presents an in-depth assessment and analysis of these MAC features along with current solutions, their potentials, and key challenges, exposing how to use these novel features to meet the rigorous IoT standards.

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Section: Mac Features Of the Ieee 80211ah And Current Resource Alloca...mentioning

confidence: 99%

Section: Mac Features Of the Ieee 80211ah And Current Resource Alloca...mentioning

confidence: 99%

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Resource Management for Massive Internet of Things in IEEE 802.11ah WLAN: Potentials, Current Solutions, and Open Challenges

Farhad

Pyun

2022

Sensors

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“…The proposed analytical model was validated based on the ns-3 simulator. Later, they extended their analytical model to address the grouping of stations according to traffic priority [ 30 ].…”

Section: Related Workmentioning

confidence: 99%

An Analytical Model for the Aggregate Throughput of IEEE 802.11ah Networks under the Restricted Access Window Mechanism

Soares

Carvalho

2022

Sensors

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The IEEE 802.11ah is an amendment to the IEEE 802.11 standard to support the growth of the Internet of Things (IoT). One of its main novelties is the restricted access window (RAW), which is a channel access feature designed to reduce channel contention by dividing stations into RAW groups. Each RAW group is further divided into RAW slots, and stations only attempt channel access during the RAW slot they were assigned to. In this paper, we propose a discrete-time Markov chain model to evaluate the average aggregate throughput of IEEE 802.11ah networks using the RAW mechanism under saturated traffic and ideal channel conditions. The proposed analytical model describes the behavior of an active station within its assigned RAW slot. A key aspect of the model is the consideration of the event of RAW slot time completion during a station’s backoff operation. We study the average aggregate network throughput for various numbers of RAW slots and stations in the network. The numerical results derived from our analytical model are compared to computer simulations based on an IEEE 802.11ah model developed for the ns-3 simulator by other researchers, and its performance is also compared to two other analytical models proposed in the literature. The presented results indicate that the proposed analytical model reaches the closest agreement with independently-derived computer simulations.

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“…The authors of [18] predict the service interval of a monitoring application and schedule the subsequent frames before their arrivals without requiring any further contention. To increase the saturation throughput of the network and the relative service differentiation ratio of the lower priority class devices, the authors in [19] propose a priority-based grouping in which the devices belonging to the same priorities are grouped together. In [20], the authors propose an optimization model by using adaptive neuro-fuzzy inference system to find the optimal number of RAW slots.…”

Section: Introductionmentioning

confidence: 99%

Scalability Improvement of IEEE 802.11ah IoT Networks

Naghzali

Kazeminia

Mehrjoo

2022

Wireless Pers Commun

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In this paper, we propose a non-orthogonal multiple access (NOMA) based grouping method for IEEE 802.11ah, a promising platform for the internet of things (IoT). The grouping method improves the scalability of IoT networks, by reducing collisions in the access points (APs). The proposed method puts those IoT devices (IoT-Ds) whose channel gains are far enough from each other, i.e., who satisfy NOMA constraints, in the same group. Therefore, using successive interference cancellation (SIC), the AP is able to decode the simultaneous signal transmissions from IoT-Ds in a group. To assign IoT-Ds into groups and determine their transmission power, we formulate a total throughput maximization problem as a joint optimal grouping and power allocation problem, which is a non-convex mixed-integer programming problem. We convert it to a convex problem using quadratic fractional programming (QFP), and then we solve it using augmented Lagrange multiplier (ALM) method. Moreover, to reduce the complexity of the solution, we propose a fast grouping method to allocate power to each group in parallel. Simulation results show that the proposed methods have outstanding performance compared to conventional association identifier (AID)-based grouping method; besides, scalability of the network in terms of throughput, power consumption and channel utilization improves dramatically because of the collision reduction of IoT-Ds, which is achieved by deploying NOMA and SIC. Furthermore, the fast grouping method decreases the computational complexity greatly at the expense of a small reduction in network performance.

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Service Differentiation in IEEE 802.11ah WLAN under Restricted Access Window based MAC protocol

Cited by 10 publications

References 17 publications

Resource Management for Massive Internet of Things in IEEE 802.11ah WLAN: Potentials, Current Solutions, and Open Challenges

Resource Management for Massive Internet of Things in IEEE 802.11ah WLAN: Potentials, Current Solutions, and Open Challenges

An Analytical Model for the Aggregate Throughput of IEEE 802.11ah Networks under the Restricted Access Window Mechanism

Scalability Improvement of IEEE 802.11ah IoT Networks

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