WiFi Cards Research Articles

Leveraging the ASSURE Model for Optimized Information Technology-Based Learning Media

The evaluation of technology-based learning media is crucial in modern education to ensure its effectiveness in improving student learning outcomes and addressing various challenges. This study aims to evaluate the effectiveness of information technology-based learning media in Class VI of SD Negeri 07 Angkasa using the ASSURE model. A case study approach was employed, involving 1 teacher and 10 sixth-grade students. Data were collected through interviews, observations, and documentation. The analysis followed the stages of data reduction, data display, and conclusion drawing. The findings revealed that the teacher used PowerPoint, Word Wall, and Quizizz Paper Mode, resulting in a 50% increase in student engagement and a 21.43% improvement in student performance. Key factors supporting the use of technology-based learning media included school facilities such as modems, WiFi cards, projectors, laptops, and speakers, as well as teachers' personal smartphones. However, challenges included limited internet access and unstable electricity. Solutions such as using offline technology, portable hotspots, backup power sources, and devices with sufficient battery life were suggested. The integration of information technology-based learning media showed positive effects on student engagement and performance. However, infrastructure limitations, curriculum alignment, and time management issues need to be addressed to optimize its use. This study concludes that the application of information technology-based learning media using the ASSURE model positively impacts student learning processes and outcomes. Overcoming infrastructural and logistical challenges is necessary to enhance its effectiveness in classrooms.

Enabling WiFi Sensing on New-generation WiFi Cards

The last few years have witnessed the rapid development of WiFi sensing with a large spectrum of applications enabled. However, existing works mainly leverage the obsolete 802.11n WiFi cards (i.e., Intel 5300 and Atheros AR9k series cards) for sensing. On the other hand, the mainstream WiFi protocols currently in use are 802.11ac/ax and commodity WiFi products on the market are equipped with new-generation WiFi chips such as Broadcom BCM43794 and Qualcomm QCN5054. After conducting some benchmark experiments, we find that WiFi sensing has problems working on these new cards. The new communication features (e.g., MU-MIMO) designed to facilitate data transmissions negatively impact WiFi sensing. Conventional CSI base signals such as CSI amplitude and/or CSI phase difference between antennas which worked well on Intel 5300 802.11n WiFi card may fail on new cards. In this paper, we propose delicate signal processing schemes to make wireless sensing work well on these new WiFi cards. We employ two typical sensing applications, i.e., human respiration monitoring and human trajectory tracking to demonstrate the effectiveness of the proposed schemes. We believe it is critical to ensure WiFi sensing compatible with the latest WiFi protocols and this work moves one important step towards real-life adoption of WiFi sensing.

CREATION OF AN INTELLIGENT SYSTEM FOR OPTIMIZING ELECTRICAL ENERGY CONSUMPTION FOR LIGHTING IN A BUILDING FOR DOMESTIC OR ADMINISTRATIVE USE

This article consists of the implementation of an intelligent system for the management and optimization of electrical energy consumption in a building for domestic or administrative use. Waste of energy is usually due to laziness, forgetfulness and ignorance.The systems that we have proposed in this part are solutions for optimizing the consumption of electrical energy for lighting. In the creation of these systems we used two kinds of microcontroller cards such as the Arduino card and the ESP8266 Wifi card to control our different sensors and system components.The solutions that we will put in place must manage the factors that lead to the waste of electrical energy in order to be able to manage and optimize the consumption of electrical energy. Our system is called intelligent because the entire lighting system can be done remotely by sending SMS.

WiMeasure

In the past few years, a large range of wireless signals such as WiFi, RFID, UWB and Millimeter Wave were utilized for sensing purposes. Among these wireless sensing modalities, WiFi sensing attracts a lot of attention owing to the pervasiveness of WiFi infrastructure in our surrounding environments. While WiFi sensing has achieved a great success in capturing the target's motion information ranging from coarse-grained activities and gestures to fine-grained vital signs, it still has difficulties in precisely obtaining the target size owing to the low frequency and small bandwidth of WiFi signals. Even Millimeter Wave radar can only achieve a very coarse-grained size measurement. High precision object size sensing requires using RF signals in the extremely high-frequency band (e.g., Terahertz band). In this paper, we utilize low-frequency WiFi signals to achieve accurate object size measurement without requiring any learning or training. The key insight is that when an object moves between a pair of WiFi transceivers, the WiFi CSI variations contain singular points (i.e., singularities) and we observe an exciting opportunity of employing the number of singularities to measure the object size. In this work, we model the relationship between the object size and the number of singularities when an object moves near the LoS path, which lays the theoretical foundation for the proposed system to work. By addressing multiple challenges, for the first time, we make WiFi-based object size measurement work on commodity WiFi cards and achieve a surprisingly low median error of 2.6 mm. We believe this work is an important missing piece of WiFi sensing and opens the door to size measurement using low-cost low-frequency RF signals.

Embracing Consumer-level UWB-equipped Devices for Fine-grained Wireless Sensing

RF sensing has been actively exploited in the past few years to enable novel IoT applications. Among different wireless technologies, WiFi-based sensing is most popular owing to the pervasiveness of WiFi infrastructure. However, one critical issue associated with WiFi sensing is that the information required for sensing can not be obtained from consumer-level devices such as smartphones or smart watches. The commonly-seen WiFi devices in our everyday lives actually can not be utilized for sensing. Instead, dedicated hardware with a specific WiFi card (e.g., Intel 5300) needs to be used for WiFi sensing. This paper involves Ultra-Wideband (UWB) into the ecosystem of RF sensing and makes RF sensing work on consumer-level hardware such as smartphones and smart watches for the first time. We propose a series of methods to realize UWB sensing on consumer-level electronics without any hardware modification. By leveraging fine-grained human respiration monitoring as the application example, we demonstrate that the achieved performance on consumer-level electronics is comparable to that achieved using dedicated UWB hardware. We show that UWB sensing hosted on consumer-level electronics is able to achieve fine granularity, robustness against interference and also multi-target sensing, pushing RF sensing one step towards real-life adoption.

AuthIoT: A Transferable Wireless Authentication Scheme for IoT Devices Without Input Interface

Wireless Internet of Things (IoT) applications have penetrated every aspect of our society and become increasingly important in smart homes, smart cities, and smart hospitals. However, many WiFi-based IoT devices (e.g., light switches, door/window open alert sensors, and Google Home) do not have input interfaces such as keypad or touchscreen due to their limits in physical size, power consumption, and/or manufacturing cost, making it inconvenient and onerous for end users to authenticate those IoT devices for wireless Internet access. In this article, we present AuthIoT, a learning-based authentication scheme for wireless IoT devices without input interfaces. The key component of AuthIoT is a channel state information (CSI)-based character classification algorithm for a WiFi access point (AP), which recognizes the passcode from an IoT device when an end user holds it in hand and writes the passcode over the air. AuthIoT has two salient features: 1) it is transferable for cross-environment applications and 2) it works in more realistic scenarios where AP is equipped with nonlinear antenna array. We have built a prototype of AuthIoT and evaluated its performance on two testbeds: 1) Intel 5300 WiFi card with three linear antennas and 2) USRP N310 with four nonlinear (square-shaped) antennas. The experimental results show that AuthIoT achieves 84% and 83% recognition accuracy on the two testbeds.

Battery Share: A New Revolution of Sharing Your Smartphone Battery Power

Abstract: The majority of individuals in the globe nowadays struggle with their mobile phone's batteries dying while they are out and about. The concept to create a system that allows consumers to transfer battery life from one phone to another was born to address this problem. In a cell phone battery, an electric charge is chemically stored. Batteries for mobile phones have a static charge that is stored. This concept is centred on the usage of a technology, which transforms static electric charge into data packets and transmits them from source to destination (i.e., from one mobile to another mobile) via a wireless network (much like sharing using file sharing applications utilising Wi-Fi) using a technology called Wi-Fi Direct. To connect with another device, it makes use of the Wi-Fi card in the smartphone. It establishes a secure link between the two devices so that data may be sent at fast rates without interruption and is converted back to static energy to extend battery life. This is not the one same as the existing wireless charging technology in which phone has to be kept on a charging pad and it starts charging. Instead, here in this proposed technology the electric charge will be converted in form of data packets and transfer them over a wireless communication channel like, Wi-Fi.

Single-Antenna Device-to-Device Localization in Smart Environments With Backscatter

A long-standing vision of indoor localization is to eliminate infrastructure and deployment costs. Recent innovations make it possible to enable device-to-device (D2D) localization while requiring multiple antennas for the systems. We ask the following question: can we localize the more generally used single-antenna devices (e.g., IoT) using another single-antenna device (e.g., smartphone or smartwatch) in a smart environment where low-cost backscatter tags are widely deployed on walls or smart objects? In this article, we present TagLoc, a lightweight system that enables D2D localization without relying on large antenna arrays. Our observation is that the reflected signals from the ambient smart environment can be exploited to eliminate the requirement of bulky antenna arrays that are unachievable for the simple-designed IoT devices. Specifically, TagLoc creates multiple direction signatures using backscatter arrays in smart environments. Then, the receiver can accurately estimate the direction signatures from the transmitter to the arrays and then localize the target by cooperating all tag arrays. We prototype TagLoc using two single-antenna Intel NUCs with off-the-shelf Intel 5300 WiFi cards and customized backscatter tags. The results show TagLoc can achieve robust performance in a real indoor environment with a median localization error of 0.82 m.

Development of IOT Based Digital Display Board for Students’ Lectures Update

Technology is generally improving the advertisement sector and the use of electronic medium in communication system is on geometric rise due to its flexibility, attractiveness, speed and efficiency. Various methods of information dissemination have been adopted from the use of posters, traditional billboards to electronic billboards. This work presents the development of an IOT (Internet of Things) based display board for Engineering Students in Federal Polytechnic Ile Oluji, Ondo State, Nigeria for lecture update using wireless communication channel (wi-fi); this provides ease and comfort of updating the content from remote location in the wireless network range. The command is sent to the digital display board through the Mobile Application interfaced with the wireless module to give regular update and seamless communication with students.
 The digital board basically contains a digital display module, a 5volts DC power pack, a wi-fi card, Microcontroller and Light Emitting Diodes. The administrator sends out message through the mobile led art light application to keep students updated on their lecture information. The text is received through the wireless module connected to the application and transferred to the microcontroller which decodes the message for users. The decoded message will automatically switch ON and OFF appropriate LEDs in order to display respective message sent which scrolls on the display. The display board can accept texts in different modes and styles as provided in the mobile application. The display board is useful in academic environment to regularly update students on lectures and other relevant information.

Decimeter Level Indoor Localization Using WiFi Channel State Information

Indoor localization using WiFi signal parameters is challenging, with encouraging decimeter localization results available with enough line-of-sight coverage and hardware infrastructure. This paper proposes a new 2-dimensional multiple packets based matrix pencil (2D M-MP) method to estimate the Angle of Arrival (AoA) and Time of Flight (ToF) based on WiFi channel state information (CSI). Compared with the conventional parameter estimation algorithms, this method has two advantages. First, 2D M-MP method uses the discrete Fourier transform (DFT) to convert the complex computation into real computation to reduce the computational complexity significantly without losing accuracy. Second, it accumulates multiple CSI packets to improve the parameter estimation accuracy effectively, especially at low values of signal-to-noise-ratio (SNR) environment. To verify the practicability of our proposed 2D M-MP method, we set up a localization system in an actual scenario using commodity WiFi cards which demonstrates that the performance of 2D M-MP method is better than conventional parameter estimation algorithms and can achieve a localization accuracy of 42 cm in indoor hall deployment.

Countering Cascading Denial of Service Attacks on Wi-Fi Networks

Recent work demonstrates that IEEE 802.11 networks are vulnerable to cascading DoS attacks, wherein a single node can remotely and suddenly congest an entire network. In this paper, we propose, analyze, simulate, and experimentally verify a counter-measure against such attacks. Our main idea is to optimize the duration of packet transmissions in order to weaken coupling effects between neighboring pairs of nodes. Toward that end, we propose a new theoretical model that relates the utilization of neighboring pairs of nodes using a sequence of iterative equations. The model captures important specifications of the IEEE 802.11 MAC layer. Through a fixed point analysis of the sequence, we show how to optimally set the packet duration so that, on one hand, cascading DoS attacks are avoided and, on the other hand, throughput is maximized. We validate the analysis through extensive ns-3 simulations and demonstrate the effectiveness of the mitigation through experiments with real Wi-Fi cards. A key insight is that IEEE 802.11 networks with relatively large MAC overhead are less susceptible to cascading DoS attacks than networks with smaller MAC overhead.

Security-Oriented Smart Door Lock à la Internet of Things

In this study, a framework to open a door lock with a keypad is designed. The system has a database for the authorized people who has the privilege for the access to a restricted area. Authorized individuals are permitted after keying in the respective passwords. The ID of those who access the restricted area is logged on the internet, to a Google Spread Sheet table with a time stamp. An economical wi-fi module was used for internet connection. A Solenoid is used for the locking mechanism driven by a mosfet power drive. An oled screen is also provided for the visual feedback to the users. Wi-fi card is coded in MicroPython. This card is responsible for transfering the passwords from the keypad to a web server. This web server is coded in PHP which compares the incoming passwords with the passwords in the previously generated user database, and as a result sends the wi-fi card either the name of the user who opened the door or the wrong password information. The result then is conveyed to the door lock controller. Each time this is performed, wi-fi card sends the ID of the person who opens the door or the invalid password information to an IFTTT Webhook service, and the Webhook service transfers this information to a Google Spread Sheet table with its timestamp. An administrator panel has also been created with PHP codes so that new users can also be added without dealing with the codes.

Peer-to-Peer Localization for Single-Antenna Devices

Some important indoor localization applications, such as localizing a lost kid in a shopping mall, call for a new peer-to-peer localization technique that can localize an individual's smartphone or wearables by directly using another's on-body devices in unknown indoor environments. However, current localization solutions either require pre-deployed infrastructures or multiple antennas in both transceivers, impending their wide-scale application. In this paper, we present P2PLocate, a peer-to-peer localization system that enables a single-antenna device co-located with a batteryless backscatter tag to localize another single-antenna device with decimeter-level accuracy. P2PLocate leverages the multipath variations intentionally created by an on-body backscatter tag, coupled with spatial information offered by user movements, to accomplish this objective without relying on any pre-deployed infrastructures or pre-training. P2PLocate incorporates novel algorithms to address two major challenges: (i) interference with strong direct-path signal while extracting multipath variations, and (ii) lack of direction information while using single-antenna transceivers. We implement P2PLocate on commercial off-the-shelf Google Nexus 6p, Intel 5300 WiFi card, and Raspberry Pi B4. Real-world experiments reveal that P2PLocate can localize both static and mobile targets with a median accuracy of 0.88 m.

Cascading Attacks on Wi-Fi Networks: Theory and Experiments

We unveil the existence of a vulnerability in Wi-Fi (802.11) networks, which allows an adversary to remotely launch a denial-of-service (DoS) attack that propagates both in time and space. This vulnerability stems from a coupling effect induced by hidden nodes. Cascading DoS attacks can congest an entire network and do not require the adversary to violate any protocol. We demonstrate the feasibility of such attacks through experiments with real Wi-Fi cards, theoretical analysis, and ns-3 simulations. The experiment shows that an attacker can cause the throughput of a node outside its communication range to vanish. To gain insight into the root causes of the attack, we model the network as a dynamical system and analyze its limiting behavior and stability. The model predicts that a phase transition (and hence a cascading attack) is possible in linear networks when the retry limit parameter of Wi-Fi is greater or equal to 7, and also characterizes the phase transition region in terms of the system parameters.

Device-free near-field human sensing using WiFi signals

Wireless device-free human sensing is an emerging technique of Internet of Things, which holds great potential for ubiquitous location-based services and human-interaction applications. Although existing studies can detect human appearance, they still neglect to further identify whether a user is approaching a sensor or not, which is critical for fine-grained recognition of human behaviors. In this paper, we first conduct comprehensive experiments to measure relationships between signal fading and human positions. The experimental results show that signal fading stepwise changes with different distances of the human to a sensor. Moreover, the signal fading is worse when the human is located closer to an antenna of the sensor. Motivated by these observations, we propose NSee, a novel system for device-free near-field human sensing without site-survey fingerprints. Specifically, we cluster signal fading features of different antennas by a Gaussian mixture model, and further propose a cluster identification algorithm to identify clusters in correspondence to different near-field subareas of human appearance. Based on cluster characteristics, NSee can recognize near-field human presence with online sensing. We implement a prototype of NSee system based on a commercial WiFi card with multiple antennas. Extensive experimental results illustrate that the proposed system can achieve an averaged accuracy of 90% in device-free near-field human recognition.

Reliable Physical-Layer Cross-Technology Communication With Emulation Error Correction

Physical-Layer Cross-Technology Communication (PHY-CTC), which achieves direct communication among heterogeneous technologies, brings great opportunities to help diverse IoT devices achieve harmonious coexistence through explicit coordination. The core technique of PHY-CTC is signal emulation which utilizes the signal of one technology (e.g., WiFi) to emulate the signal of another technology (e.g., ZigBee). The signal emulation based approach, however, inevitably introduces emulation errors which further lead to unreliable communication. In this paper, we aim to recover the intrinsic emulation errors and establish reliable PHY-CTC. We propose TwinBee which (i) explores chip-level error patterns and (ii) corrects emulation errors with symbol-level chip-combining coding/decoding and soft mapping. To achieve this, TwinBee dose not require accessing chip information as well as making hardware changes. We implement TwinBee on commodity devices (i.e., Laptops with Atheros AR2425 WiFi card and TelosB motes) and the USRPN210 platform (for physical layer evaluation). Experiment results show that TwinBee significantly improves the Packet Reception Ratio (PRR) of PHY-CTC from 50%–60% to more than 99%. Furthermore, we demonstrate the reliability of TwinBee in a data dissemination application over a network of 20 TelosB nodes, achieving over $42\times $ reduction of data dissemination delay compared to the state-of-the-art.

Centrino and the Restructuring of Wi-Fi Supply

In this paper we examine Intel’s launch of Centrino and interpret it as platform leader’s attempt to restructure a supply chain. We provide a narrative of key actions and how they coordinated changes and offer a framework of the predictable consequences for complementary markets. We then collect data and test these predictions on outcomes in several related complementary markets. The overall findings are consistent with our framework. We show that the launch of Centrino increased the likelihood of exit from internal and external Wi-Fi cards markets, and we find that the magnitude of the effect was largest for internal cards. We also show that the launch of Centrino’s stimulated product introduction in the markets for complements overall and find that the effect varied across type of routers. Finally, we show that experience with producing Wi-Fi cards shaped product introduction by router providers. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Through-the-Wall Image Reconstruction via Reweighted Total Variation and Prior Information in Radio Tomographic Imaging

This paper focuses on the application of radio tomographic imaging in through-the-wall image reconstruction. By using the simple wireless communication devices, e.g., Wi-Fi cards, the whole building layout can be reconstructed with only the received signal strength (RSS). For the reconstruction algorithms, the total variation (TV) minimization algorithm has been used to reconstruct the image. However, some false artifacts may exist in the image results due to the inherent defects of the algorithm. The artifacts can be misestimated as wall structures. In this paper, a reweighted total variation and prior information regularization algorithm named as RTV-PIR is proposed to reconstruct the image. This algorithm is based on the TV minimization of the image. And the prior information that the wall is only oriented horizontally or vertically is also considered simultaneously, which is used to keep the wall orientation. To verify the performance of the proposed algorithm, the simulations and experiments based on real data are conducted. It is shown that the RTV-PIR algorithm can get a better result with respect to the root mean square error (RMSE) and the structural similarity (SSIM), in comparison with the state-of-the-art algorithms.

Channel Selective Activity Recognition with WiFi: A Deep Learning Approach Exploring Wideband Information

WiFi-based human activity recognition explores the correlations between body movement and the reflected WiFi signals to classify different activities. State-of-the-art solutions mostly work on a single WiFi channel and hence are quite sensitive to the quality of a particular channel. Co-channel interference in an indoor environment can seriously undermine the recognition accuracy. In this paper, we for the first time explore wideband WiFi information with advanced deep learning toward more accurate and robust activity recognition. We present a practical Channel Selective Activity Recognition system (CSAR) with Commercial Off-The-Shelf (COTS) WiFi devices. The key innovation is to actively select available WiFi channels with good quality and seamlessly hop among adjacent channels to form an extended channel. The wider bandwidth with more subcarriers offers stable information with a higher resolution for feature extraction. Conventional classification tools, e.g., hidden Markov model and k-nearest neighbors, however, are not only sensitive to feature distortion but also not smart enough to explore the time-scale correlations from the extracted spectrogram. We accordingly explore advanced deep learning tools for this application context. We demonstrate an integration of channel selection and long short term memory network (LSTM), which seamlessly combine the richer time and frequency features for activity recognition. We have implemented a CSAR prototype using Intel 5300 WiFi cards. Our real-world experiments show that CSAR achieves a stable recognition accuracy around 95 percent even in crowded wireless environments (compared to 80 percent with state-of-the-art solutions that highly depend on the quality of the working channel). We have also examined the impact of environments and persons, and the results reaffirm its robustness.

A Hybrid Key Agreement Scheme for Smart Homes Using the Merkle Puzzle

Cryptographic keys should be established for the smart home devices in order to secure home area networks. In certain smart home applications, however, the devices might be produced by different factories. As a result, it becomes impractical to assume devices are preloaded with secrets before leaving factories. Moreover, in some scenarios, smart home devices have no access to an online trusted third party. These problems make conventional key agreement schemes inapplicable for these devices. It is investigated that devices can extract secrets from received signal strength (RSS) measurements at the physical layer. However, the bit extraction rate is low. The Merkle puzzle is introduced to design the key agreement scheme by expanding the low entropy seed to high entropy secret key. However, it introduces considerable time and computation costs. To alleviate these problems, in this article, we design a hybrid key agreement scheme for smart homes. Namely, smart home devices first extract short random keys at the physical layer. Then, they establish secret communication keys at higher layers by making use of the Merkle puzzle. In this way, secret keys can be established without using any preloaded secrets or online trusted third party. We prove the security of the new scheme and present a prototype implementation using Ralink WiFi cards. Moreover, we evaluate the performance of our scheme and compare it with other related schemes. The analysis shows that comparing with the related schemes, in our scheme, the time cost is at least one order of magnitude lower, the computation cost is at least five orders of magnitude lower, and the extra communication cost is moderate.