WLAN Environment Research Articles

Overview and Performance Evaluation of Wi-Fi 7

The arrival of Wi-Fi 6 in late 2019, which is based on IEEE 802.11 ax, marked a giant leap forward in improving the capacity, efficiency, and coverage in most WLAN environments. While Wi-Fi 6 introduced various features to enhance the network performance and user experience such as in high-dense deployment scenarios, emerging applications like 4K/8K video, augmented reality, virtual reality, industrial Internet of Things, and real-time collaborations demand more deterministic connectivity. Meeting the most stringent requirements in throughput and latency in a deterministic fashion is beyond the capabilities of Wi-Fi 6 and therefore motivates the development of a new Wi-Fi 7 generation. As a result, the IEEE 802.11 Task Group be has been formed to define extreme high throughput (EHT) PHY and MAC layers capable of supporting a maximum throughput of at least 30 Gb/s, as well as reducing worst case latency and jitter to improve support for latency-sensitive applications. In this article, our primary goal is to identify and describe the main PHY and MAC elements that will shape Wi-Fi 7, which will operate in the 2.4 GHz, 5 GHz, and 6 GHz bands. For each of the main features, including key PHY enhancements, multi-link operation, and enhanced quality of service management, we discuss how Wi-Fi 7 is designing the corresponding enabling mechanism and present performance results as appropriate.

Proactive Cross-Layer Framework Based on Classification Techniques for Handover Decision on WLAN Environments

In recent years, modern technology has been increasing, and this has grown a derivate in big challenges related to the network and application infrastructures. New devices have been providing more high functionalities to users than ever before; however, these devices depend on a high functionality of network in order to ensure a correct functioning ability over applications. This is essential for mobile networking systems to evolve in order to meet the future requirements of capacity, coverage, and data rate. In addition, when a network problem happens, it could be converted into somethingmore disastrous and difficult to solve. A crucial point is the network physical change and the difficulties, such as loss continuity of services and the decision to select the future network to be connected. In this article, a new framework is proposed to forecast a future network to be connected through a mobile node in WLAN environments. The proposed framework considers a decision-making process based on five classifiers and the user’s position and acceleration data in order to anticipate the network change, reaching up to 96.75% accuracy in predicting the connection of this future network. In this way, an early change of network is obtained without packet and time loss during the network change.

WITHDRAWN: Evaluation, prediction and implementation patterns of network traffic malware using machine learning

Abstract The network environments are now days quite susceptible and vulnerable to assorted assaults because of the increasing devices from different sources. In this research work, the fetching and analytics of the network traffic is planned and simulated with the machine learning based approaches so that the overall prediction of the network malware can be done. The fetching of the network traffic from WLAN environment using Wireshark as packet capturing tool is done and the .pcap file is generated from Wireshark. After that Snort IDS is used for the classification of the network traffic and further training of the datasets. The approaches of support vector machine and random forest are taken in the work for training of the data. The approach of support vector machine is earlier work done as the traditional approach. Against Artificial Neural Network (ANN) and SVC, we have used the Random Forest (RF) Approach for the prediction of malware in the new testing network traffic as RF is one of the prominent approaches for the machine learning and classification. Once the results obtained from ANN and RF both, then have compare the performance of both ANN and RF and proved that RF is better than ANN in the prediction patterns on assorted parameters. The users on wireless environment including Wi-Fi are very vulnerable to the malware. The users connect to the public Wi-Fi hotspots without validations of their traffic and sniffing attempts. Many times, the public wireless hotspots capture the data of users who connect to them. There is need to work on the algorithms of machine learning and knowledge discovery for the better accuracy to get the malware in the wireless environment. This work is having the key focus towards the prediction of random network traffic so that the assaults can be predicted with higher degree of accuracy.

Hybrid Fuzzy Logic Engine for Ping-Pong Effect Reduction in Cognitive Radio Network

The spectrum is a scarce resource and shall be used efficiently. It is observed that fixed spectrum allocation techniques, currently in use, may not be able to accommodate increased number of users trying simultaneously to access the network. Researches suggest that this problem of spectrum scarcity can be addressed by cognitive radio networks; which permits the dynamic use of spectrum. One of the basic requirements of dynamic spectrum access in cognitive radio network is spectrum handoff. There is an associated issue with frequent spectrum handoffs and that is of the ping-pong ect. The ping-pong ect is caused due to the motion of mobile users between the adjacent cells, thus, initiating unnecessary spectrum handoffs. The purpose of this study is to develop and analyse a system that has the ability to perform cient decision about the execution of spectrum handoffs and in turn reduce the chances of ping-pong ect. Therefore, a fuzzy logic based system has been developed in a cognitive radio WLAN and UMTS environment and handoff is investigated between primary and secondary users. Our proposed hybrid system uses a two-stage fuzzy logic controller to reduce the number of ping-pong handoffs. In the rst stage, the system is designed to control the power of SU and to avoid any interference to PU. In the second stage, the system is designed to take the decision to execute handoff.

Developing TAMEx Model For Availability Aspect of E-Exam Security in WLAN Environment

The internet has provided tremendous impact in educational development throughout the world. E-Exam system is one of educational component which has been used increasing dramatically for evaluating educational process. As an e-exam feature, uniformity of system access and time duration of the exam to all examinees is very important to be considered. Exam manager usually utilizes a fixed network to connect all e-exam terminal in order to maintain service stability on e-exam systems. However, the use of a fixed network will be difficult to be implemented if the number of examines is large. There are not many Education institutions have adequate ICT infrastructure or e-exam terminals for all students. Most institutions develop WLAN networks as an alternative to being able to provide ICT services to all students. With the development of mobile device technology (Handphone or Laptop) that strongly supports the learning process through mobile devices. This research aims to develop application of the Time Adaptive for Mobile E-Exam (TAMEx) Model. It serves to maintain the reliability of e-exam implementation on WLAN networks. The possible connection disruptions is anticipate by seeking time compensation only to participants who has experienced the connection problem. The time compensation must be accordance to the duration of the occurrence of connection loss. Application development has been carried out and is functioning properly. The report of application shows that it has succeeded in identifying the connection disturbance duration and provide time compensation to the proper examinee.

SDN-Based Centralized Channel Assignment Scheme Using Clustering in Dense WLAN Environments

IEEE WLAN 802.11 uses a contention-based medium access control protocol. Adjacent WLAN access points (APs) and stations (STAs) sharing the same channels causes interference, and affect network performance. Existing distributed channel selection schemes are prone to channel oscillation and ripple effect problems. Additionally, centralized schemes may provide optimal or sub-optimal performance but the computation time complexity increases with number of APs. In this paper, we propose a clustering-based centralized channel selection scheme implemented using software-defined networking architecture to overcome these limitations. In the proposed scheme, we first group the APs using a similarity-based clustering algorithm using proximity information. The channel assignment problem is then formulated using an integer linear programming model. Performance evaluation using simulations indicate that the proposed scheme outperforms the common least congested channel selection (LCCS), in terms of throughput and required number of channels switching. Additionally, for N number of APs, the computation complexity is reduced from $$\mathcal {O}\left( 3^{N}\right)$$ to $$\mathcal {O}\left( (N)^2\right)$$ , in comparison to the common centralized channel selection scheme. The test bed results validate simulation results by achieving better data rates for the STAs compared with the LCCS scheme.

Indoor , Outdoor Characterization for WLAN Environments

Indoor , Outdoor Characterization for WLAN Environments

Self-Adaptive Dynamic Ranging Model-Based Real-Time Hybrid Algorithm for Accurate Indoor Localization

The reliability of location information still maintains a crucial impact on restricting the development of location based services in indoor environment. However, in wireless local area network, received signal strength indicator (RSSI) is prone to be interfered by indoor complex environment, resulting in low accuracy and instability of real-time positioning. Here, the new self-adaptive dynamic ranging model-based real-time hybrid algorithm was proposed to realize accurate and undisturbed localization in indoor scenes. A self-adaptive dynamic ranging model was initially constructed to update the environmental parameters and correct the ranging values of mobile terminals in real time. Based on this model, a hybrid KNN algorithm and a hybrid Bayesian algorithm were severally presented. Location fingerprint database and real-time RSSI data of test points were then obtained through data acquisition. Finally, the acquired data was further used to verify the two hybrid algorithms proposed, and compared with the results of several conventional algorithms. As a result, the stability and accuracy of dual hybrid algorithms were better than those of the traditional ones. The range of average location error of both hybrid algorithms maintained 1.26-1.38 m, which was significantly lower than the error level of 2-5 m under the current WLAN environment. This newly proposed hybrid algorithm could effectively improve the stability and accuracy of indoor localization with real-time positioning algorithm, providing a promising solution for RSSI-based indoor positioning system.

Priority Scheme for Enhancing the Capacity of Peer-to-Peer Networks in Mobile Environments

Given the pervasive use of wireless systems, based on the IEEE 802.11 and LTE standards, Peer-to-Peer (P2P) networks take relevance due to their inherent flexibility and scalability. In a mobile environment, users experience different channel conditions. This has a major impact on the system’s performance as it is perceived by users in different coverage zones. Additionally, users have different degrees of mobility which can improve or degrade the quality of the received signal. In this work, a wireless mobile P2P network is studied. Building on this, two different scenarios are considered. Namely, a WLAN environment where nodes usually know the placement of the access point and can move closer to it in order to improve their channel quality and a cellular system where nodes usually do not know where their attending base station is located. Building on this, a priority model to improve the system’s performance is proposed, studied and analyzed. This priority scheme is based on serving first users that experience a better channel condition in order to improve the file downloading process. The aforementioned networks are analyzed using both a fluid model and a Continuous Time Markov Chain.

A context-awareness positioning scheme in hospital WLAN environment

A context-awareness positioning scheme in hospital WLAN environment

Teletraffic Analysis for VoIP Services in WLAN Systems with Handoff Capabilities

In this work, a TDMA-based system is studied for VoIP services with and without mobility. The system is considered to be dedicated for voice-only services such as a WLAN environment under the PCF mode or a cellular system. In such environments, mobiles usually have mobility. This introduces the need to study the system when a handoff procedure is enabled. The teletraffic analysis considers call arrivals, call completions, and ON/OFF activity processes for individual VoIP sessions. The VoIP system is simulated in order to verify the accuracy of the analytical results. Since many papers published in the literature consider a fixed number of VoIP users in the system, this analysis can be useful to estimate the system's performance when the system is in statistical equilibrium. As an additional feature of this work, a simple fluid model to calculate the number of active and inactive users in a VoIP system with and without handoff capabilities is proposed and developed.

On the Feasibility of an Adaptive Movable Access Point System in a Static Indoor WLAN Environment

On the Feasibility of an Adaptive Movable Access Point System in a Static Indoor WLAN Environment

A Multi-Criteria Decision Framework for network selection over LTE and WLAN

The most appealing topic for next generation wireless networks – 5G and upcoming generations – is the vertical handover, as many heterogeneous wireless communication technologies have been introduced recently to fulfil the needs of mobile users under all circumstances. Especially with the growing applications that are greedy in terms of Quality of Service (QoS). Thus, being Always Best Connected (ABC) remains a difficult task to achieve.The purpose of this article is to tackle the network selection issue levering a combination of Multi-Criteria Decision Making (MCDM) methods to ensure that appropriate network is selected for a certain type of traffic and considering a number of QoS metrics that due to their interdependency, a weight distribution phase is required. To ascertain thoroughly the weights of QoS metrics impacting the decision for each application, Analytical Hierarchy Process (AHP) is generally applied. But its classical form comes with biased rules that are dealt with through an enhanced version FAHP (Fuzzy AHP) that has proven its performance when combined with other well reputed ranking methods, namely: ”Multiplicative Exponential Weighting (MEW)”, ”Simple Additive Weighting (SAW)”, ”Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS)” and ”VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR)”.The implementation and simulation experiments in LTE and WLAN environments using Network Simulator NS3 are presented in order to evaluate the reviewed algorithms focusing on Packet Delay and Packet Loss Rate as key selection criteria. The empirical results show that FAHP algorithms over perform the classical AHP ones. The most suitable algorithms combination will be assessed and suggested for each type of traffic.

Mobility management in IEEE 802.11 WLAN using SDN/NFV technologies

IEEE 802.11 wireless LAN is proliferating since the increased trend in the wireless network utilization on mobile devices. Accuracy, fast content delivery, and reliable mobility support are essential features of any network to support the changing trend in a wireless network. However, traditional architectures in wireless LAN (WLANs or WiFi) always endured from challenges such as the provision of consistent mobility, real-time packet flow, and seamless handoff. Generally, most of the WLAN only relies on signal strength for handoff which is not sufficient enough for fair selection of an access point (AP) and therefore causes imperfect performance of the network. We present a novel mobility management scheme for WLANs to deal with the mobility management issues, and load balancing by software-defined network (SDN) and network function virtualization (NFV) technologies. The proposed scheme is based on logical AP (LAP) that keeps a connection with the user/mobile terminal (MT) during handoff triggered by either the user or the SDN controller for seamless mobility. It also involves the current state of each AP in addition to traditional parameters of WLAN. We implemented the proposed scheme on a real testbed in a WLAN environment. The evaluation results authenticate that our proposed scheme provides robust handover without throughput degradation and load imbalance among adjacent APs, and allocates the best AP in the neighboring region. Moreover, our proposed scheme is feasible to implement since it did not require any modification at the mobile terminal.

60 GHz indoor WLANs: insights into performance and power consumption

This paper presents a feasibility study of 60 GHz indoor WLANs. We evaluate 60 GHz performance in a typical academic office building under the primary assumption that 60 GHz WLAN APs and clients will be equipped with relatively wide-beam antennas to cope with client mobility. In contrast to previous works which measured performance at a single layer using custom, non-standard compliant hardware, we investigate performance across multiple layers using primarily 802.11ad-compliant wide-beam COTS devices. Our study shows that the large number of reflective surfaces in typical indoor WLAN environments combined with wider beams makes performance highly unpredictable and invalidates several assumptions that hold true in static, narrow-beam, Line-Of-Sight scenarios. Additionally, we present the first measurements, to our best knowledge, of power consumption of an 802.11ad NIC and examine the impact of a number of factors on power consumption.

Composite Peer Hand-Shake Radio Map for Indoor WLAN Localization

Wi-Fi fingerprint localization using received signal strength indicator (RSSI) values has been widely adopted for indoor WLAN environments due to its accuracy and low deployment cost. However, the indoor environment comprises different schematic dimensions, e.g., for offices, corridors, and lobby spaces, under the same Wi-Fi coverage range. We propose a novel fingerprinting approach that leverages the schematic dimensional size of the target area to construct the radio map: The Peer Hand-shake (PHS) database. It deploys competitive pattern learning for representation between adjacent candidate reference vector points during offline calibration phase. Our extensive experimental analysis in a 15 m-by-9 m target area, using conventional nearest neighbor-based localization algorithms, show that PHS outperforms comparisons with average localization error below 2.7 m for all localization algorithms at affordable cost.

가상화된 WLAN 환경에서 트래픽 변화를 고려한 SDN 기반 대역폭 제어 기법

가상화된 WLAN 환경에서 트래픽 변화를 고려한 SDN 기반 대역폭 제어 기법

Hidden Naive Bayes Indoor Fingerprinting Localization Based on Best-Discriminating AP Selection

Indoor fingerprinting localization approaches estimate the location of a mobile object by matching observations of received signal strengths (RSS) from access points (APs) with fingerprint records. In real WLAN environments, there are more and more APs available, with interference between them, which increases the localization difficulty and computational consumption. To cope with this, a novel AP selection method, LocalReliefF-C( a novel method based on ReliefF and correlation coefficient), is proposed. Firstly, on each reference location, the positioning capability of APs is ranked by calculating classification weights. Then, redundant APs are removed via computing the correlations between APs. Finally, the set of best-discriminating APs of each reference location is obtained, which will be used as the input features when the location is estimated. Furthermore, an effective clustering method is adopted to group locations into clusters according to the common subsets of the best-discriminating APs of these locations. In the online stage, firstly, the sequence of RSS observations is collected to calculate the set of the best-discriminating APs on the given location, which is subsequently used to compare with cluster keys in order to determine the target cluster. Then, hidden naive Bayes (HNB) is introduced to estimate the target location, which depicts the real WLAN environment more accurately and takes into account the mutual interaction of the APs. The experiments are conducted in the School of Environmental Science and Spatial Informatics at the China University of Mining and Technology. The results validate the effectiveness of the proposed methods on improving localization accuracy and reducing the computational consumption.

Novel learning-based spatial reuse optimization in dense WLAN deployments

To satisfy the increasing demand for wireless systems capacity, the industry is dramatically increasing the density of the deployed networks. Like other wireless technologies, Wi-Fi is following this trend, particularly because of its increasing popularity. In parallel, Wi-Fi is being deployed for new use cases that are atypically far from the context of its first introduction as an Ethernet network replacement. In fact, the conventional operation of Wi-Fi networks is not likely to be ready for these super dense environments and new challenging scenarios. For that reason, the high efficiency wireless local area network (HEW) study group (SG) was formed in May 2013 within the IEEE 802.11 working group (WG). The intents are to improve the “real world” Wi-Fi performance especially in dense deployments. In this context, this work proposes a new centralized solution to jointly adapt the transmission power and the physical carrier sensing based on artificial neural networks. The major intent of the proposed solution is to resolve the fairness issues while enhancing the spatial reuse in dense Wi-Fi environments. This work is the first to use artificial neural networks to improve spatial reuse in dense WLAN environments. For the evaluation of this proposal, the new designed algorithm is implemented in OPNET modeler. Relevant scenarios are simulated to assess the efficiency of the proposal in terms of addressing starvation issues caused by hidden and exposed node problems. The extensive simulations show that our learning-based solution is able to resolve the hidden and exposed node problems and improve the performance of high-density Wi-Fi deployments in terms of achieved throughput and fairness among contending nodes.

Arithmetic-BICM for Seamless Rate Adaptation for Wireless Communication Systems

Bit-interleaved coded modulation (BICM) has been widely used in capacity-achieving communication systems. In this paper, we propose an enhanced BICM, namely, arithmetic-BICM (A-BICM), which is able to achieve high channel throughput and seamless rate adaptation in time-varying wireless channels. The A-BICM generates modulation symbols through arithmetic weighted sum operations, and it adapts to data rate via adjusting the number of transmitted symbols. Our contributions in the design of A-BICM are twofold. First, we use an extrinsic information transfer chart to analyze a demapping procedure, and this facilitates the selection of the weighting multiset, which is a key design parameter in A-BICM mapping. Second, we employ a systematic channel code in A-BICM and design an adaptation scheme to strike a balance between the performances in both high- and low-SNR regions. Simulation results show that the A-BICM outperforms the conventional BICM with length-864 low-density parity check coding in both additive white Gaussian noise and fading channels. The experiments in real WLAN environments reveal that A-BICM achieves 42.1% and 20.9% throughput gain over conventional BICM and hybrid automatic repeat request rate adaptation schemes. Furthermore, the channel coding rate and its constellation in A-BICM do not need to change within a fairly wide SNR region from 1 to 30 dB to reduce communication overhead.