Search

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.

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]

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]

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]

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.

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]

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]

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]

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]

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]

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]

Hyperconnectivity, real-time processing, and interoperable communication facilities are key to the Next Generation (NG) Internet of Things (IoT). However, existing IoT architectures employ a diverse range of communication technologies to support desired data rates and coverage range, making them more complex to manage. The emerging IEEE 802.11ah standard, marketed as WiFi HaLow™, enables seamless communication among large-scale and energy-efficient sensor/actuator nodes to the Internet. IEEE 802.11ah introduces key features like Restricted Access Window (RAW) for reducing contention, Hierarchical Traffic Indication Map (TIM) and Association Identifier (AID) for efficient scheduling and device identification, and multiple Modulation and Coding Schemes (MCSs) for adaptive data transmission, enhancing IoT connectivity in Next-Generation IoT (NGI) communication. In this paper, we discuss IEEE 802.11ah for NGI concerning different requirements in terms of communication and computation. We survey the suitability of 802.11ah for NGI as compared to the existing state-of-the-art connectivity solutions. Thereafter, an IEEE 802.11ah-based NGI architecture and protocol stack are proposed and discussed, according to the different distinct features of this novel technology. Finally, we conclude by enumerating research and future directions for the proposed network architecture. • Proposed a Next-Generation Internet of Things (NGI) Architecture using IEEE 802.11ah. • The proposed NGI is for diverse communication and computation needs. • Exploration of communication technologies, standards, and NGI architecture prerequisites. • Suitability analysis of 802.11ah for NGI against current connectivity solutions. • Comparative assessment with related architectures, emphasizing programmability and intelligence. • Comparative study of existing communication standards in the context of NGI. • Presentation of IEEE 802.11ah-based NGI architecture and protocol stack with unique features. • Conclusion includes insights on research directions for the proposed NGI architecture. [ABSTRACT FROM AUTHOR]

Restricted Access Window (RAW) and group-based media access are new features that are exploited by IEEE 802.11ah standard to resolve massive access problem in Internet of Things (IoT) applications. Nevertheless, inefficient device grouping and inappropriate contention resolution in a RAW may still result in collisions and consequently lead to degradation of QoS, energy efficiency, and channel utilization. In existing works, contention resolution schemes schedule all failed devices to retransmit in the next slot of the RAW, which increases the probability of collisions. In this paper, we propose a new retransmission scheme that allows the collided devices of each RAW slot to have another transmission chance in one of the next µ upcoming slots, randomly. We represent the proposed retransmission method via a probabilistic model and formulate a problem based on it, to adjust the number of slots of each RAW with the aim of increasing overall energy efficiency and overall channel utilization regarding delay constraint of devices. Solving the formulated problem, a meta-heuristic algorithm is used to adjust the number of RAW slots and so the size of each RAW regarding the results of the device grouping algorithm. To better exploit the capabilities of the proposed idea, we suggest a load-aware and distance-based device grouping algorithm that not only considers the hidden node problem, but also attends to the load balance of the groups. Simulation results show that the proposed retransmission and RAW adjustment scheme alongside the proposed device grouping algorithm, improve energy efficiency and channel utilization by 25 % and 17 % respectively, and reduce the access delay by 31 % in average, compared to the previous retransmission method. [ABSTRACT FROM AUTHOR]

IEEE 802.11ah, a new amendment to the Wi-Fi standard, adapts Wi-Fi networks to the emerging Internet of Things (IoT). A key component of .11ah is the Restricted Access Window (RAW), a new channel access mechanism, which reduces contention when even thousands of IoT devices operate in the same area by assigning them different channel times. This paper shows that existing studies incorrectly understand the RAW behavior, oversimplify its modeling and thereby overestimate the real system throughput in several times, especially for short durations of the reserved RAW slots. The core contribution of this paper is a new mathematical model based on a completely different approach, which yields more accurate results and thereby enables better IoT system dimensioning. The developed model is suitable for many scenarios typical for IoT. It allows finding RAW parameters that optimize system performance in terms of throughput, power consumption, and packet loss ratio. The proposed solution is can be used for various traffic patterns: when each device transmits a single packet, a batch of packets of random size, or it has full-buffer traffic.

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.

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.

In Wi-Fi HaLow networks, sensors can transmit data only after the link set-up procedure (LSP). After a power outage or when a swarm of sensors appears in an Internet of Things network, all of them contend for the channel to set up links with the access point. For thousands of sensors the LSP can last for hours. However, it can be shortened with advanced LSP coordination algorithms. We show that the best standard-compatible solution found in the literature leaves a gap between the achievable link set-up time and the estimated lower bound. In this paper, we design and evaluate an adaptive solution to control the LSP that fills in this room for improvement and provides significant gains against the existing algorithms. [ABSTRACT FROM AUTHOR]

Aside from vast deployment cost reduction, Industrial Wireless Sensor and Actuator Networks (IWSAN) introduce a new level of industrial connectivity. Wireless connection of sensors and actuators in industrial environments not only enables wireless monitoring and actuation, it also enables coordination of production stages, connecting mobile robots and autonomous transport vehicles, as well as localization and tracking of assets. All these opportunities already inspired the development of many wireless technologies in an effort to fully enable Industry 4.0. However, different technologies significantly differ in performance and capabilities, none being capable of supporting all industrial use cases. When designing a network solution, one must be aware of the capabilities and the trade-offs that prospective technologies have. This paper evaluates the technologies potentially suitable for IWSAN solutions covering an entire industrial site with limited infrastructure cost and discusses their trade-offs in an effort to provide information for choosing the most suitable technology for the use case of interest. The comparative discussion presented in this paper aims to enable engineers to choose the most suitable wireless technology for their specific IWSAN deployment. [ABSTRACT FROM AUTHOR]

IEEE 802.11ah, marketed as Wi-Fi HaLow, operates at Sub 1 GHz spectrum to provide broad coverage, high throughput, energy efficiency and scalability. This makes IEEE 802.11ah a promising candidate for the Internet of Things (IoT). One of the major enhancements in the MAC layer is the Restricted Access Window (RAW) mechanism, which focuses on mitigating the channel contention in dense networks. The RAW mechanism reduces the channel contention among the group of devices by restricting their channel access to the allocated RAW slots. Since the standard does not specify the optimal RAW configuration parameters, choosing the number of RAW slots has significant impact on the performance of the RAW mechanism. In this paper, we develop an optimization framework by exploiting the Multilayer Perceptron Artificial Neural Network (MLP-ANN) to find the optimal number of RAW slots that can maximize the performance of the RAW mechanism in terms of throughput, delay and energy consumption. We train the ANN using the network size, Modulation and Coding Schemes, duration of the RAW period and the optimal number of RAW slots found using the analytical model presented in this paper. Further, we evaluate the performance of the RAW mechanism by choosing the optimal number of RAW slots provided by the ANN-based optimization framework. Results show that the proposed scheme significantly enhances the performance of the RAW mechanism. Finally, the analytical results are corroborated using extensive simulations done in ns-3. [ABSTRACT FROM AUTHOR]

IEEE 802.11ah defines amendments to IEEE 802.11 to support the Internet of Things (IoT). IEEE 802.11ah implements restricted access window (RAW) mechanism to reduce the contention and energy consumption in dense IoT networks. The RAW mechanism is a group-based MAC protocol that partitions the devices into various groups and confines the channel access of a group of devices to the restricted time interval known as the RAW slot. However, the standard does not specify, grouping mechanism, duration of RAW slots, and the number of RAW slots while configuring the RAW mechanism. In an IoT network, each device has distinct transmission requirements. Thus, it is necessary to find the optimal number of RAW slots that can maximize the network performance, to group the devices with similar transmission requirements and to assign a RAW slot that adaptively varies with the traffic requirements of the respective group. In this paper, we exploit fuzzy logic to find the optimal number of RAW slots by considering network size, collision probability, and modulation and coding schemes. Further, we propose a traffic-aware adaptive RAW slot allocation (TARA) scheme that uses fuzzy c-means clustering algorithm to group the devices with similar traffic requirements and to assign each group with a RAW slot whose duration adaptively varies with the transmission requirements of the devices. We have also presented a simple yet accurate analytical model to evaluate the performance of the RAW mechanism. Results show that the optimal number of RAW slots found using fuzzy logic significantly enhances the performance of the RAW mechanism in terms of throughput and energy consumption. Further, it is observed that the TARA scheme can effectively meet the traffic requirements of different group of devices when compared to the uniform grouping scheme. Finally, extensive simulations are conducted using ns-3 to validate the analytical results. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) can connect billions of devices and services at anytime, anyplace with diverse applications. Heterogeneous network provides Always Best Connected network between the various technologies. We consider a hybrid network among recent Bluetooth and Wi-Fi technologies to provide the IoT applications. Bluetooth low energy (BLE) works with the minimum amount of power and data rate, sufficient to handle the IoT applications. Wi-Fi Alliance also developed the IEEE 802.11ah which is specifically designed for IoT applications. In this paper, we performed heterogeneous wireless network scenario between the advanced IEEE 802.15.1 (BLE) and IEEE 802.11ah (HaLow) based on Receive Signal Strength as threshold parameter for vertical handover. The simulation results show the Bluetooth versions of Basic Rate, BLE, and Wi-Fi (HaLow) network with the data rate increment after the handover. [ABSTRACT FROM AUTHOR]

IEEE 802.11ah, also known as Wi-Fi HaLow, improves the scalability of the Internet of Things (IoT) by connecting a large number of low-power devices. IEEE 802.11ah has developed a novel feature called Restricted Access Window (RAW) that aims to address one of the main problems of the IoT: excessive channel contention in large sensor networks. All stations can access the channel from the same group when using RAW, which allows the Access Point (AP) to split stations into different groups. Existing station grouping schemes support only the homogeneous traffic scenarios, having the same data transmission interval, packet size, and Modulation and Coding Scheme (MCS). In this paper, we highlight two contributions to solving this problem. The first is a Machine Learning (ML) based model that predicts RAW configuration parameters and performance under specific network conditions. It predicts accurately and is very quick to train. Second, a technique for a channel access mechanism that uses an ML model to determine the optimal RAW station grouping configuration for dynamic traffic conditions and heterogeneous stations. We compare our ML-based model with the existing state-of-the-art solutions. We calculated the performance of our proposed model and compared it to traditional 802.11ah and the Traffic Adaptive RAW Optimization Algorithm (TAROA). The results show that our proposed model outperforms a significant improvement in terms of throughput, packet received ratio, delay, and execution time. [ABSTRACT FROM AUTHOR]

The recent Wi-Fi HaLow technology focuses on adopting Wi-Fi for the needs of the Internet of Things. A key feature of Wi-Fi HaLow is the Restricted Access Window (RAW) mechanism that allows an access point to divide the sensors into groups and to assign each group to an exclusively reserved time interval where only the stations of a particular group can transmit. In this work, we study how to optimally configure RAW in a scenario with a high number of energy harvesting sensor devices. For such a scenario, we consider a problem of device grouping and develop a model of data transmission, which takes into account the peculiarities of channel access and the fact that the devices can run out of energy within the allocated intervals. We show how to use the developed model in order to determine the optimal duration of RAW intervals and the optimal number of groups that provide the required probability of data delivery and minimize the amount of consumed channel resources. The numerical results show that the optimal RAW configuration can reduce the amount of consumed channel resources by almost 50%.

Wi-Fi HaLow is an adaptation of the widespreadWi-Fi technology for the Internet of Things scenarios. Such scenarios often involve numerous wireless stations connected to a shared channel, and contention for the channel significantly affects the performance in such networks. Wi-Fi HaLow contains numerous solutions aimed at handling the contention between stations, two of which, namely, the Centralized Authentication Control (CAC) and the Distributed Authentication Control (DAC), address the contention reduction during the link set-up process. The link set-up process is special because the access point knows nothing of the connecting stations and its means of control of these stations are very limited. While DAC is self-adaptive, CAC does require an algorithm to dynamically control its parameters. Being just a framework, the Wi-Fi HaLow standard neither specifies such an algorithm nor recommends which protocol, CAC or DAC, is more suitable in a given situation. In this paper, we solve both issues by developing a novel robust close-to-optimal algorithm for CAC and compare CAC and DAC in a vast set of experiments. [ABSTRACT FROM AUTHOR]

So far, existing sub-GHz wireless communication technologies focused on low-bandwidth, long-range communication with large numbers of constrained devices. Although these characteristics are fine for many Internet of Things (IoT) applications, more demanding application requirements could not be met and legacy Internet technologies such as Transmission Control Protocol/Internet Protocol (TCP/IP) could not be used. This has changed with the advent of the new IEEE 802.11ah Wi-Fi standard, which is much more suitable for reliable bidirectional communication and high-throughput applications over a wide area (up to 1 km). The standard offers great possibilities for network performance optimization through a number of physical- and link-layer configurable features. However, given that the optimal configuration parameters depend on traffic patterns, the standard does not dictate how to determine them. Such a large number of configuration options can lead to sub-optimal or even incorrect configurations. Therefore, we investigated how two key mechanisms, Restricted Access Window (RAW) grouping and Traffic Indication Map (TIM) segmentation, influence scalability, throughput, latency and energy efficiency in the presence of bidirectional TCP/IP traffic. We considered both high-throughput video streaming traffic and large-scale reliable sensing traffic and investigated TCP behavior in both scenarios when the link layer introduces long delays. This article presents the relations between attainable throughput per station and attainable number of stations, as well as the influence of RAW, TIM and TCP parameters on both. We found that up to 20 continuously streaming IP-cameras can be reliably connected via IEEE 802.11ah with a maximum average data rate of 160 kbps, whereas 10 IP-cameras can achieve average data rates of up to 255 kbps over 200 m. Up to 6960 stations transmitting every 60 s can be connected over 1 km with no lost packets. The presented results enable the fine tuning of RAW and TIM parameters for throughput-demanding reliable applications (i.e., video streaming, firmware updates) on one hand, and very dense low-throughput reliable networks with bidirectional traffic on the other hand. [ABSTRACT FROM AUTHOR]

The emerging IEEE 802.11ah is a promising communication standard that supports a massive number of heterogeneous devices in the Internet of Things (IoT). It provides attractive features like improved scalability, low energy consumption, and coverage of large area. In this paper, we analyze the performance of IEEE 802.11ah, and compare it with a prominent alternative, the IEEE 802.15.4. The simulation results show that the new 802.11ah standard performs better than the 802.15.4 in terms of association time, throughput, delay, and coverage range.

Minimizing the energy consumption is one of the main challenges in internet of things (IoT) networks. Recently, the IEEE 802.11ah standard has been released as a new low-power Wi-Fi solution. It has several features, such as restricted access window (RAW) and target wake time (TWT), that promise to improve energy consumption. Specifically, in this article we study how to reduce the energy consumption thanks to RAW and TWT. In order to do this, we first present an analytical model that calculates the average energy consumption during a RAW slot. We compare these results to the IEEE 802.11ah simulator that we have extended for this scope with an energy life-cycle model for RAW and TWT. Then we study the energy consumption under different conditions using RAW. Finally, we evaluate the energy consumption using TWT. In the results, we show that the presented model has a maximum deviation from the simulations of 10% in case of capture effect (CE) and 7% without it. RAW always performs better than carrier-sense multiple access with collision avoidance (CSMA/CA) when the traffic is higher and the usage of more slots has showed to have better energy efficiency, of up to the 76%, although also significantly increasing the latency. We will show how TWT outperforms pure RAW, by over 100%, when the transmission time is over 5 min.

A great number of Internet of Things (IoT) and machine-to-machine (M2M) based applications, which are telecommunication areas with the highest foreseen growth in the future years, require energy efficient, long range and low data rate wireless communication links. In order to offer a competitive solution in these areas, IEEE 802.11 standardization group has defined the 'ah' amendment, the first sub-1 GHz WLAN standard, with flexible channel bandwidths, starting from 1 MHz, up to 16 MHz, and many other physical and link layer improvements, enabling long-range and energy efficient communications. However, for some regions, like Europe, the maximum transmitted power in dedicated frequency band is limited to only 10 mW, thus disabling the achievement of ranges which would be close to targeted of up to 1 km. In this paper we examine possibilities for range extension through implementation of half-duplex decode-and forward (DF) relay station (RS) in communication between an access point (AP) and an end-station (ST). Assuming a Rician fading channel between AP and RS, and a Rayleigh fading channel on RS-ST link, we analytically derive results on achievable ranges for the most robust modulation and coding schemes (MCSs), both on downlink (DL) and uplink (UL). Analyses are performed for two different standard adopted deployment scenarios on RS-ST link, and variable end-to-end link outage probabilities. Moreover, we have analyzed whether the considered most robust MCSs, known for supporting the longest range, but the lowest data rates, can meet the defined requirement of at least 100 kb/s for the greatest attainable AP-RS-ST distances. We examine data rate enhancements, brought by coding and using of short packets, for both DL and UL. Finally, we present bit error rate results, obtained through simulations, of a dual-hop DF IEEE 802.11ah relay system for the considered MCs. All presented results confirm that IEEE 802.11ah systems through deployment of relay stations, become an interesting solution for M2M and IoT based applications, due to flexibility they offer in many aspects, meeting requirements for wide transmission ranges in such applications. [ABSTRACT FROM AUTHOR]