Device-free localization of people and objects indoors not equipped with radios is playing a critical role in many emerging applications. This paper presents an accurate model-based device-free localization system LiFS, implemented on cheap commercial off-the-shelf (COTS) Wi-Fi devices. Unlike previous COTS device-based work, LiFS is able to localize a target accurately without offline training. The basic idea is simple: channel state information (CSI) is sensitive to a target's location and by modelling the CSI measurements of multiple wireless links as a set of power fading based equations, the target location can be determined. However, due to rich multipath propagation indoors, the received signal strength (RSS) or even the fine-grained CSI can not be easily modelled. We observe that even in a rich multipath environment, not all subcarriers are affected equally by multipath reflections. Our pre-processing scheme tries to identify the subcarriers not affected by multipath. Thus, CSIs on the "clean" subcarriers can be utilized for accurate localization. We design, implement and evaluate LiFS with extensive experiments in three different environments. Without knowing the majority transceivers' locations, LiFS achieves a median accuracy of 0.5 m and 1.1 m in line-of-sight (LoS) and non-line-of-sight (NLoS) scenarios respectively, outperforming the state-of-the-art systems. Besides single target localization, LiFS is able to differentiate two sparsely-located targets and localize each of them at a high accuracy.

Passive localization is fundamental for many applications such as activity monitoring and real-time tracking. Existing received signal strength (RSS)-based passive localization approaches have been proposed in the literature, which depend on dense deployment of wireless communication nodes to achieve high accuracy. Thus, they are not cost-effective and scalable. This paper proposes the RSS distribution-based localization (RDL) technique, which can achieve high localization accuracy without dense deployment. In essence, RDL leverages the RSS and the diffraction theory to enable RSS-based passive localization in sensor networks. Specifically, we analyze the fine-grained RSS distribution properties at a variety of node distances and reveal that the structure of the triangle is efficient for low-cost passive localization. We further construct a unit localization model aiming at high accuracy localization. Experimental results show that RDL can improve the localization accuracy by up to 50%, compared to existing approaches when the error tolerance is less than 1.5 m. In addition, we apply RDL to facilitate the application of moving trajectory identification. Our moving trajectory identification includes two phases: an offline phase where the possible locations can be estimated by RDL and an online phase where we precisely identify the moving trajectory. We conducted extensive experiments to show its effectiveness for this application - the estimated trajectory is close to the ground truth.

Device-free localization (DFL) plays an important role in many applications, such as wildlife population and migration tracking. Most of current DFL systems leverage the distorted received signal strength (RSS) changes to localize the target(s). However, they assume a fixed distribution of the RSS change measurements, although they are distorted by different types of targets. It inevitably causes the localization to fail if the targets for modeling and testing belong to different categories. This paper presents TLCS-a transferring compressive sensing based DFL approach-which employs a rigorously designed transferring function to transfer the distorted RSS changes across different categories of targets into a latent feature space, where the distributions of the distorted RSS change measurements from different categories of targets are unified. A benefit of this approach is that the same transferred sensing matrix can be shared by different categories of targets, leading to a substantial reduction in the human efforts. The results of experiments illustrate the efficacy of the TLCS.

Without relying on devices carried by the target, device-free localization (DFL) is attractive for many applications, such as wildlife monitoring. There still exist many challenges for DFL for multiple targets without dense deployment of sensor nodes. To fit the gap, in this paper, we propose a multi-target localization method based on compressive sensing, named LCS. The key observation is that given a pair of nodes, the received signal strength (RSS) will be different when a target locates at different locations. Taking advantage of compressive sensing in sparse recovery to handle the sparse property of the localization problem, (i.e., the vector which contains the number and location information of k targets is an ideal k-sparse signal), we presented a scalable compressive sensing based multiple target counting and localization method i.e., LCS, and rigorously justify the validity of the problem formulation. The results from our realistic deployment in a 12m×12m open space are promising. For 12 people with 24 nodes, the worst localization error ratio and counting error ratio of our LCS is no more than 8.3% and 33.3% respectively.

With parallel decoding for backscatter communication, tags are allowed to transmit concurrently and more efficiently. Existing parallel decoding mechanisms, however, assume that signals of the tags are highly stable, and hence may not perform optimally in the naturally dynamic backscatter systems. This paper introduces FlipTracer, a practical system that achieves highly reliable parallel decoding even in hostile channel conditions. FlipTracer is designed with a key insight: although the collided signal is time-varying and irregular, transitions between signals' combined states follow highly stable probabilities, which offers important clues for identifying the collided signals, and provides us with an opportunity to decode the collided signals without relying on stable signals. Motivated by this observation, we propose a graphical model, called one-flip-graph (OFG), to capture the transition pattern of collided signals, and design a reliable approach to construct the OFG in a manner robust to the diversity in backscatter systems. Then Flip-Tracer can resolve the collided signals by tracking the OFG. We have implemented FlipTracer and evaluated its performance with extensive experiments across a wide variety of scenarios. Our experimental results have shown that FlipTracer achieves a maximum aggregated throughput that approaches 2 Mbps, which is 6× higher than the state-of-the-art.

Parallel backscatter is a promising technique for high throughput, low power communications. The existing approaches of parallel backscatter are based on a common assumption, i.e. the states of the collided signals are distinguishable from each other in either the time domain or the IQ (the In-phase and Quadrature) domain. We in this paper disclose the superclustering phenomenon, which invalidates that assumption and seriously affects the decoding performance. Then we propose an interstellar travelling model to capture the bursty Gaussian process of a collided signal. Based on this model, we design Hubble, a reliable signal processing approach to support parallel backscatter in the wild. Hubble addresses several technical challenges: (i) a novel scheme based on Pearson's Chi-Square test to extract the collided signals' combined states, (ii) a Markov driven method to capture the law of signal state transitions, and (iii) error correction schemes to guarantee the reliability of parallel decoding. Theoretically, Hubble is able to decode all the backscattered data, as long as the signals are detectable by the receiver. The experiment results demonstrate that the median throughput of Hubble is 11.7× higher than that of the state-of-the-art approach.