Few-shot class-incremental learning (FSCIL) presents a greater challenge compared with few-shot task-incremental learning (FSTIL) due to the need to classify all previous classes without prior knowledge of the session identifier (session-ID). To address this, we propose Bamboo, a novel framework for FSCIL that introduces a cascading inference mechanism to explicitly infer the session-ID for each sample. This mechanism is enabled by a novel, session-specific equiangular tight frame prototype (ETF-P) classifier. By adaptively fusing session-agnostic and session-specific semantics, the ETF-P classifier reliably determines if a sample belongs to its associated session, which is the core decision required at each step of the cascade. Considering the incremental nature of the learning process, which resembles the continuous growth of bamboo, we treat the base session classifier as the foundational bamboo node and progressively add new session classifiers as additional nodes on top. During the testing phase, each sample flows sequentially through the bamboo nodes, from top to bottom, to determine its session-ID and to be classified accordingly. Overall, the Bamboo framework is capable of perceiving session-ID without prior knowledge and classifying each sample within the correct session, leading to state-of-the-art performance on multiple benchmark datasets.

Electroencephalography (EEG) signals have emerged as a novel biometric feature in identity authentication. However, in highly sensitive scenarios such as remote access control and sensitive operation confirmation, identity authentication alone is insufficient to ensure system security. This paper proposes EEGAuth, an EEG-based secure and lightweight authentication system with cryptographic key generation, addressing the demand for integrated systems that enhance both security and user convenience by combining identity authentication and key generation into a unified solution. The proposed system employs a genetic algorithm for optimal channel selection, integrates a discrete wavelet transform with an autoencoder-based feature extraction framework, and implements a CNN-based architecture for robust identity authentication. In addition, the system discretizes feature vectors to generate unique and repeatable seeds, which are used as inputs to a secure hash function to produce keys. The evaluation results show that our model achieves a classification accuracy of 99.38% with only 15 channels, significantly outperforming state-of-the-art methods and baseline models. The generated cryptographic keys demonstrate robust security properties, as evidenced by their successful passage through NIST statistical test suite for randomness verification, scale index analysis for aperiodicity assessment, and autocorrelation testing for bit-sequence independence, collectively confirming their resistance to cryptographic attacks and compliance with security standards.

In mobile augmented reality applications, how to build a secure device connection between two previously unassociated devices without prior set-up is challenging, which also refers to the problem of device pairing. Most existing approaches to device pairing either have certain limits to be utilized in the environment of augmented reality or lack considerations of security issues. In this article, we design WVC, an intuitive, user-friendly, and secure device paring system for mobile augmented reality. It enables users to connect to a neighboring device by waving a finger to click towards it in the air. The system uses critical features from finger tracking trajectories to understand which device a user wants to interact with. Then, it designs key generation and correction algorithms to enhance security in device communication. In addition, we present solutions to defend against malicious attacks. We implement and test the system with 10 volunteers. The experimental results demonstrate the feasibility and effectiveness of the system under varied scenarios in which devices are close to each other at a small angle or located at different heights within 2 m .

Mobile devices are promising to apply two-factor authentication to improve system security. Existing solutions have certain limits of requiring extra user effort, which might seriously affect user experience and delay authentication time. In this paper, we propose PPGPass, a novel mobile two-factor authentication system, which leverages Photoplethysmography (PPG) sensors available in most wrist-worn wearables. PPGPass simultaneously performs a password/pattern/signature authentication and a physiological-based authentication. To realize both nonintrusive and secure, we design a two-stage algorithm to separate clean heartbeat signals from PPG signals contaminated by motion artifacts so that users do not have to deliberately keep their bodies still. In addition, to deal with noncancelable issues when biometrics are compromised, we design a repeatable and non-invertible method to generate cancelable feature templates as alternative credentials. We leverage the great power ofRandom ForestandSupport Vector Data Descriptionto detect adversaries and verify a user's identity. To the best of our knowledge, PPGPass is the first nonintrusive and secure mobile two-factor authentication based on PPG sensors. Extensive experiments demonstrate that PPGPass can achieve the false acceptance rate of 3.11% and the false recognition rate of 3.71%, which confirms its high effectiveness, security, and usability.

Indoor navigation is essential to a wide spectrum of applications in the era of mobile computing. Existing vision-based technologies suffer from both start-up costs and the absence of semantic information for navigation. We observe an opportunity to leverage pervasively deployed surveillance cameras to deal with the above drawbacks and revisit the problem of indoor navigation with a fresh perspective. In this paper, we proposeiSAT, a system that enables public surveillance cameras, as indoor navigating satellites, to locate users on the floorplan, tell users with semantic information about the surrounding environment, and guide users with navigation instructions. However, enabling public cameras to navigate is non-trivial due to 3 factors: absence of real scale, disparity of camera perspective, and lack of semantic information. To overcome these challenges,iSATleverages POI-assisted framework and adopts a novel coordinate transformation algorithm to associate public and mobile cameras, and further attaches semantic information to user location. Extensive experiments in 4 different scenarios show thatiSATachieves a localization accuracy of 0.48m and a navigation success rate of 90.5 percent, outperforming the state-of-th-art systems by$> 30\%$. Benefiting from our solution, all areas with public cameras can upgrade to smart spaces with visual navigation services.

The past decades have witnessed a surge in human fall detection with sensors, cameras, and wireless signals. Among them, Wi-Fi-based fall detection has been one of the most attractive solutions due to the ubiquitous and pervasive deployment of Wi-Fi infrastructures. However, these approaches are still difficult to be put into practical use. To push forward Wi-Fi-based fall detection for wide deployment, three major limitations concerningenvironmental diversity,motion diversity, anduser diversityare required to be resolved. In this paper, we propose FallDar, a Wi-Fi-based deep learning-assisted fall detection system that outperforms state-of-the-art works on the three criteria simultaneously. First, to deal with environmental diversity, FallDar characterizes falls with the speed of the body, which is the most relevant and inherent feature of falling activities, making the system resilient to environmental changes. Second, to deal with motion diversity, FallDar simulates a large amount of fall data of various falling types with a DNN-based generative model. Training with these data, FallDar is endowed the capability of detecting more types of falls. Third, to deal with user diversity, FallDar proposes to incorporate the fall detection network with a user identification network. The network is designed to extract user-independent features, requiring no fall data from new users for system adjustment. We implement FallDar on commercial Wi-Fi devices and conduct experiments in home and office environments for six months. The evaluation results show that FallDar achieves a false alarm rate of 5.7% and a missed alarm rate of 3.4% across all factors, making a fundamental step towards ubiquitous fall detection with Wi-Fi.

Environment depth estimation by fusing camera and radar enables a broad spectrum of applications such as autonomous driving, environmental perception, context-aware localization and navigation. Various pioneering approaches have been proposed to achieve accurate and dense depth estimation by integrating vision and LiDAR through deep learning. However, due to the challenges of sparse sampling of in-vehicle LiDARs, high ground-truth annotation overhead, and severe dynamics in real environments, existing solutions have not yet achieved widespread deployment on commercial autonomous vehicles. In this paper, we propose LeoVR, a visual-LiDAR fusion based self-supervised approach that enables accurate environment depth estimation. LeoVR digs into the vehicle's motion information and designs two effective system frameworks based on it to (i) optimize the depth estimation results, and (ii) provide supervision signals to train a DNN. We fully implement LeoVR on a robotic testbed and commercial vehicle to conduct extensive experiments across 6 months. The results demonstrate that LeoVR achieves remarkable performance with an average depth estimation error of 0.17m, outperforming existing state-of-the-art solutions by > 43%. Besides, even cold-start in real environments by self-supervised training, LeoVR still achieves an average error of 0.21m, outperforming the related works by > 45% and comparable to those supervised training methods.

Among numerous indoor localization systems, WiFi fingerprint-based localization has been one of the most attractive solutions, which is known to be free of extra infrastructure and specialized hardware. To push forward this approach for wide deployment, three crucial goals on delightful deployment ubiquity, high localization accuracy, and low maintenance cost are desirable. However, due to severe challenges about signal variation, device heterogeneity, and database degradation root in environmental dynamics, pioneer works usually make a trade-off among them. In this paper, we propose iToLoc, a deep learning based localization system that achieves all three goals simultaneously. Once trained, iToLoc will provide accurate localization service for everyone using different devices and under diverse network conditions, and automatically update itself to maintain reliable performance anytime. iToLoc is purely based on WiFi fingerprints without relying on specific infrastructures. The core components of iToLoc are a domain adversarial neural network and a co-training based semi-supervised learning framework. Extensive experiments across 7 months with 8 different devices demonstrate that iToLoc achieves remarkable performance with an accuracy of 1.92m and > 95% localization success rate. Even 7 months after the original fingerprint database was established, the rate still maintains > 90%, which significantly outperforms previous works.

Existing smartphone-based Augmented Reality (AR) systems are able to render virtual effects on static anchors. However, today's solutions lack the ability to render follow-up effects attached to moving anchors since they fail to track the 6 degrees of freedom (6-DoF) poses of them. We find an opportunity to accomplish the task by leveraging sensors capable of generating sparse point clouds on smartphones and fusing them with vision-based technologies. However, realizing this vision is non-trivial due to challenges in modeling radar error distributions and fusing heterogeneous sensor data. This study proposes FollowUpAR, a framework that integrates vision and sparse measurements to track object 6-DoF pose on smartphones. We derive a physical-level theoretical radar error distribution model based on an in-depth understanding of its hardware-level working principles and design a novel factor graph competent in fusing heterogeneous data. By doing so, FollowUpAR enables mobile devices to track anchor's pose accurately. We implement FollowUpAR on commodity smartphones and validate its performance with 800,000 frames in a total duration of 15 hours. The results show that FollowUpAR achieves a remarkable rotation tracking accuracy of 2.3° with a translation accuracy of 2.9mm, outperforming most existing tracking systems and comparable to state-of-the-art learning-based solutions. FollowUpAR can be integrated into ARCore and enable smartphones to render follow-up AR effects to moving objects.

Micro finger gesture recognition is an emerging approach to realize more friendly interaction between human and smart devices, especially for small wearable devices, such as smartwatches and virtual reality glasses. This paper proposes airFinger, a novel solution utilizing NIR light sensing to realize both real-time gesture recognition and finger tracking aiming at micro finger gestures. Using a custom NIR-based sensor with novel algorithms to capture subtle finger movements, airFinger enables to detect a rich set of micro finger gestures and track finger movements in terms of scrolling direction, velocity, and displacement. Besides, airFinger is capable of effective noise mitigation, gesture segmentation, and reducing false recognition due to the unintentional actions of users. Extensive experimental results demonstrate that airFinger has robustness against individual diversity, gesture inconsistency, and many other impacts. The overall performance reaches an average accuracy as high as 98.72% over a set of 8 micro finger gestures among 10, 000 gesture samples collected from 10 volunteers.

Mobile devices are promising to apply two-factor authentication in order to improve system security and enhance user privacy-preserving. Existing solutions usually have certain limits of requiring some form of user effort, which might seriously affect user experience and delay authentication time. In this paper, we propose PPGPass, a novel mobile two-factor authentication system, which leverages Photoplethysmography (PPG) sensors in wrist-worn wearables to extract individual characteristics of PPG signals. In order to realize both nonintrusive and secure, we design a two-stage algorithm to separate clean heartbeat signals from PPG signals contaminated by motion artifacts, which allows verifying users without intentionally staying still during the process of authentication. In addition, to deal with non-cancelable issues when biometrics are compromised, we design a repeatable and non-invertible method to generate cancelable feature templates as alternative credentials, which enables to defense against man-in-the-middle attacks and replay attacks. To the best of our knowledge, PPGPass is the first nonintrusive and secure mobile two-factor authentication based on PPG sensors in wearables. We build a prototype of PPGPass and conduct the system with comprehensive experiments involving multiple participants. PPGPass can achieve an average F1 score of 95.3%, which confirms its high effectiveness, security, and usability.

In this article, we propose points-of-interest (POI)-tagging App-assisted incentive mechanism (PTASIM), an incentive mechanism that explores the cooperation with POI-tagging App for mobile edge crowdsensing (MEC). PTASIM requests App to tag some edges to be POI, which further guides App users to perform tasks at that location. We further model the interactions of users, platform, and App by a three-stage decision process. App first determines the POI-tagging price to maximize its payoff. Platform and users subsequently decide how to determine tasks reward and select edges to be tagged, and how to select the best task to perform, respectively. We analyze the optimal solution in those stages. Specifically, we prove that greedy algorithm could provide the optimal solution for platform's payoff maximization in polynomial time. The numerical results show that: 1) the cooperation with App brings long-term and sufficient participation; and 2) the optimal strategies reduce platform's tasks cost as well as improve App's revenues.

The usage of hybrid energy supplied sensors in the Internet of Things has enabled longer lifetime of sensors and expanded scope of applications. These sensors can combine advantages of environmental energy harvesting techniques and wireless energy harvesting techniques. However, how to coordinate them is still a challenge and has not been studied extensively. In this article, we present a system based on mobile crowd wireless charging to manage energy of hybrid energy supplied sensors. When environmental energy is insufficient, the system will utilize smart devices carried by mobile users as chargers to provide wireless energy. We construct and study a W3W problem in the system: when to leverage mobile crowd wireless charging to support rechargeable sensors, where to perform wireless energy transfer, and whom to allocate and incentivize as chargers to maximize useful energy value over all sensors subject to a budget. In order to control the actual quality of wireless energy charging, we propose a design principle named task completion trustfulness. We consider offline and online conditions and design corresponding algorithms with incentive allocations. Extensive simulations are conducted to demonstrate the effectiveness of our algorithms, which also validates our theoretical results.

Wireless energy harvesting is promising to be a new opportunity to prolong the lifetime of rechargeable sensors in the Internet of Things. However, how to recharge the sensors and manage charging energy is still a problem. Most existing methods are based on robots or vehicles carrying battery packs to charge sensors. They are vulnerable to the sensors distributed in complex terrain and have high hardware maintaining cost. To address this issue, we present crowd-charging (CC), a novel crowdsourcing-based wireless energy charging model for rechargeable sensors. It leverages smart devices carried by users as mobile crowd energy resources (chargers) to provide wireless energy to sensors. The challenge is how to incentivize and allocate mobile users to optimize the total charging quality of all the sensors. Besides monetary energy revenues, we design a bonus game to entertain participated users. We also propose three user allocation algorithms, CC algorithm (CCA), and two improved versions of CCA, i.e., CC and CC . The improved algorithms give great improvement for our model and they have different advantages suitable for different situations. We conduct extensive simulations and demonstrate the effectiveness of our algorithms with numerical results.