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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.

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.

For the large scale wireless networks, restricted access window (RAW) mechanism is a promising technique for realizing large-scale sensor access with the limited collision probability. In this paper, we are committed to designing the traffic distribution based sensor grouping scheme to balance the energy efficiency (EE) of different groups in the large scale access networks. Specifically, by adopting the Markov chain model, we formulate the optimization problem of max-min EE by taking into account traffic demands with even distribution of different all groups, but the formulated problem is an integer nonlinear programming (INLP) problem. In order to solve the INLP problem, we propose an optimal traffic grouping algorithm (OTGA) by utilizing the branch-and-bound method (BBM) to accommodate for the congestion level among groups. Though the traffic demands of each group can be obtained from the traffic grouping scheme, different combination of heterogeneous sensors can generate the same traffic demands, which make it difficult to find the optimal solution of sensor grouping from the proposed traffic grouping scheme. Furthermore, a heuristic traffic-sensor mapping algorithm (HTMA) is presented to make the traffic demands of each group appropriate. Thus, the proposed scheme can achieve a sub-optimal performance with the individual EE. The numerical results are provided to verify the effectiveness of the proposed schemes.

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.

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]

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this letter, we propose a novel channel access mechanism for IEEE 802.11ah to reduce delay and energy consumption in large-scale networks. The proposed protocol predicts the service interval of a monitoring application and schedules the subsequent frames before their arrivals without requiring any further contention. By doing so, the proposed protocol significantly reduces access delay and unnecessary wake-ups. In saturated network conditions, the proposed protocol shows throughput improvement up to 25%, and reduces average packet latency and energy consumption up to 55.5% and 48.4% respectively as compared to the traditional scheme. [ABSTRACT FROM AUTHOR]

In Machine-to-Machine (M2M) networks, an enormous number of stations with low energy budgets contend for the channel access in order to get the transmission opportunity, leading to huge amount of congestion. Energy beamforming technology enables wireless devices to replenish their energy much faster than the traditional energy harvesting strategies. In this paper, a group-scheduled Medium Access Control (GSMAC) protocol is proposed as a solution of managing the access of stations that transmit bursts of data packets while taking into account the benefit of energy beamforming technology in M2M networks. In the proposed protocol, nodes are grouped by multiple Power Beacons in order to reduce the high collision probability. Nodes first contend the channel access in each group for reservation opportunities so that the reserved nodes can transmit data and harvest energy simultaneously by Access Point scheduling the data and energy transfer order. Simulation and analytical results show that GSMAC has significant improvements compared with the traditional distributed coordination function in terms of saturation throughput, delay, and energy-related performances. [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]

We are on the brink of a new era for the wireless telecommunications, an era that will change the way that business is done. The fifth generation (5G) systems will be the first realization in this new digital era where the various networks will be interconnected forming a unified system. With support for higher capacity as well as low-delay and machine-type communication services, the 5G networks will significantly improve performance over the current fourth generation (4G) systems and will also offer seamless connectivity to numerous devices by integrating different technologies, intelligence, and flexibility. In addition to ongoing 5G standardization activities and technologies under consideration in the Third Generation Partnership Project (3GPP), the Institute of Electrical and Electronic Engineers (IEEE)-based technologies operating on unlicensed bands, will also be an integral part of a 5G eco-system. Along with the 3GPP-based cellular technology, the IEEE standards and technologies are also evolving to keep pace with the user demands and new 5G services. In this paper, we provide an overview of the evolution of the cellular and Wi-Fi standards over the last decade with a particular focus on the Medium Access Control and Physical layers, and highlight the ongoing activities in both camps driven by the 5G requirements and use-cases.

Recently, the Wi-Fi alliance announced a new Wi-Fi standard known as IEEE 802.11ah (or Wi-Fi HaLow) to efficiently support Internet of Things (IoT) applications. However, the existing registration method under IEEE 802.11ah, based on carrier-sense multiple access with collision avoidance (CSMA/CA), was analyzed as not efficient enough for registration of large-scale machine-to-machine (M2M) communications where a massive number of devices try to access a single, centralized access point (AP). In this paper, we propose a hybrid slotted-CSMA/CA-time-division multiple access (TDMA) (HSCT) medium access control (MAC) protocol for efficient massive registration of IoT devices (up to 8000) in M2M networks. We focus on situations, where a large number of M2M devices simultaneously try to register at a single, centralized AP. In the proposed HSCT, contention-based slotted-CSMA/CA allows devices to send an authentication request via randomly selected backoff slots, whereas contention-free TDMA permits those devices to send/receive the subsequent association request/association response via an individually allocated TDMA slot. In addition, a centralized authentication control (CAC)-based mechanism with modified algorithms for optimal selection of CAC parameters and the slotted fixed-window CSMA protocol with Sift geometric probability distribution are used to mitigate severe contention between massive registrations upon network (re-)initialization from an AP reboot. This paper also analyzes the performance of the proposed scheme and determines the optimal configuration to enhance registration performance. Simulation results demonstrate that the proposed HSCT MAC protocol achieves substantial improvement, compared with the contention-free transmission, a combined authentication/association scheme, and the conventional IEEE 802.11ah with CSMA/CA.

Rapid development of wireless network impacts the development of IEEE 802.11x standard technology. However, the existing standard technologies still can not solve the problem of capacity, coverage, and power consumption. Due to these limitations, IEEE 802.11ah standard was made. IEEE 802.11ah is a standard of Wi-Fi technology that supports wireless networking scenarios, such as ability to arrange the large number of devices, a wide range of coverage, and power consumption mechanism on a limited energy condition. Wireless network is a best-effort network in delivering package. But VoIP service on the wireless network has lower QoS value, compared to the network using cables. This paper addresses the performance analysis of 802.11ah standard against VoIP services, especially at the MAC layer. To analyze the influence of node density and performance of RAW mechanism on VoIP service, a simulation is conducted through two scenarios, which are changing the number of nodes and RAW station. The simulation is performed using network simulator NS3 version 3.23. Analysis results show with the increasing number of nodes, the value of throughput and delay increase by 60.09% and 93.43%. Then, with the increasing number of RAW station, throughput and delay value have increased by 81.54% and 43.45%. The increase stops in a constant value when the number of RAW station has exceeded the number of nodes in the network.

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]

The Restricted Access Window (RAW) mechanism proposed by IEEE 802.11ah promises to address one of the major problems of the Internet of Things (IoT): high channel contention in large-scale densely deployed sensor networks. The RAW feature allows the Access Point (AP) to divide stations into different groups, with only the stations in the same group being allowed to access the channel simultaneously. Existing station grouping strategies only support homogeneous scenarios, where all sensor stations have the same fixed data transmission interval, modulation and coding scheme (MCS) and packet size. In this paper, we present two contributions to address this issue. First, we propose a surrogate modeling methodology to predict RAW performance for both throughput and energy efficiency given specific network conditions and RAW configuration parameters. The models are fast to train and can be solved in real-time. Second, we extend the original version of Model-Based RAW Optimization Algorithm (MoROA) which only focused on throughput. The extended MoROA uses the surrogate models to determine the optimal RAW configuration for both throughput and energy efficiency in real-time through multi-objective optimization. We compare the accuracy of our surrogate model to simulation results. Performance of MoROA is compared to existing RAW optimization algorithms and traditional 802.11 channel access methods. The results show that the trained surrogate models can accurately predict RAW performance with a relative error less than 7% and 10% for 90% and 95% of the RAW configurations respectively. Compare to the traditional 802.11 channel access functions, by using the objective weight and assigning stations into homogeneous groups, MoROA achieves either 65% higher throughput, or 96% less energy consumption, or a weighted solution in between, in dense heterogeneous networks. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Internet of Things (IOT) offers a new dimension of technology and information where connectivity is available anywhere, anytime, and for any purpose. IEEE 802.11 Wireless Local Area Network group is a standard that developed to answer the needs of wireless communication technology (WI-Fi). Recently, IEEE 802.11 working group released the 802.11ah technology or Wi-Fi HaLow as a Wi-fi standard. This standard works on the 1 GHz frequency band with a broader coverage area, massive device and the energy efficiency issues. This research addresses, the influence of Doppler Effect using Random Waypoint mobility model on 802.11ah with different RAW slot and RAW slot duration are analyzed. The design of the simulation system is done by changing RAW slot and RAW slot duration. Based on the result, it can be concluded that the overall performance of the network with all of the parameter scenarios is decreasing along with the increasing RAW slot, RAW slot duration, and fluctuation. In the RAW slot = 5 scenario with v = 10 km/h has the worst performance with an average delay which is about 0.128225 s, and average throughput is about 0.284337 Mbps while for RAW slot = 1 with an average PDR which is about 99.1076%. While in the RAW slot duration = 0.020 s scenario with v = 10 km/h has the worst performance with an average delay which is about 0.135581 s, average throughput is about 0.286828 Mbps, and average PDR which is about 99.3165%. [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%.

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]

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]

IEEE 802.11ah is proven to be a suitable communication standard for Internet of Things. It supports a wide range of modulation and coding schemes (MCSs) along with different data rates. The Restricted Access Windows (RAW)-based channel access mechanism facilitates scalable communication among a large number of devices. However, due to the absence of RAW size adjustment, it fails to optimally utilize the resources in a dynamic network environment. In this paper, we propose a method to estimate the RAW size based on traffic loads and provide relay node support for stations to use different MCSs. The relay nodes dynamically allocate bandwidth to stations belonging to different relay groups. The proposed scheme is seamlessly assimilated into 802.11ah which shows significant performance improvement in terms of throughput and delay. [ABSTRACT FROM AUTHOR]

This paper presents MFAM (Multiple Frequency Adaptive Model-based localization method), a novel model-based indoor localization method that is capable of using multiple wireless signal frequencies simultaneously. It utilizes indoor architectural model and physical properties of wireless signal propagation through objects and space. The motivation for developing multiple frequency localization method lies in the future Wi-Fi standards (e.g., 802.11ah) and the growing number of various wireless signals present in the buildings (e.g., Wi-Fi, Bluetooth, ZigBee, etc.). Current indoor localization methods mostly rely on a single wireless signal type and often require many devices to achieve the necessary accuracy. MFAM utilizes multiple wireless signal types and improves the localization accuracy over the usage of a single frequency. It continuously monitors signal propagation through space and adapts the model according to the changes indoors. Using multiple signal sources lowers the required number of access points for a specific signal type while utilizing signals, already present in the indoors. Due to the unavailability of the 802.11ah hardware, we have evaluated proposed method with similar signals; we have used 2.4 GHz Wi-Fi and 868 MHz HomeMatic home automation signals. We have performed the evaluation in a modern two-bedroom apartment and measured mean localization error 2.0 to 2.3 m and median error of 2.0 to 2.2 m. Based on our evaluation results, using two different signals improves the localization accuracy by 18% in comparison to 2.4 GHzWi-Fi-only approach. Additional signals would improve the accuracy even further. We have shown that MFAM provides better accuracy than competing methods, while having several advantages for real-world usage. [ABSTRACT FROM AUTHOR]

One of the main challenges when designing a new self-powered Low Power Wide Area Network (LPWAN) technology is the vast operational dependence on its scarce energy resources. Therefore, a thorough identification and characterisation of the main energy consumption processes may lay the foundation for developing novel mechanisms aimed to make a more efficient use of devices’ batteries, as well as for planning future network deployments. This paper provides an analytical energy consumption model for IEEE 802.11ah WLANs, which are expected to become one of the technology drivers in the future development of the Internet of Things (IoT). Given the network characteristics, the presented analytical model is able to provide an estimation of the average energy consumed by a station in the network, predicting its battery lifetime, based on a set of closed-form equations. In addition, this model can be used as a tool to understand the effects of the main network parameters on the network energy consumption, and to find the most suitable network configuration for a given application scenario. [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]

This brief presents a wake-up mechanism for orthogonal frequency division multiplexing (OFDM) modulation-based systems by coding a low data-rate wake-up signal in transmitter without extra circuitry overhead. It is achieved by controlling data pattern for each OFDM subcarrier in one symbol of frame to produce an equivalent amplitude-modulated (AM) signal as a wake-up query without contaminating protocol integrity. A low-power wake-up receiver is used to demodulate this signal and interpret the wake-up query. A system using proposed wake-up mechanism has been built based on IEEE 802.11ah standard. Measurement results demonstrate that an AM-type wake-up signal with a data-rate of 31.25 Kb/s is generated through the proposed method. Moreover, an envelope detector with −35-dBm sensitivity at 900 MHz has been implemented in 0.13- \mu \textm CMOS technology for wake-up signal detection and consumes 120-nW power. [ABSTRACT FROM PUBLISHER]

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 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.

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.

In 2016, the IEEE task group ah (TGah) released a new standard called IEEE 802.11ah, and industrial Internet of Things (IoT) is one of its typical use cases. The restricted access window (RAW) is one of the core MAC mechanisms of IEEE 802.11ah, which aims to address the collision problem in the dense wireless networks. However, in each RAW period, stations still need to contend for the channel by Distributed Coordination Function and Enhanced Distributed Channel Access (DCF/EDCA), which cannot meet the real-time requirements of most industrial applications. In this paper, we propose a channel-aware contention window adaption (CA-CWA) algorithm. The algorithm dynamically adapts the contention window based on the channel status with an external interference discrimination ability, and improves the real-time performance of the IEEE 802.11ah. To validate the real-time performance of CA-CWA, we compared CA-CWA with two other backoff algorithms with an NS-3 simulator. The results illustrate that CA-CWA has better performance than the other two algorithms in terms of packet loss rate and average delay. Compared with the other two algorithms, CA-CWA is able to support industrial applications with higher deadline constraints under the same channel conditions in IEEE 802.11ah.

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.

One of new mobile technology is being developed by 3GPP is Long Term Evolution (LTE). LTE usually used by user because provide high data rate. Many traffic sending over LTE, makes several users didn't get good Quality of Service (QoS). Traffic diversion is needed to increasing QoS value. It can be done with offloading data method from LTE to Wi-Fi network. This paper using 802.11ah standard to evaluate Wi-Fi network. IEEE 802.11ah have 1000 meters coverage area and efficiency energy mechanism, which is proposed for M2M in 5G techonology. Some research has proven that traffic diversion with offloading can increasing network performance. The contribution of this paper is to evaluate the impact of traffic offload between LTE and IEEE 802.11ah standard. This paper propose two scenarios using increment number of user and increment mobility speed of user to evaluate throughput and delay value before and after the offload process. The simulation will simulate using Network Simulator-3. We can conclude that network performance after offloading is better for every scenario. For increment number of user scenario, throughput value increasing 29.08%, and delay decreasing 8.12%. Scenario with increment mobility speed of user obtain throughput value increasing 37,57%, and delay value decreasing 27.228%. [ABSTRACT FROM AUTHOR]

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]