With the fast penetration of commercial Virtual Reality (VR) and Augmented Reality (AR) systems into our daily life, the security issues of those devices have attracted significant interests from both academia and industry. Modern VR/AR systems typically use head-mounted devices (i.e. headsets) to interact with users, and often store private user data, e.g. social network accounts, online transactions or even payment information. This poses significant security threats, since in practice the headset can be potentially obtained and accessed by unauthenticated parties, e.g. identity thieves, and thus cause catastrophic breach. In this paper, we propose a novel GaitLock system, which can reliably authenticate users using their gait signatures. Our system doesn't require extra hardware, e.g. fingerprint sensors or retina scanners, but only uses the on-board inertial measurement units (IMUs) equipped in almost all mainstream VR/AR headsets to authenticate the legitimate users from intruders, by simply asking them to walk a few steps. To achieve that, we propose a new gait recognition model Dynamic-SRC, which combines the strength of Dynamic Time Warping (DTW) and Sparse Representation Classifier (SRC), to extract unique gait patterns from the inertial signals during walking. We implement GaitLock on Google Glass (a typical AR headset), and extensive experiments show that GaitLock outperforms the state-of-the-art systems significantly in recognition accuracy ($>98\%$ success in 5 steps), and is able to run in-situ on the resource-constrained VR/AR headsets without incurring high energy cost.

Radio frequency identification (RFID) technology, with its advantages such as battery-free tags, low cost, and scalability, has been playing an important role in many application domains, such as large-scale storage systems, supermarkets, construction sites, etc. Many of those application scenarios also require indoor positioning technologies, for example, warehouse goods positioning, item positioning in production assembly lines, and worker positioning in construction sites. However,indoor positioning using RFID faces accuracy degradation in dynamic environments, especially when tracking randomly moving targets. In this paper, we propose PSOTrack , a continuous RFID-based tracking system for random moving targets in unconstrained indoor environments. In PSOTrack , a data preprocessed, and an optimized particle swarm optimization algorithm is applied to determine the initial position, after that a dynamic correction method for trajectory prediction is proposed for continuous tracking. Results show that the proposed algorithm effectively improves the positioning accuracy and is able to achieve 1 m localization accuracy in dynamic indoor environments, which makes it a promising technology to support future pervasive RFID-based tracking applications.

Research has shown the potential of device-free WiFi sensing for human identification. Each and every human has an unique gait and prior works suggest WiFi devices are able to capture the unique signature of a person’s gait. In this paper, we show for the first time that the monitored gait could be inconsistent and have mirrorlike perturbations when individuals walk through WiFi devices in different directions, provided that WiFi antenna array is horizontal to walking path. Such inconsistent mirrored patterns are to negatively affect the uniqueness of gait and accuracy of human identification. Therefore, we propose a system called Gate-ID for accurately identifying individuals’ identities irrespective of different walking directions. Gate-ID employs theoretical communication model and real measurements to demonstrate that antenna array orientations and walking directions contribute to the mirrorlike patterns in WiFi signals. A novel heuristic algorithm is proposed to infer individual’s walking directions. A set of methods are employed to extract and augment the representative spatial-temporal features of gait and enable the system performing irrespective of walking directions. We further propose a novel attention-based deep learning model that fuses various weighted features and ignores ineffective noises to uniquely identify individuals. We implement Gate-ID on commercial off-the-shelf devices. Extensive experiments demonstrate that our system can uniquely identify people with average accuracy of 90.7% to 75.7% from a group of 6 to 20 people, respectively, and improve the accuracy by 12.5%-43.5% compared with baselines.

Internet of Vehicles (IoV) has greatly improved safety and quality of services in intelligent transportation system (ITS). However, the deployed dedicated short-range communication (DSRC) protocol broadcasts messages after every 100–300 ms. This presents some challenges in message validation within this short duration. As such, most of the current authentication schemes which incur heavy computation and communication overheads are not suitable in this environment. In this article, an efficient authentication scheme is presented based on lightweight cryptographic primitives, such as collision-resistant one-way hashing functions and exclusive OR (XOR) operations. In our protocol, two-factor authentication is attained using physically unclonable function (PUF) generated identities and random nonces, as well as passwords. Extensive formal security verification using the Real or Random (RoR) model shows that it is provably secure. In addition, elaborate semantic security analysis shows that it offers anonymity, untraceability, and key secrecy as well as resilience against numerous IoV attack vectors. In terms of performance, comparative evaluations demonstrate that it reduces computation and energy consumptions by 42.31%. Moreover, it increases the supported security features by 26.67%.

While location is one of the most important context information in mobile and pervasive computing, large-scale deployment of indoor localization system remains elusive. In this work, we propose MPiLoc, a multi-floor indoor localization system that utilizes data contributed by smartphone users through participatory sensing for automatic floor plan and radio map construction. Our system does not require manual calibration, prior knowledge, or infrastructure support. The key novelty of MPiLoc is that it clusters and merges walking trajectories annotated with sensor and signal strengths to derive a map of walking paths annotated with radio signal strengths in multi-floor indoor environments. We evaluate MPiLoc over five different indoor areas. Evaluation shows that our system can derive indoor maps for various indoor environments in multi-floor settings and achieve an average localization error of 1.82 m.