This paper presents the design, implementation and evaluation of In-N-Out, a software-hardware solution for far-field wireless power transfer. In-N-Out can continuously charge a medical implant residing in deep tissues at near-optimal beamforming power, even when the implant moves around inside the human body. To accomplish this, we exploit the unique energy ball pattern of distributed antenna array and devise a backscatter-assisted beamforming algorithm that can concentrate RF energy on a tiny spot surrounding the medical implant. Meanwhile, the power levels on other body parts stay in low level, reducing the risk of overheating. We proto-type In-N-Out on 21 software-defined radios and a printed circuit board (PCB). Extensive experiments demonstrate that In-N-Out achieves 0.37 mW average charging power inside a 10 cm-thick pork belly, which is sufficient to wirelessly power a range of commercial medical devices. Our head-to-head comparison with the state-of-the-art approach shows that In-N-Out achieves 5.4X-18.1X power gain when the implant is stationary, and 5.3X-7.4X power gain when the implant is in motion.

This paper presents PLoRa, an ambient backscatter design that enables long-range wireless connectivity for batteryless IoT devices. PLoRa takes ambient LoRa transmissions as the excitation signals, conveys data by modulating an excitation signal into a new standard LoRa "chirp" signal, and shifts this new signal to a different LoRa channel to be received at a gateway faraway. PLoRa achieves this by a holistic RF front-end hardware and software design, including a low-power packet detection circuit, a blind chirp modulation algorithm and a low-power energy management circuit. To form a complete ambient LoRa backscatter network, we integrate a light-weight backscatter signal decoding algorithm with a MAC-layer protocol that work together to make coexistence of PLoRa tags and active LoRa nodes possible in the network. We prototype PLoRa on a four-layer printed circuit board, and test it in various outdoor and indoor environments. Our experimental results demonstrate that our prototype PCB PLoRa tag can backscatter an ambient LoRa transmission sent from a nearby LoRa node (20 cm away) to a gateway up to 1.1 km away, and deliver 284 bytes data every 24 minutes indoors, or every 17 minutes outdoors. We also simulate a 28-nm low-power FPGA based prototype whose digital baseband processor achieves 220 μW power consumption.

Wireless localization technology is a vital component in many long-term monitoring applications, such as activity monitoring and real-time tracking. Most existing localization methods however require the target to carry communicationcapable devices to send or receive messages, which may not hold for wildlife monitoring or intrusion detection. Prior proposals are based on device-free localization techniques, such as Channel State Information (CSI). However, they cost huge human effort in fingerprint collection when locate the target in different scenarios with different area size. This paper proposes a robust and accurate at low-cost devicefree localization system named LiReT. To reduce the time cost and human effort in fingerprint collection when the monitoring environment changed, we represent a LiReT algorithm based on a multivariable linear regression model to transfer the CSI measurements (fingerprint) at distance L to L'. Thus, LiReT can locate the target accurately at low-cost. Result from experiments demonstrate that our system can improve the localization accuracy by up to 51.68%, which is competitive with existing solutions.