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Wi-Fi is an integral and invaluable part of our media practices. Wireless networks are blended into our media environment and, in terms of infrastructural importance, have become comparable with electricity or water. This article offers a new transnational perspective on the underexplored history of IEEE 802.11 standards by focusing on the tensions between the United States and Europe in terms of development trajectories of wireless technology. The goal is to analyze the standardization of wireless networking through a transnational lens and to contribute to enhanced understanding of the global proliferation of Wi-Fi technology. Four particular aspects of the transnational development of Wi-Fi technology are discussed: the rivalry between US and European standards, the constitutive choice to focus on data transmission, radio spectrum availability, and the peculiarities of network authentication. [ABSTRACT FROM AUTHOR]

IEEE 802.11 based Wireless Local Area Networks (WLANs) are becoming popular in home, enterprise and public access areas primarily due to their low cost, simplicity of installation and high data rates. While WLANs continue to be predominantly data centric, there is growing interest in using WLANs for voice and text data especially in enterprise markets. In this paper, a comprehensive analysis comparison to measure the delay, throughput packet loss and retransmission attempts was done to different scenarios using different trending wireless protocols such as 802.11n (2.4GHz, 5GHz), 802.11g, 802.11b, 802.11a while transferring low and high data load in FANET, with two major routing protocols AODV, and OLSR. As concluded in the result, recent wireless technology 802.11n with frequency band such of 2.4GHz can give better performance as they transfer higher data rates with longer distances and frequency band of 5GHz with higher data rate and less distance than 2.4GHz.

IoE (Internet of Everything) là xu hướng kết nối mọi thứ vào mạng internet, điều này đòi hỏi các chuẩn công nghệ mới với các yêu cầu cao về độ rộng băng thông, độ linh hoạt trong sử dụng băng tần. Trong bài báo này, một anten ba băng được đề xuất dựa trên cấu trúc anten phẳng nhằm đơn giản về chế tạo và dễ dàng tích hợp trong các mạch thu phát. Anten sử dụng cấu trúc mặt phẳng đất khuyết DGS (Defected Ground Structure) để chủ động tối ưu từng tần số cộng hưởng. Kết hợp với cấu trúc ống dẫn sóng tích hợp trên chất nền SIW (Substrate Integrated Waveguide), anten đề xuất có đặc tính ngăn chặn rò rỉ bức xạ của điện trường, mang lại sự cải thiện về hiệu suất, hệ số khuếch đại cũng như độ cộng hưởng cực đại trên từng băng tần hoạt động. Cộng hưởng tại ba băng tần 2,4GHz, 5GHz và 5,9 GHz với độ lợi cao, băng thông rộng, anten ba băng DGS-SIW có thể sử dụng cho các thiết bị đầu cuối theo các chuẩn không dây tiên tiến như 802.11ax, be, bd, …

Emerging immersive and delay-sensitive applications such as AR, VR, and XR are driving the fast development of the next generation Wi-Fi 8 standard. Whereas Wi-Fi 7 have focused on extremely fast transmission, Wi-Fi 8 aims to ensure reliability. We first explore the technical requirements, current standardization status, new features, use cases, and research challenges for the IEEE 802.11bn ultra-high reliability (UHR), which is the foundation of Wi-Fi 8. We then investigate the innovative key features such as distributed Multi-link operation, determinism, and AP coordination. Finally, we suggest how these could be adopted in the coordination framework, one of the core protocol solutions of the Wi-Fi 8. [ABSTRACT FROM AUTHOR]

Aggregation-enabled wireless local area network (WLAN) technologies such as IEEE 802.11n and 802.11ac are deployed densely for wireless access to the high-speed Internet. Conventional approaches for performance modeling of 802.11n/ac assume fully saturated traffic, where stations always have enough packets to transmit aggregate frames of full or fixed aggregation size. However, such an assumption is unrealistic, since stations usually transmit aggregate frames with different aggregation sizes depending on several dynamic factors, such as offered traffic load, number of active stations, transmission rate, and random backoff time. In this work, we propose a new performance model of aggregation-enabled 802.11n/ac WLANs for different traffic loads and buffer sizes with the assumption that stations always have at least one packet to transmit. Unlike conventional models where stations always contend to transmit aggregate frames of maximum or other fixed aggregation size, the proposed model dynamically determines the aggregation size based on given offered traffic load, number of stations, transmission rate, and random backoff process. Performance evaluations showed that the proposed model produces remarkably accurate performance estimates for throughput, especially for higher traffic loads.

When wireless communication signals impinge on a moving human they are affected by micro-Doppler. A passive receiver of the resulting signals can calculate the spectrogram that produces different signatures depending on the human activity. This constitutes a significant privacy breach when the human is unaware of it. This paper presents a methodology for preventing this when we want to do so by injecting into the transmitted signal frequency variations that obfuscate the micro-Doppler signature. We assume a system that uses orthogonal frequency division multiplexing (OFDM) and a passive receiver that estimates the spectrogram based on the instantaneous channel state information (CSI). We analyze the impact of our approach on the received signal and we propose two strategies that do not affect the demodulation of the digital communication signal at the intended receiver. To evaluate the performance of our approach we use an IEEE 802.11-based OFDM system and realistic human signal reflection models.

IEEE 802.11 Wi-Fi networks are prone to many denial of service (DoS) attacks due to vulnerabilities at the media access control (MAC) layer of the 802.11 protocol. Due to the data transmission nature of the wireless local area network (WLAN) through radio waves, its communication is exposed to the possibility of being attacked by illegitimate users. Moreover, the security design of the wireless structure is vulnerable to versatile attacks. For example, the attacker can imitate genuine features, rendering classification-based methods inaccurate in differentiating between real and false messages. Although many security standards have been proposed over the last decades to overcome many wireless network attacks, effectively detecting such attacks is crucial in today’s real-world applications. This paper presents a novel resource exhaustion attack detection scheme (READS) to detect resource exhaustion attacks effectively. The proposed scheme can differentiate between the genuine and fake management frames in the early stages of the attack such that access points can effectively mitigate the consequences of the attack. The scheme is built through learning from clustered samples using artificial neural networks to identify the genuine and rogue resource exhaustion management frames effectively and efficiently in the WLAN. The proposed scheme consists of four modules which make it capable to alleviates the attack impact more effectively than the related work. The experimental results show the effectiveness of the proposed technique by gaining an 89.11% improvement compared to the existing works in terms of detection. [ABSTRACT FROM AUTHOR]

The most widely used Wi-Fi wireless communication system, which is based on OFDM, is currently developing quickly. The receiver must, however, accurately estimate the carrier frequency offset between the transmitter and the receiver due to the characteristics of the OFDM system that make it sensitive to carrier frequency offset. The autocorrelation of training symbols is typically used by the conventional algorithm to estimate the carrier frequency offset. Although this method is simple to use and low in complexity, it has poor estimation performance at low signal-to-noise ratios, which has a significant negative impact on the performance of the wireless communication system. Meanwhile, the design of the communication physical layer using deep-learning-based (DL-based) methods is receiving more and more attention but is rarely used in carrier frequency offset estimation. In this paper, we propose a DL-based carrier frequency offset (CFO) model architecture for 802.11n standard OFDM systems. With regard to multipath channel models with varied degrees of multipath fadding, the estimation error of the proposed model is 70.54% lower on average than that of the conventional method under 802.11n standard channel models, and the DL-based method can outperform the estimation range of conventional methods. Besides, the model trained in one channel environment and tested in another was cross-evaluated to determine which models could be used for deployment in the real world. The cross-evaluation demonstrates that the DL-based model can perform well over a large class of channels without extra training when trained under the worst-case (most severe) multipath channel model. [ABSTRACT FROM AUTHOR]

Wireless technologies are essential for modern people to maintain uninterrupted connection to the Internet. The most popular standards for wireless technologies are standards of the IEEE 802.11 family. Currently, data transmission rate achievable by IEEE 802.11ac or 802.11ax standards can reach up to 10 Gbit/s. Different IEEE standards have specific data transmission rates. For example, the IEEE 802.11ah standard or Wi-Fi HaLow (code name) operates in the 900 MHz band, which is an unlicensed frequency band below 1 GHz, and is called the “Sub-1-GHz” range. In theory, this standard can provide coverage range of up to 543 meters indoors and data transfer rate of up to 347 Mbit/s (using a maximum of four spatial streams and 16 MHz channel bandwidth). The great benefit of the 802.11ah standard is low energy consumption, which enables communication between devices from the Internet of Things (IoT) over long distances without using a lot of energy. The Wi-Fi HaLow standard is being studied by the authors of the presented article in the ns-3 network simulation program with the 802.11ah module installed and implemented in Docker containers, VirtualBox Virtual Machines (VMs) with a running Linux operating system. During the simulations, results were obtained for the Docker containers simulation with a limited number of stations over different simulation times. These results have been studied in different scenarios. In the scenarios, the results of the Wi-Fi HaLow network simulation were converted into another simulation time, and thus were compared with each other.

Multicast communication over wireless networks has many potential applications, such as real-time audiovisual content distribution or digital signage systems with multiple remote terminals. However, today’s common 802.11 networks cannot fully support such applications at the link layer due to the technical challenges in achieving both high transmission rate and high reliability for multicast data transfers. Those challenges primarily emerge from the inapplicability of immediate acknowledgment mechanisms of unicast transmissions to the multicast case and the need for finding a common transmission rate for all receivers with diverse channel conditions. The research literature in this domain mainly addresses the problem at the higher layers of the protocol stack. In this article, we present a novel approach that combines wireless link rate selection with an adaptive packet-level Forward Error Correction (FEC) mechanism in order to achieve high-rate and highly reliable multicast in 802.11 wireless networks. The integration of FEC into the 802.11 MAC layer allows direct interaction between the transmission rate and the coding scheme. As a result, potential packet losses caused by higher link rates can be compensated by the adjustable redundancy provided by FEC. In combination with an aggregated receiver feedback mechanism, this yields improved transmission efficiency and reliability for wireless multicast. We investigate this approach in a simulation environment under a realistic wireless channel model in various application scenarios with up to 50 receivers. The results represent significant performance improvements, in terms of throughput, channel utilization, and packet loss, over the state-of-the-art methods for reliable wireless multicast.

Real-time applications (RTA) are an important use case for IEEE 802.11be, a new amendment to the Wi-Fi standard. This amendment introduces new complicated mechanisms to provide low delay and high reliability for RTA, but many of them are not supported by legacy devices that may be present in future Wi-Fi networks. In contrast, the preliminary channel access (PCA) method is designed to satisfy strict RTA requirements even in the presence of legacy devices and does not require significant changes to the Wi-Fi protocol. However, it significantly reduces the capacity for non-RTA traffic. This paper introduces a Smart PCA method, which improves the performance of all the stations in scenarios with multiple RTA stations. Extensive simulation shows that the Smart PCA method guarantees low delays for intensive RTA traffic in these scenarios. Moreover, it doubles the network capacity for the stations with non-RTA traffic. [ABSTRACT FROM AUTHOR]

Intrusion detection in wireless and, more specifically, Wi-Fi networks is lately increasingly under the spotlight of the research community. However, the literature currently lacks a comprehensive assessment of the potential to detect application layer attacks based on both 802.11 and non-802.11 network protocol features. The investigation of this capacity is of paramount importance since Wi-Fi domains are often used as a stepping stone by threat actors for unleashing an ample variety of application layer assaults. In this setting, by exploiting the contemporary AWID3 benchmark dataset along with both shallow and deep learning machine learning techniques, this work attempts to provide concrete answers to a dyad of principal matters. First, what is the competence of 802.11-specific and non-802.11 features when used separately and in tandem in detecting application layer attacks, say, website spoofing? Second, which network protocol features are the most informative to the machine learning model for detecting application layer attacks? Without relying on any optimization or dimensionality reduction technique, our experiments, indicatively exploiting an engineered feature, demonstrate a detection performance up to 96.7% in terms of the Area under the ROC Curve (AUC) metric. [ABSTRACT FROM AUTHOR]

With the arrival of the peak smartphone era, users are upgrading their smartphones less frequently, and data growth is decelerating. To ensure effective spectrum management decisions, policy makers require a thorough understanding of prospective wireless broadband technologies, current trends and emerging issues. Here, we review the sixth cellular generation ('6G'), in comparison to two new Wi-Fi standards, including IEEE 802.11be ('Wi-Fi 7') and IEEE 802.11bn ('Wi-Fi 8'). We identify three emerging issues necessary for successful telecommunication policy. Firstly, evidenced-based policy making needs to be able to measure effectively how much demand takes place where and how. Thus, new datasets are needed reflecting real usage by different wireless broadband technologies, for indoor and outdoor users. Secondly, with data consumption growth slowing, there needs to be an urgent reassessment of spectrum demand versus allocation. Past forecasts do not reflect recent data and regulators urgently need to re-evaluate the implications for spectrum management. Finally, regulators need new and improved Lifecycle Impact Assessment metrics of cellular versus Wi-Fi architectures, to support successful policy decisions which mitigate energy and emissions impacts. • The rate of data growth is slowing in the peak smartphone era. • Policy issues associated with 6G, Wi-Fi 7 and Wi-Fi 8 are identified and evaluated. • New datasets are required measuring where and how demand takes place. • Spectrum managers need to urgently reassess spectrum demand versus allocation. • Lifecycle Impact Assessment metrics are required for future 6G and Wi-Fi architectures. [ABSTRACT FROM AUTHOR]

Wireless technologies are essential for modern people to maintain uninterrupted connection to the Internet. The most popular standards for wireless technologies are standards of the IEEE 802.11 family. Currently, data transmission rate achievable by IEEE 802.11ac or 802.11ax standards can reach up to 10 Gbit/s. Different IEEE standards have specific data transmission rates. For example, the IEEE 802.11ah standard or Wi-Fi HaLow (code name) operates in the 900 MHz band, which is an unlicensed frequency band below 1 GHz, and is called the "Sub-1-GHz" range. In theory, this standard can provide coverage range of up to 543 meters indoors and data transfer rate of up to 347 Mbit/s (using a maximum of four spatial streams and 16 MHz channel bandwidth). The great benefit of the 802.11ah standard is low energy consumption, which enables communication between devices from the Internet of Things (IoT) over long distances without using a lot of energy. The Wi-Fi HaLow standard is being studied by the authors of the presented article in the ns-3 network simulation program with the 802.11ah module installed and implemented in Docker containers, VirtualBox Virtual Machines (VMs) with a running Linux operating system. During the simulations, results were obtained for the Docker containers simulation with a limited number of stations over different simulation times. These results have been studied in different scenarios. In the scenarios, the results of the Wi-Fi HaLow network simulation were converted into another simulation time, and thus were compared with each other. [ABSTRACT FROM AUTHOR]

Due to the exponential growth in the use of Wi-Fi networks, it is necessary to study its usage pattern in dense environments for which the legacy IEEE 802.11 MAC (Medium Access Control) protocol was not specially designed. Although 802.11ax aims to improve Wi-Fi performance in dense scenarios due to modifications in the physical layer (PHY), however, MAC layer operations remain unchanged, and are not capable enough to provide stable performance in dense scenarios. Potential applications of Deep Learning (DL) to Media Access Control (MAC) layer of WLAN has now been recognized due to their unique features. Deep Reinforcement Learning (DRL) is a technique focused on behavioral sensitivity and control philosophy. In this paper, we have proposed an algorithm for setting optimal contention window (CW) under different network conditions called DRL-based Contention Window Optimization (DCWO). The proposed algorithm operates in three steps. In the initial step, Wi-Fi is being controlled by the 802.11 standards. In the second step, the agent makes the decisions concerning the value of CW after the TRAIN procedure for the proposed algorithm. The final phase begins after the training, defined by a time duration specified by the user. Now, the agent is fully trained, and no updates will be no longer received. Now the CW is updated via the OPTIMIZE process of DCWO. We have selected total network throughput, instantaneous network throughput, fairness index, and cumulative reward, and compared our proposed scheme DCWO with the Centralized Contention window Optimization with DRL (CCOD). Simulation results show that DCWO with Double Deep Q-Networks (DDQN) performs better than CCOD with (i) Deep Deterministic Policy Gradient (DDPG) and (ii) Deep Q-Network (DQN). More specifically, DCWO with DDQN gives on average 28% and 23% higher network throughput than CCOD in static and dynamic scenarios. Whereas in terms of instantaneous network throughput DCWO gives around 10% better results than the CCOD. DCWO achieves almost near to optimal fairness in static scenarios and better than DQN and DDPG with CCOD in dynamic scenarios. Similarly, while the cumulative reward achieved by DCWO is almost the same with CCOD with DDPG, the uptrend of DCWO is still encouraging. [ABSTRACT FROM AUTHOR]

Many home networks integrate a small number (typically 2-4) of Wi-Fi Access Points (APs), with heterogeneous characteristics: different 802.11 variants, capabilities and security schemes. This paper proposes the consideration of these specific characteristics in order to improve the management of network resources. Three use cases are presented in order to showcase the potential benefits. By the use of a user-space AP, which works in coordination with a controller, the network is able to assign each connected station to the AP that best fits with its characteristics. The system also manages security, avoiding the need of adding specific elements for authentication, encryption or decryption. Extensions are proposed to an existing protocol that defines the communication between the AP and the controller, in order to communicate and store the specific characteristics of each AP and end device. This includes new association and handoff schemes that do not introduce any additional delay. The system has been implemented in a real environment, and a battery of tests has been run using three hardware platforms of different characteristics. The results show that handoffs between bands are possible, and estimate the processing delays, the Round-Trip Time and the handoff delay, which is small enough in order not to produce any significant disruption to the user (10-50 ms). Finally, the scenarios of interest have been replicated in a simulation environment, showing that significant benefits can be achieved if the specific characteristics of each AP and station are considered.

Despite the rapid growth of Wireless Local Area Networks (WLANs), the energy consumption caused by wireless communication remains a significant factor in reducing the battery life of power-constrained wireless devices. To reduce the energy consumption, static and adaptive power saving mechanisms have been deployed in WLANs. However, some inherent drawbacks and limitations remain. We have developed the concept of Context-Aware Listen Interval (CALI), in which the wireless network interface, with the aid of a Machine Learning (ML) classification model, sleeps and awakes based on the level of network activity of each application. In this paper we develop the power saving modes of CALI. The experimental results show that CALI consumes up to 75% less power when compared to the currently deployed power saving mechanism on the latest generation of smartphones, and up to 14% less energy when compared to Pyles’ et al. SAPSM power saving approach, which also employs an ML classifier.

In the last two decades, Wireless Local Area Networks (WLANs) based on 802.11 have become crucial in offering Internet services with high data transmission rates. Under crowded scenarios like airports, shopping centers, and stadiums, WLANs can be easily congested due to their random access nature and limited operational capability. This may result in a reduced user multimedia experience. With a proper Transmission Opportunity (TXOP) allocation and scheduling scheme, the user service quality could be improved under such scenarios. In this paper, a Congestion-Aware Transmission Opportunity (CA-TXOP) scheme is proposed, allocating transmission resources by considering network level congestion and information like buffer status, channel quality, and data rates. The CA-TXOP scheme prioritizes congested and noncongested user stations and improves the overall network performance. Simulation results show that the CA-TXOP scheme attained 45%, 35%, and 20% enhancement compared with Fixed, Bi-level, and FRA-TXOP scheduling schemes. Such scheme applies to future congested scenarios like smart buildings and organizations that require satisfying video quality to all the user stations.

We explore the use of synchronization bits from a standard 802.11 preamble for high-accuracy localization. The multi-tone short training field present in 802.11 standards that use a Legacy preamble is investigated in comparison to a common remote sensing waveform known as the stepped frequency waveform that shares similar waveform characteristics to the preamble. We present the 802.11 standard and the connection to the stepped frequency waveform, derive the theoretical lower bound on the short training field, present a comparison of simulated results, and present experimental wireless measurements demonstrating ranging accuracies on the order of 1.9 mm.

Industry 4.0 and Industrial Internet refer to the expected revolution in production, utility management and, in general, fully automated, interconnected and digitally managed industrial ecosystems. One of the key enablers for Industry 4.0 lies on reliable and timely exchange of information and large scale deployment of wireless communications in industry facilities. Wireless will bring solutions to overcome the main drawbacks of the current wired systems: lack of mobility, deployment costs, cable damage dependency and scalability. However, the strict requirements in reliability and latency of use cases such as Factory Automation (FA) and Process Automation (PA) are still a major challenge and a barrier for massive deployment of currently available wireless standards. This paper proposes a PHY/MAC wireless communication solution for FA and PA based on Non-Orthogonal Multiple Access (NOMA) in combination with the 802.11n standard. The communication system proposed aims at delivering two different sets of services. The first service class is composed of Critical Services (CS) with strict restrictions in reliability and latency. The same communication system should convey also a second group of services, referred as Best Effort (BE) with more relaxed boundary conditions. The proposal theoretical background, a detailed transmission-reception architecture, the physical layer performance and the MAC level system reliability are presented in this paper. The solution provides significantly better reliability and higher flexibility than TDMA systems, jointly with a predictable control-cycle latency.

When the preambles of a wideband 802.11n/ac frame transmission are affected by a collision with a 20MHz frame on one of the subchannels, a receiver might fail to decode the whole wideband frame. Experimental evaluations with off-the-shelf 802.11n hardware showed that the vulnerability of wideband frame transmission to a narrowband collision/interference is a real problem. To mitigate the severe impact of narrowband collision/interference on wideband transmission, we propose to transmit several 20MHz frames in parallel instead of a single wideband frame so that a narrowband collision on one of the subchannels affects only the parallel frame transmitted on that subchannel. Performance evaluations demonstrated that the proposed parallel frame transmission scheme significantly improves the throughput and delay performance of wideband transmissions under different traffic and channel conditions. While the throughput performance of the proposed scheme for saturated traffic conditions was analytically validated, extensive simulation-based evaluations showed that the proposed scheme achieves great performance improvement for unsaturated traffic conditions and hidden node environment.

The most widely used Wi-Fi wireless communication system, which is based on OFDM, is currently developing quickly. The receiver must, however, accurately estimate the carrier frequency offset between the transmitter and the receiver due to the characteristics of the OFDM system that make it sensitive to carrier frequency offset. The autocorrelation of training symbols is typically used by the conventional algorithm to estimate the carrier frequency offset. Although this method is simple to use and low in complexity, it has poor estimation performance at low signal-to-noise ratios, which has a significant negative impact on the performance of the wireless communication system. Meanwhile, the design of the communication physical layer using deep-learning-based (DL-based) methods is receiving more and more attention but is rarely used in carrier frequency offset estimation. In this paper, we propose a DL-based carrier frequency offset (CFO) model architecture for 802.11n standard OFDM systems. With regard to multipath channel models with varied degrees of multipath fadding, the estimation error of the proposed model is 70.54% lower on average than that of the conventional method under 802.11n standard channel models, and the DL-based method can outperform the estimation range of conventional methods. Besides, the model trained in one channel environment and tested in another was cross-evaluated to determine which models could be used for deployment in the real world. The cross-evaluation demonstrates that the DL-based model can perform well over a large class of channels without extra training when trained under the worst-case (most severe) multipath channel model.

In this article, we propose a medium access control (MAC) scheme, called STation pREsence-Aware MAC (STREAM), that offers equal contention opportunities to the stations in a network. STREAM is designed to preserve its performance in crowded and high-traffic networks. The scheme improves channel access, utilization, and service fairness of stations. STREAM detects collided transmissions or idle events in the network to estimate the number of active stations. STREAM sets the contention period as a function of the number of stations in the network. As a result, the number of contention opportunities is enlarged and the access to the transmission channel in the network is improved. In this way, STREAM overcomes the performance degradation of IEEE 802.11 under heavy traffic and crowded networks. We analyze the throughput of STREAM, fairness of service, and the probability that a station experiences collisions as a function of the number of stations in the network. We also present performance evaluations and comparisons with IEEE 802.11 and other high-performing schemes. We extend the application of STREAM on traffic with different priorities and quality of service. Our evaluations show that STREAM outperforms IEEE 802.11 and the compared schemes. We show that STREAM is an ideal candidate as a fallback mechanism for IEEE 802.11 under overwhelming conditions. [ABSTRACT FROM AUTHOR]

Real-time applications (RTA) are an important use case for IEEE 802.11be, a new amendment to the Wi-Fi standard. This amendment introduces new complicated mechanisms to provide low delay and high reliability for RTA, but many of them are not supported by legacy devices that may be present in future Wi-Fi networks. In contrast, the preliminary channel access (PCA) method is designed to satisfy strict RTA requirements even in the presence of legacy devices and does not require significant changes to the Wi-Fi protocol. However, it significantly reduces the capacity for non-RTA traffic. This paper introduces a Smart PCA method, which improves the performance of all the stations in scenarios with multiple RTA stations. Extensive simulation shows that the Smart PCA method guarantees low delays for intensive RTA traffic in these scenarios. Moreover, it doubles the network capacity for the stations with non-RTA traffic.

Energy is a vital resource in wireless computing systems. Despite the increasing popularity of wireless local area networks (WLANs), one of the most important outstanding issues remains the power consumption caused by wireless network interface controller. To save this energy and reduce the overall power consumption of wireless devices, most approaches proposed to-date are focused on static and adaptive power saving modes. Existing literature has highlighted several issues and limitations in regards to their power consumption and performance degradation, warranting the need for further enhancements. In this paper, we propose a novel context-aware network traffic classification approach based on machine learning (ML) classifiers for optimizing WLAN power saving. The levels of traffic interaction in the background are contextually exploited for application of ML classifiers. Finally, the classified output traffic is used to optimize our proposed context-aware listen interval power saving modes. A real-world dataset is recorded, based on nine smartphone applications’ network traffic, reflecting different types of network behavior and interaction. This is used to evaluate the performance of eight ML classifiers in this initial study. The comparative results show that more than 99% of accuracy can be achieved. This paper indicates that ML classifiers are suited for classifying smartphone applications’ network traffic based on the levels of interaction in the background.

Intrusion detection in wireless and, more specifically, Wi-Fi networks is lately increasingly under the spotlight of the research community. However, the literature currently lacks a comprehensive assessment of the potential to detect application layer attacks based on both 802.11 and non-802.11 network protocol features. The investigation of this capacity is of paramount importance since Wi-Fi domains are often used as a stepping stone by threat actors for unleashing an ample variety of application layer assaults. In this setting, by exploiting the contemporary AWID3 benchmark dataset along with both shallow and deep learning machine learning techniques, this work attempts to provide concrete answers to a dyad of principal matters. First, what is the competence of 802.11-specific and non-802.11 features when used separately and in tandem in detecting application layer attacks, say, website spoofing? Second, which network protocol features are the most informative to the machine learning model for detecting application layer attacks? Without relying on any optimization or dimensionality reduction technique, our experiments, indicatively exploiting an engineered feature, demonstrate a detection performance up to 96.7% in terms of the Area under the ROC Curve (AUC) metric.

This letter proposes an algorithm for the dynamic tuning of the maximum size of aggregated frames in 802.11 WLANs. Traffic flows with opposed requirements may coexist in these networks: traditional services as web browsing or file download that need high throughput, and services with real-time requirements that need low latency. The proposed algorithm allows the network manager to find an optimal balance (i.e. the “sweet spot” between throughput and latency: a “delay budget” can be assigned to real-time flows, with the objective of keeping the latency as close as possible to that budget, while penalizing the throughput of traditional services as little as possible. [ABSTRACT FROM AUTHOR]

Due to the exponential growth in the use of Wi-Fi networks, it is necessary to study its usage pattern in dense environments for which the legacy IEEE 802.11 MAC (Medium Access Control) protocol was not specially designed. Although 802.11ax aims to improve Wi-Fi performance in dense scenarios due to modifications in the physical layer (PHY), however, MAC layer operations remain unchanged, and are not capable enough to provide stable performance in dense scenarios. Potential applications of Deep Learning (DL) to Media Access Control (MAC) layer of WLAN has now been recognized due to their unique features. Deep Reinforcement Learning (DRL) is a technique focused on behavioral sensitivity and control philosophy. In this paper, we have proposed an algorithm for setting optimal contention window (CW) under different network conditions called DRL-based Contention Window Optimization (DCWO). The proposed algorithm operates in three steps. In the initial step, Wi-Fi is being controlled by the 802.11 standards. In the second step, the agent makes the decisions concerning the value of CW after the TRAIN procedure for the proposed algorithm. The final phase begins after the training, defined by a time duration specified by the user. Now, the agent is fully trained, and no updates will be no longer received. Now the CW is updated via the OPTIMIZE process of DCWO. We have selected total network throughput, instantaneous network throughput, fairness index, and cumulative reward, and compared our proposed scheme DCWO with the Centralized Contention window Optimization with DRL (CCOD). Simulation results show that DCWO with Double Deep Q-Networks (DDQN) performs better than CCOD with (i) Deep Deterministic Policy Gradient (DDPG) and (ii) Deep Q-Network (DQN). More specifically, DCWO with DDQN gives on average 28% and 23% higher network throughput than CCOD in static and dynamic scenarios. Whereas in terms of instantaneous network throughput DCWO gives around 10% better results than the CCOD. DCWO achieves almost near to optimal fairness in static scenarios and better than DQN and DDPG with CCOD in dynamic scenarios. Similarly, while the cumulative reward achieved by DCWO is almost the same with CCOD with DDPG, the uptrend of DCWO is still encouraging.

Contribution: A novel approach to remote instruction in 802.11 technology is described using the virtual lab technology. Background: Lab-based education has been a staple of computing education for decades. By interacting with the technology, students are able to gain a much greater understanding of the subject through hands-on activities. Recently, virtual labs have provided a mechanism to allow both co-located and online students access to these lab environments without the time, space, and monetary constraints of traditional labs. Computer networking and security is one area where virtual labs provide a highly useful testbed for learning security concepts. One problem is implementing virtual educational labs pertaining to 802.11 technologies given the inherently physical location-based nature of the wireless medium. Intended Outcomes: This article builds on work by others by developing and testing a new hybrid physical/virtual lab to enable students to learn concepts pertaining to 802.11 networking technologies remotely in a virtual lab setting. Application Design: The virtual lab environment uses the ESXi virtualization technology coupled with arrays of USB 802.11 adapters to enable the use of 802.11 technologies remotely. Findings: The experimental results show the proposed system to be as effective as a traditional physical lab environment with regards to usefulness, ease of use, satisfaction, self-efficacy, and exercise completion time. [ABSTRACT FROM AUTHOR]

In the last two decades, Wireless Local Area Networks (WLANs) based on 802.11 have become crucial in offering Internet services with high data transmission rates. Under crowded scenarios like airports, shopping centers, and stadiums, WLANs can be easily congested due to their random access nature and limited operational capability. This may result in a reduced user multimedia experience. With a proper Transmission Opportunity (TXOP) allocation and scheduling scheme, the user service quality could be improved under such scenarios. In this paper, a Congestion-Aware Transmission Opportunity (CA-TXOP) scheme is proposed, allocating transmission resources by considering network level congestion and information like buffer status, channel quality, and data rates. The CA-TXOP scheme prioritizes congested and noncongested user stations and improves the overall network performance. Simulation results show that the CA-TXOP scheme attained 45%, 35%, and 20% enhancement compared with Fixed, Bi-level, and FRA-TXOP scheduling schemes. Such scheme applies to future congested scenarios like smart buildings and organizations that require satisfying video quality to all the user stations. [ABSTRACT FROM AUTHOR]

The representative media access control (MAC) mechanism of IEEE 802.11 is a distributed coordination function (DCF), which operates based on carrier-sense multiple access with collision avoidance (CSMA/CA) with binary exponential backoff. The next amendment of IEEE 802.11 being developed for future Wi-Fi by the task group-be is called IEEE 802.11be, where the multi-link operation is mainly discussed when it comes to MAC layer operation. The multi-link operation discussed in IEEE 802.11be allows multi-link devices to establish multiple links and operate them simultaneously. Since the medium access on a link may affect the other links, and the conventional MAC mechanism has just taken account of a single link, the DCF should be used after careful consideration for multi-link operation. In this paper, we summarize the DCFs being reviewed to support the multi-radio multi-link operation in IEEE 802.11be and analyze their performance using the Markov chain model. Throughout the extensive performance evaluation, we summarize each MAC protocol's pros and cons and discuss essential findings of the candidate MAC protocols. [ABSTRACT FROM AUTHOR]

The next article shows of prototype of monitoring and alarm that it allows for the detection of white frost, which is the base to develop a prototype for detention and predictions of other types of frost. The device shows the atmospheric variables of temperature and humidity, this data is sent to a local server broker with Wifi protocol MQTT. This one retransmit the info to end devices like cellphones and laptops. The system is capable of alerting with an alarm and it allows for the events communication to different end devices. To do this was necessary to select the appropriate devices, technologies and protocols, design and validate them, emulating the appropriate temperature and humidity conditions.