Device-free passive human activity recognition is playing an important role in many applications such as smart homes, identification, health care, etc. However, existing human activity recognition systems either require dedicated device or do not meet the scenarios of the signals through the wall. To address this challenge, we present TW-See, a device-free passive human activity recognition system with Wi-Fi signals, which does not require any dedicated device and meets the scenarios of the signals through the wall. TW-See mainly exploits two key techniques to recognize different human activities. First, we propose an Opposite robust PCA (Or-PCA) approach to obtain the correlation between human activity and its resulting changes in CSI values, which can eliminate the influence of the background environment on the correlation extraction. Second, we propose a normalized variance sliding windows algorithm to segment human action time from Or-PCA waveforms, which can distinguish the true or false of the start and end time of human actions. Furthermore, we implemented TW-See with commodity Wi-Fi devices and evaluated it in several different environments. Experiment results show that TW-See achieves average 94.46% accuracy, when the signals pass through the wall.

Leveraging a wireless multipath channel as the source of common randomness, many key generation methods have been proposed according to the information-theory security. However, existing schemes suffer a low generation rate and a low entropy, and mainly rely on nodes' mobility. To overcome this limitation, we present a key generation protocol with known artificial interference, named SmokeGrenade, a new physical-layer approach for secret key generation in a narrowband fading channel. Our scheme utilizes artificial interference to contribute to the change of measured values on channel states. Our theoretical analysis shows that the key generation rate increases with the increment of the interference power. Particularly, the achievable key rate of SmokeGrenade gains three times better than that of the traditional key generation schemes when the average interference power is normalized to 1. Simulation results also demonstrate that SmokeGrenade achieves a higher generation rate and entropy compared with some state-of-the-art approaches.

Regular free-weight exercise helps to strengthen natural movements and stabilize muscles that are important to strength, balance, and posture of human beings. Prior works have exploited wearable sensors or RF signal changes for activity sensing, recognition, and counting, etc.. However, none of them have incorporated three key factors necessary for a practical free-weight exercise monitoring system: recognizing free-weight activities on site, assessing their qualities, and providing useful feedbacks to the bodybuilder promptly. Our FEMO system provides an integrated free-weight exercise monitoring service that incorporates all the essential functionalities mentioned above. FEMO achieves this by attaching passive RFID tags on the dumbbells and leveraging the Doppler shift profile of the reflected backscatter signals for on-site free-weight activity recognition and assessment. The rationale behind FEMO is 1) since each free-weight activity owns unique arm motions, the corresponding Doppler shift profile should be distinguishable to each other. 2) Doppler profile of each activity has a strong spatial-temporal correlation that implicitly reflects the quality of the activity. We implement FEMO with COTS RFID devices and conduct a two-week experiment. The preliminary result from 15 volunteers demonstrates that FEMO can be applied to a variety of free-weight activities, and provide valuable feedbacks for activity alignment.

Most existing voice-based user authentication systems mainly rely on microphones to capture the unique vocal characteristics of an individual, which are vulnerable to various acoustic attacks and may suffer high-security risks. In this work, we present Accuth$^+$${}^{+}$ , a novel authentication system on the wrist-worn device that takes advantage of a low-cost accelerometer to verify the user's identity and resist spoofing acoustic attacks. Accuth$^+$${}^{+}$ captures unique sound vibrations during the human pronunciation process and extracts multi-level features to verify the user's identity. Specifically, we analyze and model the differences between the physical sound field of human beings and loudspeakers, and extract a novel sound-field-level liveness feature to defend against spoofing attacks. Accuth$^+$${}^{+}$ is an effective complement to existing wearable authentication approaches as it only leverages a ubiquitous, low-cost, and small-size accelerometer. In real-world experiments. Accuth$^+$${}^{+}$ achieves over 92.85% averaged identification accuracy among 15 human participants and an averaged equal error rate (EER) of 1.91% for spoofing attack detection.

Thanks to the increasing number and massive coverage, delivery drones, equipped with various sensors, have demonstrated significant but unexplored potentials for large-scale and low-cost urban sensing during package delivery. In this paper, we propose novel studies on the reutilization of such delivery drone resources to fill this void in urban crowdsensing. Accounting for interdependency between flying/sensing and drone delivery weight, we jointly optimize route selection, sensing time, and delivery weight allocation, to maximize delivery and sensing utility under drones’ energy constraints. This problem is formulated as a non-convex mixed-integer non-Linear programming problem, which is proved to be NP-hard. To address this intricate problem, we propose near-optimal algorithms that leverage the equivalent objective function construction, the local search scheme, and the alternating iteration technique. Theoretical analysis indicates that our algorithms can achieve $\frac{1}{4+\varepsilon }$ -approximation ratio (where $\varepsilon$ is an arbitrarily small positive parameter) and the convergence guarantee in polynomial time, for the scenarios of fixed and adjustable delivery weights, respectively. Extensive trace-based simulations, field experiments, and the real-world application demonstrate that ours can significantly improve the delivery & sensing utility by $124.7\%$ and the energy utilization rate by $72.2\%$ on average, compared with the drone delivery without reusing.

With the rapid development of smart cities, smart buildings are generating a massive amount of building sensing data by the equipped sensors. Indeed, building sensing data provides a promising way to enrich a series of data-demanding and cost-expensive urban mobile applications. In this paper, as a preliminary exploration, we study how to reuse building sensing data to predict traffic volume on nearby roads. Compared with existing studies, reusing building sensing data has considerable merits of cost-efficiency and high-reliability. Nevertheless, it is non-trivial to achieve accurate prediction on such cross-domain data with two major challenges. First, relationships between building sensing data and traffic data are not unknown as prior, and the spatio-temporal complexities impose more difficulties to uncover the underlying reasons behind the above relationships. Second, it is even more daunting to accurately predict traffic volume with dynamic building-traffic correlations, which are cross-domain, non-linear, and time-varying. To address the above challenges, we design and implement BuildSenSys , a first-of-its-kind system for nearby traffic volume prediction by reusing building sensing data. Our work consists of two parts, i.e., Correlation Analysis and Cross-domain Learning . First, we conduct a comprehensive building-traffic analysis based on multi-source datasets, disclosing how and why building sensing data is correlated with nearby traffic volume. Second, we propose a novel recurrent neural network for traffic volume prediction based on cross-domain learning with two attention mechanisms. Specifically, a cross-domain attention mechanism captures the building-traffic correlations and adaptively extracts the most relevant building sensing data at each predicting step. Then, a temporal attention mechanism is employed to model the temporal dependencies of data across historical time intervals. The extensive experimental studies demonstrate that BuildSenSys outperforms all baseline methods with up to 65.3 percent accuracy improvement (e.g., 2.2 percent MAPE) in predicting nearby traffic volume. We believe that this work can open a new gate of reusing building sensing data for urban traffic sensing, thus establishing connections between smart buildings and intelligent transportation.