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Register a new userMultiple-Perspective Clustering of Passive Wi-Fi Sensing Trajectory Data
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Information about the spatiotemporal flow of humans within an urban context has a wide plethora of applications. Currently, although there are many different approaches to collect such data, there lacks a standardized framework to analyze it. The focus of this paper is on the analysis of the data collected through passive Wi-Fi sensing, as such passively collected data can have a wide coverage at low cost. We propose a systematic approach by using unsupervised machine learning methods, namely k-means clustering and hierarchical agglomerative clustering (HAC) to analyze data collected through such a passive Wi-Fi sniffing method. We examine three aspects of clustering of the data, namely by time, by person, and by location, and we present the results obtained by applying our proposed approach on a real-world dataset collected over five months.
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Taking advantage of the rich information provided by Wi-Fi measurement setups, Wi-Fi-based human behavior sensing leveraging Channel State Information (CSI) measurements has received a lot of research attention in recent years. The CSI-based human sensing algorithms typically either rely on an explicit channel propagation model or, more recently, adopt machine learning so as to robustify feature extraction. In most related work, the considered CSI is extracted from a single dedicated Access Point (AP) communication setup. In this paper, we consider a more realistic setting where a legacy network of multiple APs is already deployed for communications purposes and leveraged for sensing benefits using machine learning. The use of legacy network presents challenges and opportunities as many Wi-Fi links can present with richer yet unequally useful data sets. In order to break the curse of dimensionality associated with training over a too large dimensional CSI, we propose a link selection mechanism based on Reinforcement Learning (RL) which allows for dimension reduction while preserving the data that is most relevant for human behavior sensing. The method is based on a sequential state decision-making process in which the CSI is modeled as a part of the state. From actual experiment results, our method is shown to perform better than state-of-the-art approaches in a scenario with multiple available Wi-Fi links.
Wi-Fi is among the most successful wireless technologies ever invented. As Wi-Fi becomes more and more present in public and private spaces, it becomes natural to leverage its ubiquitousness to implement groundbreaking wireless sensing applications such as human presence detection, activity recognition, and object tracking, just to name a few. This paper reports ongoing efforts by the IEEE 802.11bf Task Group (TGbf), which is defining the appropriate modifications to existing Wi-Fi standards to enhance sensing capabilities through 802.11-compliant waveforms. We summarize objectives and timeline of TGbf, and discuss some of the most interesting proposed technical features discussed so far. We also introduce a roadmap of research challenges pertaining to Wi-Fi sensing and its integration with future Wi-Fi technologies and emerging spectrum bands, hoping to elicit further activities by both the research community and TGbf.
Transit ridership flow and origin-destination (O-D) information is essential for enhancing transit network design, optimizing transit route and improving service. The effectiveness and preciseness of the traditional survey-based and smart card data-driven method for O-D information inference have multiple disadvantages due to the insufficient sample, the high time and energy cost, and the lack of inferring results validation. By considering the ubiquity of smart mobile devices in the world, several methods were developed for estimating the transit ridership flow from Wi-Fi and Bluetooth sensing data by filtering out the non-passenger MAC addresses based on the predefined thresholds. However, the accuracy of the filtering methods is still questionable for the indeterminate threshold values and the lack of quantitative results validation. By combining the consideration of the assumed overlapped feature space of passenger and non-passenger with the above concerns, a three steps data-driven method for estimating transit ridership flow and O-D information from Wi-Fi and Bluetooth sensing data is proposed in this paper. The observed ridership flow is used as ground truth for calculating the performance measurements. According to the results, the proposed approach outperformed all selected baseline models and existing filtering methods. The findings of this study can help to provide real-time and precise transit ridership flow and O-D information for supporting transit vehicle management and the quality of service enhancement.
Data traffic over cellular networks is exhibiting an ongoing exponential growth, increasing by an order of magnitude every year and has already surpassed voice traffic. This increase in data traffic demand has led to a need for solutions to enhance capacity provision, whereby traffic offloading to Wi-Fi is one means that can enhance realised capacity. Though offloading to Wi-Fi networks has matured over the years, a number of challenges are still being faced by operators to its realization. In this article, we carry out a survey of the practical challenges faced by operators in data traffic offloading to Wi-Fi networks. We also provide recommendations to successfully address these challenges.
According to the LTE-U Forum specification, a LTE-U base-station (BS) reduces its duty cycle from 50% to 33% when it senses an increase in the number of co-channel Wi-Fi basic service sets (BSSs) from one to two. The detection of the number of Wi-Fi BSSs that are operating on the channel in real-time, without decoding the Wi-Fi packets, still remains a challenge. In this paper, we present a novel machine learning (ML) approach that solves the problem by using energy values observed during LTE-U OFF duration. Observing the energy values (at LTE-U BS OFF time) is a much simpler operation than decoding the entire Wi-Fi packets. In this work, we implement and validate the proposed ML based approach in real-time experiments, and demonstrate that there are two distinct patterns between one and two Wi-Fi APs. This approach delivers an accuracy close to 100% compared to auto-correlation (AC) and energy detection (ED) approaches.
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