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IEEE 802.11ah, primarily developed for Internet of things (IoT) applications. A restricted access window (RAW) mechanism is employed for channel access to minimize contention among the nodes in dense IoT networks. Here, we present an analytical model for IEEE 802.11ah RAW mechanism with unsaturated conditions. In IEEE 802.11ah, determination of optimal number of RAW slots/groups affects the network performance. Thus, choosing the optimal number of RAW slots according to network size, RAW duration and modulation coding scheme (MCS) can improve the performance of IEEE 802.11ah network. Recently, deep learning (DL) becomes a thriving field for communication and networking applications. In this research work, we propose a long short-term memory (LSTM) based DL-recurrent neural network (DL-RNN) to predict the optimal RAW slots. Using genetic algorithm, we obtain optimal RAW slots from the presented analytical model with unsaturated conditions for the IEEE 802.11ah IoT network. Further, the dataset with optimal number of RAW slots for each of the combinations (network sizes, RAW durations and MCSs) is used for training the LSTM based DL-RNN. Based on the optimal RAW slots, throughput and energy efficiency (EE) are computed for IEEE 802.11ah network. From results, we notice that optimal RAW slots generated by LSTM based DL-RNN model significantly improve throughput and EE of IEEE 802.11ah network comparison to existed machine learning and Fuzzy optimization techniques. [ABSTRACT FROM AUTHOR]

The widespread adoption of IoT-enabled communication owes much to the introduction of the IEEE 802.11ah standard for Wi-Fi communications. Operating within the sub 1 GHz band, this standard offers numerous enhanced features, including scalability to accommodate more stations with lower energy consumption compared to existing technologies. Ensuring optimal energy efficiency in IoT networks is particularly critical in densely populated, high contention environments to support long-range connectivity. Given the promising future of IEEE 802.11ah in enabling autonomous IoT communications, enhancing the Restricted Access Window (RAW) mechanism becomes pivotal for prolonging device lifetimes and fostering innovation in protocol development. This study's highlight lies in investigating energy efficiency within the RAW. Preceding the formulation of an optimization function for maximizing energy efficiency, a comprehensive energy performance analysis was conducted. Both analytical and simulation results across various scenarios corroborate the efficacy of the proposed methodology. [ABSTRACT FROM AUTHOR]

Wireless local area networks (WLANs) play a crucial role in the internet of things (IoT) landscape, facilitating constant data exchange among devices. However, the inherent security vulnerabilities in these networks, stemming from limited computational resources, pose significant challenges to deploying robust security measures. This research addresses the security concerns surrounding IoT devices within the IEEE 802.11ah WLAN environment by introducing the SecureScanML algorithm, a novel machine learning (ML) approach designed to optimize Internet-wide port scans (IWPS) for enhanced device security while preserving network performance. The SecureScanML algorithm leverages Q-learning, a reinforcement learning technique, to dynamically adjust the scan rates of IoT devices adaptively. Through this approach, the algorithm effectively reduces vulnerabilities, achieving a notable 35.7% reduction, while maintaining key network performance metrics. With a throughput of 2.8 Mbps, a packet delivery ratio of 97.3%, an adaptability index of 0.91, a convergence speed of 420 s, and a low latency of 42 ms, the proposed algorithm surpasses existing methods such as TA, RA, SSR, CNN-LSTM, RLA, and RSR. Simulation results corroborate the efficacy of SecureScanML in mitigating vulnerabilities without compromising network efficiency. The algorithm strikes a fine balance between proactive vulnerability management and network performance preservation. Moreover, the study explores the impact of security-performance weight parameters on the algorithm's behavior, providing valuable insights for fine-tuning the system to achieve specific security and performance objectives. Notably, for a security-performance weight of 0.5, the algorithm demonstrates high throughput and vulnerability reduction alongside low latency. [ABSTRACT FROM AUTHOR]

The IEEE 802.11ah standard is introduced to address the growing scale of internet of things (IoT) applications. To reduce contention and enhance energy efficiency in the system, the restricted access window (RAW) mechanism is introduced in the medium access control (MAC) layer to manage the significant number of stations accessing the network. However, to achieve optimized network performance, it is necessary to appropriately determine the RAW parameters, including the number of RAW groups, the number of slots in each RAW, and the duration of each slot. In this paper, we optimize the configuration of RAW parameters in the uplink IEEE 802.11ah-based IoT network. To improve network throughput, we analyze and establish a RAW parameters optimization problem. To effectively cope with the complex and dynamic network conditions, we propose a deep reinforcement learning (DRL) approach to determine the preferable RAW parameters to optimize network throughput. To enhance learning efficiency and stability, we employ the proximal policy optimization (PPO) algorithm. We construct network environments with periodic and random traffic in an NS-3 simulator to validate the performance of the proposed PPO-based RAW parameters optimization algorithm. The simulation results reveal that using the PPO-based DRL algorithm, optimized RAW parameters can be obtained under different network conditions, and network throughput can be improved significantly. [ABSTRACT FROM AUTHOR]

Iterative subcarrier regularization (ISR) has been recently proposed to mitigate the nonlinear noise caused by the saturation of power amplifiers in orthogonal frequency-division multiplexing systems. The generalized Papoulis–Gerchberg algorithm (GPGA) used in ISR exploits the frequency-domain prior knowledge specified by standards and time-domain prior knowledge reserved by low nonlinearity amplitudes. It is a reconstruction-based method that may cause bit error rate (BER) degradation when coding techniques are applied. In this work, a dual-loop structure of the GPGA is presented to integrate the cancellation-based methods for maintaining the coding gain. By using a well-known cancellation-based method, iterative clipping noise cancellation (ICNC), as an example, simulations based on the IEEE 802.11ah verified that the proposed scheme effectively maintains the contribution of both the reconstruction-based and cancellation-based methods, thus significantly outperforming the ISR or ICNC in terms of coded BER.

IEEE 802.11ah, which is a wireless network protocol designed for Internet of Things (IoT), devised the Restrict Access Window (RAW) mechanism to tackle the collision problem. However, some RAW's slots may be overloaded; some RAW's slots may be lightly loaded or have no load because no stations (STA) allocated in these slots need to access channel. This work adopted (i) the Registered Backoff Time (RBT) mechanism such that each STA can register the backoff time for its future channel access in access point (AP) and (ii) the Claiming RAW mechanism such that STAs are allowed to notify AP that they have uplinked data to transmit. In this way, AP can (1) know which STAs have uplinked data to transmit and thus (2) re-schedule those STAs from the overloaded slots to the slots that (i) are underloaded or (ii) no STAs need to access channel based on STAs' RBTs in advance. As a result, the proposed Registration-based Regrouping for Load-Balance Channel Access (RRG-LBCA) method can achieve load balance among slots to increase the IEEE 802.11ah network's performance. The performance evaluation results shown that the proposed RRG-LBCA method can eliminate the collision situation and increase the aggregate throughput. [ABSTRACT FROM AUTHOR]

The rapid proliferation of Internet of Things (IoT) devices has spurred the need for robust security mechanisms within wireless local area network (WLAN) environments. Specifically, the IEEE 802.11ah (WiFi-HaLow) technology offers low-power, long-range communication capabilities ideally suited for IoT devices, but these devices face security threats due to constrained computational resources. This research addresses the challenge of optimizing Internet-wide port scanning to enhance IoT security while minimizing disruptions to network performance. In this paper, we propose a novel reinforcement learning-based approach to achieve this optimization. Our approach harnesses the power of the Proximal Policy Optimization (PPO) algorithm to guide decision-making for IoT security and network performance enhancement. The proposed solution entails training an agent to dynamically adjust the port scanning rate, ensuring the delicate balance between security improvement and network responsiveness. We establish a comprehensive system model, encompassing WiFi-HaLow infrastructure, IPv6-enabled IoT devices, and the integration of Internet-security (IPSec) protocols. Mathematical formulations encapsulate constraints such as resource limitations and security-performance trade-offs. Our experimental setup utilizes a carefully curated dataset and a simulation environment to rigorously evaluate the proposed solution's effectiveness. The proposed approach demonstrates remarkable security enhancement through the prevention of unauthorized access attempts, data breaches, and improved intrusion detection. Moreover, network performance is optimized, with reductions in latency, improvements in throughput, and energy-efficient operation. By presenting a plethora of comparison scenarios, we validate the superiority of our solution against various benchmarks. This research contributes a comprehensive framework that not only advances IoT security within WLAN environments but also highlights the intricate relationship between security and network performance optimization. [ABSTRACT FROM AUTHOR]

IEEE 802.11ah, known as Wi-Fi HaLow standard, specifically promoted for next-generation Internet of things (IoT) applications. Restricted access window (RAW) mechanism is introduced in IEEE 802.11ah medium access control (MAC) layer. The RAW mechanism reduces the collisions among contending devices by partitioning RAW period into RAW sots and allocates a RAW slot to each group. Thus, the specific group of devices alone contend for channel access in the respective RAW slot. In this paper, we develop an accurate analytical model to compute the throughput and energy efficiency of IEEE 802.11ah with the RAW mechanism. In IEEE 802.11ah, the choice of optimal number of RAW slots is an open research problem. Thus, we present a gated recurrent unit (GRU) based deep learning-recurrent neural network (DL-RNN) to estimate the optimal RAW slots that improves the performance of IEEE 802.11ah for dense IoT networks. From the results, we observe that throughput and energy efficiency performance are significantly improved by using optimal number of RAW slots obtained using GRU. The analytical works conducted are validated with extensive simulation works. [ABSTRACT FROM AUTHOR]

The IEEE 802.11ah standard is introduced to address the growing scale of internet of things (IoT) applications. To reduce contention and enhance energy efficiency in the system, the restricted access window (RAW) mechanism is introduced in the medium access control (MAC) layer to manage the significant number of stations accessing the network. However, to achieve optimized network performance, it is necessary to appropriately determine the RAW parameters, including the number of RAW groups, the number of slots in each RAW, and the duration of each slot. In this paper, we optimize the configuration of RAW parameters in the uplink IEEE 802.11ah-based IoT network. To improve network throughput, we analyze and establish a RAW parameters optimization problem. To effectively cope with the complex and dynamic network conditions, we propose a deep reinforcement learning (DRL) approach to determine the preferable RAW parameters to optimize network throughput. To enhance learning efficiency and stability, we employ the proximal policy optimization (PPO) algorithm. We construct network environments with periodic and random traffic in an NS-3 simulator to validate the performance of the proposed PPO-based RAW parameters optimization algorithm. The simulation results reveal that using the PPO-based DRL algorithm, optimized RAW parameters can be obtained under different network conditions, and network throughput can be improved significantly.

IEEE 802.11ah, or Wi-Fi HaLow, is a long-range Internet of Things (IoT) communication technology with promising performance claims. Being IP-based makes it an attractive prospect when interfacing with existing IP networks. Through real-world performance experiments, this study evaluates the network performance of Wi-Fi HaLow in terms of throughput, latency, and reliability against IEEE 802.11n (Wi-Fi n) and a competing IoT technology LoRa. These experiments are enabled through three proposed network evaluation architectures that facilitate remote control of the devices in a secure manner. The performance of Wi-Fi HaLow is then assessed against the network requirements of various smart grid applications. Wi-Fi HaLow offers promising performance when compared to rival technology LoRa. This study is the first to evaluate Wi-Fi HaLow in an authentic experimental way, providing performance data and insights that are not possible through simulation and modelling alone. This work provides the basis for further evaluation and implementation of this emerging technology. [ABSTRACT FROM AUTHOR]

IEEE 802.11ah is a new Wi-Fi standard for sub-1Ghz communications, aiming to address the challenges of the Internet of Things (IoT). Significant changes in the legacy 802.11 standards have been proposed to improve the network performance in high contention scenarios, the most important of which is the Restricted Access Window (RAW) mechanism. This mechanism promises to increase the throughput and energy efficiency by dividing stations into different groups. Under this scheme, only the stations belonging to the same group may access the channel, which reduces the collision probability in dense scenarios. However, the standard does not define the RAW grouping strategy. In this paper, we develop a new mathematical model based on the renewal theory, which allows for tracking the number of transmissions within the limited RAW slot contention period defined by the standard. We then analyze and evaluate the performance of RAW mechanism. We also introduce a grouping scheme to organize the stations and channel access time into different groups within the RAW. Furthermore, we propose an algorithm to derive the RAW configuration parameters of a throughput maximizing grouping scheme. We additionally explore the impact of channel errors on the contention within the time-limited RAW slot and the overall RAW optimal configuration. The presented analytical framework can be applied to many other Wi-Fi standards that integrate periodic channel reservations. Extensive simulations using the MATLAB software validate the analytical model and prove the effectiveness of the proposed RAW configuration scheme.

IEEE 802.11ah is a protocol that adopts the CSMA/CA-based channel access way for Internet of Thing (IOT). This work proposed a newly devised Restricted Access Window (RAW) called the Claiming RAW to let those spontaneous stations having high priority's uplinked data frames do claims of intending to uplink high priority's data to reduce collisions. In addition, instead of generating the random backoff time after the collision happens, which the regular CSMA/CA-based 802.11* protocols adopt, this work adopted the registered backoff time mechanism, for which a station registers its next backoff time to IEEE 802.11ah AP when its current channel access is finished, i.e., a station generates the backoff time before the collision happens. Thus, IEEE 802.11ah AP is able to schedule stations' channel accesses based on stations' registered backoff time to avoid collisions more effectively. Through the use of the Claiming RAW and the registered backoff time mechanisms, IEEE 802.11ah AP can know which stations have high priority's uplinked data frames and then schedule these stations to have the higher privilege to access the channel. The simulation results shown that. Comparing with the traditional IEEE 802.11ah, the simulation results shown that the proposed Claim-based Priority-Discriminated Channel Access (CPDCA)method has a lower collision rate and higher throughput in the network environment having the higher number of stations in each time slot; the transmission time of those stations with high priority's uplinked data frames using the CPDCA mechanism is much lower than that of using the traditional IEEE 802.11ah. [ABSTRACT FROM AUTHOR]

The recent IEEE 802.11ah amendment has proven to be suitable for supporting large-scale devices in Internet of Things (IoT). It is essential to provide a minimum level of Quality of Service (QoS) for critical applications such as industrial automation and healthcare. In this paper, we propose a QoS-aware Medium Access Control (MAC) layer solution to enhance network reliability and reduce critical traffic latency by an adaptive station grouping and a priority traffic scheduling scheme. First, a link layer representation of traffic categories as per the delay and reliability requirements is proposed. Second, a novel backoff size-based slot scheduling scheme for Restricted Access Window (RAW) is proposed to support QoS. Third, a grouping scheme is proposed to calculate the current traffic load and balance it among different RAW groups. Finally, a Markov-chain model is developed to study the throughput and latency behaviors of the traffic generated from the critical application. The proposed protocol shows significant delay improvement for priority traffic. The overall throughput performance improves up to 12.7% over the existing RAW grouping scheme. [ABSTRACT FROM AUTHOR]

A large number of sensor and actuator devices are being deployed for sensing and automation in a smart environment. While enabling communication for a large number of stations with RAW in IEEE 802.11ah, the state-of-the-art solutions for channel access are deficient in dealing with both periodic uplink and event-driven downlink actuation at the same time, as per the application’s criteria. In this paper, we propose IoT-MAC, a downlink traffic-aware Medium Access Control (MAC) protocol for automation in smart spaces. The proposed scheme uses new RAW frames to schedule downlink actuation traffic, considering the periodicity and freshness of uplink traffic. IoT-MAC identifies the periodicity of uplink traffic and schedules a frame without further contention. It then prioritizes critical downlink traffic without losing fresh uplink data. The performance analysis of the proposed scheme shows significant improvement in terms of throughput, delay, power consumption and packet loss for running different IoT applications.

This paper proposes an algorithm for the dynamic assignment of Restricted Access Window (RAW) for IEEE 802.11 ah. The Internet of Things (IoT) has groups of sensors and stations interacting over a common network. Wireless Local Area Network (WLAN) standard IEEE 802.11ah is best for IoT because it has high data rate and wide coverage while consuming low power. IEEE 802.11ah uses cautiously selected fixed RAW through an Access Point (AP), so that the chances of packet collision is minimized and energy efficiency is improved. By keeping it static, however, the effectiveness of RAW to utilize channels is reduced. We are suggesting an algorithm in which AP roughly calculates network packet transmission and the number of transmitting stations, and assigns resources to IoT networks by modifying RAW dynamically. The proposed algorithm shows improvement in average delay, throughput, PDR and jitter. The proposed algorithm achieves one of the best performances with an average delay of 0.43 sec, throughput of about 23 kbps, maximum packet delivery ratio (PDR) of about 98%, and jitter of about 80 sec. [ABSTRACT FROM AUTHOR]

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, 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. [ABSTRACT FROM AUTHOR]

The IEEE 802.11ah standard is intended to adapt the specifications of IEEE 802.11 to the Internet of Things (IoT) scenario. One of the main features of IEEE 802.11ah consists of the Restricted Access Window (RAW) mechanism, designed for scheduling transmissions of groups of stations within certain periods of time or windows. With an appropriate configuration, the RAW feature reduces contention and improves energy efficiency. However, the standard specification does not provide mechanisms for the optimal setting of RAW parameters. In this way, this paper presents a grouping strategy based on a genetic algorithm (GA) for IEEE 802.11ah networks operating under the RAW mechanism and considering heterogeneous stations, that is, stations using different modulation and coding schemes (MCS). We define a fitness function from the combination of the predicted system throughput and fairness, and provide the tuning of the GA parameters to obtain the best result in a short time. The paper also includes a comparison of different alternatives with regard to the stages of the GA, i.e., parent selection, crossover, and mutation methods. As a proof of concept, the proposed GA-based RAW grouping is tested on a more constrained device, a Raspberry Pi 3B+, where the grouping method converges in around 5 s. The evaluation concludes with a comparison of the GA-based grouping strategy with other grouping approaches, thus showing that the proposed mechanism provides a good trade-off between throughput and fairness performance. [ABSTRACT FROM AUTHOR]

In this article, we propose non-orthogonal multiple access (NOMA) based restricted access window (RAW) mechanism for IEEE 802.11ah compliant dense Internet of Things networks. The proposed scheme utilizes the principles of NOMA at the physical layer and distributed coordination function with the RAW mechanism at the data link layer. Using an analytical model, we evaluate the throughput of IEEE 802.11ah wireless local area network (WLAN) under the proposed RAW-NOMA integrated mechanism. From the results, we establish that the throughput and scalability of IEEE 802.11ah WLAN are significantly improved under the proposed channel access scheme. Finally, analytical findings are corroborated with ns-3 simulations. [ABSTRACT FROM AUTHOR]

IEEE 802.11ah, or Wi-Fi HaLow, is a long-range Internet of Things (IoT) communication technology with promising performance claims. Being IP-based makes it an attractive prospect when interfacing with existing IP networks. Through real-world performance experiments, this study evaluates the network performance of Wi-Fi HaLow in terms of throughput, latency, and reliability against IEEE 802.11n (Wi-Fi n) and a competing IoT technology LoRa. These experiments are enabled through three proposed network evaluation architectures that facilitate remote control of the devices in a secure manner. The performance of Wi-Fi HaLow is then assessed against the network requirements of various smart grid applications. Wi-Fi HaLow offers promising performance when compared to rival technology LoRa. This study is the first to evaluate Wi-Fi HaLow in an authentic experimental way, providing performance data and insights that are not possible through simulation and modelling alone. This work provides the basis for further evaluation and implementation of this emerging technology.

Low-Power Wide-Area Network (LPWAN) multi-Radio Access Technology (RAT) devices combine features of different low-power network technologies to flexibly manage heterogeneous requirements stemming from various Internet of Things (IoT) applications. IEEE 802.11ah is a novel technology that is envisioned to provide a “bridge” between Wi-Fi and s, thus it has been often considered as one of the supporting technologies in multi-RAT devices. In such multi- RAT scenarios, network discovery and handover procedures need to be utilized for determining the availability of a given technology and managing if the connection to the technology should be initiated. However, traditional discovery and handover procedures, such as beacon listening, have to be performed periodically and, therefore, consume substantial amount of energy, making them unsuitable for battery-powered IoT devices. To address this issue, we present a mechanism that is able to make more optimal discovery and handover decisions by leveraging the physical location information of the multi- RAT devices. We demonstrate how this approach is feasible in performing energy efficient handovers between a technology (NB-IoT) and IEEE 802.11ah based on estimated location. We do that by showing that the location-based procedure substantially reduces the energy consumption of the mobile device compared to the traditional discovery based on periodical listening for beacons, while maintaining comparable duration of the device’s association to IEEE 802.11ah. Moreover, we evaluate the energy and delay overheads caused by the localization service, showing only slight effects on the performance of the mechanism. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

One of the latest protocols developed for the Internet of Things networks is IEEE 802.11ah, proposed by the WiFi Alliance. The new channel access mechanism in IEEE 802.11ah, which is called Restricted Access Window, aims at reducing the contention between the stations by allowing only selected stations to transmit data at certain time slots. Stations may exhibit selfish behavior to maximize their own throughput. This will come at the cost of the overall network quality of service. In this paper, we first analyze the default behavior of the IEEE 802.11ah protocol in terms of fairness. We then introduce various percentages of selfish stations and observe how the network's quality of service in terms of fairness, throughput and packet-loss are affected. After establishing the inherent fairness of IEEE 802.11ah, we analyze applicability of two existing selfish behavior detection algorithms designed for IEEE 802.11 to the IEEE 802.11ah protocol. Due to their poor performance, we propose a new definition of 'selfish behavior' specifically for IEEE 802.11ah, based on which we present a new algorithm for detecting selfish behavior. To combat selfish behavior and to create a better fairness, throughput and lower packet loss, we consequently present a novel mitigation algorithm called Selfish Stations Quarantine Punishment Algorithm (SSQPA). The proposed algorithm takes advantage of the RAW grouping to isolate selfish stations from the honest stations, thus mitigating the effect of the selfish behavior. SSQPA comes in two variants: honest stations-centric and network-centric. Our experimental results show that both variants can successfully mitigate selfish behavior effects in IEEE 802.11ah networks and either one can be used depending on the goal of the network. [ABSTRACT FROM AUTHOR]

IEEE 802.11ah is a new sub-GHz Wi-Fi technology that provides several advantages over traditional Wi-Fi such as a higher communication range, enhanced scalability, and lower energy consumption, however at the cost of substantially lower throughput. With the aim of simultaneously benefiting from multiple Wi-Fi technologies, recent proposals suggest combining a number of these technologies into a single device. This, however, compromises the energy efficiency of the device, as it implies concurrent utilization of different radio access interfaces. To mitigate this issue, the device should utilize the interface of a certain technology only when there is a high probability of establishing communication over that technology. Traditional vertical handover algorithms are not designed for this purpose as they rely on continuous beacon listening or active probing, even if the device is not in the range of a given technology. To address this issue, vertical handover algorithms based on the combination of devices’ physical locations and either Radio Environmental Maps (REM) or propagation modeling have been proposed. Moreover, their suitability and encouraging performance have been demonstrated for a number of the established Low-Power Wide-Area Network (LPWAN) technologies. However, their appropriateness for Wi-Fi-based networks with IEEE 802.11ah is currently unknown, which provides the main motivation for this work. Specifically, we carry out an extensive experimental performance evaluation of two location-based vertical handover algorithms in the context of Wi-Fi-based networks with IEEE 802.11ah. Our results demonstrate the feasibility of location-based handovers in this context. We base our findings on the fact that location-based algorithms can maintain comparable data communication quality as the beacon listening-based baseline, while simultaneously reducing the utilization of the IEEE 802.11ah and IEEE 802.11n radio access interfaces by a factor of 2 and 10, respectively.

Motivated by the explosion of the Internet of Things (IoT), we examined Sub-1 GHz (frequencies below 1 GHz) band wireless technologies that are essential to enable various IoT applications. IEEE 802.15.4g and IEEE 802.11ah are two wireless technologies developed for outdoor IoT applications such as smart utility, smart city and infrastructure monitoring for which both technologies operate in Sub-1 GHz Bands. Our coexistence simulation of IEEE 802.15.4g and IEEE 802.11ah using standard defined coexistence mechanisms shows serious interference problems due to fundamental protocol differences and channel access parameter differences. Accordingly, we proposed IEEE 802.19.3 Task Group formation to lead the IEEE 802.19.3 standard development of IEEE 802.15.4g and IEEE 802.11ah coexistence in the Sub-1 GHz band. In addition to our coexistence methods contributed to IEEE 802.19.3 standard, we propose a novel Active Carrier Sense based CSMA/CA mechanism for IEEE 802.15.4g to reduce CSMA/CA failure packet discard under interference from IEEE 802.11ah traffic and to keep interoperability with conventional IEEE 802.15.4g CSMA/CA mechanism. Our proposed coexistence techniques can improve fair spectrum sharing between IEEE 802.15.4g and IEEE 802.11ah networks for IoT applications.

Industry 4.0 is being enabled by a number of new wireless technologies that emerged in the last decade, aiming to ultimately alleviate the need for wires in industrial use cases. However, wireless solutions are still neither as reliable nor as fast as their wired counterparts. Closed loop communication, a representative industrial communication scenario, requires high reliability (over 99%) and hard real-time operation, having very little tolerance for delays. Additionally, connectivity must be provided over an entire industrial side extending across hundreds of meters. IEEE 802.11ah fits this puzzle in terms of data rates and range, but it does not guarantee deterministic communication by default. Its Restricted Access Window (RAW), a new configurable medium access feature, enables flexible scheduling in dense, large-scale networks. However, the standard does not define how to configure RAW. The existing RAW configuration strategies assume uplink traffic only and are dedicated exclusively to sensors nodes. In this article, we present an integer nonlinear programming problem formulation for optimizing RAW configuration in terms of latency in closed loop communication between sensors and actuators, taking into account both uplink and downlink traffic. The model results in less than 1% of missed deadlines without any prior knowledge of the network parameters in heterogeneous time-changing networks.

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. [ABSTRACT FROM AUTHOR]

The progress on the Internet of Things brings a challenge of providing communications to a tremendous number of simultaneously connected devices. The more stations are transmitting in a wireless network, the higher is the contention for the channel and the slower is the data transmission. The IEEE 802.11ah standard introduces a Restricted Access Window (RAW) mechanism that allows reducing contention for the channel by reservation of channel time for a predefined group of stations. This study is aimed at estimating the network capacity, i.e., the maximum number of stations in a RAW that allows satisfying the requirements for the reliability and data delivery delay at specified channel timeshare restriction and traffic intensity. To estimate the network capacity, an analytical model for servicing sparse traffic when using a periodic RAW with short slots is developed. The problem of optimizing the parameters of a periodic RAW is solved. [ABSTRACT FROM AUTHOR]

The emerging IEEE 802.11ah is a promising communication standard for large-scale networks particularly the Internet of Things (IoT). The single-channel-based centralized channel access mechanism employed in 802.11ah does not scale well in such networks and leads to poor data reception quality. In this paper, we propose an Enhanced and Scalable Medium Access Control (ES-MAC) protocol, which employs multi-band sectorization and dynamic load balancing. These features facilitate multi-hop communication more efficiently and enhance network capacity. Traffic congestion issues prevailing around the access point node due to the large volume of uplink traffic is mitigated by allowing simultaneous transmission using multiple orthogonal channels and sectors. Simulation and analytical results establish the essence of the novel protocol by showing significant improvements in terms of throughput and average packet delay over the existing schemes. The proposed network architecture improves throughput and delay performance up to 150% and 100% respectively compared to the relevant schemes. [ABSTRACT FROM AUTHOR]

The IEEE 802.11ah standard is intended to adapt the specifications of IEEE 802.11 to the Internet of Things (IoT) scenario. One of the main features of IEEE 802.11ah consists of the Restricted Access Window (RAW) mechanism, designed for scheduling transmissions of groups of stations within certain periods of time or windows. With an appropriate configuration, the RAW feature reduces contention and improves energy efficiency. However, the standard specification does not provide mechanisms for the optimal setting of RAW parameters. In this way, this paper presents a grouping strategy based on a genetic algorithm (GA) for IEEE 802.11ah networks operating under the RAW mechanism and considering heterogeneous stations, that is, stations using different modulation and coding schemes (MCS). We define a fitness function from the combination of the predicted system throughput and fairness, and provide the tuning of the GA parameters to obtain the best result in a short time. The paper also includes a comparison of different alternatives with regard to the stages of the GA, i.e., parent selection, crossover, and mutation methods. As a proof of concept, the proposed GA-based RAW grouping is tested on a more constrained device, a Raspberry Pi 3B+, where the grouping method converges in around 5 s. The evaluation concludes with a comparison of the GA-based grouping strategy with other grouping approaches, thus showing that the proposed mechanism provides a good trade-off between throughput and fairness performance.

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.

An Access Point can support up to 8192 stations with a coverage area of 1 km in the IEEE 802.11ah network. Due to its large communication area, this network severely suffers from hidden station problem. The various grouping strategies are used to separate hidden pairs, but with more communication overheads and computational complexity. To mitigate this, the efficient Randomness Region-based Grouping (RRG) and Proximity-based Regrouping Scheme (PRS) are proposed in this paper. The RRG partitions the entire network into a number of regions based on their closeness to reduce the active hidden stations. The PRS further reduces hidden stations using distance matrix and regroups into existing or new groups based on the Centroid of the neighbour groups. The proposed Markov model and NS3 simulation results reveal that the proposed algorithms outperform the other algorithms in terms of throughput, packet loss, packet collision rate, and fairness index. [ABSTRACT FROM AUTHOR]

Rapidly evolving, the Internet of Things imposes new challenges for the developers of wireless networks. Various critical infrastructure monitoring scenarios require fast and reliable alert delivery. In such systems, multiple sensors are entrusted to react to the same emergency event. Thus, it is enough to receive an alert message from any of these sensors. However, such a message shall be reliably delivered as soon as possible. The recently published Wi-Fi HaLow standard defines the Restricted Access Window (RAW) mechanism that coordinates transmissions of numerous devices. Thus, it can improve reliability and reduce delays. The paper is the first to study the usage of RAW in a scenario of emergency alerts, where the alert shall be received from at least one sensor. The paper presents an easy-to-calculate mathematical model of alert delivery with RAW. The model allows dynamic online reconfiguration of RAW parameters to select such parameters that minimize consumed channel timeshare while providing satisfactory reliability and delivery delay for an alert. Intensive performance evaluation shows that the RAW is fruitful for mission-critical data delivery in the considered scenario.

An important problem of modern Wi-Fis is the interferences caused by hidden stations active in the same area, or in multihop communications. All these issues significantly degrade the efficiency of the random channel access methods. Recent standardization and research activities are focused on solving coordination problems between various Wi-Fi devices. For example, the ongoing development of Wi-Fi 7 includes a coordinated schedule between the access points as a candidate solution. Consequently, Wi-Fi has many deterministic channel access mechanisms, which schedule channel time in a periodic manner well in advance and, thus, are utilized for streaming QoS sensitive data. However, both random traffic intensity and error-prone nature of the wireless channel complicate choosing such reservation parameters, i.e., the duration and the period of the reserved time intervals, that satisfy QoS requirements while minimizing channel time consumption. This paper introduces a general mathematical framework to solve the problem of choosing appropriate reservations parameters. The comparison of the analytical and simulation results show the high accuracy of the proposed framework. Finally, the paper gives an example of how to use the developed framework to maximize the network capacity.

Considering the feature of periodical uplink data transmission in IEEE 802.11ah low-power wide area network (LWPAN),a real-time RAW setting (RTRS) algorithm was proposed.Multiple node send data to an access point (AP),and the uplink channel resources were divided into Beacon periods in time.During a Beacon period,AP firstly predicted the next data uploading time and the total amount of devices that will upload data in the next Beacon period.The AP calculated the optimal RAW parameters for minimum energy cost and broadcasted the information to all node.Then all devices upload data according to the RAW scheduling.The simulation results show that the current network state can be predicted accurately according to the upload time of the terminal in the last period.According to the predicted state,raw configuration parameters can be dynamically adjusted and the energy efficiency can be significantly improved.

In view of need of safety monitoring wireless sensor network in special communication environment of underground coal mine and limitation of access device number and transmission distance of existing safety monitoring wireless sensor network, IEEE 802.11ah was applied to underground safety monitoring wireless sensor network, and NS-3 simulation platform was used to analyze its performances, such as transmission distance, throughput, delay, energy consumption.The analysis results show that the transmission distance of IEEE 802.11ah is much longer than that of traditional WiFi communication technology,and the packet loss rate of IEEE 802.11ah is still 0 when the transmission distance is 600 m. When the number of nodes is small and the network is not saturated, and the channel competition and collision of nodes are not serious, the performance advantages of IEEE 802.11ah in terms of throughput, time delay and energy consumption are not obvious. However,when the number of access nodes is large, IEEE 802.11ah can well guarantee the performances of throughput, time delay, energy consumption. As the number of access nodes increases after network saturated, performance of the traditional WiFi communication technology network continues to drop sharply, performance of IEEE 802.11ah declines less and becomes gradually stable. In addition, performance of IEEE 802.11ah network uplink data transmission is related to selection of restricted access window parameters, accurate selection of restricted access window parameters can better exploit performance advantages of underground safety monitoring wireless sensor network based on IEEE 802.11ah.

The IEEE 802.11ah standard was developed from IEEE 802.11, which works on a Wireless Local Area Networks (WLAN). This standard works in the sub-band 1GHz, to increase the range of an Access Point (AP) up to 1 km2, with the ability to handle approximately 8000 stations (STA). The IEEE 802.11ah standard occurs in the MAC and PHY layers. However, the high number of STAs produces high collision, therefore this standard introduces the Restricted Access Window (RAW) at the MAC layer. This research accurately examines a surrogate model to predict RAW performance under hidden nodes scenario. The solution to the surrogate model was analyzed using the Markov chain and numerically simulated with Matlab. Furthermore, the Markov chain solution is used to determine the performance measure parameters, namely throughput, energy consumption, and average delay. This research also examines the effect of hidden nodes on the IEEE 802.11ah standard network’s performance, especially in RAW, with variables obtained using Bianchi’s approach. The result showed that the simulated RAW slot duration of 52 μs on the IEEE 802.11ah standard made performance results better than 104 μs and 156 μs. The effect of the hidden nodes makes the successful transmission time probability longer due to its increment. [ABSTRACT FROM AUTHOR]

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.