Wi-Fi Technology Research Articles

STREAMING MICROSCOPIC IMAGE DATA TO THE CLOUD STORAGE FOR DETECTION OF ACUTE LYMPHOBLASTIC LEUKEMIA USING CNN

Abstract. In the modern world, the problem of the prevalence of cancer remains quite widespread, including acute lymphoblastic leukemia. It is very important to diagnose such diseases in the early stages in order to prescribe timely treatment and achieve patient remission. However, the problem that remains to this day is that the diagnosis of the disease based on microscopic images of a human blood smear are manual. This method of diagnosis is prone to errors due to many factors, due to inattention, absence of a specialist in the locality, etc. Therefore, the need for automated data collection of microscope images and their analysis with high accuracy is urgent. Research into the creation of low-cost devices and the creation of neural network architectures that can control the process of analysis and disease detection. It is proposed to develop a network consisting of hardware and software capable of transferring acquired data from the microscope to cloud storage for further use by a convolutional neural network to classify human blood smear images to detect healthy or blast blood cells. A hardware and software complex has been developed for collecting and transferring values from the microscope to cloud storage. The main module for receiving and transferring data to the cloud is a Raspberry Pi single-board computer that works on Wi-Fi technology. In conclusion, the proposed system is capable of ensuring the effectiveness of diagnosing blood cancers not only of lymphoblastic leukemia, but also of other types.

Wi-Fi signal for soil moisture sensing

Measuring soil moisture is essential in various scientific and engineering disciplines. Over recent decades, numerous technologies have been employed for in situ monitoring of soil moisture. Currently, dielectric-based sensors are the most popular measurement technology and provide acceptable accuracy for various measurement purposes. However, these sensors are relatively expensive, and alternative technologies, which are cheaper, are not accurate enough for scientific purposes. Recently, the idea of using a Wi-Fi signal to measure soil moisture has been presented. Theoretically, the use of Wi-Fi technology in soil sensing follows the same concepts as the previous dielectric sensors. The main advantage of Wi-Fi technology is the possibility of providing a relatively accurate and cost-effective solution for soil moisture measurement. In this work, we try to investigate the possibility of using Wi-Fi signal characteristics for soil sensing. Therefore, a series of small-scale laboratory and field experiments were conducted to test the concept. The results of these experiments were promising, showing strong linear relationships between Wi-Fi signal properties (received signal strength indicator, RSSI) and soil water content, with R2 values ranging between 0.92 and 0.99, indicating a strong correlation. They also illustrate the possibility of using this technology to develop an inexpensive and accurate device for measuring soil moisture. However, observations from the experiments also point to problematic factors involving the hardware and software used in the measurements. It is important to control these factors in the next steps to develop a reliable measurement device.

RADIO FREQUENCY BASED INPAINTING FOR INDOOR LOCALIZATION USING MEMORYLESS TECHNIQUES AND WIRELESS TECHNOLOGY

Recently, the Internet of Things (IoT) has grown to encompass the surveillance of devices through the utilization of Indoor Positioning Systems (IPS) and Location Based Services (LBS). One commonly used method for developing an Intrusion Prevention System (IPS) is to utilize wireless networks to determine the location of the target. This is achieved by leveraging devices with known positions. Location-based services (LBS) play a vital role in many smart building applications, enabling the creation of efficient and effective work environments. This study examines four memoryless positioning algorithms, namely K-Nearest Neighbour (KNN), Decision tree, Naïve Bayes and Random Forest regressor. The algorithms are compared based on their performance in terms of Mean Square Error, Root Mean Square Error, Mean Absolute Error and R2. A comparative analysis has been conducted to verify the outcomes of different memoryless techniques in Wi-Fi technology. Based on empirical evidence, Naïve Bayes has been determined to be the localization strategy that exhibits the highest level of accuracy. The dataset containing the Received Signal Strength Indicator (RSSI) measurements from all the studies is accessed online.

Integrating Wi-Fi, Li-Fi, and BPL Technologies for a Secure Indoor Communication System.

In today's digital age, there is an increasing demand for integrated wireless and wired technologies; however, there is a difficulty in achieving secure and reliable communications within buildings and facilities. This paper presents a proposal for maintaining the infrastructure while expanding it to implement communication technologies with high transmission and reception speeds and high levels of data confidentiality to enhance the operational efficiency of organizations. Three main technologies have emerged as promising solutions for this purpose: Wi-Fi, Li-Fi, and BPL. Despite the advantages that each technology offers, some drawbacks appear in these technologies that affect data transmission. Wi-Fi, Li-Fi, and BPL can be combined to achieve maximum security and reduce noise and interference via the ESP8266 module. This combination could be an important step toward achieving an integrated and secure indoor communication system. The paper provides a comprehensive overview of the three techniques and how they are applied in practice. In addition, OSE, ABPF, and ASBF filters are used to detect and eliminate interference and attack secure internal networks.

Biometric Technology Based on Wireless Sensing: Principles, Applications, and Challenges

With the rapid advancement of computer technology, image processing, and artificial intelligence, biometric identification technology has seen widespread application, gradually permeating areas such as national security, public security, and home security. However, traditional biometric technologies exhibit certain limitations. In contrast, biometric identification based on wireless sensing offers distinct advantages, including non-contact operation and strong concealment, making it a promising alternative in various security applications. This paper provides a comprehensive overview of the history and current state of biometric technology, alongside the development background of wireless sensing technology. It details the principles of wireless sensing-based biometric identification, covering essential characteristics of wireless signals, and related technologies such as RFID, Wi-Fi, Bluetooth, and millimeter wave technology, as well as relevant signal processing algorithms. Furthermore, the paper analyzes the application of this technology in areas like human posture and motion recognition and physiological feature recognition, supported by practical case studies. Finally, it addresses the environmental dependencies, data privacy, and security concerns associated with this technology, while offering insights into future development directions.

An IoT-enabled monitoring and control system for electrochromic smart windows

Electrochromic (EC) smart windows are highly efficient in preventing solar irradiation. Nevertheless, an insufficient automated monitoring and control system for EC windows has been observed. This study outlined the framework for designing a control system that effectively regulated the voltage polarity of an EC smart window. Integrating Internet of Things (IoT) capabilities and environmental sensors enhanced the smart window control system. This integration allowed for automated monitoring and adjustment of light intensity (below and above 30,000 lx), temperature (below and above 32 °C), and UV index (below 3 and above 7) for the smart window. The magnitude of the current supplied from the output load of the operational circuit module was directly correlated with the switching time of the electrochromic window. Moreover, voltage fluctuation was observed during the evaluation testing of the control system, which was ascribed to the reliability of the breadboard platform. This study also provided a functional design analysis for a Wi-Fi-enabled control system, presented insights into manual and automatic control modes under diverse environmental conditions, and improved user interaction through real-time data visualization using the IoT Blynk platform. Overall, this study presented a range of cost-effective approaches for managing the dynamic EC window using Wi-Fi and sensor-enabled technology.

Microstrip Antenna Design Supported by Generative Adversarial Networks

We report on the effectiveness of using generative neural networks in an antenna design. We considered the modeling of microstrip antennas as they have significant advantages, such as a low profile, lightness, and ease of manufacture, which make them versatile for various applications. We designed, trained, and analyzed generative models applied to the modeling of these antennas without losing the generalizability of the application of these models to any antenna. We started with a Generative Adversarial Network (GAN), which was trained with data related to the antenna models for operation within the frequency range of 1 to 30 GHz. Using the synthetic data produced by the GAN resulted in antenna designs with dimensions and electromagnetic properties that were very close to the expected values. Next, a model was developed using a Conditional GAN (CGAN), which was trained to generate antenna characteristic data conditioned on an arbitrary central frequency, i.e., 2.4 GHz (generally used for Bluetooth, Wi-Fi, and ZigBee technologies), to enable better control over the process of generating these synthetic data. The CGAN model could satisfactorily generate synthetic data for this frequency range, simultaneously considering substrates with different dielectric permittivities. This study reveals that both generative models could produce synthetic data that were very close to the expected data, as evidenced by the low error values. Additionally, in terms of application, the models could provide both geometries and more than one antenna characteristic (resonance, bandwidth, and quality factor), which is very useful for direct application to practical designs.

Current Scenario of Maintenance 4.0 and Opportunities for Sustainability-Driven Maintenance

Industry 4.0, a shift from Industry 3.0, aims to enhance productivity and efficiency in operations and supply chain management. Maintenance plays a crucial role in this process, and IoT-enabled (Ind. 4.0) condition monitoring is a key component of this technology. However, challenges persist in implementing effective IoT-enabled condition monitoring solutions. The triple bottom line perspective (Economical, Ecological, and Social) is also crucial for realizing Ind. 4.0. This paper investigates the state of IoT-enabled industrial condition monitoring (Maintenance 4.0) and sustainability-driven maintenance (Maintenance 5.0), focusing on the challenges associated with implementing these concepts. The IoT-enabled technologies are divided into three layers: the application layer, the networking layer, and the physical layer. The physical layer, the lowest layer, faces numerous challenges in realizing maintenance 4.0 effectively. A new system configuration for vibration-based condition monitoring in an Ind. 4.0 environment is proposed to address these shortcomings. Wi-Fi technology is found to be the best option for high-throughput communication needs in the current scenario. The literature review reveals that while the economic aspect of maintenance 5.0 has been thoroughly examined, the environmental and social aspects have not been thoroughly assessed. Future research should focus on developing a new sustainable maintenance model that incorporates IoT-enabled technologies and investigates sustainable performance indicators to understand sustainability aspects quantitatively.

Wi-TCG: a WiFi gesture recognition method based on transfer learning and conditional generative adversarial networks

Abstract With the rapid progress of WiFi technology, WiFi-based wireless sensing technology has opened up new ways for contactless human-computer interaction. However, hand gesture recognition technology faces low-quality data sets, insufficient model robustness, poor universality when the application scenario changes, high training costs, and weak generalization ability. To this end, this study innovatively proposes the Wi-TCG method, which combines transfer learning and conditional generative adversarial network (CGAN) to optimize WiFi gesture recognition. This method uses commercial Wi-Fi devices to collect channel state information (CSI) of gesture actions. It innovatively extracts Doppler shift image data as the input of CGAN to generate virtual data with similar characteristics to expand the training sample set. The network is fine-tuned using transfer learning techniques to recognize multiple gesture action categories in different scenarios accurately. In tests of two new natural scenes and six new gesture categories, the Wi-TCG method achieved a high recognition accuracy of 93.1%, providing strong support for applying WiFi-based wireless sensing technology in contactless human-computer interaction.

Impact of Structural Design of Knitted Fabrics on Shielding from Radiation of Wi-Fi Technologies

Abstract The goal of the study is to determine and compare the attenuation of electromagnetic waves at 2.4 - 2.5 GHz frequency between plain single jersey and 1x1 rib knitted fabric. For knitting the samples, three different types of metal-contained yarns, their combinations, and cotton yarn were used. Two horn antennas and a network analyzer were used to characterize the transmission of the samples in the frequency range of 2.4-2.5 GHz. Obtained results show, that in mentioned frequency range single jersey knitted samples attenuate better than the rib samples.

Improving the performance of IoT devices that use Wi-Fi

<p>Providing quality service to users of the internet of things (IoT) entails addressing two crucial aspects: one related to security and the other concerning the limited resources of IoT devices. We will face a challenge while using timesensitive applications within a network that utilizes a high-performance Wi-Fi technology with exceeding energy consumption. Due to this research challenge, we propose a new algorithm, IoT-quality of service (QoS), designed to achieve a true balance between enhancing the security aspects of IoT devices and improving network-hardware performance. Thus, the algorithm efficiently manages the limited energy resources by monitoring energy levels, communication quality, and queuing delay at access points. This is accomplished by utilizing a streamlined identity management system capable of achieving authentication and access authorization with reduced loading for IoT devices. The research hypothesis underwent validation through a comparative analysis of its performance against the conventional model of a Wi-Fi-based IoT device. This evaluation was conducted utilizing the NS3 simulator and was based on a predetermined set of parameters influencing the examined performance metrics, including power consumption, throughput, delay, and response time. The findings exposed the superiority of the proposed algorithm.</p>

Wireless sensor network-based health monitoring and smart home assistance for the older adults in sports and wellness towns.

Integrating technology with rural development is essential for addressing the unique challenges faced by aging populations in rural areas. China's national rural revitalization strategy emphasizes the importance of developing characteristic towns that focus on health and wellness demands, particularly for the older adults. However, there is a gap in the literature regarding the systematic use of technology to support the health and daily living needs of this demographic. This study aims to bridge this gap by proposing a wireless sensor network (WSN)-based system that integrates health monitoring and smart home assistance, specifically tailored for older adults residents in sports and wellness towns. The system's design involves collecting and analyzing historical activity data and basic physiological parameters from older adults residents' homes. The data are cleaned, combined and stored to prepare it for behavior analysis, which is vital for controlling smart home equipment. Preliminary health assessments are conducted using the collected physiological data. A hybrid network leveraging Sub-G and Wi-Fi technologies is optimized for the purposes of collecting and uploading data, based on a comparative analysis of wireless communication options. This study significantly contributes to the advancement of sports and wellness towns by promoting healthy aging through cutting-edge technological solutions.

The Impact of Mobile Phone Electromagnetic Waves on the Neurons and Blood Brain Barrier Integrity in the Chick Embryo

The electromagnetic environment surrounding us has dramatically evolved over the past decade, with the proliferation of Wi-Fi, Bluetooth, and other wireless technologies becoming commonplace in our daily lives. Mobile phones emit non-ionizing low-frequency electromagnetic waves (EW). To examine the effects of EW on living cells, this study aims to explore the impact of cell phone EW on the developing brain of chick embryos. The fertilized eggs were allowed to develop under exposure to electromagnetic waves emitted by cell mobile. A cell phone was placed inside the incubator with 20 eggs and was called from outside on a precise schedule. The same number of fertilized eggs were placed in another incubator without a mobile phone and served as the control. Embryos were sacrificed on days 10 and 15, and the cerebral cortex and cerebellum were removed and sent for electron microscopy. In the control group, cerebral neurons appeared healthy, with a large, centrally placed nucleus, visible oligodendrocytes, and a less dense extracellular matrix. In contrast, neurons from the exposed group were smaller, fewer in number, with unclear nuclear margins, signs of shrinkage, and apoptosis and a dense extracellular matrix. In the cerebellum, the exposed group revealed a reduced number of Purkinje neurons and noticeable mitochondrial swelling. The blood-brain barrier remained intact in the control group but was compromised in the exposed group. We conclude that electromagnetic waves emitted by cell phones adversely affect the normal development of the brain in chick embryos.

INDUSTRIAL DEVICE CONTROL USING WI-FI MODULE

This paper presents a design and prototype implementation of new industrial automation system that uses Wi-Fi technology as a network infrastructure connecting its parts. The proposed system is better from the scalability and flexibility point of view than the commercially available industrial automation systems. In Industry we have different types of loads at different locations. We can control all loads at a same time from one place (control room) without connecting any physical wire between loads and control room, in this paper we are using WI-FI module, IOT, relay, voltage sensor, current sensor, speed sensor. In this paper Wi-Fi is being used by phone and the loads are operated with it. In this paper we should not connect AC loads directly to microcontroller since AC may enter into controller due to this our controller may bedestroyed. To avoid such type of drawback we need some drivers. In this paper weare using relay as load controller. Key word: Sensor, Microcontroller, IOT, Motor, Relay

Design of an active wing-folding biomimetic flapping-wing air vehicle

In nature, birds and bats dynamically alter their wing shapes to suit various flight environments and tasks. This paper focuses on the design and validation of a biomimetic flapping-wing aerial vehicle, named FlexiWing, which features a unique mechanism for active wing deformation. This mechanism allows the wings to adjust their shapes flexibly in response to flight demands, significantly enhancing attitude control and maneuverability.’ ‘This study began with an in-depth exploration of biomimetic principles, focusing particularly on how birds and bats achieve precise control during flight through active wing deformation. Subsequently, we present a detailed account of the design and fabrication process of the active folding biomimetic flapping-wing aerial vehicle, including the design of mechanical mechanisms and material selection. Utilizing lightweight nylon materials and hollow carbon fiber rods, we successfully constructed a mechanically foldable wing structure. To achieve precise control over the aircraft’s movement, an embedded control system was designed, comprising an onboard embedded flight controller and ground-based equipment. The onboard controller uses a high-performance ESP32-C3 processor and a JY901 inertial measurement unit to acquire real-time attitude information of the aircraft. The control system incorporates Wi-Fi communication technology, enabling operators to send commands via a remote control or personal computer to manage flight modes and attitudes. Ultimately, a series of flight experiments were conducted to validate the performance of FlexiWing. The results demonstrate that FlexiWing exhibits remarkable maneuverability and stability, capable of achieving high-precision attitude control through active wing folding, making it adaptable to complex environments and tasks.’

HUMAN ACTIVITY RECOGNITION BASED ON DEVICE-FREE WI-FI SENSING: A COMPREHENSIVE REVIEW

Within the context of the current era’s research and advancement in technology, in recent years, the Internet of Things (IoT) has been consistent in the development of applications in several fields like smart cities, smart homes, smart grids, smart agriculture, and so on. Most of the existing research in human activity recognition relies on vision-based, wearable devices, object-tagged, and sensor-based approaches. Despite the superior performance of these approaches, a number of issues have arisen related to the invasion of privacy, light dependency, cost effectiveness, and feasibility. Moreover, these approaches require domain knowledge of different tasks, which may make them complicated for practical deployment. Wi-Fi technology, on the other hand, offers robust possibilities in indoor and outdoor environments for recognizing applications and, combined with a few significant features, makes it a far more attractive option compared to other sensing technologies. Hence, the device-free Wi-Fi-sensing approach is more practical in the smart home environment as it does not require the targeted human to have any device for day-to-day activities. This article’s contributions can be summarized as, first, providing primary knowledge on a wireless LAN, the Wi-Fi sensing model; second, sharing the findings of the comprehensive survey scrutinizing the latest developments in human activity or gesture recognition systems based on device-free Wi-Fi sensing systems; third, sharing the analysis of the limitations and key research challenges that need to be addressed in order to expand the device-free Wi-Fi sensing system; and lastly, a discussion and future directions of existing device-free Wi-Fi sensing techniques.

АНАЛІЗ БЕЗПЕКИ СУЧАСНИХ ПРОТОКОЛІВ ЗАХИСТУ МЕРЕЖ WI-FI: ОЦІНКА СТІЙКОСТІ ПРОТОКОЛУ WPA3 ПІД АТАКИ НА БАЗІ УТІЛІТИ DRAGONBLOOD

With the constant development of information technology and the growing threat of cyber attacks the security of Wi-Fi wireless networks is of particular relevance. This article aims to provide an in-depth analysis of modern Wi-Fi security protocols such as WPA2 WPA3 and OWE focusing on their strengths and weaknesses in securing the network against the most common types of attacks. This paper looks at wireless network security threats including man-in-the-middle attacks Wi-Fi access point phishing and exploits that target specific security mechanisms. An important part of the research is a description of test methods attack tools such as Aircrack-ng and Wireshark and a detailed analysis of the results obtained. This work focuses on a detailed security analysis of the WPA3 protocol using Dragonblood utilities to identify possible vulnerabilities in its implementation. Through targeted attacks and simulations that mimic real-world cyberattacks the goal is to identify potential breaches of secure wireless networks using WPA3. This allows us to evaluate the effectiveness of the encryption and authentication mechanisms used within the framework of this standard and develop recommendations for increasing the level of security of information systems. During the study practical experiments will be conducted to modify the code of the wireless access point server and data obtained using the Wireshark program will be analyzed to assess the impact of attacks on the functionality of the network. The results of the work highlight the need for continuous improvements in Wi-Fi security technologies to provide reliable security in the face of growing cyber threats. Keywords of the article: Wireless networks Wi-Fi security protocols WPA2 WPA3 attacks on Wi-Fi methods of protecting against attacks information system security vulnerability analysis attack tools increasing the level of security user education network component software data encryption VPN.

Locating people in tunnels using Wi-Fi technology

The aim of the project is to investigate the possibility of using people's mobile phones to locate people in a tunnel environment using the mobile phone's Wi-Fi connection. In total, 39 different trials were carried out under different conditions in a road tunnel in Stockholm, Sweden. In the trials, the Wi-Fi-based predicted location has been compared with the actual location of the recruited 16 participants in the tunnel. The conditions include the number of people in a group, the number of available access points in the tunnel, whether the mobile phone has an active or passive connection, whether a person is moving or standing still and whether the mobile phone is held in the hand or is stored in the person's pocket. The results indicate that the mean value for the distance between actual and predicted locations is in the order of 20 m or less, which is higher than reported in other studies. Despite this, there is a good potential to locate individuals in a tunnel emergency as the distance between emergency exits is often much longer than the uncertainties in the predicted locations of people. Improving the location algorithms will possibly reduce the uncertainty of the predicted location.

System For Penetrative Emergency Communication System

System For Penetrative Emergency Communication System

5G in construction: from deployment to evaluation for robotic applications

AbstractA stable, low-latency, and high-bandwidth communication infrastructure is indispensable for effective teleoperation or automated control of construction machinery. Despite the vital importance of this aspect, limited exploration has been undertaken thus far. Our work presents a comprehensive study that begins by detailing the strategic deployment of a 5G network, underscoring its tailored features and functionalities designed specifically to meet the demanding requirements of construction sites. Through a series of diverse experiments involving different types of full-scale construction machines, we vividly demonstrate the tangible benefits of 5G technology in this context. Leveraging comparative studies with WiFi technology and real-world tests, this methodology highlights the improvements in communication facilitated by 5G networks. This holistic exploration not only fills a critical gap in understanding the potential of 5G in construction machinery communication but also offers a roadmap for leveraging this technology to further develop the construction industry.