The wireless local area network (WLAN), particularly the wireless fidelity (WiFi) communication technology, has greatly facilitated human productivity and entertainment in recent decades. Accurate ...channel models are identified as a key point for the optimization of transceivers and network deployment in WiFi system. In this paper, we conduct a passive measurement for indoor WiFi channels at 2.4GHz and 5.8GHz bands. Three scenarios are investigated including 1) small office; 2) office-to-corridor; 3) office-to-office. The large-scale and small-scale channel characteristics, including path loss (PL), shadowing, power delay porfiles (PDPs), Rician K-factor and RMS delay spread (RMS-DS), as well as interband correlation are analyzed and compared with the current standard channel model as well as other existing works. Elaborated physical interpretations are provided for the modeling results, revealing that the channels at 2.4GHz and 5.8GHz cannot be accurately described by a uniform model. The obtained results are essential for the accurate understanding of the WiFi propagation mechanisms and can greatly contribute to the optimization of WiFi deployments.
Wi-Fi has become the de facto wireless technology for achieving short- to medium-range device connectivity. While early attempts to secure this technology have been proved inadequate in several ...respects, the current more robust security amendments will inevitably get outperformed in the future, too. In any case, several security vulnerabilities have been spotted in virtually any version of the protocol rendering the integration of external protection mechanisms a necessity. In this context, the contribution of this paper is multifold. First, it gathers, categorizes, thoroughly evaluates the most popular attacks on 802.11 and analyzes their signatures. Second, it offers a publicly available dataset containing a rich blend of normal and attack traffic against 802.11 networks. A quite extensive first-hand evaluation of this dataset using several machine learning algorithms and data features is also provided. Given that to the best of our knowledge the literature lacks such a rich and well-tailored dataset, it is anticipated that the results of the work at hand will offer a solid basis for intrusion detection in the current as well as next-generation wireless networks.
In the battery test process, the gas data index in the test environment is very important. The system adopts the relevant air index sensor, based on the STM32F103C8T6 minimum system single-chip ...microcomputer as the core chip, to build the air quality monitoring system design in the battery test workshop. Use various types of sensors related to the main gas contained in the environment in the environment to collect the main gas data in the battery test workshop environment, and display the relevant environmental data in the configured OLED display screen, and send the relevant data to the user through the WIFI module to realize real-time monitoring of the environmental state. This air quality testing system has a wide range of application, excellent performance, and convenient maintenance. It can be applied to the environmental gas test in the test workshop under many special circumstances, which has certain practical value and research significance.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
In modern-day world ladies are much less comfortable and feature many issues regarding their safety cause. They should undergo amongst various tough situations and should Prove themselves in all ...significant circumstances every moment. For their safety and protection reasons, therefore, the authorities have provided security through policies and regulations to society. Despite the reality that there are many present safety-related systems, the need for superior smart protection gadgets is accelerated. In order to conquer such problems, smart safety gadgets are used for females. This document defines roughly a safe and secure digital phone for women, consisting of a NodeMCU, WiFi,which acts as a Transceiver and an IOT Bell,which will be fixed to all houses,shops,police stations etc… A router is introduced at certain zone which gives a wide bandwith organize.
Wireless Fidelity (Wi-Fi) sensing utilization has been widespread, especially for human behavior/activity recognition. It provides high flexibility since it does not require the person/object to ...carry any device known as device-free. This "passive" concept is also helpful for another application of Wi-Fi sensing, i.e., indoor localization. The "sensing" is conducted using particular parameters extracted from communication links of Wi-Fi devices, i.e., channel state information (CSI). This paper explores the recent trends in CSI-based indoor localization with Wi-Fi technology as its core, including their advantages, challenges, and future directions. We found tremendous benefits can be gained by employing Wi-Fi sensing in localization supported by its performance and integrability for other intelligent systems for activity recognition.
With the advancement of wireless sensing technology, human identification based on WiFi sensing has garnered significant attention in the fields of human–computer interaction and home security. ...Despite the initial success of WiFi sensing based human identification when the environment is fixed, the performance of the trained identity sensing model will be severely degraded when applied to unfamiliar environments. In this paper, a cross-domain human identification system (CATFSID) is proposed, which is able to achieve environment migration of trained model using up to 3-shot. CATFSID utilizes a dual adversarial training network, including cross-adversarial training between source and source domain classifiers, and adversarial training between source and target domain discriminators to extract environment-independent identity features. Introducing a method based on pseudo-label prediction, which assigns labels to target domain samples similar to the source domain samples, reduces the distribution bias of identity features between the source and target domains. The experimental results show accuracy of 90.1% and F1-Score of 89.33% when using 3 samples per user in the new environment.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Wireless sensing is a promising method that integrates wireless mechanisms with strong sensing capabilities. The current focus of using WiFi Channel State Information (CSI) for human activity ...recognition (HAR) is the line-of-sight (LoS) path, which is mainly affected by human activities and is very sensitive to environmental changes. However, the signal on non-line-of-sight (nLoS) paths, particularly those passing through walls, is unpredictable due to the weak reflected signals destroyed by the wall. This work proposes a method to achieve high-accuracy wireless sensing based on CSI behavior recognition with low-cost resources by showing through-wall and wider-angle predictions using WiFi signals. The technique utilizes MIMO to exploit multipath propagation and increase the capability of signal transmission and receiving antennas. The signals captured by the multi-antenna are delivered into parallel channels with different spatial signatures. An RPi 4 B is attached to an ALFA AWUS 1900 adapter utilizing Nexmon firmware monitors and extracts CSI data with flexible C-based firmware for Broadcom/Cypress WiFi chips. Preprocessing techniques based on CSI are applied to improve the feature extraction from the amplitude data in an indoor environment. Furthermore, a deep learning algorithm based on RNN with an LSTM algorithm is used to classify the activity instances indoors, achieving up to 97.5% accuracy in classifying seven activities. The experiment shows CSI can achieve accurate wireless sensing in nLoS scenarios with extended antennas and a deep learning approach.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Indoor localisation could benefit greatly from non‐line‐of‐sight (NLOS) identification and mitigation, since the major challenge for WiFi indoor ranging‐based localisation technologies is multipath ...and NLOS. NLOS identification and mitigation on commodity WiFi devices, however, are challenges due to limited bandwidth and coarse multipath resolution with mere MAC layer received signal strength index. In this study, the authors explore and exploit the finer‐grained PHY layer channel state information (CSI) to identify and mitigate NLOS. Key to the authors’ approach is exploiting several statistical features of CSI, which are proved to be particularly effective. The approaches, NLOS identification support vector machine (NISVM) and related channel information regression model (RCIRM), based on machine learning are proposed to identify NLOS and mitigate NLOS error, respectively. Experiment results in various indoor scenarios with severe interferences demonstrated an overall NLOS identification rate of 94.12% with a false alarm rate of 5.88% and a better mitigation performance.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Wireless sensing and communication evolved separately in the past. However, Integrated Sensing and Communication (ISAC) unlocks a new era of mobile network capabilities, with WiFi emerging as a prime ...candidate. By leveraging existing WiFi infrastructure and frequencies, ISAC enables powerful services like accurate localization and human activity recognition (HAR). WiFi-based HAR is a prime example powered by the magic of ISAC. WiFi Channel State Information (CSI) is susceptible to human movement disturbances; the alterations in CSI mirror the dynamic attributes of human activities. Given the intricate relationship between human activities and CSI, numerous deep learning models have been introduced to enhance HAR accuracy. Recently, transformer-based models have achieved excellent performance in various tasks, including speech recognition, natural language processing, and image classification. This has spurred research into incorporating transformer-based models into WiFi sensing applications. However, their application in WiFi-based HAR remains nascent. Vision transformer is well-suited for analyzing WiFi CSI signals in the form of spectra, such as the Doppler frequency spectrum frequently utilized in related studies, owing to its data structure mimicking that of images. In this study, we explored five widely used Vision Transformer architectures (vanilla ViT, SimpleViT, DeepViT, SwinTransformer, and CaiT) for WiFi CSI-based HAR using two publicly available datasets, UT-HAR and NTU-Fi HAR. Our work aims to assess and compare the performance of diverse ViT architectures for WiFi CSI-based HAR and provide guidelines for WiFi-based HAR modeling and ViT selection, considering accuracy, model size, and computational efficiency.
A real-time pedestrian monitoring system provides information about traffic flow, speeds, travel times, and time spent in areas or transportation facilities of interest. This is useful in travel ...information systems and crowd management strategies, as well as in planning and emergencies in public spaces, such as airports, parks, malls, and university campuses. While there are technologies that can obtain count data for non-motorized transportation at specific locations, most technologies cannot provide origin-destination information, trip paths, travel times, or time spent. To overcome these shortcomings, some studies have explored the use of Bluetooth (BT) sensors to capture the unique media access control (MAC) addresses of mobile devices carried by pedestrians. However, this collection method may suffer from low-detection rates. As an alternative, collecting MAC data from WiFi signals has emerged. The objective of this paper is three-fold: 1) develop and evaluate the performance of an integrated WiFi-BT system to monitor pedestrian-cyclists activity traffic; 2) develop and validate a classification method for differentiating pedestrians from bicycles; and 3) propose a simple extrapolation method that combines counts and MAC data. Among other results, relatively high detection rates were obtained for the developed WiFi system in comparison with BT sensors. In addition, high correlation between estimated and ground truth speeds and low classification errors are observed. Finally, the extrapolated WiFi counts and ground truth counts were found to be highly correlated. These results demonstrate the feasibility of the proposed system and methods to estimate travel times (speeds), to classify bicycle-pedestrian WiFi signals, and to extrapolate pedestrian MAC counts.
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