Recent years have witnessed a remarkable growth of micro aerial vehicle (MAV) technologies, which are desirable for many applications, e.g ., warehouse inventory management and home entertainment. These indoor needs significantly improve work efficiency while posing fundamental challenges in the design of MAV state estimation. The state includes a vehicle's position, orientation, and velocity, which are fundamental to guide the motor control and adjust that vehicle's actions in autonomous flight. Existing vision-based solutions only work in well-lit texture-rich environments, while laser-based solutions are limited to MAVs' payload and budget. This paper presents WiSion, a robust and low-cost state estimator that leverages ubiquitous WiFi to estimate six-degree-of-freedom states for MAVs. Our observation is that the multipath of WiFi conceals a wealth of information about a vehicle's state, which helps combat the temporal drift of inertial sensors for smooth state estimation. We realize WiSion by an absolute-relative WiFi sensing module and a WiFi-inertial state estimation module. It works without knowing access points' (APs') positions. We implement the prototype with off-the-shelf products and conduct experiments in indoor venues. Our results show that WiSion achieves the position error of 35.25 cm and the attitude error of $\rm {2.6}^{\circ }$ with a maximum linear velocity of 1.74 m/s. Moreover, WiSion can recover APs' positions and is robust to indoor hindrances such as obstacles and multipath.

The design paradigm for backscatter tags is to avoid complex functionality and make tags as simple as possible. However, such design principle leads to the prevalence of signal collision as tags cannot sense other tags’ ongoing transmissions. The high probability of tag collision will result in low overall throughput. Although there are some existing efforts to resolve tag collisions, they either require good channel conditions or can only resolve a limited number of tags as channel capacity is deficient when SNR is low. In this paper, to overcome this limitation, we bring in antenna arrays to boost the channel capacity. We propose FreeScatter, which can support scalable concurrent backscatter transmission using an antenna array. FreeScatter extracts the path that signals travelled and formulates tags’ channel coefficient using the path representations. FreeScatter further exploits the frequency agnostic property so that it can support more spatial streams than the number of antennas. Experiment results show that we can enable up to 20 tags transmitting concurrently. With the ubiquitous connectivity of battery-free tags in the near future, FreeScatter can significantly boost the network throughput.

Wireless sensing is gaining increasing attention from both academia and industry. Various wireless signals, such as Wi-Fi, UWB, and acoustic signals, have been leveraged for sensing. While promising in many aspects, two critical limitations still exist: a) limited sensing coverage; and b) the requirement for dedicated sensing signals, which may interfere with the original function of the wireless technology. To address these issues, we propose to utilize GPS signals for sensing, as GPS signals are already pervasive and emitted from satellites 24/7 at pre-allocated frequency bands, causing no interference. To make GPS sensing possible, we reconstruct signals with amplitude and phase information which is critical for sensing using the raw measurements reported by commercial GPS receiver module. We also develop sensing models to tailor the unique properties of GPS signals such as extremely long transmission distance. Finally, we introduce the concept of distributed sensing and design signal processing methods to fuse signals from multiple satellites to improve sensing performance. With all these designs, we prototype the first GPS wireless sensing system on commercial GPS receiver modules. Comprehensive experiments demonstrate that the proposed system can realize meaningful sensing applications such as human activity sensing, passive trajectory tracking, and respiration monitoring.

Low-power wide-area networks (LPWANs), such as LoRaWAN, play an essential role and are expanding quickly in miscellaneous intelligent applications. However, the collision problem is also expanding significantly with the mass promotion of LPWAN nodes and providing collision-resilient techniques that are urgently needed for these applications. This paper proposes BackLoRa, a lightweight method that enables collision-resilient LoRa transmission with extra propagation information provided by backscatter tags. BackLoRa uses several backscatter tags to create multipath propagation features related to the LoRa nodes’ positions and offers a lightweight algorithm to extract the feature and correctly distinguish each LoRa node. Further, BackLoRa proposes a quick-phase acquisition algorithm with low time complexity that can carry out the iterative recovery of symbols for robust signal reconstructions in low-SNR conditions. Finally, comprehensive experiments were conducted in this study to evaluate the performance of BackLoRa systems. The experimental results show th compared with the existing scheme, our scheme can reduce the symbol error rate from 65.3% to 5.5% on average and improve throughput by 15× when SNR is −20 dB.