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The new standard IEEE 802.11ah is ideal targeting large-scale and dense deployments of IoT and M2M solutions due to three specific characteristics: the low operating frequency allowing coverage up to 1km, the ability to simultaneously connect a large number of devices per access point, and low power consumption. Several studies on the characteristics of radio propagation in the bands used by the IEEE standard are being carried out worldwide. In this paper, we evaluate two urban path loss models and four specific path loss models for vegetated environments, based on a measurement campaign performed in an urban environment with a strong presence of vegetation, typical of living areas, squares and urban parks where IoT technologies should be commonly implemented in the coming years. The models proposed by TGah were also evaluated. Based on empirical data, the most accurate models are determined. The characterization of large-scale fading and small-scale fading are presented.

IEEE 802.11ah, marketed as Wi-Fi HaLow, is the new Wireless LAN (WLAN) standard for the Internet of Things (IoT) with many enhancements to the 802.11 standard. One of the new features, mentioned as the Restricted AccessWindow (RAW), focuses on enhancing scalability in extremely dense deployments. RAW divides stations into groups and reduces contention and collisions by permitting channel access to one group at a time. However, the standard does not mandate any optimum RAW grouping strategy. Existing station grouping schemes for enhanced throughput and Quality of Service (QoS) considered a fixed number of groups, though it is one of the important factors in determining the overall throughput. In this paper, we have proposed a real-time grouping scheme non-constrained on the number of groups for homogeneous periodic traffic with the same QoS. The proposed scheme uses Agglomerative Hierarchical Clustering for station grouping where fairness and throughput are used as metrics for distance measure and level selection. Level throughput is estimated from its clusters(groups), using a Neural Network regressor with a 3-dimensional input vector. Evaluation of the network is done after every beacon interval and optimum station grouping and group parameters are broadcasted before the start of a next beacon interval. The proposed model was tested against uniform grouping and random grouping, an improvement of 20%, and 50% in the normalized throughput was observed. Fairness ratio of 0.945 was achieved with the proposed model.

In recent years, vulnerable Intenet of Things (IoT) devices have engendered several distributed denial of service (DDoS) attacks owing to the generation of massive IoT botnets by various IoT malware such as Mirai, Persirai and among others. IoT devices are vulnerable owing to constrained memory and computation resources that restrict the implementation of complex security protocols and anti-malware programs. In recent times, there have been attempts to implement periodic Internet-Wide port scan (IWPS) to identify vulnerable IoT devices at each wireless local area network (WLAN). IoT WLAN standard such as IEEE 802.11ah has been designed with the objective of low power consumption and massive connectivity, but is constrained by its low network performance particularly on traditional scan traffic; thus it can result in security degradation. Hence, in this paper, we propose novel models for security analyses of IoT based on the network performance of IWPS over IEEE 802.11ah. The results verify that the network-Oblivious IWPS can degrade IoT security and increase risk.

ISBN 978-1-7281-6802-9e-ISBN 978-1-7281-6801-2; International audience; Internet of Things (IoT) will continue to grow while demand is needed in lots of civil areas. The new Wi-Fi 802.11ah standard from the IEEE supports IoT development. One of the 802.11ah's application is in the healthcare domain. The IoT sensor networks used by patients could support medical staff to diagnose the emergency cases and to monitor continuously. However, the human body and movement can cause interference to the transmitted signal from sensor nodes, which affects the receiving process at Access Point (AP). Thus, the AP may not reply with the Acknowledge (ACK). When the sensor nodes do not receive the ACK from the AP, the sensor nodes will retransmit the data, which will certainly increase Packet Error Rate (PER). Here, we propose a new cross layer protocol to overcome this problem. The proposal takes advantages of beacon power measurements in the node's PHY layer to determine whether there is interference from the human body or not. This information is used by the MAC layer to decide the transmission of packets. Our results show that there are significant improvements of the PER while maintaining the throughput relatively the same as the conventional protocol.

In this paper, we consider dense multi-rate IEEE 802.11ah based Internet of Things (IoT) network with rate adaptation. Here, the nodes access the channel using a group based channel access mechanism known as group-synchronized distributed coordination function (GS-DCF) with default uniform grouping scheme. GS-DCF implements a novel restricted access window (RAW) mechanism, that divides the channel time into several RAW slots, nodes into various groups, and allocates each RAW slot to a group of nodes. GS-DCF plays an important role in reducing collisions and relieving from high channel contention. In such networks, we have observed that the default uniform grouping scheme leads to groups with distinct data rate nodes, which results in severe throughput degradation due to performance anomaly. In this paper, with an accurate analytical model, we report the performance anomaly of GS-DCF and the results are validated with extensive simulations.

In recent years, the Internet of Things (IoT) has received significant attention globally by interconnecting every device from a small household object to large industrial equipment through the Internet. Among diverse applications, the Internet of Vehicles (IoV) is that interconnects numerous vehicles to communicate significant information through the IoT enabled network. The revolution in IoV made every vehicle as a smart object that captures, processes, and communicates information among the connected vehicles. Due to modernization, technological advancements have facilitated an explosive growth in the usage of the number of vehicles. Consequently, it leads to severe traffic congestion, increased number of accidents, the rise in pollution, etc. Hence, the IoV plays a prominent role to effectively administer and manage the traffic conditions. Among several IoT communication technologies, Wireless Personal Area Networks (WPAN) is limited to short transmission ranges, whereas Low Power Wide Area Networks (LPWAN) although it has extended coverage but suffers from lower throughput. Despite the popularity of Wireless Local Area Networks (WLAN), it suffers from severe contention in the dense networks. Hence, IEEE 802.11ah is introduced by the IEEE 802 task group to support IoT applications. IEEE 802.11ah operates at sub-1 GHz International Scientific Medicine (ISM) frequency bands. The standard provides an extended coverage up to 1 km, the data rate of 78 Mbps and connectivity to 8192 devices. It provides several enhancements to the Physical (PHY) and Medium Access Control (MAC) layer. The standard reduces the control overhead by implementing Null Data Packet (NDP) MAC frames, short MAC headers, etc. Hierarchical Association Identifiers (AID) is implemented to enhance the scalability. Above all, Restricted Access Window (RAW) mechanism is introduced to reduce the channel contention among the devices. Analyst forecast that there will be two billion number of vehicles by 2035. To support such dense IoV networks, IEEE 802.11ah is a perfect candidate. However, there exist several design issues and challenges when we propose IEEE 802.11ah for IoV. In this chapter, we discuss the various design issues and challenges in detail.

In this paper, we present an IEEE 802.11ah (HaLow) dongle that provides sub-1 GHz wireless channel and USB (universal serial bus) interface for power supply and wired data link for easy Internet of Things (IoT) wireless networking. The HaLow dongle is implemented using CC1352P1 sub-1GHz smart RF chipset that provides 50Kbps ~ 4Mbps in 10m ~ 1Km range. The major advantages of the HaLow dongle include i) easy installation using USB interface, ii) energy efficient wireless networking with flexible channel selection and Tx rate/power adjustments, and iii) user-friendly API (application programming interface) for raw-socket-based application developments. The functional architecture of IEEE 802.11ah (HaLow) dongle is explained in detail and the performance is analyzed in practical environments.

The IEEE 802.11 Wi-Fi network (wireless fidelity) currently continues to grow with a variety of different functions and capabilities, but has their respective weaknesses, especially to support emergency communication. However, these problems can be resolved by the IEEE 802.11ah standard which has the advantage of coverage area, power consumption, and traffic. In this paper, IEEE 802.11ah Network Challenges Supports Covid-19 Prevention Team with an explanation of IEEE 802.11ah concepts and technology compared to other IEEE 802.11 technologies, based on existing research IEEE 802.11ah can be implemented in monitoring covid-19 sufferers in realtime and from any location. Finally, the IEEE 802.11ah Implementation Model Plans 1 and 2 that have been designed in this paper are implemented.

Wi-Fi (wireless fidelity) IEEE 802.11 network is growing rapidly, but still has much weakness in coverage area, power consumption, and traffic. However, to overcome this weakness can use the IEEE 802.11ah standard, which has advantages to overcome area coverage, power consumption, and traffic. This paper comprehensively explains the development of the IEEE 802.11ah network and provides opportunities and challenges for the development of IEEE 802.11ah in the future. We are currently researching the IEEE 802.11ah challenge with the novelty of combining game theory with the winning game strategy to optimize the IEEE 802.11ah QoS network. (Abstract)

IEEE 802.11ah reduces the number of collisions and improves energy efficiency by implementing the restricted access window (RAW) mechanism. The RAW mechanism partitions the network and allows each group to access the channel in a RAW slot. Finding optimal RAW parameters plays a vital role in enhancing network performance. In this article, we formulate an optimization problem using the artificial neural network to find the optimal number of RAW slots and to improve network performance. The results show that the proposed optimization problem improves the throughput of the RAW mechanism. Finally, the analytical findings are validated using ns-3 simulations.

In this paper, we propose a channel access scheme for relay-based IEEE 802.11ah network and analyze the feasibility and efficiency of it for multi-hop and large-scale Internet of Things (IoT). A new Restricted Access Window (RAW) scheme is designed to consider the currently active number of stations in a group for channel access. The proposed protocol facilitates multi-hop communication efficiently by utilizing the available channels. Accordingly, the relay node can maintain the best possible frame size for multi-hop communication. Simulation and analysis results show that the proposed protocol improves throughput performance up to 30.7% and the average frame delay up to 41.6% over the traditional scheme.

IEEE 802.11ah can support machine-to-machine (M2M) communications among many sensor devices, which is rapidly increasing with the Internet of Things (IoT). In 802.11ah, a restricted access window (RAW) is designed for a beacon interval (BI). RAW is divided into many slots that allow sensor devices to contend for transmission. This paper proposes a dynamic slot allocation scheme (DSAS) in 802.11ah networks. To alleviate the contention in crowded M2M environments, the DSAS defines two sets of thresholds, high and low, to determine the sleeping-time level of a machine and the contention level for a slot, respectively. The innovative aspect of the DSAS is that machines with short sleeping time are allocated to less congested slots whereas machines with long sleeping time are allocated to more congested slots. To avoid unnecessary slot reallocation, machines with sleeping-time levels or contention levels between the high and low thresholds continue to use their original transmission contention slots. Simulations performed on NS-3 show the DSAS can significantly improve average backoff time and packet delay and increase the overall system throughput by 30%.

IEEE802.11ah introduces the restricted access window (RAW) mechanism to mitigate intense channel access contentions in IoT networks with a huge number of connected devices. In this mechanism, the wireless station (STA) grouping method has a significant impact on the network performance, and therefore several methods have been proposed for the STA grouping. Although the existing methods target the networks with geographical uniform STA distribution, the STAs may be deployed non-uniformly in real environments. Consequently, the conventional methods do not always provide better performance under the network with the non-uniform STA distribution. This paper proposes a twolevel STA grouping method to improve the network performance even under the such conditions of STA distribution. The evaluation through computer simulation shows that the proposed method improves the network throughput and the fairness in user throughput among STAs, compared with the conventional methods.

Wi-Fi systems based on the IEEE 802.11 standards are the most popular wireless interfaces that use Listen Before Talk (LBT) method for channel access. The distinctive feature of a majority of LBT-based systems is that the transmitters use preambles that precede the data to allow the receivers to perform packet detection and carrier frequency offset (CFO) estimation. Preambles usually contain repetitions of training symbols with good correlation properties, while conventional digital receivers apply correlation-based methods for both packet detection and CFO estimation. However, in recent years, data-based machine learning methods are disrupting physical layer research. Promising results have been presented, in particular, in the domain of deep learning (DL)-based channel estimation. In this paper, we present a performance and complexity analysis of packet detection and CFO estimation using both the conventional and the DL-based approaches. The goal of the study is to investigate under which conditions the performance of the DL-based methods approach or even surpass the conventional methods, but also, under which conditions their performance is inferior. Focusing on the emerging IEEE 802.11ah standard, our investigation uses both the standard-based simulated environment, and a real-world testbed based on Software Defined Radios., 13 pages, journal publication

IEEE 802.11ah enables the Internet of Things (IoT) applications because of its extended coverage, high throughput and large scalability. It reduces contention, energy consumption and hidden terminals in the network by implementing restricted access window (RAW) mechanism. The RAW mechanism groups the devices and spreads the contention across various RAW slots. One of the major challenges in RAW mechanism is to choose the optimal number of RAW slots (ORS). In this article, we find the ORS by using fuzzy logic. Further, we analytically evaluate the throughput and energy consumption of RAW mechanism. Results show that the throughput is significantly improved and the energy consumption is decreased by choosing the ORS. Finally, the analytical finding are corroborated using ns-3 simulations.

IEEE 802.11ah is a novel sub-GHz license-free WiFi technology that is of great interest for Smart Cities, primarily due to its long range, low energy consumption, reliability, and ubiquitousness. To guarantee a long lifetime of battery-powered IEEE 802.11ah Mobile Terminals (MTs), network discovery based on beacon listening or active probing should ideally be avoided. Alternative discovery approaches heavily rely on crowd-sourcing, which in turn relies on propagation modeling. Due to the novelty of IEEE 802.11ah, the accuracy of the available propagation models is currently all but clear, and this paper makes one of the first steps in bridging this gap. Specifically, for a number of Smart City-relevant environments we experimentally evaluate the accuracy of an exhaustive set of propagation models by comparing their outputs against real measurements obtained using IEEE 802.11ah-compatible hardware. Our results are encouraging, showing that the existing models are indeed suitable for IEEE 802.11ah. However, they also indicate that diverse models perform best in distinct types of deployment environments, suggesting the need for an environment-tailored design of IEEE 802.11ah-based systems that utilize propagation modeling.

IEEE 802.11ah is an Internet of Things enabling technology, where the efficient management of thousands of devices is a key function. To this aim, IEEE 802.11ah provides the restricted access window (RAW) mechanism, which reduces contention by enabling transmissions for small groups of stations. Optimal grouping of RAW stations requires an evaluation of many possible configurations. In this paper, we propose an analytical model (named e-model) that provides an evaluation of the RAW configuration performance, allowing a fast adaptation of RAW grouping policies, in accordance to varying channel conditions. We base the e-model in known saturation models, which we adapted to include the IEEE 802.11ah’s PHY and MAC layer modifications and to support different bitrates and packet sizes. As a proof of concept, we use the proposed model to compare the performance of different grouping strategies, showing that the e-model is a useful analysis tool in RAW-enabled scenarios. We validate the model with existing IEEE 802.11ah implementation for NS-3.

In 2016, the IEEE task group ah (TGah) published a new standard IEEE 802.11ah, aimed at providing network connectivity among a large number of Internet of Things (IoT) devices. Restricted access window (RAW) is one of the fundamental MAC mechanisms of IEEE 802.11ah. It reduces the contention overhead in the dense wireless environment by dividing stations into different RAW groups. However, how to optimize the RAW parameters is still an open issue, especially in the run-time environment. In this paper, we propose a run-time RAW optimization scheme, namely RO-RAW, to improve the performance of RAW in the IEEE 802.11ah networks. RO-RAW adopts the Extended Kalman Filter method to estimate the channel status and adjusts the RAW parameters according to the number of competing stations in real-time. The evaluation via NS-3 simulations shows that, by tuning the RAW parameters appropriately, RO-RAW substantially improves throughput, latency, and packet loss performance compared with another RAW optimization scheme in different simulation scenarios. The results further show that, when the channel is relatively congested, RO-RAW improves the RAW performance more significantly.

IEEE 802.11ah is a recently published standard for low-rate wireless communications over smart devices used for Internet of Things (IoT) systems. As a member of 802.11 Wireless Local Area Network (WLAN) protocol family, it presents a fine solution to extend the life of a network of resource-constrained devices and enhance the efficiency of low-rate communications. Although the performance of the Media Access Control (MAC) layer has been improved, the security functionality, especially for the authentication, is inherited from the legacy version. Lightweight authentication protocols to support the IoT devices with the requirements of low-power consumption and low-latency are much demanded. To meet the new demands, in this paper, an enhanced authentication scheme based on Fast Initial Link Setup (FILS) specified in IEEE 802.11ai standard is proposed, named as Fast Key Re-authentication, (FKR). It is designed for the authentication process in the link setup procedure to improve the overall performance of the 802.11ah networks.

IEEE 802.11 standard is a WLAN (Wireless LAN) standard that has been used in all over the world. IEEE 802.11ah is the newer technology that designed to supports Internet of Things (IoT) and Machine-to-machine Communication (M2M). IEEE 802.11ah has a feature called Restricted Access Window (RAW) that capable to reduce power usage and have satisfying Quality of Service (QoS). In this research, Enhanced Distributed Channel Access (EDCA) is also applied. Same as RAW, EDCA also be able to affect QoS by modified the MAC Layer in 802.11 standard. This research used 3 different scenarios for RAW parameters: Modifying the number of RAW Group, Modifying the number of RAW Slot, and Comparing 2 Datamode. The EDCA Parameters that used in this research were: Contention Window and Arbitrary inter-frame Spacing Number. The values that expected to be the output in this research are: Delay, Throughput, Packet Delivery Ratio, Availability, and Reliability. After the research has been simulated, the results are: First, the lowest of average delay was Ngroup = 1, the highest of PDR was Ngroup = Nsta/2, and the highest of Throughput was Ngroup = Nsta/2. Second, the lowest of average delay was RAW Slot = 6, the highest of PDR were RAW Slot = 3 and 4, and the highest of Throughput was RAW Slot = 4. Third, the lowest of average delay was Datamode 3,9 Mbps BW 2 MHz, the highest of PDR was Dat mode 3,9 Mbps BW 2 MHz, and the highest of Throughput was Datamode 3,9 Mbps BW 2 MHz. Reliability, Availability, and Energy Consumption also can be affected by modifying RAW parameters, in 802.11ah Energy Consumption can be reduced by increasing the number of RAW Stations and RAW Groups.

Recently, IEEE 802.11ah standard has been proposed to extend the range of wireless local area network operating in the sub-1-GHz frequency band. This standard along with other protocols can provide communication services to the Internet of Things applications. However, in future, this band is also expected to be crowded like 2.45 GHz ISM band and cause interference to other devices operating in the same band. For a communication channel affected by additive white Gaussian noise, the least square (LS)-based estimator and Euclidean distance-based Viterbi decoder give optimal performance. However, the receiver’s performance with LS estimator followed by the Viterbi decoder degrades for high interference affected communication channels. In this paper, a new orthogonal frequency division multiplexing-based receiver structure operating in high interference environment is proposed. The proposed receiver is based on nonparametric maximum likelihood channel estimation followed by Viterbi decoder. The Viterbi decoder’s branch metric is updated based on the distribution of residual error. The proposed receiver structure is tested on IEEE 802.11ah-based receiver in two different type of additive interference: 1) IEEE 802.15.4 device and 2) impulsive noise. Both simulations and real-world experimental results on standard compliant platform show that the proposed algorithm performs better in terms of bit error rate than other receivers in all the considered interference models. Additionally, we also derive analytical expression for the probability of symbol error.

The IEEE 802.11ah standard has been developed to provide Internet access to a large number of devices in the Internet of Things (IoT) and machine-to-machine (M2M) networks. To handle contention from a large number of devices and reduce the collision probability, IEEE 802.11ah partitions nodes into groups by adopting a group- based MAC protocol. The formed groups may consist of nodes with different traffic patterns and hence, the data rate requirements of nodes in a group (and consequently the groups themselves) may not be uniform. To maximize the throughput while minimizing unfairness across groups, this paper formulates fair scheduling in IEEE 802.11ah networks as a multi-objective optimization problem. To maintain fairness among the nodes in a group, contention window size selection of nodes is formulated as an integer programming problem. Since it is difficult to solve these problems in real time, heuristic methods are also proposed. Performance of the proposed methods is evaluated in a dense IoT network and compared with the existing methods. As the number of nodes and groups increase, the proposed method consistently shows a superior performance in terms of fairness, throughput, delay, and power consumption, compared to the existing methods.

IEEE 802.11ah Task Group has introduced group-based contention mechanism called Restricted Access Window (RAW) for accessing the shared medium. The RAW mechanism is an initial solution to control the collision that occurs due to the heavy contention in large networks. The Distributed Coordination Function (DCF) is a contention-based medium access method that uses Binary Exponential Backoff (BEB) to resolve contention among the stations inside the RAW group. As mentioned in several literatures, BEB suffers from high probability of collisions. This problem is mitigated through an efficient BO algorithm with appropriate integer sequences for calculating new Contention Window (CW) without any additional control overheads and complex computations. An analytical model is developed for computing saturated throughput, attained by the proposed algorithm. A comprehensive simulation is also carried out in NS3 network simulator with different packet sizes. It is observed from analytical and simulation results that, the proposed backoff algorithm outperforms BEB in terms of saturated throughput, packet delivery ratio, packet loss ratio and delay.

IEEE 802.11ah is a new task group on the IEEE 802.11 standard designed to work on the 900 MHz. It is with a range of communication coverage up to 1kilometer, lower energy consumption, and up to 8191 stations. There are two types of STAs in 802.11ah: sensor type to support sensor service and non-sensor type for offload service. In this research, it only focuses on non-sensor STA. For non-sensor STA, maximizing throughput is more important than power consumption. This research aims to see the performance of IEEE 802.1 1ah with Enhanced Distributed Channel Access (EDCA). To achieve that purpose, a mechanism is needed to provide guarantees various services required by theSTA. EDCA is an access mechanism used to set the Quality of Service (QoS) for the IEEE 802.11 standard through modifications in MAC layer. In this research,it focuses on one of the EDCA parameters, Arbitration Inter-Frame Space (AIFS). In addition, this research also focuses on the 802.11ah feature is Restricted Access Window (RAW) by changing the number of the RAW groups. From the results of the research, it is found that the improvement scheme with Arbitration Inter-Frame Space Number (AIFSN) value AC BK = 2, AC BE = 1, AC VI = 1, AC VO = 1 has better performance compared to the default scheme with AIFSN value AC BK = 7, AC BE = 3, AC VI = 2, AC VO = 2) with an average throughput of 1.504598 Mbps, average overall delay of 0.066242 second and average PDR of 62%. In addition, changes in the number of RAW groups and RAW slots affect network performance. This feature can improve the value of throughput, average delay, and Packet Delivery Ratio. The goals of this research is to know the effect of AIFSN value changes on AIFSN parameters, variation of RAW group and RAW slot number to throughput,average delay and packet delivery ratio.

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. This work was partially supported by the Spanish and Catalan governments through the projects TEC2016-79510-P and SGR2014-1173 (both with FEDER funds), respectively. It has also been funded by the ENTOMATIC project from the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no 605073.

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.

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.

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.

With the massive integration of distributed renewable energy sources (RESs) into the power system, the demand for timely and reliable network quality monitoring, control, and fault analysis is rapidly growing. Following the successful deployment of Phasor Measurement Units (PMUs) in transmission systems for power monitoring, a new opportunity to utilize PMU measurement data for power quality assessment in distribution grid systems is emerging. The main problem however, is that a distribution grid system does not normally have the support of an infrastructure network. Therefore, the main objective in this paper is to develop a Machine-to-Machine (M2M) communication network that can support wide ranging sensory data, including high rate synchrophasor data for real-time communication. In particular, we evaluate the suitability of the emerging IEEE 802.11ah standard by exploiting its important features, such as classifying the power grid sensory data into different categories according to their traffic characteristics. For performance evaluation we use our hardware in the loop grid communication network testbed to access the performance of the network.

A 1.3 nJ/b IEEE 802.11ah TX for IoT applications is presented. A fully-digital polar architecture consisting of an all-digital PLL-based frequency modulator and an AM-retiming $\Delta \Sigma $ switched-capacitor PA (SC-PA) achieves more than $10\times $ power reduction than state-of-the-art OFDM TXs. Several circuit-design techniques such as LSB truncation error feedback are proposed to efficiently pre-process the AM/PM data to improve the TX performance. A design approach of the SC-PA for optimum overall efficiency is introduced. The PLL spur level is reduced to 55 dBc by a switched-capacitor based digital-to-time converter. A dynamic divider is implemented together with a 1.8 GHz oscillator for efficient LO generation. Fabricated in a 40 nm CMOS process, this TX fulfills all the IEEE 802.11ah mandatory-mode PHY requirements with 4.4% EVM and > 4.8 dB spectral mask margin, while consuming 7.1 mW from a 1 V supply when delivering 0 dBm output power.

Today, the growth in the number of users is increasing with the development of advanced technology. In addition, this is also accompanied by higher community needs due to technology that is able to support the needs of the community. IEEE 802.11ah is a standardization of the development of IEEE 802.11 that is able to overcome these problems, because the coverage area of the transmission range is up to 1 km, has lower energy consumption, and is capable of serving up to 8191 stations (STA). In this research, an analysis of changes in modulation and coding scheme (MCS) and restricted access window (RAW) Slot will be carried out on the IEEE 802.11ah standard for VoIP services by comparing two codecs, namely G.729 and G.711. In this research, we used network simulators NS-3 on VoIP services by comparing two codecs namely G.729 and G.711 and investigated how they affect network performance. We compare the average delay, throughput, and packet delay ratio (PDR) as network performance parameters.

IEEE 802.11ah Wireless Local Area Network (WLAN) is a new wireless network technology standard used for Internet of Things (IoT) applications. Under IEEE 802.11ah, up to 8,191 IoT devices can connect to the same access point (AP) from a range of up to 1 km, and multirate connections are possible with speeds ranging from 150 Kbps to 346.6 Mbps. Before transmitting data frames, each IoT device must register with the AP using the centralized authentication control method. One problem with this is that simultaneous registration by many devices can result in registration taking a long time. Some papers have proposed algorithms to reduce registration time by dynamically adjusting the number of devices allowed to register in each time period. However, these proposed algorithms assume a constant rate is used when transmitting authentication and association frames. In our study, we evaluated the effect on registration speed of allowing multiple transmission speeds to be used. In our evaluation, we used the multirate Minstrel algorithm. The results from extensive simulation with an NS-3 network simulator show that the use of multirate capabilities during registration can improve IoT-device registration speed by 5–15% relative to registration at a constant transmission rate.

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The new medium access method of IEEE 802.11ah, called Restricted Access Window (RAW), divides stations into different groups, and only allows stations in the same group to access the channel simultaneously, in order to reduce collisions and thus achieve better performance (e.g., throughput). However, the existing station grouping strategies only support homogeneous scenarios where all stations use the same modulation and coding scheme (MCS) and packet size. A surrogate model is an efficient mathematical model that represents the behavior of a complex system, trained with a limited set of labeled input-output data samples. In this paper, we present a surrogate model that can accurately predict RAW performance under a given Restricted Access Window (RAW) configuration in heterogeneous networks. Different from the homogeneous scenario, heterogeneous networks are defined by a large number of parameters, leading to an enormous design space, i.e., the order of 1017 possible data points. This is too big to achieve feasible training convergence. In this paper, we present a novel training methodology that leads to a new design space with highly reduced size, i.e., the order of 105 data points. The surrogate model converges when less than 6000 labeled data points are used for training, which is only a tiny portion of the whole design space. The results show that, the relative error between model prediction and simulation results is less than 0.1 for 95% of the data points, in the areas of the design space studied. Its low complexity and high precision make the proposed model a valuable tool to develop real-time RAW optimization algorithms for heterogeneous IEEE 802.11ah networks.

Recently, Network Slicing in Radio Access Technologies (RATs) has been proposed as an approach to divide the wireless network infrastructure into isolated logical slices which are defined following their requirements and features in a service-driven way. To the best of our knowledge, the application of Network Slicing has not been explored in IEEE 802.11ah Networks. In this work, we propose a Virtual Network Slicing Broker (VNSB), a Virtual Network Function (VNF) instantiated inside a Software Defined Network (SDN) controller which communicates with an IEEE 802.11ah network Access Point (AP) via a southbound Application Program Interface (API). Based on information obtained by the northbound API, the slicing broker gets the information contained in the slicing templates, which describes the services features and respective Quality of Service (QoS) restrictions. With these data, the slicing broker makes logical slices in the context of the Restricted Access Windows (RAW) which are periodically sent by the AP to the stations (STA) via the Raw Parameter Set (RPS). Since there is no hardware with the IEEE 802.11ah standard available on the market, we validate the proposed solution through extensive simulations in a typical smart city scenario. Results showed the broker capacity to build and manage logical slices per service, respecting the QoS restrictions of each offered service. Moreover, the proposed slicing broker makes use of available resources of neighbor slices reducing delay, packet loss, and efficiently maximizing throughput per slice.

The wide range of Internet-of-Things applications has increased the number of connected devices massively. This growth may cause more contention in accessing the channel, challenging the legacy IEEE 802.11. In the IEEE 802.11ah standard, the grouping technique is exploited to make the stations (STAs) compete in a group to mitigate the contention. However, how to group the STAs in the network is still an open issue. In this paper, we propose a new strategy to group STAs in a dense network to address the above issues. We apply the MaxMin fairness criterion to the STAs’ throughput to increase the overall network’s performance with better fairness. Formulation of the problem results in a non-convex integer programming optimization problem which avoids hidden terminals opportunistically. As solving the optimization problem is difficult and time consuming, we apply the Ant Colony Optimization method to the problem to find the solution. Extensive simulations have been conducted to validate the solution. The proposed approach can achieve approximately up to 40% gain in the total throughput, 37% gain in the minimum per-STA throughput in the network, and 11% reduction in the number of hidden terminals compared to the existing strategies such as K-means.

Since Internet of Things (IoT) is configured to support up to 8000 various IoT devices scattered within 1 Km range, smart controls of transmission power and wireless channel according to the communication environment and channel status are essential feature for energy-efficient IoT networking. Also smart channel selection and cognitive switching are necessary in order to mitigate the channel interferences and environmental noises. In this paper, we propose a smart control for energy efficient networking of IEEE 802.11ah-based IoT with hybrid slotted CSMA/CA-TDMA (HSCT) MAC protocol. The proposed HSCT MAC protocol enhances the performance of IEEE 802.11ah on sub-1GHz RF channel with limited transmission rate. In the HSCT, the contention among IoT devices IoT devices is minimized by employing scheduled TDMA slot (T-slot), while T-slot requests are handled by CSMA/CA contention-based MAC scheme. The proposed smart RF control scheme has been implemented on TI CC1312R high-performance Sub-1GHz wireless launch pad. This paper also provides the performance analysis of the proposed scheme based on measured results.

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 for IoT. It has several features, such as Restricted Access Window (RAW) and Target Wake Time (TWT), that promise to improve energy consumption. In this paper, we present an extension of the IEEE 802.11ah module in ns-3 with support for the TWT feature. We study how the energy consumption is affected by the coexistence of RAW and TWT stations (STAs). The results show that the presence of RAW STAs can have an extreme negative effect on the energy efficiency of TWT STAs. Proper scheduling of channel access for both RAW and TWT STAs can mitigate this effect without negatively affecting throughput of RAW STAs.

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.

The IEEE 802.11ah is one of the most recent wireless protocols that has emerged, aiming to improve Internet of Things (IoT) communication devices. Among the 802.11ah related works that evaluate the Restricted Access Window (RAW) and others medium access control mechanisms, only a few consider Quality of Service (QoS) and inherent traffic heterogeneity in IoT. Nevertheless, the purpose of this work is to compare two RAW scenarios, which consider two types of devices with different traffic patterns and QoS requirements. The results suggest that the increase of medium containment within RAW slots in dense networks may be a limiting factor for providing QoS.

Drones are going to be extensively engaged in real life, security and surveillance network. However, a reliable UAV network requires multi-node and multi-hop communication. Designing an efficient UAV network requires designing a network architecture that not only extends the control and operating range but also provides reliable, low latency communication message to all the nodes. In this paper we have proposed an architecture of an UAV control network using IEEE 802.11ah relay scheme.

The IEEE 802.11ah standard can provide cost-effective Internet access to a large number of devices in newly evolving Internet-of-Things (IoT) and machine-to-machine (M2M) networks. To handle high collision probability caused by a large number of devices, it adopts a group-based protocol at the MAC layer and divides nodes (or sensors) into a number of groups. The formed groups may not be uniform in terms of data rate requirements, since each group is a combination of sensors with different traffic characteristics. To achieve fair resource utilization across the groups which in turn maximizes the channel utilization, this paper formulates fair grouping in IEEE 802.11ah networks as an optimization problem, and we develop a heuristic method to solve the problem in real-time. In addition, to ensure fair channel utilization by the nodes in each group, a contention window selection and adjustment method is proposed. Results from extensive simulations conducted in a dense IoT network show that the proposed fairness model achieves a superior performance than the existing methods in terms of throughput, packet delay, energy efficiency, and fairness.