Significant strides have been made in the field of WiFi-based human activity recognition, yet recent wireless sensing methodologies still grapple with the reliance on copious amounts of data. When ...assessed in unfamiliar domains, the majority of models experience a decline in accuracy. To address this challenge, this study introduces Wi-CHAR, a novel few-shot learning-based cross-domain activity recognition system. Wi-CHAR is meticulously designed to tackle both the intricacies of specific sensing environments and pertinent data-related issues. Initially, Wi-CHAR employs a dynamic selection methodology for sensing devices, tailored to mitigate the diminished sensing capabilities observed in specific regions within a multi-WiFi sensor device ecosystem, thereby augmenting the fidelity of sensing data. Subsequent refinement involves the utilization of the MF-DBSCAN clustering algorithm iteratively, enabling the rectification of anomalies and enhancing the quality of subsequent behavior recognition processes. Furthermore, the Re-PN module is consistently engaged, dynamically adjusting feature prototype weights to facilitate cross-domain activity sensing in scenarios with limited sample data, effectively distinguishing between accurate and noisy data samples, thus streamlining the identification of new users and environments. The experimental results show that the average accuracy is more than 93% (five-shot) in various scenarios. Even in cases where the target domain has fewer data samples, better cross-domain results can be achieved. Notably, evaluation on publicly available datasets, WiAR and Widar 3.0, corroborates Wi-CHAR's robust performance, boasting accuracy rates of 89.7% and 92.5%, respectively. In summary, Wi-CHAR delivers recognition outcomes on par with state-of-the-art methodologies, meticulously tailored to accommodate specific sensing environments and data constraints.
With the development of signal processing technology, the ubiquitous Wi-Fi devices open an unprecedented opportunity to solve the challenging human gesture recognition problem by learning motion ...representations from wireless signals. Wi-Fi-based gesture recognition systems, although yield good performance on specific data domains, are still practically difficult to be used without explicit adaptation efforts to new domains. Various pioneering approaches have been proposed to resolve this contradiction but extra training efforts are still necessary for either data collection or model re-training when new data domains appear. To advance cross-domain recognition and achieve fully zero-effort recognition, we propose Widar3.0, a Wi-Fi-based zero-effort cross-domain gesture recognition system. The key insight of Widar3.0 is to derive and extract domain-independent features of human gestures at the lower signal level, which represent unique kinetic characteristics of gestures and are irrespective of domains. On this basis, we develop a one-fits-all general model that requires only one-time training but can adapt to different data domains. Experiments on various domain factors (i.e. environments, locations, and orientations of persons) demonstrate the accuracy of 92.7% for in-domain recognition and 82.6%-92.4% for cross-domain recognition without model re-training, outperforming the state-of-the-art solutions.
WiFi is increasingly used by carriers for opportunistically offloading the cellular network infrastructure or even for increasing their revenue through WiFi-only plans and WiFi on-demand passes. ...Despite the importance and momentum of this technology, the current deployment of WiFi access points (APs) by the carriers follows mostly a heuristic approach. In addition, the prevalent free-of-charge WiFi access policy may result in significant opportunity costs for the carriers, as this traffic could yield non-negligible revenue. In this paper, we study the problem of optimizing the deployment of WiFi APs and pricing the WiFi data usage with the goal of maximizing carrier profit. Addressing this problem is a prerequisite for the efficient integration of WiFi to next-generation carrier networks. Our framework considers various demand models that predict how traffic will change in response to alteration in price and AP locations. We present both optimal and approximate solutions and reveal how key parameters shape the carrier profit. Evaluations on a dataset of WiFi access patterns indicate that WiFi can indeed help carriers reduce their costs while charging users about 50% lower than the cellular service.
High Performance SDN WLAN Architecture Košťál, Kristián; Bencel, Rastislav; Ries, Michal ...
Sensors (Basel, Switzerland),
04/2019, Letnik:
19, Številka:
8
Journal Article
Recenzirano
Odprti dostop
Wireless Local Area Network (WLAN) infrastructure is a dominant technology for direct access to the Internet and for cellular mobile data traffic offloading to WLANs. Additionally, the enterprise ...infrastructure can be used to provide functionality for the Internet of Things and Machine to Machine scenarios. This work is focused on improvements of radio resources control scalability similar to mobile networks via handover between cells. We introduce an improved IEEE 802.11 architecture utilizing Software-Defined Networks (SDNs). The proposed architecture allows communications during device movements without losing a quality of service (QoS). The fast seamless handover with QoS enables efficient usage of radio resources in large networks. Our improvements consist of integrating wireless management to OpenFlow protocol, separating encryption and decryption from an access point. In parallel, this feature as a side effect unloads processing at the Access Points (APs). Finally, the functionality of architecture design and scalability was proven by Colored Petri Nets (CPNs). The second proof of our concept was performed on two scenarios. The first scenario was applied to a delay sensitive use case. The second scenario considers a network congestion in real world conditions. Client's mobility was integrated into both scenarios. The design was developed to demonstrate SDN WLAN architecture efficiency.
The accuracy of smartphone-based positioning systems using WiFi usually suffers from ranging errors caused by non-line-of-sight (NLOS) conditions. Previous research usually exploits several ...distribution features from a long time series (hundreds of samples) of WiFi received signal strength (RSS) or WiFi round-trip time (RTT) to achieve a high identification accuracy. However, the long time series or large sample size attributes to high power and time consumption in data collection for both training and testing. This will also undoubtedly be detrimental to user experience as the waiting time for getting enough samples is quite long. Therefore, this paper proposes three new real-time NLOS/LOS identification methods for smartphone-based indoor positioning systems using WiFi RSS and RTT distance measurement (RDM). Based on our extensive analysis of RSS and RDM dispersion features, three machine learning algorithms were chosen and developed to separate the samples for NLOS/LOS conditions. Experiments show that our best method achieves a discrimination accuracy of over 96% with a sample size of 10. Considering the theoretically shortest WiFi ranging interval of 100ms of the RTT-enabled smartphones, our algorithm is able to provide the shortest latency of 1s to get the testing result among all of the state-of-art methods.
Monitoring the presence and movements of individuals or crowds in a given area can provide valuable insight into actual behavior patterns and hidden trends. Therefore, it is crucial in areas such as ...public safety, transportation, urban planning, disaster and crisis management, and mass events organization, both for the adoption of appropriate policies and measures and for the development of advanced services and applications. In this paper, we propose a non-intrusive privacy-preserving detection of people's presence and movement patterns by tracking their carried WiFi-enabled personal devices, using the network management messages transmitted by these devices for their association with the available networks. However, due to privacy regulations, various randomization schemes have been implemented in network management messages to prevent easy discrimination between devices based on their addresses, sequence numbers of messages, data fields, and the amount of data contained in the messages. To this end, we proposed a novel de-randomization method that detects individual devices by grouping similar network management messages and corresponding radio channel characteristics using a novel clustering and matching procedure. The proposed method was first calibrated using a labeled publicly available dataset, which was validated by measurements in a controlled rural and a semi-controlled indoor environment, and finally tested in terms of scalability and accuracy in an uncontrolled crowded urban environment. The results show that the proposed de-randomization method is able to correctly detect more than 96% of the devices from the rural and indoor datasets when validated separately for each device. When the devices are grouped, the accuracy of the method decreases but is still above 70% for rural environments and 80% for indoor environments. The final verification of the non-intrusive, low-cost solution for analyzing the presence and movement patterns of people, which also provides information on clustered data that can be used to analyze the movements of individuals, in an urban environment confirmed the accuracy, scalability and robustness of the method. However, it also revealed some drawbacks in terms of exponential computational complexity and determination and fine-tuning of method parameters, which require further optimization and automation.
Indonesia, facing the dual challenges of rapid economic development and rising vehicle theft rates due to economic inequalities, has prompted a need for innovative security solutions in the ...automotive sector. This research aims to address this growing concern by introducing a sophisticated motor vehicle security system utilizing Radio Frequency Identification (RFID) technology. The proposed system employs RFID keys, which use ID tag cards as unique identifiers to activate motor vehicles. The methodology centers around the integration of RFID technology with a microcontroller, creating a security system that is both advanced and user-friendly. This system includes an alarm feature that is triggered when an ID tag card not registered with the vehicle's RFID is used, providing an additional layer of security against unauthorized access. The results of implementing this system have been promising. Testing revealed that the RFID locks, when combined with the microcontroller, effectively recognized authorized ID tag cards and successfully denied access when unauthorized cards were used. The alarm system also functioned as intended, activating when discrepancies in ID tag data were detected. This integration of RFID with a microcontroller not only enhanced the security of motor vehicles but also contributed to a significant reduction in vehicle thefts in the community, showcasing the potential of RFID technology as a viable solution to the prevalent issue of vehicle security in Indonesia.
Offloading cellular traffic through WiFi Access Points (APs) has been a promising way to relieve the overload of cellular networks. However, data offloading process consumes a lot of resources (e.g., ...energy, bandwidth, etc.). Given that the owners of APs are rational and selfish, they will not participate in the data offloading process without receiving the proper reward. Hence, there is an urgent need to develop an effective incentive mechanism to stimulate APs to take part in the data offloading process. This paper proposes a novel Delay-constraint and Reverse Auction-based Incentive Mechanism, named DRAIM. In DRAIM, we model the reverse auction-based incentive problem as a nonlinear integer problem from the business perspective, aiming to maximize the revenue of the Mobile Network Operator (MNO), and jointly consider the delay constraint of different applications in the optimization problem. Then, two low-complexity methods: Greedy Winner Selection Method (GWSM), and Dynamic Programming Winner Selection Method (DPWSM) are proposed to solve the optimization problem. Furthermore, an innovative standard Vickrey-Clarke-Groves scheme-based payment rule is proposed to guarantee the individual rationality and truthfulness properties of DPWSM. At last, extensive simulation results show that the proposed DPWSM is superior to the proposed GWSM and the Random Winner Selection Method in terms of the MNO's utility and traffic load under different scenarios.
Wireless fidelity (WiFi) indoor positioning has attracted the attention of thousands of researchers. It faces many challenges, and the primary problem is the low positioning accuracy, which hinders ...its widespread applications.
To improve the accuracy, we propose a WiFi indoor positioning algorithm based on support vector regression (SVR) optimized by particle swarm optimization (PSO), termed PSOSVRPos. SVR algorithm devotes itself to solving localization as a regression problem by building the mapping between signal features and spatial coordinates in high dimensional space. PSO algorithm concentrates on the global-optimal parameter estimation of the SVR model. The positioning experiment is conducted on an open dataset (1511 samples, 154 features). The PSOSVRPos algorithm could achieve positioning accuracy with a mean absolute error of 1.040 m, a root mean square error (RMSE) of 0.863 m and errors within 1 m of 59.8%.
Experimental results indicate that the PSOSVRPos algorithm is a precise approach for WiFi indoor positioning as it reduces the RMSE (35%) and errors within 1 m (14%) compared with state-of-the-art algorithms such as convolutional neural network (CNN) based methods.
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