Wireless Access Points Research Articles

Blind Federated Edge Learning

We study federated edge learning (FEEL), where wireless edge devices, each with its own dataset, learn a global model collaboratively with the help of a wireless access point acting as the parameter server (PS). At each iteration, wireless devices perform local updates using their local data and the most recent global model received from the PS, and send their local updates to the PS over a wireless fading multiple access channel (MAC). The PS then updates the global model according to the signal received over the wireless MAC, and shares it with the devices. Motivated by the additive nature of the wireless MAC, we propose an analog `over-the-air' aggregation scheme, in which the devices transmit their local updates in an uncoded fashion. However, unlike recent literature on over-the-air FEEL, here we assume that the devices do not have channel state information (CSI), while the PS has imperfect CSI. On the other hand, the PS is equipped with multiple antennas to alleviate the destructive effect of the channel, exacerbated due to the lack of perfect CSI. We design a receive beamforming scheme at the PS, and show that it can compensate for the lack of perfect CSI when the PS has a sufficient number of antennas. We also derive the convergence rate of the proposed algorithm highlighting the impact of the lack of perfect CSI, as well as the number of PS antennas. Both the experimental results and the convergence analysis illustrate the performance improvement of the proposed algorithm with the number of PS antennas, where the wireless fading MAC becomes deterministic despite the lack of perfect CSI when the PS has a sufficiently large number of antennas.

Telecommunication Network Performances and Evaluation of Radio Frequency Electromagnetic Radiation

The ongoing mobile communication technology intensification had occasioned the inevitable multiplications in the ratio of the radio frequency base service stations which had raised public consciousness over the considerable health hazards of the radioactive emissions from the communication systems. The current paper analysed the sequences of electromagnetic field measurements performed on the selected three states in the North West Nigeria in order to establish the compliance of radiation levels of cellular base stations and wireless fidelity access points with respect to internationally approved recommendations. The measured power densities of wireless fidelity access points are minimal and do not surpass 1% of the level allowed by International Commission on Non-Ionizing Radiation (ICNIRP). The result confirmed the environmental safety of the RF energy maintained by the telecommunication operators within the general public indicating an insignificant health hazards to the citizens.

On-Body Edge Computing Through E-Textile Programmable Logic Array

E-textiles have received tremendous attention in recent years due to the capability of integrating sensors into a garment, enabling high-precision sensing of the human body. Besides sensing, a number of solutions for e-textile garments have also integrated wireless interfaces, allowing sensing data to be transmitted, and also inbuilt capacitive touch sensors, allowing users to provide instructions. While this has provided a new level of sensing that can result in unprecedented applications, there has been little attention placed around on-body edge computing for e-textiles. This study focuses on the need for a noninvasive and remote health-monitoring solution with inbuilt on-body edge computing, and how enabling such sensing and computing capabilities in a fabric environment can act as a new method for healthcare monitoring through the use of embedded computing intelligence in smart garments. Facilitating computing in e-textiles can result in a new form of on-body edge computing, where sensor information is processed very close to the body before being transmitted to an external device or wireless access point. This form of computing can provide new security and data privacy capabilities and, at the same time, provide opportunities for new energy-harvesting mechanisms to process the data through the garment. This study proposes this concept through embroidered programmable logic arrays (PLAs) integrated into e-textiles. In the same way that PLAs have programmable logic circuits by interconnecting different AND, NOT, and OR gates, we propose e-textile–based gates that are sewn into a garment and connected through conductive thread stitching. Two designs are proposed, and this includes single- and multi-layered PLAs. Experimental validations have been conducted at the individual gates and the entire PLA circuits to determine the voltage utilization and logic computing reliability. The multilayered PLA garment superseded the single-layered garment with higher levels of accuracy in the yielded results due to the enhanced design layout, which reduces the potential for short circuits and errors occurring. Our proposed approach can usher in a new form of on-body edge computing for e-textile garments for future wearable technologies, and, in particular, with the current pandemic that requires noninvasive remote health-monitoring applications.

COMPARISON OF WIRELESS ADAPTERS INTERFERENCE BASED ON DIFFERENCES OF FLOOR POSITION

The wireless network adapter has now been widely developed. The wireless adapter receives signal strength has several levels. It depends on the signal strength of the device with respect to the wireless signal and access point. The external adapter is used by computers that are not equipped with a wireless network adapter. Usually, internet users use a USB wireless adapter. An external adapter can also amplify signal reception from hotspots. The problem is how users consider using an external wireless adapter or just an onboard wireless adapter when there are many networks interfering with the weak signal strength of the access point. The method is implemented by measuring the value of the Received Signal Strength Indicator (RSSI). The purpose of this study is to compare the received signal strength in conditions where the signal before interference and after experiencing interference (interference). Meanwhile, the measured RSSI value is divided into four floors and with different distances for each floor. Each floor is measured by five distances. The average value of signal strength at a distance of 10 meters is -74 dBm using the onboard wireless adapter and -69 dBm using a USB wireless adapter. The value obtained after the interference is -75 dBm and the USB wireless adapter gets -61 dBm. The reduction of the RSSI value between interference and non-interference is 3%. The onboard wireless adapter is affected by interference while the external wireless adapter is not affected.

Energy Efficiency Hybrid Precoding for UAV Inspection Communication System

In order to reduce the communication power consumption during UAV power line inspection, a energy efficiency hybrid recoding scheme based on one phase shifter is proposed in its communication subsystem. The solution is realized by using a partially connected hybrid precoder architecture in the UAV communication terminal, and using a beam combiner at the wireless network access point. At the same time, in order to reduce the complexity of hybrid precoding and combiner beam optimization design, convolutional neural networks are used to dynamically design and select the optimized beam required for UAV inspection and communication. Under the premise of efficiency, the communication energy efficiency can be improved as high as possible, thereby effectively improving the battery life cycle and cruise time of the UAV. The simulation results show that, compared with the manifold optimization and orthogonal matching pursuit algorithms, the proposed algorithm can significantly improve the energy efficiency of communication coding and battery life cycle.

Genetic Algorithm Applied to Planning IEEE 802.11g Networks

The problem of planning local wireless network IEEE 802.11g consists of automatically positioning and setting up wireless access points (APs) in order to provide access to the local network with the desired coverage and the required quality of service (QOS).In addition to the complexity of predicting the Quality of Service (QoS) of a network from the variables of the problem (positions, parameters and frequency of the APs), the planning of WLAN networks faces several difficulties. In particular, the location of APs and the allocation of frequencies. There is no single model to solve the problem of designing wireless local networks. Depending on the situations and the hypotheses studied, different criteria can be considered and expressed in terms of constraints to be observed or in terms of objectives to be optimized. The first distinction is to separate the financial criteria from the network quality criteria. The nature of these two criteria being fundamentally different. Then there are a variety of service quality criteria, but we can still group them into three main categories: coverage criteria, interference criteria and capacity criteria.. In this article, we will use an optimization method based on an algorithm of stochastic optimization, which is also based on the mechanisms of natural selection and of genetic. It is genetic algorithm. Our goal consist of minimizing the total interaction between the APs to perform the good choices when deploying a network 802.11g in a way that gives users signal-to-interference ratios (SIR) greater than the required threshold ß.

Optimization of IoT-Based Motion Intelligence Monitoring System

We design and implement an intelligent IoT-based motion monitoring system to realize the monitoring of three important parameters, namely, the type of movement, the number of movements, and the period of movement in physical activities, and optimize the system to support the simultaneous use by multiple users. Considering the motion monitoring scenario for smart fit, the framework of an IoT-based motion monitoring system is proposed. The framework contains components such as active acquisition nodes, wireless access points, data processing servers, and terminals. In terms of algorithm optimization, research related to active pattern recognition and periodic calculation methods is conducted. For active pattern recognition, two types of classification algorithms with different complexity are proposed based on Support Vector Machine (SVM) and deep neural networks, respectively, to adapt to scenarios with different computational capabilities. For period calculation, a method based on over-zero detection and wavelet transform is proposed to count the number of actions and calculate the period of each action. In 100 times action cycle calculation experiments, the count statistic calculation method achieves 100% calculation accuracy and the active cycle calculation results are close to the real value, which proves the effectiveness of the cycle calculation method. The system provides a multiuser-oriented communication method and realizes accurate and reliable human movement monitoring, which has a wide application prospect in the fields of physical education and rehabilitation training.

An efficient mobility prediction model for resource allocation in mobile cloud computing

Mobile cloud computing (MCC) is a technology which provides cloud server resources to mobile users with optimized latency. MCC allows mobile device to access cloud resources and to offload tasks to cloud let servers at any time and from anywhere. The cloud let servers are attached to wireless Access points. But mobility plays an important role which leads to the loss of connectivity of mobile devices because of varying signal strengths. On the other hand, optimal code execution is a challenge. In this paper, a connectivity base mobility prediction method is proposed to assign the cloud resources to the user without loss in the connection. The past accessing history of the users and path loss factors are taken into consideration to predict proper access point. From the performance evaluation the performance of the proposed method is increased by 15.23% when compared to other existing methods.

WNV-Detector: automated and scalable detection of wireless network vulnerabilities

The security of wireless routers receives much attention given by the increasing security threats. In the era of Internet of Things, many devices pose security vulnerabilities, and there is a significant need to analyze the current security status of devices. In this paper, we develop WNV-Detector, a universal and scalable framework for detecting wireless network vulnerabilities. Based on semantic analysis and named entities recognition, we design rules for automatic device identification of wireless access points and routers. The rules are automatically generated based on the information extracted from the admin webpages, and can be updated with a semi-automated method. To detect the security status of devices, WNV-Detector aims to extract the critical identity information and retrieve known vulnerabilities. In the evaluation, we collect information through web crawlers and build a comprehensive vulnerability database. We also build a prototype system based on WNV-Detector and evaluate it with routers from various vendors on the market. Our results indicate that the effectiveness of our WNV-Detector, i.e., the success rate of vulnerability detection could achieve 95.5%.

An Online Calibration Method Based on n-Tuple and Opportunistic Communication for Mine Mass Portable Gas Sensors.

Due to the increasing deployment of the Internet of Things (IoT) in the mining industry, portable gas monitoring devices have been widely used. Sensor calibration of large-scale portable gas monitoring devices is becoming an urgent problem to be solved. An online sensor calibration algorithm based on n-tuple and opportunistic communication is proposed based on the specific characteristics (i.e., ‘single-sensor, multi-position’ and ‘multi-sensor, single-position’) of each portable gas monitoring device employed. In this paper, data collected from portable and fixed sensors were defined as multi-dimensional data points and gas monitoring data pairs, respectively. The cluster-based self-adaptive weighted data fusion algorithm and multi-period single sensor reliability fusion algorithm were proposed and used for overall judging. The overall judgments were broadcast to each wireless access point by network, and the reliability of the calibration information transmission was enhanced by opportunistic communications. The simulation results revealed that efforts required for the calibration of portable sensors were reduced significantly, and their reliability was improved.

A comparative study of different network simulation tools and experimentation platforms for underwater communication

Study of computer networks and their performance parameters like delay, bandwidth utilization, throughput, latency, jittering, and packet loss. have gained significant importance in the recent times. Simulation studies have been preferred for these parameters in computer networks, which in a real time is a difficult task. A network consists of many networking devices as gateways, routers, bridges, wireless access points and hub connected to it. To implement any new protocol or algorithm in a network is costlier and time consuming. Recently lot of research is going on underwater wireless sensor networks (UWSNs). Conducting real time experiments for underwater applications are overpriced, so as an alternative solution for this, we can conduct simulation studies to reduce the cost and quicken the research activities.In this paper we explore the different experimentation platforms and simulation tools available that help the network architects to develop new protocols or do changes to the existing protocol in a productive manner. We classify the tools based on various parameters and provide guidelines for researchers to choose a suitable platform based on their requirements.

User data selection scheme to reduce database update errors for fingerprint localization

We are investigating the fingerprint method using wireless LAN access points (APs) to estimate user location. We focused on database update as a way to prevent the loss of localization accuracy. Updating a database using user-measured data presents the issue of update errors. In this paper, we have proposed and verified this issues.

A Generative Method for Indoor Localization Using Wi-Fi Fingerprinting.

Indoor localization is an enabling technology for pervasive and mobile computing applications. Although different technologies have been proposed for indoor localization, Wi-Fi fingerprinting is one of the most used techniques due to the pervasiveness of Wi-Fi technology. Most Wi-Fi fingerprinting localization methods presented in the literature are discriminative methods. We present a generative method for indoor localization based on Wi-Fi fingerprinting. The Received Signal Strength Indicator received from a Wireless Access Point is modeled by a hidden Markov model. Unlike other algorithms, the use of a hidden Markov model allows ours to take advantage of the temporal autocorrelation present in the Wi-Fi signal. The algorithm estimates the user’s location based on the hidden Markov model, which models the signal and the forward algorithm to determine the likelihood of a given time series of Received Signal Strength Indicators. The proposed method was compared with four other well-known Machine Learning algorithms through extensive experimentation with data collected in real scenarios. The proposed method obtained competitive results in most scenarios tested and was the best method in 17 of 60 experiments performed.

Underground location algorithm based on random forest and environmental factor compensation

Aiming at the poor location accuracy caused by the harsh and complex underground environment, long strip roadway, limited wireless transmission and sparse anchor nodes, an underground location algorithm based on random forest and compensation for environmental factors was proposed. Firstly, the underground wireless access point (AP) network model and tunnel environment were analyzed, and the fingerprint location algorithm was built. And then the Received Signal Strength (RSS) was analyzed by Kalman Filter algorithm in the offline sampling and real-time positioning stage. Meanwhile, the target speed constraint condition was introduced to reduce the error caused by environmental factors. The experimental results show that the proposed algorithm solves the problem of insufficient location accuracy and large fluctuation affected by environment when the anchor nodes are sparse. At the same time, the average location accuracy reaches three meters, which can satisfy the application of underground rescue, activity track playback, disaster monitoring and positioning. It has high application value in complex underground environment.

EBA: Efficient Bandwidth Aggregation for Connected Vehicles With MPTCP

Multipath TCP (MPTCP) recently emerges as a promising tool to improve the network service to vehicles through transparent aggregation of wireless infrastructures on the Earth (e.g., current terrestrial BSs) and over the air [e.g., unmanned aerial vehicles (UAVs)-based BSs], e.g., using both LTE and WiFi at the same time for a data transfer. Currently, such an approach is challenged by the fast dynamism of wireless link characteristics (e.g., RTT and loss rate) during the handoff between wireless access points under vehicle mobility, which could easily cause Head-of-Line (HoL) blocking at the receiver and significantly lower the overall network performance. The existing solutions generally rely on asynchronous scheduling at the sender to tackle the HoL blocking. However, they fall short in handling the packet losses and excessive delay during the handoff. In this article, we propose a unique MPTCP scheduling scheme named efficient bandwidth aggregation (EBA) to tackle the drawback in current approaches. EBA adaptively enables packet duplication over parallel paths (i.e., redundant scheduling), which can effectively prevent the HoL blocking but losses some bandwidth. Thus, we carefully model the conditions under which packet duplication can improve network performance. We then dynamically turn on and off redundant scheduling based on the modeling to achieve the best bandwidth performance for vehicles. We have implemented EBA inside the current Linux kernel MPTCP and tested it extensively through simulated scenarios. Experimental results show that EBA can effectively enhance the network service for connected vehicles.

Research on WiFi Penetration Testing with Kali Linux

Aiming at the vulnerability of wireless network, this paper proposed a method of WiFi penetration testing based on Kali Linux which is divided into four stages: preparation, information collection, simulation attack, and reporting. By using the methods of monitoring, scanning, capturing, data analysis, password cracking, fake wireless access point spoofing, and other methods, the WiFi network penetration testing with Kali Linux is processed in the simulation environment. The experimental results show that the method of WiFi network penetration testing with Kali Linux has a good effect on improving the security evaluation of WiFi network.

Evaluation of load balancing algorithms on overlappiing wireless accesspoints

<span>Wireless networks came into the computing world replacing the costlier and more complex wired method of connecting numerous equipment in the same or different location via the use of cables. It provides the user devices a connection to one another and the greater internet via connections to access points. Generally, 802.11 access point products follow a default strongest signal first approach in selecting user devices or nodes to connect to the access point or overlapping access points. This standard does not make provisions for even distribution of load and hence the quality of service and the throughput in areas of congestion would be reduced. This article brings forward two algorithms used in load balancing and they include round-robin technique and the weighted round-robin technique to be used in the simulation of the distribution of the load amongst the access points with the results collated and compared to clearly show which algorithm is best suited to be used as a standard for access point load distribution.</span>

Tracking of Wearable IoT Devices Through WAP Using Intelligent Rule-Based Location Aware Approach

Usage of the internet is increasing in the daily life of humans due to the need for speedy task completion for their daily services. Most of the living time is spent in some indoor environment which provides WiFi which is the basic need of internet connectivity using Wireless Access Points (WAP). Nowadays, most of the devices are IoT-based ones, which connect with the outer world through the access points in the existing environment. The wearable IoT devices may be misplaced somewhere and we need a specific scenario which helps to identify the misplaced mobile devices based on access points where they are connected by their unique identity such as MAC address. Most of the time, unrestricted WiFi access provided in the public environment is used by the end-user. In that scenario, the tracking of misplaced mobile devices is creating an issue when the WiFi is in switch-off mode. This paper proposes a technique for tracking a mobile device by using a location-aware approach with KNN and intelligent rules by tracking the channel accessed by the user to find the misplaced path by examining the device connected WAP positions.

Estimating PQoS of Video Streaming on Wi-Fi Networks Using Machine Learning.

Video on demand (VoD) services such as YouTube have generated considerable volumes of Internet traffic in homes and buildings in recent years. While Internet service providers deploy fiber and recent wireless technologies such as 802.11ax to support high bandwidth requirement, the best-effort nature of 802.11 networks and variable wireless medium conditions hinder users from experiencing maximum quality during video streaming. Hence, Internet service providers (ISPs) have an interest in monitoring the perceived quality of service (PQoS) in customer premises in order to avoid customer dissatisfaction and churn. Since existing approaches for estimating PQoS or quality of experience (QoE) requires external measurement of generic network performance parameters, this paper presents a novel approach to estimate the PQoS of video streaming using only 802.11 specific network performance parameters collected from wireless access points. This study produced datasets comprising 802.11n/ac/ax specific network performance parameters labelled with PQoS in the form of mean opinion scores (MOS) to train machine learning algorithms. As a result, we achieved as many as 93–99% classification accuracy in estimating PQoS by monitoring only 802.11 parameters on off-the-shelf Wi-Fi access points. Furthermore, the 802.11 parameters used in the machine learning model were analyzed to identify the cause of quality degradation detected on the Wi-Fi networks. Finally, ISPs can utilize the results of this study to provide predictable and measurable wireless quality by implementing non-intrusive monitoring of customers’ perceived quality. In addition, this approach reduces customers’ privacy concerns while reducing the operational cost of analytics for ISPs.

Dynamic Metasurface Antennas for 6G Extreme Massive MIMO Communications

Next generation wireless base stations and access points will transmit and receive using an extremely massive numbers of antennas. A promising technology for realizing such massive arrays in a dynamically controllable and scalable manner with reduced cost and power consumption utilizes surfaces of radiating metamaterial elements, known as metasurfaces. To date, metasurfaces are mainly considered in the context of wireless communications as passive reflecting devices, aiding conventional transceivers in shaping the propagation environment. This article presents an alternative application of metasurfaces for wireless communications as active reconfigurable antennas with advanced analog signal processing capabilities for next generation transceivers. We review the main characteristics of metasurfaces used for radiation and reception, and analyze their main advantages as well as their capability to reliably communicate in wireless networks. As current studies unveil only a portion of the potential of metasurfaces, we detail a list of exciting research and implementation challenges which arise from the application of metasurface antennas for wireless transceivers.