Due to the cost-effective advantage, range-free localization schemes are attractive for low-cost Internet of Things applications. As a distinguishing range-free scheme, DV-Hop localization can localize those unknown nodes which have less than 3 or even no neighbor anchors. However, the localization results by existing DV-Hop based algorithm are found to be inconsistent with the real connectivity between nodes. The inconsistency inspires us to propose two algorithms to improve localization accuracy. First a Centralized Connectivity-based DV-Hop (CCDV-Hop) algorithm is proposed to optimize the accuracy of DV-Hop localization. Establishing an optimization problem which takes the real connectivity between any two nodes as the constraints, the proposed algorithm can make the localization results conform to the real connectivity. Then an algorithm with lower complexity is proposed, namely Distributed Connectivity-based DV-Hop (DCDV-Hop) algorithm, which can obtain near-optimal localization performance in distributed networks. Without including the connectivity of all nodes, the constraints in the proposed DCDV-Hop algorithm only consider the real connectivity within two hops. Simulation results show that despite higher complexity, the proposed algorithms can achieve much better accuracy than other DV-Hop based methods.

Due to robustness against multi-path effect, frequency domain CSI (Channel State Information) of OFDM (Orthogonal Frequency Division Multiplexing) systems is supposed to provide good ranging measurement for indoor localization. Since lower bound of positioning error for CSI-based localization has been rarely reported, this paper proposes a Cramer-Rao lower bound (CRLB) for CSI-based localization. This CRLB is derived based on indoor wireless propagation model integrating both shadowing effect and multi-path effect. The main factors that can influence the CRLB are analyzed. Through simulations and analysis, the gap between CSI-based localization error and the proposed CRLB is presented, and the influence by three main factors is thoroughly investigated.

Green-computing technology and energy-saving design have become the focus of research in various fields in recent years. As a ubiquitously deployed infrastructure, WiFi can be considered as a platform for green sensing, and a plethora of efforts have been made in WiFi-based passive detection recently. However, little work has been done on the exercise activity recognition. In this paper, we propose SEARE, a novel energy-efficient solution using WiFi for exercise activity recognition. It is prototyped by fine-grained CSI extracted from existing commercial WiFi devices. Different from traditional features like mean or max value exploited in previous activity recognition works, involving either time or frequency information, we select CSI-waveform shape as activity feature, which contains the information from both of these two domains. A series of de-noise methods are designed, including low-pass, PCA, and median filtering, where PCA can remove the in-band noise that traditional low-pass filters fail to do. Finally the evaluation of activities quality can be made. Extensive experimental result validates the great performance of SEARE in both LOS and NLOS scenarios, with average recognition accuracies of 97.8% and 91.2% respectively.

Contactless WiFi based human action recognition exhibits remarkable prospects in the fields such as human-computer interaction and smart home. However, domain dependency restricts its generalization into the real-world deployment. Since it is expensive to label enough new data for retaining a model, it is beneficial to explore few-shot learning for cross-domain sensing with limited target labels. Nevertheless, there are two challenges to be addressed. The first challenge is how to select a suitable dataset from a series of available source domains to prevent negative transfer. The second is to mine action-related characteristics by the feature learning model for the following effective knowledge transfer. In order to tackle the above challenges, we present a cross-domain sensing framework named CDFi, which consists of Nearest Neighbor based Domain Selector (NNDS) and Fine-to-Coarse-Grained Transformer Network (FCGTN). NNDS is proposed to evaluate the source-target domain similarities by measurements among local and global feature distributions. Besides, FCGTN embeds convolution map based hierarchical transformer structures and the modified linear layer into an end-to-end deep network, which can quickly adapt to the unseen domain by few samples. Comprehensive experiments show that CDFi can effectively realize cross-domain action recognition, and achieve about 4% and 10% improvement on cross-user and cross-scene cases, respectively, compared to the state-of-the-art.

Respiration is a vital indicator of the state of the human body. Monitoring human respiration enables the realization of a variety of intelligent applications, including smart medical and sleep monitoring. Traditional methods that are dependent upon wearable devices are more costly and inconvenient for users. Recent studies have evidenced that low-cost commodity WiFi devices can be used to accomplish contactless respiration monitoring. In this article, we present BreatheBand, a fine-grained and robust respiration monitoring system based on commercial WiFi signals. We first remove the time-varying phase shift in the channel state information (CSI) by developing the Multi-antenna CSI–Subpopulation Genetic algorithm. Then we separate human respiratory components from WiFi signals by employing subcarrier selection and Independent Component Analysis. Next, applying a Mixed Cluster Gaussian–Hidden Markov Model, we generate a respiration signal resembling that of wearable devices. Finally, we integrate the BreatheBand system into commercial WiFi infrastructure. The results show that the BreatheBand’s respiration signal is remarkably identical to the signal collected by the wearable device in various scenarios. In particular, the mean absolute error of the BreatheBand’s respiration rate is approximately 0.1 bpm, outperforming state-of-the-art algorithms.