Crowd Counting Through Walls Using WiFi

Counting the number of people inside a building, from outside and without entering the building, is crucial for many applications. In this paper, we are interested in counting the total number of people walking inside a building (or in general behind walls), using readily-deployable WiFi transceivers that are installed outside the building, and only based on WiFi RSSI measurements. The key observation of the paper is that the inter-event times, corresponding to the dip events of the received signal, are fairly robust to the attenuation through walls (for instance as compared to the exact dip values). We then propose a methodology that can extract the total number of people from the inter-event times. More specifically, we first show how to characterize the wireless received power measurements as a superposition of renewal-type processes. By borrowing theories from the renewal-process literature, we then show how the probability mass function of the inter-event times carries vital information on the number of people. We validate our framework with 44 experiments in five different areas on our campus (3 classrooms, a conference room, and a hallway), using only one WiFi transmitter and receiver installed outside of the building, and for up to and including 20 people. Our experiments further include areas with different wall materials, such as concrete, plaster, and wood, to validate the robustness of the proposed approach. Overall, our results show that our approach can estimate the total number of people behind the walls with a high accuracy while minimizing the need for prior calibrations.Comment: 10 pages, 14 figure

Occupancy Detection and People Counting Using WiFi Passive Radar

Occupancy detection and people counting technologies have important uses in many scenarios ranging from management of human resources, optimising energy use in intelligent buildings and improving public services in future smart cities. Wi-Fi based sensing approaches for these applications have attracted significant attention in recent years because of their ubiquitous nature, and ability to preserve the privacy of individuals being counted. In this paper, we present a Passive Wi-Fi Radar (PWR) technique for occupancy detection and people counting. Unlike systems which exploit the Wi-Fi Received Signal Strength (RSS) and Channel State Information (CSI), PWR systems can directly be applied in any environment covered by an existing WiFi local area network without special modifications to the Wi-Fi access point. Specifically, we apply Cross Ambiguity Function (CAF) processing to generate Range-Doppler maps, then we use Time-Frequency transforms to generate Doppler spectrograms, and finally employ a CLEAN algorithm to remove the direct signal interference. A Convolutional Neural Network (CNN) and sliding-window based feature selection scheme is then used for classification. Experimental results collected from a typical office environment are used to validate the proposed PWR system for accurately determining room occupancy, and correctly predict the number of people when using four test subjects in experimental measurements

Artificial Intelligence and Ambient Intelligence

This book includes a series of scientific papers published in the Special Issue on Artificial Intelligence and Ambient Intelligence at the journal Electronics MDPI. The book starts with an opinion paper on “Relations between Electronics, Artificial Intelligence and Information Society through Information Society Rules”, presenting relations between information society, electronics and artificial intelligence mainly through twenty-four IS laws. After that, the book continues with a series of technical papers that present applications of Artificial Intelligence and Ambient Intelligence in a variety of fields including affective computing, privacy and security in smart environments, and robotics. More specifically, the first part presents usage of Artificial Intelligence (AI) methods in combination with wearable devices (e.g., smartphones and wristbands) for recognizing human psychological states (e.g., emotions and cognitive load). The second part presents usage of AI methods in combination with laser sensors or Wi-Fi signals for improving security in smart buildings by identifying and counting the number of visitors. The last part presents usage of AI methods in robotics for improving robots’ ability for object gripping manipulation and perception. The language of the book is rather technical, thus the intended audience are scientists and researchers who have at least some basic knowledge in computer science

Advancements in Wi-Fi-Based Passenger Counting and Crowd Monitoring: Techniques and Applications

The widespread use of personal mobile devices, including tablets and smartphones, created new opportunities for collecting comprehensive data on individual movements within cities while preserving their anonymity. Extensive research focused on turning personal mobile devices into tools for measuring human presence. To protect privacy, the data collected must be anonymous or pseudo-anonymous, leading to the preference for management data. A common approach involves analysing probe requests, which are Wi-Fi protocol messages transmitted by mobile devices while searching for access points. These messages contain media access control (MAC) addresses, which used to be unique identifiers. To safeguard the privacy of smartphone users, the major manufacturers (Google, Apple, and Microsoft) have implemented algorithms that generate random MAC addresses, which change often and unpredictably. This thesis focuses on the problem of fingerprinting Wi-Fi devices based on analysing management messages to overcome previous methods that relied on the MAC address and became obsolete. Detecting messages from the same source allows counting the devices in an area, calculating their permanence, and approximating these metrics with the ones of the humans carrying them. An open dataset of probe requests with labelled data has been designed, built, and used to validate the experiments. The dataset is also provided with guidelines for collecting new data and extending it. Since the dataset contains records of individual devices, the first step of this study was simulating the presence of multiple devices by aggregating multiple records in sets. Many experiments have been conducted to enhance the accuracy of the clustering. The proposed techniques exploit features extracted from individual management messages and from groups of messages called bursts. Moreover, other experiments show what happens when one or more features are split into their components or when the logarithm of their value is used. Before running the algorithm, a feature selection was performed and exploited to improve the accuracy. The clustering methods considered are DBSCAN and OPTICS

Human Crowds Estimation based on Mobile Sensing

University of Tokyo(東京大学

Real-Time Crowd Counting based on wearable Ephemeral IDs

Crowd Counting is a very interesting problem aiming at counting people typically based on density averages and/or aerial images. This is very useful to prevent crowd crushes, especially on urban environments with high crowd density, or to count people in public demonstrations. In addition, in the last years, it has become of paramount importance for pandemic management. For those reasons, giving users automatic mechanisms to anticipate high risk situations is essential. In this work, we analyze ID-based Crowd Counting, and propose a real-time Crowd Counting system based on the Ephemeral ID broadcast by contact tracing applications on wearable devices. We also performed some simulations that show the accuracy of our system in different situations

Secure Data Collection and Analysis in Smart Health Monitoring

Smart health monitoring uses real-time monitored data to support diagnosis, treatment, and health decision-making in modern smart healthcare systems and benefit our daily life. The accurate health monitoring and prompt transmission of health data are facilitated by the ever-evolving on-body sensors, wireless communication technologies, and wireless sensing techniques. Although the users have witnessed the convenience of smart health monitoring, severe privacy and security concerns on the valuable and sensitive collected data come along with the merit. The data collection, transmission, and analysis are vulnerable to various attacks, e.g., eavesdropping, due to the open nature of wireless media, the resource constraints of sensing devices, and the lack of security protocols. These deficiencies not only make conventional cryptographic methods not applicable in smart health monitoring but also put many obstacles in the path of designing privacy protection mechanisms. In this dissertation, we design dedicated schemes to achieve secure data collection and analysis in smart health monitoring. The first two works propose two robust and secure authentication schemes based on Electrocardiogram (ECG), which outperform traditional user identity authentication schemes in health monitoring, to restrict the access to collected data to legitimate users. To improve the practicality of ECG-based authentication, we address the nonuniformity and sensitivity of ECG signals, as well as the noise contamination issue. The next work investigates an extended authentication goal, denoted as wearable-user pair authentication. It simultaneously authenticates the user identity and device identity to provide further protection. We exploit the uniqueness of the interference between different wireless protocols, which is common in health monitoring due to devices\u27 varying sensing and transmission demands, and design a wearable-user pair authentication scheme based on the interference. However, the harm of this interference is also outstanding. Thus, in the fourth work, we use wireless human activity recognition in health monitoring as an example and analyze how this interference may jeopardize it. We identify a new attack that can produce false recognition result and discuss potential countermeasures against this attack. In the end, we move to a broader scenario and protect the statistics of distributed data reported in mobile crowd sensing, a common practice used in public health monitoring for data collection. We deploy differential privacy to enable the indistinguishability of workers\u27 locations and sensing data without the help of a trusted entity while meeting the accuracy demands of crowd sensing tasks