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.
Enabling WiFi Sensing on New-generation WiFi Cards Yi, Enze; Zhang, Fusang; Xiong, Jie ...
Proceedings of ACM on interactive, mobile, wearable and ubiquitous technologies,
01/2024, Volume:
7, Issue:
4
Journal Article
Peer reviewed
Open access
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.
Open Wi-Fi can be found in famous open spots like air terminals, cafés, shopping centers, eateries, and inns — and it enables you to get to the Internet for nothing. These "hotspots" are so across ...the board and regular that individuals as often as possible associate with them without reconsidering. In spite of the fact that it sounds innocuous to sign on and check your web based life record or peruse some news stories, perusing email, checking your ledger, or playing out any movement that requires a login is dangerous business on open Wi-Fi. The issue with open Wi-Fi is that there are a colossal number of dangers that accompany these systems. While entrepreneurs may accept they're giving a profitable support of their clients, odds are the security on these systems is careless or nonexistent. 1, 3,5
Software-defined Radio (SDR) is a programmable transceiver with the capability of operating various wireless communication protocols without the need to change or update the hardware. Progress in the ...SDR field has led to the escalation of protocol development and a wide spectrum of applications, with a greater emphasis on programmability, flexibility, portability, and energy efficiency in cellular, WiFi, and M2M communication. Consequently, SDR has earned a lot of attention and is of great significance to both academia and industry. SDR designers intend to simplify the realization of communication protocols while enabling researchers to experiment with prototypes on deployed networks. This paper is a survey of the state-of-the-art SDR platforms in the context of wireless communication protocols. We offer an overview of SDR architecture and its basic components, and then discuss the significant design trends and development tools. In addition, we highlight key contrasts between SDR architectures with regards to energy, computing power, and area, based on a set of metrics. We also review existing SDR platforms and present an analytical comparison as a guide to developers. Finally, we recognize a few of the related research topics and summarize potential solutions.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPUK, ZRSKP
The explosive growth of mobile data traffic and the scarcity of available licensed spectrum make resource allocation in heterogeneous networks a critical issue. A distributed resource allocation ...algorithm for software defined cellular networks for future 5G networks is proposed. The adoption of integrated femto-WiFi small cells is used to alleviate spectrum shortage, by permitting simultaneous access to both the licensed bands (via cellular interface) and unlicensed bands (via WiFi interface). A weighted utility maximization problem is formulated to optimize resource allocation, utilizing the software defined network controller's global view. A fully distributed solution based on the weighted utility maximization optimizes resource allocation, keeping the interference from small cells to macrocells below predefined thresholds. The proposed algorithm considers the sDevices, which have both cellular and WiFi interfaces, and the wDevices which have WiFi-only interfaces. Numerical simulations substantiate the superiority of the proposed resource allocation algorithm, which increases significantly the average throughput and average utility of all devices, compared with the traditional and current methods. Throughput gains as large as 41.6% in spectral efficiency for the average of all sDevices and wDevices are achieved by the new designs.
The current SARS-CoV-2, better know as COVID-19, has emerged as a serious pandemic with life-threatening clinical manifestations and a high mortality rate. One of the major complications of this ...disease is the rapid and dangerous pulmonary deterioration that can lead to critical pneumonia conditions, resulting in death. The current healthcare system around the world faces the potential problem of lacking resources to assist a large number of patients at the same time; then, the non-critical patients are mostly referred to perform self-isolation/quarantine at home. This pandemic has placed new demands on the
, asking for novel, rapid and secure ways to monitor patients in order to detect and quickly report patient's symptoms to the healthcare provider, even if they are not in the hospital. While tremendous efforts have been done to develop technologies to detect the virus, create the vaccine, and stop the spread of the disease, it is also important to develop IoT technologies that can help track and monitor diagnosed COVID-19 patients from their homes. In this paper, we explore the possibility of monitoring respiration rates (RR) of COVID-19 patients using a widely-available technology at home - WiFi. Using the at-home WiFi signals, we propose Wi-COVID, a non-invasive and non-wearable technology to monitor the patient and track RR for the healthcare provider. We first introduce the currently available applications that can be done using WiFi signals. Then, we propose the framework scheme for an end-to-end non-invasive monitoring platform of the COVID-19 patients using WiFi. Finally, we present some preliminary results of the proposed framework. We envision the proposed platform as a life-changing technology that leverages WiFi technology as a non-wearable and non-invasive way to monitor COVID-19 patients at home.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPUK, ZAGLJ, ZRSKP
This article proposes recurrent neural networks (RNNs) for the WiFi fingerprinting indoor localization. Instead of locating a mobile user's position one at a time as in the cases of conventional ...algorithms, our RNN solution aims at the trajectory positioning and takes into account the correlation among the received signal strength indicator (RSSI) measurements in a trajectory. To enhance the accuracy among the temporal fluctuations of RSSI, a weighted average filter is proposed for both input RSSI data and sequential output locations. The results using different types of RNN, including vanilla RNN, long short-term memory (LSTM), gated recurrent unit (GRU), bidirectional RNN (BiRNN), bidirectional LSTM (BiLSTM), and bidirectional GRU (BiGRU) are presented. On-site experiments demonstrate that the proposed structure achieves an average localization error of 0.75 m with 80% of the errors under one meter, which outperforms K-nearest neighbors algorithms and probabilistic algorithms by approximately 30% under the same test environment.
Hybrid LiFi and WiFi Networks: A Survey Wu, Xiping; Soltani, Mohammad Dehghani; Zhou, Lai ...
IEEE Communications surveys and tutorials,
01/2021, Volume:
23, Issue:
2
Journal Article
Peer reviewed
Open access
In order to tackle the rapidly growing number of mobile devices and their expanding demands for Internet services, network convergence is envisaged to integrate different technology domains. For ...indoor wireless communications, one promising approach is to coordinate light fidelity (LiFi) and wireless fidelity (WiFi), namely hybrid LiFi and WiFi networks (HLWNets). This hybrid network combines the high-speed data transmission of LiFi and the ubiquitous coverage of WiFi. In this article, we present a survey-style introduction to HLWNets, starting with a framework of system design in the aspects of network architectures, cell deployments, multiple access and modulation schemes, illumination requirements and backhaul. Key performance metrics and recent achievements are then reviewed to demonstrate the superiority of HLWNets against stand-alone networks. Further, the unique challenges facing HLWNets are elaborated on key research topics including user behavior modeling, interference management, handover and load balancing. Moreover, the potential of HLWNets in the application areas is presented, exemplified by indoor positioning and physical layer security. Finally, the challenges and future research directions are discussed.
Anomalies of the omnipresent earth magnetic (i.e., geomagnetic) field in an indoor environment, caused by local disturbances due to construction materials, give rise to noisy direction sensing that ...hinders any dead reckoning system. In this paper, we turn this unpalatable phenomenon into a favorable one. We present Magicol, an indoor localization and tracking system that embraces the local disturbances of the geomagnetic field. We tackle the low discernibility of the magnetic field by vectorizing consecutive magnetic signals on a per-step basis, and use vectors to shape the particle distribution in the estimation process. Magicol can also incorporate WiFi signals to achieve much improved positioning accuracy for indoor environments with WiFi infrastructure. We perform an in-depth study on the fusion of magnetic and WiFi signals. We design a two-pass bidirectional particle filtering process for maximum accuracy, and propose an on-demand WiFi scan strategy for energy savings. We further propose a compliant-walking method for location database construction that drastically simplifies the site survey effort. We conduct extensive experiments at representative indoor environments, including an office building, an underground parking garage, and a supermarket in which Magicol achieved a 90 percentile localization accuracy of 5 m, 1 m, and 8 m, respectively, using the magnetic field alone. The fusion with WiFi leads to 90 percentile accuracy of 3.5 m for localization and 0.9 m for tracking in the office environment. When using only the magnetism, Magicol consumes 9 × less energy in tracking compared to WiFi-based tracking.
PDF
