WiFi Access Points Research Articles

Enhanced Wi-Fi Access Point Positioning Using Hexagonal CNN With Mobile Data and Urban Information

Enhanced Wi-Fi Access Point Positioning Using Hexagonal CNN With Mobile Data and Urban Information

Data Mining Approach for Evil Twin Attack Identification in Wi-Fi Networks

Recent cyber security solutions for wireless networks during internet open access have become critically important for personal data security. The newest WPA3 network security protocol has been used to maximize this protection; however, attackers can use an Evil Twin attack to replace a legitimate access point. The article is devoted to solving the problem of intrusion detection at the OSI model’s physical layers. To solve this, a hardware–software complex has been developed to collect information about the signal strength from Wi-Fi access points using wireless sensor networks. The collected data were supplemented with a generative algorithm considering all possible combinations of signal strength. The k-nearest neighbor model was trained on the obtained data to distinguish the signal strength of legitimate from illegitimate access points. To verify the authenticity of the data, an Evil Twin attack was physically simulated, and a machine learning model analyzed the data from the sensors. As a result, the Evil Twin attack was successfully identified based on the signal strength in the radio spectrum. The proposed model can be used in open access points as well as in large corporate and home Wi-Fi networks to detect intrusions aimed at substituting devices in the radio spectrum where IEEE 802.11 networking equipment operates.

A cost-efficient content distribution optimization model for fog-based content delivery networks

The massive data demand requires content distribution networks (CDNs) to use evolving techniques for efficient content distribution with guaranteed quality of service (QoS). The distributed fog-based CDN model, with optimal fog node placements, is a suggested aproach by researchers to meet this demand. While many studies have focused on improving QoS by optimizing fog node placement, they have rarely considered the impact on content distribution, affected by placement, usage changes, and delivery rates. Therefore, the practical approach to fog node placement for CDN services must examine its impact on content distribution. Further, current research on fog-based CDN lacks formal methods to address key challenges: R1) strategic placement of fog nodes to process end-user requests; R2) construction of a content distribution path with guaranteed QoS; R3) cost minimization of building a fog-based CDN model. We construct this as a joint optimization problem by considering four parameters: geographical regions, open public Wi-Fi access points (OPWAPs) locations, QoS, and cost to achieve research objectives R1–R3. As a solution, we propose a dual-step framework. First, a heuristic for optimal fog node placement based on geographic regions and OPWAP locations is proposed. Second, we propose two algorithms, Greedy Performance-based Node Selection (GPDS) and Greedy Fog Node Selection algorithm (GFNSA), for selecting fog nodes, minimizing the cost of building a fog-based CDN while achieving optimal content distribution paths. The results demonstrate that the proposed methods outperform the baseline techniques and provide near-optimal solutions to the problem.

Round-Trip Time Ranging to Wi-Fi Access Points Beats GNSS Localization

Wi-Fi round-trip time (RTT) ranging has proven successful in indoor localization. Here, it is shown to be useful outdoors as well—and more accurate than smartphone code-based GNSS when used near buildings with Wi-Fi access points (APs). A Bayesian grid with observation and transition models is used to update a probability distribution of the position of the user equipment (UE). The expected value (or the mode) of this probability distribution provides an estimate of the UE location. Localization of the UE using RTT ranging depends on knowing the locations of the Wi-Fi APs. Determining these positions from floor plans can be time-consuming, particularly when the APs may not be accessible (as is often the case in order to prevent unauthorized access to the network). An alternative is to invert the Bayesian grid method for locating the UE—which uses distance measurements from the UE to several APs with known position. In the inverted method we instead locate the AP using distance measurements from several known positions of the UE. In localization using RTT, at any given time, a decision has to be made as to which APs to range to, given that there is a cost associated with each “range probe” and that some APs may not respond. This can be problematic when the APs are not uniformly distributed. Without a suitable ranging strategy, one can enter a dead-end state where there is no response from any of the APs currently being ranged to. This is a particular concern when there are local clusters of APs that may “capture” the attention of the RTT app. To avoid this, a strategy is developed here that takes into account distance, signal strength, time since last “seen”, and the distribution of the directions to APs from the UE—plus a random contribution. We demonstrate the method in a situation where there are no line-of-sight (LOS) connections and where the APs are inaccessible. The localization accuracy achieved exceeds that of the smartphone code-based GNSS.

Use of wireless technologies in IoT projects

When it comes to creating projects based on the use of the Internet of Things (IoT), wireless sensor networks are often used. Edge computing in IoT technology reduces system response delays to sensor output signals and increases the network throughput. At the same time, a short-range sensor network can work locally without access to the Internet, while long-range networks, as a rule, require access to the Internet and use both edge and cloud computing. The paper shows the features of the application of both peripheral and cloud computing in the practical implementation of IoT projects with various methods of wireless data transmission. Specific examples show the possibility of data transmission over a short distance using ESP-NOW technology, nRF24L01 radio modules and creating a local Wi-Fi access point. The range of sensor data transmission between microcontrollers is practically determined for each proposed option. The calculation of the range of the LoRa radio line is carried out for the actual sensitivity values of the receiver. The Okamura-Hata radio wave propagation model is proposed to estimate the LoRa radio line’s total loss. The operational range of LoRa modules with different types of signal modulation is practically determined. The essence of edge computing combined with digital and analogue sensors is shown. Peculiarities of peripheral computing during the implementation of IoT projects for remote control of blood saturation of patients using wireless technologies are considered. An example of data transmission of the air quality control system through a gateway based on an ESP8266 microcontroller with a graphical display of measurements in the IoT cloud service ThingSpeak and Blynk was provided.

Autonomous and reliable fingerprint map maintenance for indoor positioning system

In WiFi-based crowdsourced fingerprinting indoor positioning systems (IPS), the signal space of WiFi APs changes over time. To autonomously maintain a highly reliable localization accuracy, the fingerprint database needs regular maintenance and updates. However, this process is labor-intensive and time-consuming due to the appearance of faulty access points (APs) in crowdsourced samples received from target clients, which are mainly used for autonomous maintenance and updates. Additionally, if some APs are relocated, removed, or replaced in the indoor environment after fingerprint map construction, it can severely impact IPS accuracy. In this paper, we present Dynamic Online Fingerprint Learning with Altered APs status-aware and updating (DOFLAAU) for IPS. Our systematic methodology involves several key steps. First, we employ the Modified Group Method of Data Handling (GMDH) neural network regression for autonomous AP status monitoring and alteration detection. Next, we perform autonomous APs subset sample selection using a modified Thomson model, followed by outlier feature detection and removal based on the weighted sample density rate. Thirdly, we propose a dynamic online fingerprint map updating model using the cosine similarity distance metric. Lastly, we adopted an effective signal distance-based weighted k-nearest neighbor algorithm to improve IPS accuracy. The experiment results validate the efficiency and effectiveness of our proposed algorithms in terms of better localization accuracy compared to peer algorithms.

H2LWRF-PDR: An efficient indoor positioning algorithm using a single Wi-Fi access point and Pedestrian Dead Reckoning

Indoor localization is challenging in densely populated urban areas due to the multipath effect, attenuation, noise, and difficulty finding reliable Wi-Fi access points. In urban flats, multiple access points are not easily available or require infrastructure setup, which increases cost. To address the above issues, the article proposes a new algorithm called ‘H2LWRF-PDR’ based on Wi-Fi fingerprinting using a single access point with extended features. This algorithm is designed for static and moving objects. The proposed algorithm first converts the received signal strengths from a single access point into five statistical features used as fingerprints. Then, hierarchical clustering is applied to remove outliers from the fingerprints. Then, a random forest classifier is used to predict the cluster values. Finally, the lowess filters are used to smoothen the output values from the classifier. Furthermore, these outputs are combined with the proposed Pedestrian Dead Reckoning (PDR) algorithm. In the end, a Savitzky–Golay filter is utilized to remove the fluctuations in the trajectories. The proposed approach has been validated in urban flat and laboratory environments and compared with state-of-the-art localization algorithms. The proposed algorithm ‘H2LWRF-PDR’ showed 50% improvements in an urban flat dataset and at least 30% in the laboratory dataset.

Enhancing longevity: Sustainable channel modeling for wireless-powered implantable BANs

Wireless body area network (WBAN) has revolutionized the healthcare sector by enabling remote monitoring and control of wearable and implantable devices, providing freedom of mobility to patients. However, wireless channel modeling in BAN is a crucial aspect for designing an efficient off-body, on-body and in-body communication. Due to the unique characteristics of the human body, it aims to characterize the signal propagation through skin, tissues, internal organs and biological fluids of a patient’s body. Moreover, it is important to enhance the battery life of the low-powered devices for a sustainable BAN. In this work, we provide a hybrid communication channel model for wireless power transfer in a BAN including both off-body and in-body communication channels. An indoor room scenario is considered in which a movable patient having an implant inside its body is present along with an RF power source (for example, a Wi-Fi access point) situated in a ceiling corner. Implant is assumed to inhibit energy harvesting capability. For practicability, we have considered the effect of path loss, partition walls, floor attenuation factor along with other important body parameters. Specifically, we aim to statistically characterize this hybrid communication system, for which unique closed-form expressions of the probability distribution functions of the received power have been derived, thereby first calculating the instantaneous power at different layers of human body and then obtaining the closed-form expression for average received power. All the derived mathematical expressions have been verified via numerical simulations. Further, for elongating the lifespan of implants, we investigated the average power harvested by an implant and its power outage probability for analyzing the sustainability of implants. The results are numerically validated, considering different types of indoor room scenarios, in addition to providing key design insights.

Enhancing WiFi Access Point Localization With AI-Based Filtering

Enhancing WiFi Access Point Localization With AI-Based Filtering

Design and build multiple input multiple output (MIMO) wideband element microstrip antennas for WiFi access-point applications

Design and build multiple input multiple output (MIMO) wideband element microstrip antennas for WiFi access-point applications

Wireless wearable potentiometric sensor for simultaneous determination of pH, sodium and potassium in human sweat

This paper reports on the development of a flexible-wearable potentiometric sensor for real-time monitoring of sodium ion (Na+), potassium ion (K+), and pH in human sweat. Na0.44MnO2, polyaniline, and K2Co[Fe(CN)6] were used as sensing materials for Na+, H+ and K+ monitoring, respectively. The simultaneous potentiometric Na+, K+, and pH sensing were carried out by the developed sensor, which enables signal collection and transmission in real-time to the smartphone via a Wi-Fi access point. Then, the potentiometric responses were evaluated by a designed android application. Na+, K+, and pH sensors illustrated high sensitivity (59.7 ± 0.8 mV/decade for Na+, 57.8 ± 0.9 mV/decade for K+, and 54.7 ± 0.6 mV/pH for pH), excellent stability, and good batch-to-batch reproducibility. The results of on-body experiments demonstrated that the proposed platform is capable of real-time monitoring of the investigated ions.

Enhancing Indoor Positioning Systems Accuracy with Optimal Placement of Wi-Fi Access Points

The indoor positioning system (IPS) has generated a considerable amount of interest in recent times, and the prosperity of the system's development is heavily reliant on its ability to accurately locate objects. The performance measure is significantly impacted by the location of access points (APs). However, the bulk of previous studies have tended to overlook the matter of optimal AP placement and efficient design for IPS due to the dependence on pre-existing installed APs, which were chiefly formulated for coverage objectives. In this investigation, an optimal placement function - which is reliant on mean and variance - has been developed using received signal strength (RSS) measurements data. The performance evaluation in this research is based on experimentation and compared with currently employed placement methods. The results indicate that the most optimal function value for the suggested method is 1.5714, which is substantially smaller than the values for rectangular, triangular, and triangular II, which are 12.468, 5.5364, and 8.5147, respectively. When the recommended placement strategy is employed instead of the existing ones, the weighted K-nearest algorithm (WKNN) for location error, using average RSS as the fingerprint radio map database, yielded a heightened degree of precision.

Construction of the analog radio over fiber technology testbed with Linux PC-based WiFi access points

Construction of the analog radio over fiber technology testbed with Linux PC-based WiFi access points

Wi-Fi Fingerprint for Indoor Keyless Entry Systems with Ensemble Learning Regression-Classification Model

Keyless entry systems have made human life more comfortable because the possibility of someone leaving a key is non-existent. However, there is a research opportunity to use Wi-Fi fingerprint localization in an indoor keyless entry system. The use of Wi-Fi as a solution is low cost because of the already hdeployed Wi-Fi access points (AP) around buildings. This study uses ensemble learning to utilize Wi-Fi fingerprints for keyless entry systems in indoor environments. The first step of this research is to obtain the UJIIndoorLoc Data Set, an open-source Wi-Fi fingerprint dataset. The next step is to design and implement a keyless entry system based on Wi-Fi Fingerprint. We compared four ensemble learning methods: Random Forest, AdaBoost, gradient boosting, and XGBoost. We use several metrics to compare the four methods, namely r2, the area under the receiver operating curve (AUROC), and the equal error rate (EER). The test results show that for localization based on ensemble regression, XGBoost has the best performance compared to the other three ensemble methods for both longitude and latitude predictions. The r2values are 0.94 and 0.98, respectively. For authentication based on ensemble classification, the gradient-boosting model performance increases as the number of weak learners increases. The optimum number of weak learners is 60. With AUROC = 0.99, gradient boosting outperforms random forest, AdaBoost, and XGBoost on authentication. Finally, our authentication method has EER = 0.01, which outperforms other state-of-the-art keyless entry systems. This research focuses on local building map datasets for future work.

Using free Wi-Fi to assess impact of COVID-19 pandemic on traditional wet markets in Hanoi

Traditional wet markets are the main source of fresh food and the largest source of daily nutrient intake for citizens of Hanoi. Nevertheless, due to the lack of traceability and sales registration systems, food flows within these markets remain largely invisible. This makes it challenging to assess the impact of shocks, such as pandemics, on these markets. In this paper, we characterize the impact of COVID-19 by analyzing data from 25 Wi-Fi access points installed in five formally established wet markets. The study timeframe covers a pre-pandemic period from July 2019 to the end of the initial stage of the pandemic in November 2020. While providing free Internet access, data were continuously collected about devices in close vicinity to the access points. Based on this information, we tested five hypotheses about the number, frequency, time, and duration of visits to the markets as well as changes in inter-market activities. The results show that during the shock (February to mid-April 2020) and aftershock (mid-April to July 2020) periods, market actors significantly decreased the total number of market visits (-26% P < 0.001) and the frequency of market visits (up to -47% for very frequent market users, P < 0.001). The number of inter-market visits dropped sharply during the shock period (66% ±\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pm$$\\end{document} 17% of the baseline level, P < 0.001), and the peak time for market shopping shifted significantly by 90 min later in the day, P < 0.001. No change was observed in visit duration. Several factors identified in existing literature as affecting consumer behaviors provide possible explanations for the changes observed. We present a set of recommendations to limit the negative impact of the pandemic in terms of food security and livelihoods in Hanoi and to mitigate consumers’ negative perception of wet markets in terms of food safety.

An efficient indoor localization for smartphone users: Hybrid metaheuristic optimization methodology

Access points (APs) are used to define coordinates in an indoor positioning system with Wi-Fi. These systems utilize existing infrastructure and Wi-Fi APs to find out the exact location of a device based on its RSSI and MAC address. The accuracy of these devices usually depends on the number of APs located nearby and the environment in which they are deployed. Therefore, the ideal selection of these points increases the discernibility of the localization technique. The rapid development of metaheuristic algorithms in recent years has demonstrated their effectiveness in resolving challenging optimization issues. The primary research goal is to investigate how to enhance indoor localization accuracy with metaheuristic algorithms and to assess the efficacy of positioning using these methods. In this paper, we propose a novel optimization algorithm called the Improved Pathfinder Algorithm (IPFA) using metaheuristic hybridization, where our contribution is twofold. The IPFA's superiority in optimization is used to choose the important APs. Subsequently, to maintain the generality of the localization performance, we created a feature-based classification model for the chosen AP subsets. Two prominent benchmark datasets, UJIIndoorLoc and JUIndoorLoc, were used to test the proposed framework. The proposed Indoor Localization framework attained an accuracy of 98.26% with a mean absolute error (MAE) of approximately 0.79 m. The results demonstrate that the IPFA method is capable of accurately locating the position with minimal positioning errors.

Wi-Fi Fingerprint for Indoor Keyless Entry Systems with Ensemble Learning Regression-Classification Model

Keyless entry systems have made human life more comfortable because the possibility of someone leaving a key is non-existent. However, there is a research opportunity to use Wi-Fi fingerprint localization in an indoor keyless entry system. The use of Wi-Fi as a solution is low cost because of the already hdeployed Wi-Fi access points (AP) around buildings. This study uses ensemble learning to utilize Wi-Fi fingerprints for keyless entry systems in indoor environments. The first step of this research is to obtain the UJIIndoorLoc Data Set, an open-source Wi-Fi fingerprint dataset. The next step is to design and implement a keyless entry system based on Wi-Fi Fingerprint. We compared four ensemble learning methods: Random Forest, AdaBoost, gradient boosting, and XGBoost. We use several metrics to compare the four methods, namely r2, the area under the receiver operating curve (AUROC), and the equal error rate (EER). The test results show that for localization based on ensemble regression, XGBoost has the best performance compared to the other three ensemble methods for both longitude and latitude predictions. The r2values are 0.94 and 0.98, respectively. For authentication based on ensemble classification, the gradient-boosting model performance increases as the number of weak learners increases. The optimum number of weak learners is 60. With AUROC = 0.99, gradient boosting outperforms random forest, AdaBoost, and XGBoost on authentication. Finally, our authentication method has EER = 0.01, which outperforms other state-of-the-art keyless entry systems. This research focuses on local building map datasets for future work.

Simultaneous Data Dissemination Among WiFi and ZigBee Devices

Recent advances in Cross-Technology Communication (CTC) have opened a new door for cooperation among heterogeneous IoT devices to support ubiquitous applications, such as smart homes and smart offices. However, existing work mainly focuses on physical layer performance improvements. In this paper, we explore how to leverage the latest CTC techniques for network layer performance improvements. Specifically, we introduce Waves, which leverages WiFi to ZigBee CTC and WiFi access point’s adaptive transmit power control techniques for reliable and fast data dissemination in low-duty-cycle ZigBee networks. We extensively evaluate our design under various settings. Evaluation results show that Waves can provide reliable data dissemination and is 33.5 times faster than the state-of-the-art protocol in terms of dissemination time.

Privacy-Preserving Wireless Indoor Localization Systems

Recently the number of buildings and interior spaces has increased, and many systems have been proposed to locate people or objects in these environments. At present, several technologies, such as GPS, Bluetooth, Wi-Fi, Ultrasound, and RFID, are used for positioning problems. Some of these technologies provide good results for positioning outdoors whereas some others are effective for indoor environments. While GPS is used for outdoor localization systems, Wi-Fi, Bluetooth, Ultra WideBand, and RFID are used for indoor localization systems (ILSs). Today, due to the proliferation and extensive usage of Wi-Fi access points, wireless-based technologies in indoor localization are preferred more than others. However, even though the abovementioned technologies make life easier for their users, ILSs can pose some privacy risks in case the confidentiality of the location data cannot be ensured. Such an incident is highly likely to result in the disclosure of users’ identities and behavior patterns. In this paper, we aim to investigate existing privacy-preserving wireless ILSs and discuss them.

Cost-effective live video streaming for internet of connected vehicles using heterogeneous networks

Smart vehicles of Internet of Connected Vehicles (IoCV) create Ad-Hoc networks and are persistently connected to the Internet through different network access technologies like Wifi, cellular, and dedicated short range communication (DSRC). A vehicle may prefer one network access technology over another based on availability, coverage area, and network access cost. The choice also depends on application requirements, like delay-tolerant or delay-sensitive, and application objectives, like maximizing data rate, maximizing cellular traffic offloading, etc. Cellular traffic offloading using Wifi access points and vehicle-to-vehicle (V2V) collaborations are very challenging due to the intermittent short coverage areas of Wifi access points and highly dynamic connectivity of V2V Ad-Hoc network. Most existing works on traffic offloading have considered delay-tolerant or on-demand services, whereas delay-constrained live video streaming services are mostly ignored. This work is focused on live video streaming services over IoCVs, where the non-triviality of the network selection task is demonstrated by formulating the task as an integer linear programming problem (ILPP). Accordingly, a distributed algorithm is proposed, which greedily maximizes cellular traffic offloading while significantly reducing the control packet overheads. Compared to existing works, the proposed work provides the best service effectiveness with a balanced performance on traffic offloading, service efficiency, and user satisfaction.