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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. 

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. 

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. 

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.