We present NeuriCam, a novel deep learning-based system to achieve video capture from low-power dual-mode IoT camera systems. Our idea is to design a dual-mode camera system where the first mode is low power (1.1 mW) but only outputs grey-scale, low resolution and noisy video and the second mode consumes much higher power (100 mW) but outputs color and higher resolution images. To reduce total energy consumption, we heavily duty cycle the high power mode to output an image only once every second. The data for this camera system is then wirelessly sent to a nearby plugged-in gateway, where we run our real-time neural network decoder to reconstruct a higher-resolution color video. To achieve this, we introduce an attention feature filter mechanism that assigns different weights to different features, based on the correlation between the feature map and the contents of the input frame at each spatial location. We design a wireless hardware prototype using off-the-shelf cameras and address practical issues including packet loss and perspective mismatch. Our evaluations show that our dual-camera approach reduces energy consumption by 7x compared to existing systems. Further, our model achieves an average greyscale PSNR gain of 3.7 dB over prior single and dual-camera video super-resolution methods and 5.6 dB RGB gain over prior color propagation methods.

Connected devices are becoming more ubiquitous, but powering them remains a challenge. The Wireless Identification and Sensing Platform (WISP) is a fully programmable device capable of energy harvesting and backscatter communication. It can accommodate a variety of sensing modalities and operate without batteries or a wired power supply, making it a suitable device for ubiquitous computing. A new version of WISP is presented. WISP-6.0 is designed to be low-power, modular, and enable dual energy harvesting from sources like a solar panel. Additionally, an upgraded cross-platform host application is built using the latest web technologies. Compared to its predecessor, WISP-5.1, WISP-6.0 consumes 13.62% and 6.29% less power in active accelerometer and active acknowledgment modes respectively. Furthermore, WISP-6.0 is better able to harvest RF energy collected from its antenna, with the greatest improvements at higher input powers.

This work highlights the need for a low-cost and low-overhead solution to monitor pallet-level environment in the food supply chain to create traceability, accountability and reduce wastage. We identify post-harvest sensing through the supply chain as a key need to reduce food waste. Toward this end, we develop initial prototypes of two different wireless environmental sensing architectures. The first leverages an ultra-low power timer with a current consumption of 35 nA to power gate and periodically wake up the system. The second mode explores a sparse event driven sensing model leveraging the threshold detection features of low power sensors to log events of interest. We demonstrate a millimeter scale prototypes that can read and backscatter temperature and humidity data with as little as 3.2 μW of power.

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.

Wireless video streaming has traditionally been considered an extremely power-hungry operation. Existing approaches optimize the camera and communication modules individually to minimize their power consumption. However, designing a video streaming device requires power-consuming hardware components and video CODEC algorithms which makes battery-free video streaming currently infeasible. Existing RF-powered wireless camera prototypes require extensive duty-cycling on the order of tens of minutes, to capture, process and communicate a single frame. Self-powered cameras can capture an image once every few seconds, but do not have the capability to stream video wirelessly.