Wi-Fi Transmission Research Articles

Towards Enhancing Spectrum Sensing: Signal Classification Using Autoencoders

The demand for technologies relying on the radio spectrum, such as mobile communications and IoT, has been growing exponentially. As a consequence, providing access to the radio spectrum is becoming increasingly more important. The ever-growing wireless traffic and the increasing scarcity of available spectrum warrants efficient management of the radio spectrum. At the same time, machine learning (ML) is becoming ubiquitous and has found applications in many fields for its ability to identify patterns and assist with decision-making processes. Recently, machine learning algorithms have been used to address challenges in the wireless communications domain, such as radio spectrum sensing, and have shown better performance than traditional sensing methods, such as energy detection. Spectrum sensing, a method for detecting and identifying different wireless signals being transmitted in the same band of the radio spectrum, is crucial for improving dynamic spectrum sharing, which has the potential to enhance sharing and coexistence of different wireless technologies in the same frequency band and ultimately improve spectrum efficiency. To this end, this research evaluates different types of autoencoders, such as deep, variational and Long Short-Term Memory (LSTM) autoencoders, to identify and differentiate between LTE and Wi-Fi transmissions. The goal is to investigate the performance of the different types of autoencoders on an I/Q dataset consisting of LTE and a combination of Wi-Fi signals (IEEE 802.11ax and IEEE 802.11ac) for the classification task in terms of complexity, precision, and recall to identify the best algorithm. Our models have achieved up to 99.9% precision and 88.1% recall for this classification task. Additionally, with a shortest training time of approximately 47 seconds, the models are suitable for online learning and deployment in a dynamic RF environment.

Open Set Wireless Transmitter Authorization: Deep Learning Approaches and Dataset Considerations

Due to imperfections in transmitters' hardware, wireless signals can be used to verify their identity in an authorization system. While deep learning was proposed for transmitter identification, existing work has mainly focused on classification among a closed set of transmitters. Malicious transmitters outside this closed set will be misclassified, jeopardizing the authorization system. In this article, we formulate the problem of recognizing authorized transmitters and rejecting new transmitters as open set recognition and anomaly detection. We consider approaches based on one and several binary classifiers, multiclass classifiers, and signal reconstruction. We study how these approaches scale with the required number of authorized transmitters. We propose using a known set of unauthorized transmitters to assist the training and study its impact. The evaluation procedure takes into consideration that some transmitters might be more similar than others and nuances these effects. The authorization's robustness against temporal changes in fingerprints is also evaluated as a function of the approach and the dataset structure. When using 10 authorized and 50 known unauthorized WiFi transmitters from a publicly accessible testbed, we were able to achieve an outlier detection accuracy of 98% on the same day test set and 80% on the different day test set.

Implementation of an automatic secured gas stove using internet-of-things technology

It was noted that the existence of house fires caused by gas stoves is still large. Gas stoves are common devices in Indonesian household. However, they have some drawbacks that can cause house fires. This causes the need for technological improvements in gas stoves to minimize this possibility. The implemented secured automatic gas stove uses a gas solenoid valve to automatically turn it on/off and can be monitored through an Android smartphone using Wi-Fi transmission, i.e. based on the Internet of Things, which is a value-added solution to gas stoves. The system also has a motion sensor to minimize the occurrence of negligence when cooking. This information will be sent and displayed on the Android application of user’s smartphone. Warning will be given through the application so that user can act properly and immediately when getting it. Testing results were as follows. At first, the gas sensor could detect gas leak if it had passed the value of 300 ppm. Second, Android application could display information about the status whether gas stove turned on or off, and it could be turned off by pressing a button on it. At last, solenoid valve was successful in stopping the flow of gas from the gas cylinder. All functions of the overall system could work well as expected in the design stage.

Identification of Power Grid Lines with Increased Power Losses Using Mobile Electric Meters and Timers

Identifying sections of 0.4 kilovolt power grid lines with increased power losses is an urgent task, the solution of which will free up some of the funds spent by power grid companies to compensate for power losses and direct them to the reconstruction and modernization of electric networks. It is necessary to monitor electricity consumption on sections of power grid lines and on the connections of consumers, and compare data on consumption. Existing devices that analyze the quality of electrical energy and can control current and power in the network, such as power quality analyzers, do not have the full necessary qualities, since they are intended for other purposes, and they are also very expensive. (Research purpose) The research purpose is in developing technical means for identifying sections of 0.4 kilovolt power grid lines with increased power losses, which also allow performing a survey of operating modes of power grid lines. (Materials and methods) The article presents a review of literary sources and an analysis of statistical data. (Results and discussion) The article presents developed technical tools for inspection of power grid lines of 0.4 kilovolts and identification of areas with increased losses of electrical energy, portable single-phase and three-phase electric meter and timer. The single-phase mobile electric meter and timer can be used for the survey of single-phase connections of consumers, three-phase one can be used for the survey of any sections of power lines. Authors also developed a version of the portable electric meter and timer with up to six independent sensors. (Conclusions) It is most rational to use a version of the mobile meter and timer equipped with Wi-Fi data transmission technology to detect power losses on sections of 0.4 kilovolts power grid lines, since in this case simultaneous monitoring of several sections is provided with the possibility of obtaining information from sensors in real time. The data allow to calculate technological and commercial losses on power line sections and draw conclusions about whether they are overstated.

Design of power parameter monitor system based on Android mobile phone

With the continuous development of power technology, real-time and accurate monitoring of power parameters in the power grid to ensure safe and stable operation of power transmission has become a hot topic of research today. The power parameter measuring instrument can realize multifunctional detection, the overall design and the combination of hardware technology and software technology, connect the mobile phone APP and hardware through WiFi transmission, and display the transmission data in real time on the smart phone terminal. Select STM32F103 as the microprocessor, adopt software synchronous sampling method, control 16-bit high-precision AD conversion chip ADS8556 for AD sampling, and then process the data by discretization method and FFT harmonic analysis algorithm, and finally send the data to The display on the Android mobile phone can realize the measurement of single-phase AC voltage and current RMS value, active power, power factor, frequency and other power parameters, and has a harmonic analysis function. The measurement data is displayed by the smartphone terminal. Tested by a test platform composed of BD-3D multi-function calibrator, the results show that its performance indicators meet the design requirements.

Wireless Holter monitoring system with a dual-core processor

The article discusses the principles of building a wireless automated system for heart activity Holter monitoring using a WiFi module based on a dual-core processor. The system is focused on use in a medical cardiology center or at home. The article proposes the method of wireless transmission of an electro-cardio signal based on the paralleling of processing operations and Wi-Fi transmission of an electro-cardio signal. The paper presents algorithms for wavelet compression and filtering of the transmitted electro-cardio signal that ensure high reliability of the transmitted ECG information. A semi-realistic model of the Holter monitoring system has been developed, including real-time packages of the Matlab system, a PhysioBank ATM electro-cardio signal database, a hardware interface based on the PCI-1710HG Board and an external wireless ECG information transmission subsystem. The proposed methods and algorithms will provide high-quality and multi-day wireless transmission of ECG signals, characterized by low power consumption

Design an automatic clothes dryer in a cabinet with wi-fi transmission

Indonesia is a tropical country that has two seasons, dry and rainy seasons. Now this season in Indonesia is difficult to predict because of the effects of global warming. The effect of global warming is quite disturbing activities of human life, one of which is in the case of drying clothes. Then an automatic clothes dryer cabinet was made with Wi-Fi transmission media making it easier for humans to find out information in the cabinetremotely using a smartphone. The system starts when the voltage supply is turned on and the fan turns on and the other system is in standby. When the clothes are in the cabinet, the heater will turn on and the microcontroller will send information about the situation in the cabinet. The heater will turn off when it has reached the set temperature to prevent excess heat in the cabinet. Clothing weight information will appear on the smartphone through the application, if the weight of the clothes has reached <190 grams, then the clothes notification will appear dry and turn off the system on the application. The weight sensor has a role to activate the digital thermostat which has a temperature sensor to disconnect and connect the power to the heater.

Leveraging Polarization of WiFi Signals to Simultaneously Track Multiple People

This paper presents WiPolar, an approach that simultaneously tracks multiple people using commodity WiFi devices. While two recent papers have also demonstrated multi-person tracking using commodity devices, they either require the people to continuously keep moving without stopping, and/or require the number of people to be input manually, and/or keep the WiFi devices from performing their primary function of data communication. Motivated by the increasing availability of polarized antennas on modern WiFi devices, WiPolar leverages signal polarization to perform accurate multi-person tracking using commodity devices while addressing the three limitations of prior work mentioned above. The key insight that WiPolar is based on is that different people expose different instantaneous horizontal and vertical radar cross-sections to WiFi transmitters due to differences in their physiques and orientations with respect to the transmitter. This enables WiPolar to accurately separate the multipaths reflected from different people, which, in turn, allows it to track them simultaneously. To the best of our knowledge, this is the first work that leverages polarization of WiFi signals to localize and track people. We implement WiPolar using commodity WiFi devices and extensively evaluate it for tracking up to five people in three different environments. Our results show that WiPolar achieved a median tracking error of just 56cm across all experiments. It also accurately tracks people even when they were not moving. WiPolar achieved a median tracking error of 74cm for people that were either stationary or just taking a small pause.

The history of the use of wireless devices in medicine

The article discusses the main stages of using wireless devices and wireless data transfer technologies in medicine. Purpose of the study: to analyse the implementation history, the use of wireless computerised devices for medical diagnosis and monitoring of the human body. Research methods: descriptive, methodology for systematisation and classification of historical data on the introduction of wireless computerised devices in medicine. Scientific novelty lies in systematising the connectionbetween the development of wireless computerised devices and theuse of new medical diagnostic and monitoring devices in medicine.The following trends can be distinguished in the history and developmentof wireless technologies in medicine. The first is that devices that have Bluetooth and Wi-Fi transmitters in medicine are used to share medical information: mobile accessibility to the patient's catalog, ability to transmit medical test results, output diagnostics directly to personal devices (smartphones , tablets, etc.). These devices help improve patient care. However, the considerable power of radio emission and power consumptionimposes certain restrictions on the long-term use of these devices. The second trend is represented by a group of devices with data modules according to IEEEE 802.15.6 and IEEE 802.15.4, which by their energy parameters of radiation and energy consumption make it possible to create wireless monitoring, diagnostic equipment with longterm autonomous power supply, whose radius is connected with a central device, and allows the patient to move freely within the room, apartment, hospital ward. Conclusions: the use of wireless devices in medicine simplifies the process of diagndiagnosing patients, creates more comfortable conditions for momonitoring the state of the patient’s body and accelerates the prprocessing of data on the state of the human body

Hand Gesture Recognition System in Smart Environment

The smart environment allows users to interact effortlessly with their immediate surroundings using embedded sensors, displays, and computing devices. This system will not only be useful for ordinary people with no disabilities but also for people with disabilities like vision disability, cognitive, psychological and etc. Hand Gesture Recognition opens up a lot of possibilities for IoT based projects. At the end of the project, the goal is to design a cost-effective and highly interactive system through which a user can control the electric home appliances through hand gestures. This is a wireless device that is used to manipulate various devices using finger movement. The proposed device incorporates a transmitter connected to the MEMS accelerometer which detects movement warnings and transmits signals wirelessly using the radio frequency radio module (Zig Bee). The gesture alert which is input to the system is transmitted and obtained wirelessly using a wi-fi radio frequency module (ZigBee). The instrument contains a triaxial accelerometer, a microprocessor, and a ZigBee wi-fi transmission module. Acceleration warnings measured by the triaxial accelerometer are sent to a receiver via the ZigBee Wi-Fi module.

Coexistence Performance and Limits of Frame-Based Listen-Before-Talk

Frame-based listen-before-talk (FB-LBT) has been adopted as one of the channel access mechanism for Wi-Fi/LTE coexistence. We aim to explore the limits of FB-LBT by developing theoretical models to characterize the FB-LBT channel access performance under the coexistence of LTE and Wi-Fi. We first derive a steady-state model to calculate the spectrum share occupied by LTE under the assumption that the Wi-Fi transmissions have stationary distributions. The assumption does not hold when the time between two LTE transmissions is short, where the system is dominated by a dynamic phenomenon. A second model is developed that accounts for the dynamics of the Wi-Fi channel access mechanism. Our models, validated by simulation results, accurately calculate the spectrum share occupied by LTE over a range of FB-LBT frame periods and Wi-Fi traffic loads. We obtain upper bounds on the FB-LBT spectrum share when competing with heavy Wi-Fi traffic, which confirm the weakness of FB-LBT. Moreover, we demonstrate that our models can be used to control the FB-LBT spectrum share within a modest range.

Secure Verifiable Outsourcing Collection of Big Data in IoV using Cloud Environments

By the growth of vehicular communique technology, international gadgets can live interactive and are associated with the Internet. Normally, cars in VANETs have to periodically transmit protection messages. Since safety messages encompass net web page visitor’s associated data and are subtle to locality privacy, it's far essential to make certain obscurity, reliability, and traceability the posted. Supportive verbal exchange is a lively method for vehicular transportations to development Wi-Fi transmission capability and trustworthiness in fifth-technology (5G) trivial-mobile set-ups. In this paper, we check protection conversation approximately internet net web page net page website traffic associated information, accidents or particular privacy aware information are inside the shape of video reporting commercial enterprise organization. This company is completed a machine to proper now the report films to fantastic authorities and ambulance in a well timed way. Conversely, due to immoderate besides at ease avenue website online visitors information, the vehicular cloud is built scheduled lively belongings; as in keeping with a surrender very last results, it critiques numerous intrinsic traumatic situations, which growth the complexity of its implementations. Therefore, we are able to format relaxed communiqué tool using cryptography approach and furthermore located into effect greater applicable function-based completely get admission to control mechanism (ERBAC) to offer the functionality to the authority for viewing the video files in cloud storage device.

FingerDraw

This paper explores the possibility of tracking finger drawings in the air leveraging WiFi signals from commodity devices. Prior solutions typically require user to hold a wireless transmitter, or need proprietary wireless hardware. They can only recognize a small set of pre-defined hand gestures. This paper introduces FingerDraw, the first sub-wavelength level finger motion tracking system using commodity WiFi devices, without attaching any sensor to finger. FingerDraw can reconstruct finger drawing trajectory such as digits, alphabets, and symbols with the setting of one WiFi transmitter and two WiFi receivers. It uses a two-antenna receiver to sense the sub-wavelength scale displacement of finger motion in each direction. The theoretical underpinning of FingerDraw is our proposed CSI-quotient model, which uses the channel quotient between two antennas of the receiver to cancel out the noise in CSI amplitude and the random offsets in CSI phase, and quantifies the correlation between CSI value dynamics and object displacement. This channel quotient is sensitive to and enables us to detect small changes in In-phase and Quadrature parts of channel state information due to finger movement. Our experimental results show that the overall median tracking accuracy is 1.27 cm, and the recognition of drawing ten digits in the air achieves an average accuracy of over 93.0%.

Device-free crowd counting with WiFi channel state information and deep neural networks

Crowd counting is of great importance to many applications. Conventional vision-based approaches require line of sight and pose privacy concerns, while most radio-based approaches involve high deployment cost. In this paper, we propose to utilize WiFi channel state information (CSI) to infer crowd count in a device-free way, with only one pair of WiFi transmitter and receiver. The proposed method establishes the statistical relationship between the variation of CSI and the number of people with deep neural networks (DNN) and thereafter estimates the people count according to the real-time CSI through the trained DNN model. Evaluations demonstrate the effectiveness of the method. For the crowd size of 6, the counting error was within 1 person for 100% of the cases. For the crowd size of 34, the counting error was within 1 person for 97.7% of the cases and within 2 persons for 99.3% of the cases.

DYNAMIC MONITORING METHOD OF ILLEGAL BUILDINGS USING SPATIOTEMPORAL BIG DATA BASED ON URBAN HIGH LYING ZONES

Abstract. China's urban illegal buildings are emerging in an endless stream with a large number. There is a wide demand for urban illegal buildings monitoring in urban management departments, including Beijing, Shanghai, Guangzhou and other regions where urban management is facing increasingly serious problems of illegal buildings. It is urgent to solve the common problem of "urban disease" caused by urban illegal buildings, and a new automatic monitoring method that can reduce the cost of urban management is urgently needed. This kind of automated monitoring method of illegal buildings has a wide market demand in Urban Management and Law Enforcement and the Ministry of Land and Resources. Existing technologies cannot realize long-term, autonomous, rapid and intelligent dynamic real-time monitoring of urban illegal buildings, which leads to the problem that illegal buildings’ behaviors cannot be stopped in time. There is a possibility to solve these problems using the ubiquitous network of base stations in cities to monitor illegal buildings.This paper proposes a dynamic monitoring method of illegal buildings using spatiotemporal big data based on urban high lying zones. Through the Spatiotemporal sensor network technology, the tilt-type stereo camera is set up at the high lying zones around the survey area. According to the real-time ambient temperature and humidity numerical data fed back by the temperature and humidity sensor, the tilt-type stereo camera uses intelligent time-lapse photogrammetry technology to obtain multiple stereo pairs. The tilted remote sensor transmits the multiple stereo pairs to the urban dynamic analysis service network using wireless transmission. The urban dynamic analysis service network will complete a series of analysis and processing operations without any human intervention, and then transmit the results of the analysis to the early-warning terminal successively through the base station, WIFI and other wireless transmission methods. Finally, the illegal building data is uploaded to the terminal. That is to say, the urban dynamic analysis service network can realize the intelligent, automatic analysis and processing of spatial analysis server and the operation of calling all database data and storing data.The method proposed in this paper uses the high lying zones around the survey area to expand the monitoring range, improve the accuracy of monitoring data, realize long-term real-time monitoring, and fully utilize the characteristics of Spatiotemporal sensing network technology intelligent, autonomous, wireless transmission, etc., significantly reducing labor. It greatly shortens the process from the emergence to the discovery of illegal buildings in cities. The workload of monitoring has improved the efficiency of dynamic monitoring and warehousing of illegal buildings data in cities.

WiFlowCount: Device-Free People Flow Counting by Exploiting Doppler Effect in Commodity WiFi

People flow counting is to count the number of people passing through a passage or a gate. Conventional vision-based approaches require line-of-sight (LoS) and impose privacy concerns, while most radio-based approaches require dedicated equipment and incur high cost. In this article, we propose to exploit commodity WiFi to count the number of people of continuous flows in a device-free way, requiring one pair of WiFi transmitter and receiver. Leveraging the Doppler effect induced by human passing, the proposed method, named WiFlowCount, first constructs the spectrogram of Doppler shifts from channel state information. Based on the spectrogram, WiFlowCount detects the people flows and the subflows according to the power distribution in the spectrogram. An optimal rotation and segmentation algorithm is proposed to segment the spectrogram of a continuous flow into the subspectrograms of its subflows. The number of people in each subflow is estimated from its subspectrogram via convolutional neural networks, which add up to the total people count in the continuous flow. Experimental evaluations demonstrate the effectiveness of the method WiFlowCount, able to detect people flows and subflows accurately and count the number of people in continuous flows with high accuracy, outperforming the existing work.

Telecontroller design for renewable wind energy systems

The problem of geographical position in Indonesia is that there are still many villages in Indonesia that have not been able to receive PLN electricity flow. People in these remote villages can indeed enjoy electricity with their own efforts to buy diesel/premium/gas-fueled electricity generators, etc., from non-renewable fuels that are expensive and difficult to get to meet lighting needs at night. Therefore, we designed the wind power generation system for the needs of residential homes that can be controlled electronically and monitored via Cell Phones with WiFi transmission which is currently affordable at the title “Tele controller Design for Renewable Wind Energy Systems”. The system that we have designed can control the distribution of electric current in the same direction or for electric current that has been converted to alternating current, as well as the system can monitor to determine the characteristics of the wind and the resulting electrical output. The results of this study are expected to make it easier for the community in each house to generate electricity independently to be able to meet the needs of cheap night lighting and without environmental problems. With a system that is always monitored and controlled, it is expected to always be maintained and with maintenance that is easily protected from damage, it can eventually increase the service life.

Internet-Based Unobtrusive Tele-Monitoring System for Sleep and Respiration

This research is about new advances in the application of remote bio-signal monitoring technology. An unobtrusive IoT bio-signal measurement system is attached to a bed using a very thin strip sensor, then the user’s sleep efficiency and respiration rate can be measured with accuracy similar to that of an existing FDA-approved sleep tracker. In particular, in this study, we propose a ubiquitous central monitoring system that links an existing, personal use, unobtrusive measurement system to cloud-based systems via WiFi transmission. The proposed monitoring system simultaneously collects, stores, and displays the data from multiple devices using a web server as well as PC and mobile platforms such as personal smart devices. In this study, we implemented a system for the real-time transmission and display of data from multiple unobtrusive systems and validated that there were no problems associated with sending and receiving data at distances of 300 km with around a one-second delay. In addition, we evaluated the tele-monitoring system’s data processing time, CPU usage, and memory usage as the number of users was increased. Each user transmits an average of 810 bytes of data including information such as user id, time stamp, data for each channel, respiration rate and sleep status. We observed that the average data processing time was 0.15 seconds, average CPU usage was 5.01%, average memory usage was 0.1% assuming 10 users connected simultaneously. These results are expected to be useful in guiding future similar personal, public, and clinical applications of this technology.

Valutazione dell’attività di sollevamento utilizzando le caratteristiche estratte da sensori indossabili

The manual lifting tasks, which occur in the vast majority of workplaces can cause work-related low-back disorders (WLBDs), that are the most common musculoskeletal problems. Recently, to identify the relationship betweenWLBDs and risk factors, wearable monitoring devices-based biomechanical risk assessments have been proposed.The purpose of this study is to characterize from a biomechanical point of view, using wearable devices other liftingconditions to define, in the future, a risk classification tool that can be applied in each lifting condition. To do this,we recorded electromyographic data of workers during lifting tasks designed to have a growing lifting index (LI=1,2 and 3) by means of revised NIOSH lifting equation. Each lifting condition (LI=1 or LI=2 or LI=3) was obtainedin three different ways modifying the asymmetry angle. We acquired data by using Wi-Fi transmission surface electromyograph (sEMG). From the sEMG signals, analyses of time and frequency domains were performed within thelifitng cycle to extract maximum value, the average rectified value, the mean frequency defined as the gravity centerfrequency of the power spectrum of the signal. The results show that these sEMG data grew significantly with the LIand that all the lifting condition pairs are discriminated. We will test whether machine-learning techniques used formapping features extracted from wearable sensors on LI levels can improve the biomechanical risk estimation duringthese tasks. These findings suggest the use of kinematic and sEMG features to assess biomechanical risk associatedwith work activities can be integrated with methods already used for biomechanical risk assessment.

Valutazione dell’attività di sollevamento utilizzando le caratteristiche estratte da sensori indossabili

The manual lifting tasks, which occur in the vast majority of workplaces can cause work-related low-back disorders (WLBDs), that are the most common musculoskeletal problems. Recently, to identify the relationship between WLBDs and risk factors, wearable monitoring devices-based biomechanical risk assessments have been proposed. The purpose of this study is to characterize from a biomechanical point of view, using wearable devices other lifting conditions to define, in the future, a risk classification tool that can be applied in each lifting condition. To do this, we recorded electromyographic data of workers during lifting tasks designed to have a growing lifting index (LI=1,2 and 3) by means of revised NIOSH lifting equation. Each lifting condition (LI=1 or LI=2 or LI=3) was obtained in three different ways modifying the asymmetry angle. We acquired data by using Wi-Fi transmission surface electromyograph (sEMG). From the sEMG signals, analyses of time and frequency domains were performed within the lifitng cycle to extract maximum value, the average rectified value, the mean frequency defined as the gravity center frequency of the power spectrum of the signal. The results show that these sEMG data grew significantly with the LI and that all the lifting condition pairs are discriminated. We will test whether machine-learning techniques used for mapping features extracted from wearable sensors on LI levels can improve the biomechanical risk estimation during these tasks. These findings suggest the use of kinematic and sEMG features to assess biomechanical risk associate with work activities can be integrated with methods already used for biomechanical risk assessment.