Wireless Local Area Network Environment Research Articles

A Study of the Active Access-Point Configuration Algorithm under Channel Bonding to Dual IEEE 802.11n and 11ac Interfaces in an Elastic WLAN System for IoT Applications

Currently, Internet of Things (IoT) has become common in various applications, including smart factories, smart cities, and smart homes. In them, wireless local-area networks (WLANs) are widely used due to their high-speed data transfer, flexible coverage ranges, and low costs. To enhance the performance, the WLAN configuration should be optimized in dense WLAN environments where multiple access points (APs) and hosts exist. Previously, we have studied the active AP configuration algorithm for dual interfaces using IEEE802.11n and 11ac protocols at each AP under non-channel bonding (non-CB). In this paper, we study the algorithm considering the channel bonding (CB) to enhance its capacity by bonding two channels together. To improve the throughput estimation accuracy of the algorithm, the reduction factor is introduced at contending hosts for the same AP. For evaluations, we conducted extensive experiments using the WIMENT simulator and the testbed system using Raspberry Pi 4B APs. The results show that the estimated throughput is well matched with the measured one, and the proposal achieves the higher throughput with a smaller number of active APs than the previous configurations.

A Smart Internet-Wide Port Scan Approach for Improving IoT Security Under Dynamic WLAN Environments

The Internet of Things (IoT) has created acute network security concerns owing to their weak protocols and limited system resources. Vulnerable IoT devices increase the risk of compromising other devices connected to the network. Hence, vulnerability and risk assessments are necessary for IoT devices. Correspondingly, the Internet-wide port scan (IWPS) technique has garnered significant attention for its ability to discover and probe Internet-wide connected IoT devices. However, IWPS performance depends on the end wireless local area network (WLAN) state (e.g., congestion and signal-to-interference-plus-noise ratio). Scans oblivious to such dynamic WLAN factors can cause probe packet loss while increasing port-scan delays, which reduces the discovery rate, which misses the point of network security. Therefore, in this study, we propose a novel holistic approach to identifying WLAN environmental states based on round-trip time and probe-packet responses. To demonstrate the effectiveness of the proposed approach, we perform extensive experiments on real WLAN environments with various devices. The accuracy of the estimated states was validated at greater than 90% by analyzing the captured probe data at each WLAN.

Joint Coordinate Optimization in Fingerprint-Based Indoor Positioning

Fingerprint-based indoor positioning uses pattern recognition algorithms (PRAs) to estimate the users’ locations in wireless local area network environments, where satellite-based positioning methods cannot work properly. Traditionally, the training phase of PRA is separately conducted for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$y$ </tex-math></inline-formula> coordinates. However, the received signal strength from access points is a unique fingerprint for each measured point, not for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$y$ </tex-math></inline-formula> coordinates separately. In this letter, we propose a method to jointly employ the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$y$ </tex-math></inline-formula> coordinates during the training phase using a novel PRA-based Gaussian process regression (GPR), named 2D-GPR. Experimental results show that the proposed 2D-GPR improves the accuracy of positioning more than <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$40cm$ </tex-math></inline-formula> in limited data samples and has a lower calculation cost compared with conventional GPR.

QAAs: QoS provisioned artificial intelligence framework for AP selection in next-generation wireless networks

Emerging trend of ubiquitous data access is driving the demand for effective wireless communication connectivity. In essence to this, wireless local area network (WLAN) technology seems to be a reliable and cost effective access for the next-generation wireless ecosystem. But the pivotal challenge for WLAN in the next generation wireless networks is to cater the legions of heterogeneous services with characteristic sets of quality of service requirements. However, the strategies present in the existing literature are not accoutered for the application-agnostic association and are incompetent in handling the enormous WLAN state space. Realising the pitfalls of the existing strategies, a novel software-defined networking enabled artificial intelligence framework has been proposed. The proposed framework implements a novel invalid action reduction scheme and double deep reinforcement learning to guarantee the flow based association in a multi-service WLAN environment. Moreover, it allows the multi-parametric optimisation of the association decision and faster convergence to the stable solution. The analytical results validated through the extensive simulations revealed that the proposed scheme achieves high performance gain in terms of convergence, stability and network utility as compared to the other solutions in the literature.

Joint Radio Resource Management of Channel-Assignment and User-Association for Load Balancing in Dense WLAN Environment

IEEE 802.11-based wireless local area network (WLAN) is currently the most popular communication system and a lot of access points (APs) have been densely deployed over heavily populated areas. In such dense WLAN environment, the WLAN users can suffer from performance degradation due to high co-channel and adjacent-channel interference among APs. To tackling such user performance degradation problem, we propose a two-phase radio resource management (RRM) framework, of which the first phase is channel assignment (CA) and the second phase is user association for channel load balancing (UA-ChLB). In designing the RRM framework, we take account of typical WLAN environment where dual bands are supported and two types of APs coexist: controlled-APs, which are managed by a centralized controller, and stand-alone APs, which independently operate for themselves. The proposed CA method utilizes a channel bonding technique, which provides a high data rate by integrating multiple basic channels while reducing the interference among neighboring APs. The proposed hybrid UA-ChLB scheme, which is composed of distributed/centralized UA-ChLB, efficiently coordinates the channel load among neighboring APs and between two bands of 2.4 and 5 GHz, under consideration of the wireless channel quality, interference, and traffic conditions. We implement a prototype system in practice using the proposed RRM scheme. The experimental results show that the proposed RRM scheme greatly improves both throughput and fairness in dense WLAN environment, as compared with some existing schemes.

Backbone-Assisted Wireless Local Area Network

This article presents a cross-layer design of backbone-assisted wireless local area network (WLAN) for dense WLAN deployment. The popularity of 802.11-based WLANs leads to dense WLAN deployment in geographically limited space, including dense access points (AP) and dense users. With dense APs, an AP could overhear packets destined for other APs. Backbone-assisted WLAN is a new system architecture where cooperative APs share the overheard packets through a backbone network, thereby reducing packet retransmission and improving system throughput. Conventional WLAN, such as Wi-Fi, uses Stop-and-Wait ARQ. This article argues that Stop-and-Wait does not work well with backbone-assisted WLAN because of large backbone delays. We first show that with a variant of Selective Repeat ARQ tailored for backbone-assisted WLAN, a single-user backbone-assisted WLAN system can achieve substantial throughput improvement over that with Stop-and-Wait ARQ. Then, we put forth a new system architecture targeted for dense users, referred to as network-coded backbone-assisted WLAN, in which multiple users are allowed to transmit simultaneously. A distinguishing feature of this system is the joint use of physical-layer network-coding (PNC) decoding and multiuser decoding (MUD) in multipacket reception. This article is the first attempt to design an ARQ for multiuser backbone-assisted WLAN. Our overall system design solves a PNC sequence obfuscation problem and addresses long packet latency in Selective Repeat ARQ. Experiments on our software-defined radio prototype indicate that network-coded Ethernet-backbone-assisted WLAN can achieve high system throughput and low packet latency. Specifically, the system throughput can outperform an MUD-only multiuser WLAN and a single-user WLAN by 60 and 100 percent, respectively. Overall, we believe that network-coded backbone-assisted WLAN is a viable solution for boosting throughput and reducing latency in dense WLAN environments.

A user application-based access point selection algorithm for dense WLANs.

The current commercial access point (AP) selection schemes are mostly based on received signal strength, but perform poorly in many situations. To address this problem, a number of alternative schemes collect and analyze the actual load of every candidate AP. However, these schemes may incur significant latency and signaling overhead in dense wireless local area networks (WLANs). To mitigate such overhead, we propose a user application-based AP selection scheme that considers historical information about AP performance. Without inducing any signaling activity, our scheme monitors the amount of network traffic used by applications and estimates the achievable throughput of APs. Our scheme employs the characteristics of application traffic with the intent of accurately predicting AP performance. Using a measurement study in dense WLAN environments, we show that our scheme achieves higher throughput and lower association latency than those of existing schemes in places highly accessible to the user.

Deep Reinforcement Learning Paradigm for Performance Optimization of Channel Observation–Based MAC Protocols in Dense WLANs

The potential applications of deep learning to the media access control (MAC) layer of wireless local area networks (WLANs) have already been progressively acknowledged due to their novel features for future communications. Their new features challenge conventional communications theories with more sophisticated artificial intelligence-based theories. Deep reinforcement learning (DRL) is one DL technique that is motivated by the behaviorist sensibility and control philosophy, where a learner can achieve an objective by interacting with the environment. Next-generation dense WLANs like the IEEE 802.11ax high-efficiency WLAN are expected to confront ultra-dense diverse user environments and radically new applications. To satisfy the diverse requirements of such dense WLANs, it is anticipated that prospective WLANs will freely access the best channel resources with the assistance of self-scrutinized wireless channel condition inference. Channel collision handling is one of the major obstacles for future WLANs due to the increase in density of the users. Therefore, in this paper, we propose DRL as an intelligent paradigm for MAC layer resource allocation in dense WLANs. One of the DRL models, Q-learning (QL), is used to optimize the performance of channel observation-based MAC protocols in dense WLANs. An intelligent QL-based resource allocation ( ${i}$ QRA) mechanism is proposed for MAC layer channel access in dense WLANs. The performance of the proposed mechanism is evaluated through extensive simulations. Simulation results indicate that the proposed intelligent paradigm learns diverse WLAN environments and optimizes performance, compared to conventional non-intelligent MAC protocols. The performance of the proposed ${i}$ QRA mechanism is evaluated in diverse WLANs with throughput, channel access delay, and fairness as performance metrics.

Symbol Timing Synchronization for Uplink Multi-User Transmission in IEEE 802.11ax WLAN

Along with the proliferation of mobile devices and applications, IEEE 802.11 wireless local area network (WLAN) has become one of the most popular Internet access technologies. This leads to dense deployment of WLAN devices, and the calls for better user experience in such high-density WLAN environments are mounting. To keep pace with this increasing demand for high efficiency WLAN, IEEE 802.11ax, whose key advance is adopting uplink multi-user transmission (UL MU), is currently entering the final phase of standardization. The UL MU in 802.11ax WLAN, however, has a difficulty that stems from the intrinsically distributed nature of WLAN. Specifically, concurrent frames from multiple stations are loosely synchronous in time, thus resulting in symbol timing misalignment which leads to poor decoding performance. In this paper, we dissect the performance degradation caused by symbol timing misalignment and examine the required symbol timing conditions in receiving UL MU. Based on the analysis, we present a new two-step synchronization method to accommodate asynchronously arriving UL MU. We develop an 802.11ax link-level simulator and comparatively evaluate the proposed synchronization method via extensive simulations. Simulation results demonstrate that the proposed method successfully stabilizes UL MU data delivery even with large UL MU arrival timing deviations and the use of short cyclic prefix.

GrassMA: Graph-Based Semi-Supervised Manifold Alignment for Indoor WLAN Localization

Wireless local area network (WLAN) fingerprinting has been extensively studied for indoor localization due to the formidable deployability of WLAN in indoor environment, but one major bottleneck of its practical implementation is the extensive calibration effort required to construct the radio map through the fingerprinting which is time consuming and labor intensive. In response to this compelling problem, we newly design the graph-based semi-supervised manifold alignment approach which relies on the concept of graph construction to construct a cost-efficient radio map with a small number of labeled fingerprints. In addition, the unlabeled user traces are also considered to compensate for the sparsity of the raw radio map as well as enhance its robustness to the environmental change. The extensive experiments conducted in an actual indoor WLAN environment demonstrate the performance improvement (by 27.48% at most with respect to the confidence probability of errors within 3 m) by our system compared with the existing ones using the fingerprints solely.

RTS/CTS Framework Paradigm and WLAN Qos Provisioning Methods

Wireless local area network (WLAN) communications performance design and management have evolved a lot to be where they are today. They went through some technology’s amendments and innovations. But, some performance tools remained almost unchanged and play a fundamental role in contemporary networking solutions despite the latest innovations higher influence on their indisputable and important function. That is the case with Request to send (RTS) and consent to receive (CTS) protocols. They are among the former technologies, which helped for transmission control with better performance in WLAN environment. They are so important, particularly since the advent of sensitive data networking (e.g. internet telephony, audio and video materials distribution) over the internet protocol (IP). Up to recent years following today’s multimedia WLAN based networks deployment trends, RTS/CTS) contributed to provide networks with some expected good performance levels prior to the discovery of more sophisticated methods for this purpose (i.e. performance enhancements). And yet, one may question whether the new technologies have rendered RTS/CTS frameworks obsolete; or are they now used only for some specific network applications traffic management? This articles review attempts to comprehensibly study some of the research works, which have had interest in RTS/CTS mechanism as tools for WLAN applications performance support. Various researches have studied these tools from their early innovation as network node’s built-in component, through different frameworks associated with WLAN legacy (IEEE 802.11) MAC protocols. This paper analyzed RTS/CTS initial implementation as mere network performance solution from packets’ collision avoidance perspective; and then for transmission delay due to hidden nodes and their false deployment. The article closes up on a critical analysis on the possible long time contribution of these protocols into integrated schemes based WLAN QoS performance design.

Simultaneous pathway mapping and behavior understanding with crowdsourced sensing in WLAN environment

With the wide deployment of Wireless Local Area Network (WLAN), the WLAN Received Signal Strength (RSS) based indoor mapping has attracted significant attention for various of Location-based Services (LBSs). In this paper, we propose a new simultaneous pathway mapping and behavior understanding approach by crowdsourced sensing of WLAN RSS. First of all, a batch of WLAN RSS sequences with known locations are collected to optimize the parameters in density clustering by using the concept of Jaccard Coefficient (JC). Secondly, the wavelet analysis is considered for each RSS sequence with unknown locations to mitigate the noise interference, and then the process of multidimensional scaling approach based dimension reduction is followed to reduce the computation cost involved in density clustering. Thirdly, based on the RSS and timestamp relations of RSS measurements, the density clustering is conducted to merge the RSS measurements at unknown locations into different clusters. Finally, the pathway mapping and behavior understanding are realized according to the indoor mapping from the signal into physical space. The extensive experimental results show that the proposed approach can achieve superior pathway mapping performance compared with the well-known WILL and CIMLoc approaches.

Design of Miniaturized Dual-Band Microstrip Antenna for WLAN Application.

Wireless local area network (WLAN) is a technology that combines computer network with wireless communication technology. The 2.4 GHz and 5 GHz frequency bands in the Industrial Scientific Medical (ISM) band can be used in the WLAN environment. Because of the development of wireless communication technology and the use of the frequency bands without the need for authorization, the application of WLAN is becoming more and more extensive. As the key part of the WLAN system, the antenna must also be adapted to the development of WLAN communication technology. This paper designs two new dual-frequency microstrip antennas with the use of electromagnetic simulation software—High Frequency Structure Simulator (HFSS). The two antennas adopt ordinary FR4 material as a dielectric substrate, with the advantages of low cost and small size. The first antenna adopts microstrip line feeding, and the antenna radiation patch is composed of a folded T-shaped radiating dipole which reduces the antenna size, and two symmetrical rectangular patches located on both sides of the T-shaped radiating patch. The second antenna is a microstrip patch antenna fed by coaxial line, and the size of the antenna is diminished by opening a stepped groove on the two edges of the patch and a folded slot inside the patch. Simulation experiments prove that the two designed antennas have a higher gain and a favourable transmission characteristic in the working frequency range, which is in accordance with the requirements of WLAN communication.

A Passive Indoor Tracking Scheme With Geometrical Formulation

Passive tracking that aims to track targets without assistant devices, i.e., device-free targets , is addressed as an emerging field recently. This letter proposes a novel passive tracking algorithm with several pairs of position-known wireless terminals (WTs) and access points (APs), which can be implemented in wireless local area network environments. In the proposed scheme, we formulate the crossing point estimation on the line-of-sight paths (LOSPs) between APs and WTs of third parties, which reveal trajectory of the device-free target, as a geometrical optimization problem. Then, the optimization problem is solved by the particle swarm optimization algorithm. As essential knowledge, we first explore the characteristics of the wireless propagation radio frequency tomography, under LOSP. The tracking performance of the proposed scheme is evaluated via computer simulations as well as experiments in a $(5 \times 12)\hbox{-m}^2 $ monitored area. The experimental results show that the proposed scheme can provide remarkable tracking accuracy of as high as decimeters.

Distributed channel selection scheme based on the number of interfering stations in WLAN

In contention-based wireless local area networks (WLAN), adjacent WLAN access points (APs) and stations (STAs) using the same channel may degrade their performance due to contention. To solve this problem, researchers have studied various distributed channel selection schemes, where APs select the least congested channel among all available channels. However, the existing distributed channel selection schemes rarely consider contention from upstream transmission of STAs. Therefore, it is difficult to find the least congested channel in a WLAN environment where both upstream and downstream transmissions exist. In this paper, we propose a novel distributed channel selection scheme which decides a channel with minimum number of interfering STAs as the least congested channel. Unlike the existing schemes, the proposed scheme considers the contention from both downstream and upstream transmissions. In the proposed scheme, an AP can perceive the number of interfering STAs by receiving the beacon frames from its neighboring APs. For practical implementation of our proposed scheme, we also define the additional information elements of the beacon frame according to the IEEE 802.11 standard. By performing a system level simulation and experiment using a test bed, we evaluate and analyze the performance of the proposed scheme by comparing it with that of the least congested channel selection (LCCS) and the client-assisted channel assignment optimization (CACAO) schemes. The simulation result shows that the proposed scheme increases the network throughput by 11% and 20% compared to the LCCS and CACAO schemes, respectively. Under test environment, we confirm that the proposed scheme can enhance the data rate of AP up to three times comparing to that of the LCCS scheme.

Radio map updated method based on subscriber locations in indoor WLAN localization

With the rapid development of wireless local area network(WLAN) technology, an important target of indoor positioning systems is to improve the positioning accuracy while reducing the online calibration effort to overcome signal time-varying. A novel fingerprint positioning algorithm, known as the adaptive radio map with updated method based on hidden Markov model(HMM), is proposed. It is shown that by using a collection of user traces that can be cheaply obtained, the proposed algorithm can take advantage of these data to update the labeled calibration data to further improve the position estimation accuracy. This algorithm is a combination of machine learning, information gain theory and fingerprinting. By collecting data and testing the algorithm in a realistic indoor WLAN environment, the experiment results indicate that, compared with the widely used K nearest neighbor algorithm,the proposed algorithm can improve the positioning accuracy while greatly reduce the calibration effort.

Using Partially Overlapping Channels in Home 802.11g WLANs

In this paper, we revisit the channel assignment problem in 802.11 Wireless Local Area Networks (WLANs). We focus on the Orthogonal Frequency Division Multiplexing IEEE802.11g networks and report results of experimental work conducted in a dense WLAN environment. Unlike recently published work that recommends avoiding partially overlapping channels (POCs) in IEEE802.11g-based WLANs, our experimental results show that POCs can be effectively used in such networks, depending mainly on channel load and the received power from interfering nodes.

Refining TCP's RTT dependent mechanism by utilizing link retransmission delay measurement in Wireless LAN

SummaryNetwork utilization by legacy transmission control protocol (TCP) is determined by its round trip time (RTT) dependent mechanism for the growth of its sending rate. The RTT does not always reflect the actual network conditions, especially in the case of wireless local area network (WLAN). Consequently, it influences the RTT‐dependent mechanism falsely. This paper proposes a novel cross‐layer scheme between TCP and the Institute of Electrical and Electronics Engineers (IEEE) 802.11 medium access control (MAC) that compensates for any diminished growth of TCP's sending rate because of the inclusion of non‐congestion delay component in RTT, if any. The proposed scheme has two refinements. The first refinement is at the MAC layer that notifies the additional propagation delay on account of link retransmission of a TCP packet. The second refinement is at the TCP layer in which the sender adapts the sending rate by relating the aforementioned additional propagation delay to the RTT estimation. To examine the benefits, the proposed scheme is evaluated after implementing it on top of representative TCP variants; Newreno and Westwood+ (TCPW). Simulation results showed that with the proposed scheme, network utilization by native TCP is improved by more than 35% in a dedicated WLAN environment, while the performance remained protected in a shared WLAN environment. Copyright © 2015 John Wiley &amp; Sons, Ltd.

Assessment of the peak-to-average power ratio in different channel organization strategies

The benefits of organizing multichannel communications by fragmenting the data in bonded or in aggregated channels have been usually assessed at the media access control layer, being the network traffic the purpose of the studies. Despite the importance of the different kinds of channel organizations over the transmit signal peak-to average power ratio (PAPR) to size the back-off value of the power amplifier, little attention has been paid to it. In this paper the key aspect is the PAPR, which is fundamental to face transmitter designs optimizing the trade-off between linearity and the time-of-life of the batteries. The results are of interest not only in ad hoc cognitive radio networks, but also in some of the latest European Telecommunications Standards Institute and Institute of Electrical and Electronics Engineers standards supporting mobility in either wireless local area network environments or in cellular communications. A standardized radio link is used to model a realistic scenario to study different settings of the aggregate channels and to compare them with channel bonding alternatives. Besides, the suitability of some crest factor reduction techniques is here considered for different channel organizations. The results show how the channel organization (contiguous, regular and random spaced), either with bonded or aggregated strategies, affects the PAPR.

Access point selection game with mobile users using correlated equilibrium.

One of the most important issues in wireless local area network (WLAN) systems with multiple access points (APs) is the AP selection problem. Game theory is a mathematical tool used to analyze the interactions in multiplayer systems and has been applied to various problems in wireless networks. Correlated equilibrium (CE) is one of the powerful game theory solution concepts, which is more general than the Nash equilibrium for analyzing the interactions in multiplayer mixed strategy games. A game-theoretic formulation of the AP selection problem with mobile users is presented using a novel scheme based on a regret-based learning procedure. Through convergence analysis, we show that the joint actions based on the proposed algorithm achieve CE. Simulation results illustrate that the proposed algorithm is effective in a realistic WLAN environment with user mobility and achieves maximum system throughput based on the game-theoretic formulation.