Most wireless communication technologies for Internet of Things (IoT) applications face the bottleneck of dense and large-scale use cases. One solution to this problem is a periodic channel reservation strategy, in which only a small group of stations can compete for channel access during a given period. The IEEE 802.11ah standard, a.k.a. Wi-Fi HaLow, deploys this idea in its channel access protocol, named Restricted Access Window (RAW). A single RAW consists of one or more RAW slots during which only designated stations can contend for channel access. This paper considers an IEEE 802.11ah-based network with randomly distributed stations around the Access Point (AP), operating under a Rayleigh-fading channel with capture enabled. We develop an analytical model to evaluate the contention of a group of stations and propose a Load-Aware Channel Allocation (LACA) algorithm for the RAW slot period. The LACA algorithm ensures the delivery of all packets that designated stations carry, allowing for the allocation of load-aware RAW slots, which is effective in enhancing the Age of Information (AoI). We evaluate the Packet Delivery Ratio (PDR) and channel usage within a pre-allocated RAW slot to prove the effectiveness of our proposal. We further study the impact of the spatial distribution of the stations around the AP and the capture effect under a Rayleigh channel on the performance of the proposed LACA algorithm. Extensive simulations are used to validate our analytical results. Our proposal provides a load-aware and adaptive channel allocation scheme based on the dynamic conditions of the network. Our model can be implemented in a global configuration scheme for the RAW mechanism in heterogeneous networks or for alternative communication technologies that address dense scenarios with the integration of periodic channel reservations.

The IEEE 802.11ah standard, marketed as Wi-Fi Halow, introduces a new channel access mechanism called the Restricted Access Window (RAW), aiming to provide connectivity for the Internet of Things (IoT) applications over broad areas. RAW aspires to alleviate the contention by splitting the channel access into periods and allocating each period to a given group of stations. This paper develops an analytical framework based on Probability and Renewal theories for modeling and evaluating an IEEE 802.11ah-based network implementing the RAW mechanism. We consider a Rayleigh-fading channel with the presence of the capture effect: a realistic scenario for IoT networks deployed in dense urban environments. Considering a single-hop scenario of stations randomly distributed around an Access Point (AP) and the power attenuation of transmitted packets, we model the channel access under capture awareness. As the RAW mechanism presents a time-limited contention for channel access, we develop a counting process that tracks transmissions up to the end of the contention time interval. Henceforth, we evaluate the network performance in terms of throughput. We meticulously validate the derived analytical results through extensive campaigns of discrete-event simulations. Our study evaluates the impact of different parameters on the overall performance, including the contention time, the number of stations, the number of groups, and the capture threshold. We henceforth study the impact of the capture effect on enhancing the network performance under the grouping feature introduced by the RAW mechanism. This work contributes to developing an analytical modeling framework to evaluate the performance of time-limited random access mechanisms accurately and can be an excellent basis for proposing practical scheduling algorithms to configure the RAW mechanism under non-ideal channel conditions.

The Restricted Access Window (RAW) mechanism introduced in the IEEE 802.11ah standard aims to address the high channel contention of Internet of Things (IoT) networks. It allows the Access Point (AP) to periodically limit channel access to only one group of stations during a short time interval called RAW slot. Such a time limit prevents the channel access contention from reaching the steady state. Henceforth, we present a renewal theory based analytical model for the time-limited contention within the RAW slot. Furthermore, we construct a counting process to track transmissions within the contention interval and derive closed-form analytical results. As IoT networks are typically composed of battery-powered devices, it is compulsory to manage the energy consumption of the network in order to extend the lifetime of devices. Hence, we analyze and evaluate the energy consumption and energy efficiency during the RAW slot period. Extensive simulations using Matlab software validate the analytical model and prove the effectiveness of our proposals.

The Restricted Access Window (RAW) mechanism included in the IEEE 802.11ah standard is one of the promising solutions to meet the requirements of Internet of Things (IoT) applications. It splits channel time into RAW slots and limits channel access to only a group of stations at a time. In this paper, we consider an IEEE 802.11ah network operating under Rayleigh fading channel with capture effect, and we develop efficient schemes to optimize the gain of the RAW mechanism. We propose an algorithm to identify the best cases for the RAW slot duration. Then, we deploy this result to develop a resource-efficient scheme to reconfigure a RAW consisting of several RAW slots. The new configuration eliminates the extra time wasted during the RAW period, resulting in higher throughput and energy efficiency. We present numerical results and simulations to validate our findings and analyze the impact of several parameters. The proposed RAW reconfiguration scheme significantly increases RAW performance by up to 21% for throughput and up to 8% for energy efficiency. It further optimizes the channel allocation resources, providing a channel gain ratio of up to 19%.