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Realizing the vision of ubiquitous battery-free sensing has proven to be challenging, mainly due to the practical energy and range limitations of current wireless communication systems. To address this, we design the first wide-area and scalable backscatter network with multiple receivers (RX) and transmitters (TX) base units to communicate with battery-free sensor nodes. Our system circumvents the inherent limitations of backscatter systems--including the limited coverage area, frequency-dependent operability, and sensor node limitations in handling network tasks--by introducing several coordination techniques between the base units starting from a single RX-TX pair to networks with many RX and TX units. We build low-cost RX and TX base units and battery-free sensor nodes with multiple sensing modalities and evaluate the performance of the MultiScatter system in various deployments. Our evaluation shows that we can successfully communicate with battery-free sensor nodes across 23400 square feet of a two-floor educational complex using 5 RX and 20 TX units, costing $569. Also, we show that the aggregated throughput of the backscatter network increases linearly as the number of RX units and the network coverage grows. 

Occupancy detection systems are commonly equipped with high quality cameras and a processor with high computational power to run detection algorithms. This paper presents a human occupancy detection system that uses battery-free cameras and a deep learning model implemented on a low-cost hub to detect human presence. Our low-resolution camera harvests energy from ambient light and transmits data to the hub using backscatter communication. We implement the state-of-the-art YOLOv5 network detection algorithm that offers high detection accuracy and fast inferencing speed on a Raspberry Pi 4 Model B. We achieve an inferencing speed of ∼100ms per image and an overall detection accuracy of >90% with only 2GB CPU RAM on the Raspberry Pi. In the experimental results, we also demonstrate that the detection is robust to noise, illuminance, occlusion, and angle of depression. 

Despite significant research in backscatter communication over the past decade, key technical open problems remain underexplored. Here, we first systematically lay out the design space for backscatter networking and identify applications that make backscatter an attractive communication primitive. We then identify 10 research problems that remain to be solved in backscatter networking. These open problems span across the network stack to include circuits, embedded systems, physical layer, MAC and network protocols as well as applications. We believe that addressing these problems can help deliver on backscatter's promise of low-power ubiquitous connectivity. 

Backscatter communication has been a popular choice in low-power/battery-free sensor nodes development. However, the effect of RF source to receiver distance on the operating range of this communication system has not been modeled accurately. In this paper, we propose a model for a bistatic backscatter system coverage map based on the receiver selectivity, receiver sensitivity, and geometric placement of the receiver, RF source, and the tag. To verify our proposed model and simulations, we perform an experiment using a low-cost commercial BLE receiver and a custom-designed BLE backscatter tag. We also show that the receiver selectivity might depend on the interference level, and present measurement results to signify how this dependence relates the system bit error rate to the RF excitation power. 

This work presents a custom high-performance protocol for bi-directional communication with neural implants, that will eventually enable closed-loop operation. This protocol presents a flexible configuration to communicate to neural implants with different characteristics. It can support different uplink data rates, a variable number of neural channels from 2 to 16, two types of digital signal modulation (Amplitude Shift-Keying, ASK, and Binary Shift-Keying, PSK), and different RF operation frequencies (915MHz being the default). The proposed protocol is implemented in C++ (preferred to Python because it enables fast signal processing algorithms), using GNU-Radio toolkit with custom communication blocks. 

Wireless protocol design for IoT networks is an active area of research which has seen significant interest and developments in recent years. The research community is however handicapped by the lack of a flexible, easily deployable platform for prototyping IoT endpoints that would allow for ground up protocol development and investigation of how such protocols perform at scale. We introduce tinySDR, the first software-defined radio platform tailored to the needs of power-constrained IoT endpoints. TinySDR provides a standalone, fully programmable low power software-defined radio solution that can be duty cycled for battery operation like a real IoT endpoint, and more importantly, can be programmed over the air to allow for large scale deployment. We present extensive evaluation of our platform showing it consumes as little as 30 uW of power in sleep mode, which is 10,000x lower than existing SDR platforms. We present two case studies by implementing LoRa and BLE beacons on the platform and achieve sensitivities of -126 dBm and -94 dBm respectively while consuming 11% and 3% of the FPGA resources. Finally, using tinySDR, we explore the research question of whether an IoT device can demodulate concurrent LoRa transmissions in real-time, within its power and computing constraints. 

We design and prototype the first battery-free video streaming camera that harvests energy from both ambient light and RF signals. The RF signals are emitted by a nearby access point. The camera collects energy from both sources and backscatters up to 13 frames per second (fps) video at a distance of up to 150 ft in both outdoor and indoor environments. Compared to a single harvester powered by either ambient light or RF, our dual harvester design improves the camera's frame rate. Also, the dual harvester design maintains a steady 3 fps at distances beyond the RF energy harvesting range. To show efficacy of our battery-free video streaming camera for real applications such as surveillance and monitoring, we deploy our camera for a day-long experiment, from 8 AM to 4 PM, in an outdoor environment. Our results show that on a sunny day, our camera can provide a frame rate of up to 9 fps using a 4.5 cm×2.2 cm solar cell. 

We present the first wireless protocol that scales to hundreds of concurrent transmissions from backscatter devices. Our key innovation is a distributed coding mechanism that works below the noise floor, operates on backscatter devices and can decode all the concurrent transmissions at the receiver using a single FFT operation. Our design addresses practical issues such as timing and frequency synchronization as well as the near-far problem. We deploy our design using a testbed of backscatter hardware and show that our protocol scales to concurrent transmissions from 256 devices using a bandwidth of only 500 kHz. Our results show throughput and latency improvements of 14–62x and 15–67x over existing approaches and 1–2 orders of magnitude higher transmission concurrency.