WiFi Devices Research Articles

Retraction Note to: Positioning of WiFi devices for indoor floor planning using principal featured Kohonen deep structure

Retraction Note to: Positioning of WiFi devices for indoor floor planning using principal featured Kohonen deep structure

Rapid Quantification of SARS-Cov-2 Spike Protein Enhanced with a Machine Learning Technique Integrated in a Smart and Portable Immunosensor.

An IoT-WiFi smart and portable electrochemical immunosensor for the quantification of SARS-CoV-2 spike protein was developed with integrated machine learning features. The immunoenzymatic sensor is based on the immobilization of monoclonal antibodies directed at the SARS-CoV-2 S1 subunit on Screen-Printed Electrodes functionalized with gold nanoparticles. The analytical protocol involves a single-step sample incubation. Immunosensor performance was validated in a viral transfer medium which is commonly used for the desorption of nasopharyngeal swabs. Remarkable specificity of the response was demonstrated by testing H1N1 Hemagglutinin from swine-origin influenza A virus and Spike Protein S1 from Middle East respiratory syndrome coronavirus. Machine learning was successfully used for data processing and analysis. Different support vector machine classifiers were evaluated, proving that algorithms affect the classifier accuracy. The test accuracy of the best classification model in terms of true positive/true negative sample classification was 97.3%. In addition, the ML algorithm can be easily integrated into cloud-based portable Wi-Fi devices. Finally, the immunosensor was successfully tested using a third generation replicating incompetent lentiviral vector pseudotyped with SARS-CoV-2 spike glycoprotein, thus proving the applicability of the immunosensor to whole virus detection.

Development of Double C-Shaped Left-Handed Metamaterial for Dual-Band Wi-Fi and Satellite Communication Application with High Effective Medium Radio and Wide Bandwidth

The development and improvement of the dual-band 802.11ac standard Wi-Fi and wide bandwidth satellite communication devices are currently research subjects that have garnered significant interest. In this paper, double C-shaped two split-ring resonator (SRR) bounded unit cells were developed, which are applicable for S, C, and X band applications, including dual-band Wi-Fi communication devices and satellite communication applications for its effective medium ratio (EMR) of 15.6, which results in a 2.4 GHz resonance frequency and wide bandwidth (S21 < −10 dB) of 1650 MHz at an 11.5 GHz resonance frequency. A copper resonator and the popular substrate material Rogers RT 5880 (thickness of 1.575 mm) were adopted for analyzing the characteristics of this unit cell. The 8×8 mm2 structure was designed and simulated using a commercially available electromagnetic simulator CST (Computer Simulation Technology) Studio Suite 2019, which was utilized at four resonance frequencies: 2.4 GHz, 5.6 GHz, 8.93 GHz, and 11.5 GHz. The electric field, magnetic field, and surface current distribution were examined by modifying the metamaterial unit cell design structure, showing effective results. To confirm the CST simulation results, the newly designed double C-shaped double-negative metamaterial (DNG) was also simulated with the Ansys High-Frequency Structure Simulator (HFSS) and compared with the extracted results. The suggested metamaterial is advised for usage in Wi-Fi and satellite communication applications for superior long-distance communication performance and efficiency with the compactness of the structure.

Electromagnetic Signal Intelligent Identification Based on Radio Frequency Fingerprints

Due to the open nature of WIFI connection, it is exposing its private information to the attackers. Traditional WIFI security methods are no longer able to meet the current security needs, and more and more wireless-side physical layer security solutions provide solutions, among which RF fingerprinting is an endogenous security technology with potential. Constructing an effective and accurate method to identify WIFI devices that steal information is a difficulty that today’s society needs to face. The main problem is not only that the recognition accuracy is difficult to improve but also the problem of data shortage. In this paper, we first construct a large-scale WIFI real-world measurement dataset. Next, we use PSD and bispectrum features, as well as complex ResNet schemes for WIFI device identification experiments, and compare and analyze them from multiple perspectives. The experimental results show that the proposed algorithm can achieve up to 97% recognition accuracy among 100 devices. Moreover, when the SNR is 0 dB, the complex ResNet method can still achieve 78% recognition accuracy among 100 devices. Finally, this paper summarizes the experimental analysis of the measured dataset and discusses the open issues related to this area.

DeFall: Environment-Independent Passive Fall Detection Using WiFi

Fall is recognized as one of the most frequent accidents among elderly people. Many solutions, either wearable or noncontact, have been proposed for fall detection (FD) recently. Among them, WiFi-based noncontact approaches are gaining popularity due to the ubiquity and noninvasiveness. The existing works, however, usually rely on labor-intensive and time-consuming training before it can achieve a reasonable performance. In addition, the trained models often contain environment-specific information and, thus, cannot be generalized well for new environments. In this article, we propose DeFall, a WiFi-based passive FD system that is independent of the environment and free of prior training in new environments. Unlike previous works, our key insight is to probe the physiological features inherently associated with human falls, i.e., the distinctive patterns of speed and acceleration during a fall. DeFall consists of an offline template-generating stage and an online decision-making stage, both taking the speed estimates as input. In the offline stage, augmented dynamic time-warping (DTW) algorithms are performed to generate a representative template of the speed and acceleration patterns for a typical human fall. In the online phase, we compare the patterns of the real-time speed/acceleration estimates against the template to detect falls. To evaluate the performance of DeFall, we built a prototype using commercial WiFi devices and conducted experiments under different settings. The results demonstrate that DeFall achieves a detection rate above 95% with a false alarm rate lower than 1.50% under both line-of-sight (LOS) and non-LOS (NLOS) scenarios with one single pair of transceivers. Extensive comparison study verifies that DeFall can be generalized well to new environments without any new training.

Wi-Fi Can Do More: Toward Ubiquitous Wireless Sensing

The next big deal for Wi-Fi is not about communication and networking, but sensing. Wireless sensing technology is turning a Wi-Fi device into a ubiquitous sensor, which not only adds a brand new dimension to the functions, capabilities, and applications of all Wi-Fi systems, but also revolutionizes how sensing, especially human-centric sensing, is practiced. Wi-Fi sensing utilizes ambient Wi-Fi signals to analyze and interpret human and object movements, underpinning many sensing applications including motion sensing, sleep monitoring, and fall detection, among others. These new sensing functionalities can benefit the global Wi-Fi ecosystem including integrated circuit manufacturers, device manufacturers, system integrators, application developers, and ultimately end users. In this article, we introduce the concepts, principles, and challenges of Wi-Fi sensing, and share our unique technologies that have been massively deployed for real-world applications. We foresee that Wi-Fi sensing will enter billions of devices and millions of homes, creating a smarter space for a smarter life. Today is just the beginning of this revolution.

WiFi-Based Cross-Domain Gesture Recognition via Modified Prototypical Networks

Numerous deep learning studies have achieved remarkable advances in WiFi-based human gesture recognition (HGR) using channel state information (CSI). However, since the CSI patterns of the same gesture change across domains (i.e., users, environments, locations, and orientations), recognition accuracy might degrade significantly when applying the trained model to new domains. To overcome this problem, we propose a WiFi-based cross-domain gesture recognition system (WiGr) which has a domain-transferable mapping to construct an embedding space where the representations of samples from the same class are clustered, and those from different classes are separated. The key insight of WiGr is using the similarity between the query sample representation and the class prototypes in the embedding space to perform the gesture classification, which can avoid the influence of the cross-domain CSI patterns change. Meanwhile, we present a dual-path prototypical network (Dual-Path PN) which consists of a deep feature extractor and a dual-path (i.e., Path-A and Path-B substructures) recognizer. The trained feature extractor can extract the gesture-related domain-independent features from CSI, namely, the domain-transferable mapping. In addition, WiGr implements the cross-domain HGR based on only a pair of WiFi devices without retraining in the new domain. We conduct comprehensive experiments on three data sets, one is built by ourselves and the others are public data sets. The evaluation suggests that WiGr achieves 86.8%–92.7% in-domain recognition accuracy and 83.5%–93% cross-domain accuracy under the four-shot condition.

Nocturnal Seizure Detection Using Off-the-Shelf WiFi

The detection of nocturnal seizures in epilepsy patients is essential, both for the quick management of the seizure complications, and for the assessment of the ongoing seizure treatment. Traditional seizure detection products (e.g., wearables), however, are either very costly, uncomfortable, or unreliable. In this article, we then propose to utilize everyday WiFi signals for robust, fast, and noninvasive detection of nocturnal seizures. We first present a new and rigorous mathematical characterization for the spectral content/bandwidth of the WiFi signal, measured on a WiFi device placed near a sleeping patient, during different kinds of sleep motions: seizures, normal movements (e.g., posture adjustments), and breathing. Based on this mathematical modeling, we propose a novel pipeline for processing the received WiFi signals to robustly detect all nocturnal nonbreathing movements, and then classify them into normal body movements or seizures. In order to validate this, we carry out extensive experiments in seven different typical bedroom locations, where a set of 20 actors simulate the state of having seizures (a total of 260 instances), as well as normal sleep movements (a total of 410 instances). Our proposed system detects 93.85% of the seizures with a mean response time (MRT) of only 5.69 s since the onset of the seizure. Moreover, our proposed system achieves a probability of false alarm of only 0.0097, when classifying normal sleep movements. Overall, our new mathematical modeling and experimental results show the great potential the ubiquitous WiFi signals have for detecting nocturnal seizures, which can provide better support for epilepsy patients and their caregivers.

The Envi–City

Our main objective is to change people's perspective on an unbalanced city by viewing a new world through sustainable development goals. When we relate sustainability and a city there are important things to look upon and make a change on it. Some of the problems looked upon are, (i) Reduction in the number of birds due to electromagnetic radiation. (ii) 8-15% loss between powerplant and consumers. (iii) Air pollution. (iv) Waterlogging in city pockets and waste management. The final vision is to make a smart eco-city run fully on renewable energy. Using sustainable energy sources by fitting rooftop solar cells in every house and placing vertical turbines on the roadside reduces the dependency on fossil fuels and can generate employment in many technical fields. We shall even have automated air purifiers run by solar cells, which purify the air depending on the real-time PPM levels of dust and gasses. The smart city that we have a vision of filters out high bandwidth signal waves for mobile and telecommunication while utilizing short-range Wi-Fi devices interconnected with WAN cables which can reduce the harmful effects of high energy electromagnetic radiation on birds. This is an IoT-based city where most of the electronics and other devices are connected, running on automation for easing day-to-day activities. Implementing this project will produce a positive change in the ecosystem.

Machine Learning and Internet of Things (IoT) for Real-Time Image Classification in Smart Agriculture

A human's ability to survive would be impossible without agriculture. Agriculture provides a means of subsistence for a huge proportion of the global population. It also provides a large number of work opportunities for the locals. Poor yields are a consequence of farmers' desire to rely on old-fashioned farming methods. The long-term development and success of the economy will continue to depend on agricultural and related industries. Crop monitoring and tracking, disease identification and management, and other such issues are among agriculture's most difficult difficulties. In many cases, smart farming is a viable answer. The Internet of Things (IoT) and machine learning methods has made it feasible to implement smart agriculture. This article presents a framework for real-time picture categorization in agriculture. Everything from IoT cameras to mobile applications to machine learning methods is featured. Arduino Uno, sensors, and Wi-Fi devices make up the hardware. Computers may "learn" from previous examples and detect patterns from noisy or complicated datasets using machine learning, a technique that uses a variety of statistical, probabilistic, and optimization methodologies to allow computers to "learn." Because of this, machine learning algorithms are increasingly being utilized to classify photos. Real-time photos linked to smart agriculture were classified using a combination of SVM, K-nearest neighbors, and probabilistic neural network classifiers. Real-time picture classification will assist in the diagnosis of leaf disease, the monitoring of farm staff, the classification of crops, and the tracking of farm product progress.

A survey on gait recognition in IoT applications

In IoT applications, identity recognition is a basic and critical requirement. In recent years, IoT technology has developed rapidly, and IoT devices such as wearable devices, environmental sensors, and WiFi devices have been popularized and developed. The unobtrusive, low-cost, continuous advanced identity recognition methods are needed in IoT applications. Gait recognition not only has high performance but also is not easy to be forged or hidden. It has excellent potential in IoT intelligent identity recognition. This review discusses the implementation of gait analysis, representative datasets, and algorithms. Finally, we also discussed the challenges of gait analysis in IoT applications.

Secure User Authentication Leveraging Keystroke Dynamics via Wi-Fi Sensing

User authentication plays a critical role in access control of a man-machine system, where the knowledge factor, such as a personal identification number, constitutes the most widely used authentication element. However, knowledge factors are usually vulnerable to the spoofing attack. Recently, the inheritance factor, such as fingerprints, emerges as an efficient alternative resilient to malicious users, but it normally requires special equipment. To this end, in this article, we propose WiPass, a device-free authentication system only leveraging the pervasive Wi-Fi infrastructure to explore keystroke dynamics (manner and rhythm of keystrokes) captured by the channel state information to recognize legitimate users while rejecting spoofers. However, it remains an open challenge to characterize the behavioral features hidden in the human subtle motions, such as keystrokes. Therefore, we build a signal enhancement model using Ricean distribution to amplify user keystroke dynamics and a hybrid learning model for user authentication, which consists of two parts, i.e., convolutional neural network based feature extraction and support vector machine based classification. The former relies on visualizing the channel responses into time-series images to learn the behavioral features of keystrokes in energy and spectrum domains, whereas the latter exploits such behavioral features for user authentication. We prototype WiPass on the low-cost off-the-shelf Wi-Fi devices and verify its performance. Empirical results show that WiPass achieves on average 92.1% authentication accuracy, 5.9% false accept rate, and 6.3% false reject rate in three real environments.

Harvesting Ambient RF for Presence Detection Through Deep Learning.

This article explores the use of ambient radio frequency (RF) signals for human presence detection through deep learning. Using Wi-Fi signal as an example, we demonstrate that the channel state information (CSI) obtained at the receiver contains rich information about the propagation environment. Through judicious preprocessing of the estimated CSI followed by deep learning, reliable presence detection can be achieved. Several challenges in passive RF sensing are addressed. With presence detection, how to collect training data with human presence can have a significant impact on the performance. This is in contrast to activity detection when a specific motion pattern is of interest. A second challenge is that RF signals are complex-valued. Handling complex-valued input in deep learning requires careful data representation and network architecture design. Finally, human presence affects CSI variation along multiple dimensions; such variation, however, is often masked by system impediments, such as timing or frequency offset. Addressing these challenges, the proposed learning system uses preprocessing to preserve human motion-induced channel variation while insulating against other impairments. A convolutional neural network (CNN) properly trained with both magnitude and phase information is then designed to achieve reliable presence detection. Extensive experiments are conducted. Using off-the-shelf Wi-Fi devices, the proposed deep-learning-based RF sensing achieves near-perfect presence detection during multiple extended periods of test and exhibits superior performance compared with leading edge passive infrared sensors. A comparison with existing RF-based human presence detection also demonstrates its robustness in performance, especially when deployed in a completely new environment. The learning-based passive RF sensing thus provides a viable and promising alternative for presence or occupancy detection.

Enhanced Wi-Fi RTT Ranging: A Sensor-Aided Learning Approach

The fine timing measurement (FTM) protocol is designed to determine precise ranging between Wi-Fi devices using round-trip time (RTT) measurements. However, the multipath propagation of radio waves generates inaccurate timing information, degrading the ranging performance. In this study, we use a neural network (NN) to adaptively learn the unique measurement patterns observed at different indoor environments and produce enhanced ranging outputs from raw FTM measurements. Moreover, the NN is trained based on an unsupervised learning framework, using the naturally accumulated sensor data acquired from users accessing location services. Therefore, the effort involved in collecting training data is significantly minimized. The experimental results verified that the collection of unlabeled data for a short duration is sufficient to learn the pattern in raw FTM measurements and produce improved ranging results. The proposed method reduced the errors in raw distance measurements and well-calibrated ranging results requiring the collection of ground truth data by 47–50% and 17–29%, respectively. Consequently, positioning errors were reduced by 17–30% compared to the well-calibrated ranging scenario.

Breathing-based continuous non-intrusive user verification leveraging commodity WiFi

This paper opens up a new pathway of the utility of breathing pattern for user verification. We demonstrate that it is possible to capture people's breathing pattern leveraging commodity WiFi devices. While prior solutions for biometrics-based user recognition usually require dedicated devices (e.g., video cameras or IR sensors), this paper introduces the first general, low-cost breathing-based user verification system using commodity WiFi devices. The proposed system is based on the fact that the breathing pattern always keeps consistent for the same user but distinct among different people. Our innovative method successfully extracts the breathing pattern of different people based on channel state information of WiFi signal to facilitate user verification. The prototype study using two commodity WiFi devices can differentiate people with an average verification accuracy over 90%, suggesting that our breathing-based user verification system using commerical off-the-shelf (COTS) WiFi is promising to be one of the most critical methods in biometrics.

Toward Reliable Non-Line-of-Sight Localization Using Multipath Reflections

The past decade's research in RF indoor localization has led to technologies with decimeter-level accuracy under controlled experimental settings. However, existing solutions are not reliable in challenging environments with rich multipath and various occlusions. The errors can be 3-5 times compared to settings with clear LoS paths. In addition, when the direct path is completely blocked, such approaches would generate wrong location estimates. In this paper, we present NLoc, a reliable non-line-of-sight localization system that overcomes the above limitations. The key innovation of NLoc is to convert multipath reflections to virtual direct paths to enhance the localization performance. To this end, NLoc first extracts reliable multi-dimensional parameters by characterizing phase variations. Then, it models the relation between the target location and the geometric features of multipath reflections to obtain virtual direct paths. Finally, it incorporates novel algorithms to remove random ToF offsets due to lack of synchronization and compensate target orientation that determines the geometric features, for accurate location estimates. We implement NLoc on commercial off-the-shelf WiFi devices. Our experiments in multipath challenged environments with dozens of obstacles and occlusions demonstrate that NLoc outperforms state-of-the-art approaches by 44% at the median and 200% at 90% percentile.

Towards Robust Gesture Recognition by Characterizing the Sensing Quality of WiFi Signals

WiFi-based gesture recognition emerges in recent years and attracts extensive attention from researchers. Recognizing gestures via WiFi signal is feasible because a human gesture introduces a time series of variations to the received raw signal. The major challenge for building a ubiquitous gesture recognition system is that the mapping between each gesture and the series of signal variations is not unique, exact the same gesture but performed at different locations or with different orientations towards the transceivers generates entirely different gesture signals (variations). To remove the location dependency, prior work proposes to use gesture-level location-independent features to characterize the gesture instead of directly matching the signal variation pattern. We observe that gesture-level features cannot fully remove the location dependency since the signal qualities inside each gesture are different and also depends on the location. Therefore, we divide the signal time series of each gesture into segments according to their qualities and propose customized signal processing techniques to handle them separately. To realize this goal, we characterize signal's sensing quality by building a mathematical model that links the gesture signal with the ambient noise, from which we further derive a unique metric i.e., error of dynamic phase index (EDP-index) to quantitatively describe the sensing quality of signal segments of each gesture. We then propose a quality-oriented signal processing framework that maximizes the contribution of the high-quality signal segments and minimizes the impact of low-quality signal segments to improve the performance of gesture recognition applications. We develop a prototype on COTS WiFi devices. The extensive experimental results demonstrate that our system can recognize gestures with an accuracy of more than 94% on average, and significant improvements compared with state-of-arts.

Long-Term Wi-Fi Exposure From Pre-Pubertal to Adult Age on the Spermatogonia Proliferation and Protective Effects of Edible Bird's Nest Supplementation.

Children are vulnerable to the radiofrequency radiation (RFR) emitted by Wi-Fi devices. Nevertheless, the severity of the Wi-Fi effect on their reproductive development has been sparsely available. Therefore, this study was conducted to evaluate the Wi-Fi exposure on spermatogonia proliferation in the testis. This study also incorporated an approach to attenuate the effect of Wi-Fi by giving concurrent edible bird’s nest (EBN) supplementation. It was predicted that Wi-Fi exposure reduces spermatogonia proliferation while EBN supplementation protects against it. A total of 30 (N = 30) 3-week-old Sprague Dawley weanlings were divided equally into five groups; Control, Control EBN, Wi-Fi, Sham Wi-Fi, and Wi-Fi + EBN. 2.45 GHz Wi-Fi exposure and 250 mg/kg EBN supplementation were conducted for 14 weeks. Findings showed that the Wi-Fi group had decreased in spermatogonia mitosis status. However, the mRNA and protein expression of c-Kit-SCF showed no significant decrease. Instead, the reproductive hormone showed a reduction in FSH and LH serum levels. Of these, LH serum level was decreased significantly in the Wi-Fi group. Otherwise, supplementing the Wi-Fi + EBN group with 250 mg/kg EBN resulted in a significant increase in spermatogonia mitotic status. Even though EBN supplementation improved c-Kit-SCF mRNA and protein expression, the effects were insignificant. The improvement of spermatogonia mitosis appeared to be associated with a significant increase in blood FSH levels following EBN supplementation. In conclusion, the long-term Wi-Fi exposure from pre-pubertal to adult age reduces spermatogonia proliferation in the testis. On the other hand, EBN supplementation protects spermatogonia proliferation against Wi-Fi exposure.

Crowd Monitoring in Smart Destinations Based on GDPR-Ready Opportunistic RF Scanning and Classification of WiFi Devices to Identify and Classify Visitors’ Origins

Crowd monitoring was an essential measure to deal with over-tourism problems in urban destinations in the pre-COVID era. It will play a crucial role in the pandemic scenario when restarting tourism and making destinations safer. Notably, a Destination Management Organisation (DMO) of a smart destination needs to deploy a technological layer for crowd monitoring that allows data gathering in order to count visitors and distinguish them from residents. The correct identification of visitors versus residents by a DMO, while privacy rights (e.g., Regulation EU 2016/679, also known as GDPR) are ensured, is an ongoing problem that has not been fully solved. In this paper, we describe a novel approach to gathering crowd data by processing (i) massive scanning of WiFi access points of the smart destination to find SSIDs (Service Set Identifier), as well as (ii) the exposed Preferred Network List (PNL) containing the SSIDs of WiFi access points to which WiFi-enabled mobile devices are likely to connect. These data enable us to provide the number of visitors and residents of a crowd at a given point of interest of a tourism destination. A pilot study has been conducted in the city of Alcoi (Spain), comparing data from our approach with data provided by manually filled surveys from the Alcoi Tourist Info office, with an average accuracy of 83%, thus showing the feasibility of our policy to enrich the information system of a smart destination.

Multimodal Fusion-AdaBoost Based Activity Recognition for Smart Home on WiFi Platform

With the ubiquity of commodity WiFi devices and the rapid development of Internet of Things (IoT), there are increasingly intelligent sensing applications emerging by utilizing fine-grained channel state information (CSI) from WiFi signals, which can realize contactless human-computer interaction (HCI) for smart home. However, most of CSI-based activity recognition approaches are vulnerable to random noises derived from indoor environments. In this paper, we present a multimodal fusion-AdaBoost based human activity recognition scheme on WiFi platform. For this purpose, we develop two theoretical underpinnings, including a sensing model and a recognition model. The sensing model is firstly established to investigate the impact of human activities on propagation properties of WiFi signals. To this end, Fresnel zone model is used to quantify the correlation between CSI dynamics and human activities. Moreover, the recognition model is used to exploit physical activity-induced signal changes to infer potential activity information. In the process, we firstly construct a CSI tensor through integrating all CSI information at each WiFi receiver. Then, a CANDECAMP/PARAFAC (CP) decomposition method is applied to this CSI tensor for obtaining representative features. Finally, based on the features extracted, human activities are able to be recognized by combining multimodal fusion and AdaBoost. We implement the proposed scheme on a set of WiFi devices and evaluate it in both laboratory and corridor environments. The experimental results confirm that the proposed scheme can achieve average recognition accuracies of 96% and 95% in two indoor scenarios, respectively.