Micro-robots are emerging as powerful tools for search-and-rescue, precision agriculture, and cooperative manipulation, where their small size and low cost offer advantages over larger robots. However, enabling autonomous navigation on these robots remains challenging due to severe hardware constraints, such as limited memory, energy, and computational power. We explore a brain-inspired learning paradigm called Hyperdimensional Computing (HDC) to equip a cheap, lightweight navigation model that runs onboard micro-robots. We present NavHD, which features an adaptive HD encoder that learns spatial representations and incorporates loss-based training for both imitation learning and off-policy reinforcement learning. Our hardware implementation of NavHD uses eight ultrasound sensors and is optimized to run on an ARM Cortex-M4 core, using only 10.2 kB of memory, 900 clock cycles and 1.1 mJ of energy per inference. Through experiments in both simulation and the real world, we demonstrate that NavHD outperforms DNN-based and prior HDC-based RL methods in obstacle avoidance by more than 2x the performance, while achieving 2-26x more superior resource efficiency.

We present HyperCam, an energy-efficient image classification pipeline that enables computer vision tasks onboard low-power IoT camera systems. HyperCam leverages hyper-dimensional computing to perform training and inference efficiently on low-power microcontrollers. We implement a low-power wireless camera platform using off-the-shelf hardware and demonstrate that HyperCam can achieve an accuracy of 93.60%, 84.06%, 92.98%, and 72.79% for MNIST, Fashion-MNIST, Face Detection, and Face Identification tasks, respectively, while significantly outperforming other classifiers in resource efficiency. Specifically, it delivers inference latency of 0.08–0.27s while using 42.91–63.00KB flash memory and 22.25KB RAM at peak. Among other machine learning classifiers such as SVM, xgBoost, MicroNets, MobileNetV3, and MCUNetV3, HyperCam is the only classifier that achieves competitive accuracy while maintaining competitive memory footprint and inference latency that meets the resource requirements of low-power camera systems.

SARLink is a passive satellite backscatter communication system that uses existing spaceborne synthetic aperture radar (SAR) imaging satellites to provide connectivity in remote regions. It achieves orders of magnitude more range than traditional backscatter systems, enabling communication between a passive ground node and a satellite in low earth orbit. The system is composed of a cooperative ground target, a SAR satellite, and a data processing algorithm. A mechanically modulating reflector was designed to apply amplitude modulation to ambient SAR backscatter signals by changing its radar cross section. These communication bits are extracted from the raw SAR data using an algorithm that leverages subaperture processing to detect multiple bits from a target in a single image dataset. A theoretical analysis of this communication system using on-off keying is presented, including the expected signal model, throughput, and bit error rate. The results suggest a 5.5 ft by 5.5 ft modulating corner reflector could send 60 bits every satellite pass, enough to support low bandwidth sensor data and messages. Using Sentinel-1A, a SAR satellite at an altitude of 693 km, we deployed static and modulating reflectors to evaluate the system. The results, successfully detecting the changing state of a modulating ground target, demonstrate our algorithm's effectiveness for extracting bits, paving the way for ultra-long-range, low-power satellite backscatter communication.

New methods of passive wireless communication are presented where no RF carrier is needed. Instead, data is wirelessly transmitted by modulating noise sources, from those found in electronic components to extraterrestrial noise sources. Any pair of noise sources with a difference in noise temperature can be used to enable communication. We discuss using the Earth, the Moon, the Sun, the coldness of space, and Active Cold Load circuits as sources of thermal contrast. We present Cosmic Backscatter and demonstrate that wireless connectivity can be enabled by switching an antenna connection between the "cold" Sky and a "hot" 50Ω resistor. Furthermore, we present Noise Suppression Communication, where data is transmitted by controlling an Active Cold Load to selectively reduce emitted noise below ambient temperature levels.

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.

Recent work in networked systems has shown that using aerial imagery for farm monitoring can enable precision agriculture by lowering the cost and reducing the overhead of large scale sensor deployment. However, acquiring aerial imagery requires a drone, which has high capital and operational costs, often beyond the reach of farmers in the developing world. In this paper, we present TYE (Tethered eYE), an inexpensive platform for aerial imagery. It consists of a tethered helium balloon with a custom mount that can hold a smartphone (or a camera) with a battery pack. The balloon can be carried using a tether by a person or a vehicle. We incorporate various techniques to increase the operational time of the system, and to provide actionable insights even with unstable imagery. We develop path-planning algorithms and use that to develop an interactive mobile phone application that provides the user instant feedback to guide users to efficiently traverse large areas of land. We use computer vision algorithms to stitch orthomosaics by effectively countering wind-induced motion of the camera. We have used TYE for aerial imaging of agricultural land for over a year, and envision it as a low-cost aerial imaging platform for similar applications.

The proliferation of Internet of Things (IoT) devices has led to the deployment of various types of sensors in the homes, offices, buildings, lawns, cities, and even in agricultural farms. Since IoT applications rely on the fidelity of data reported by the sensors, it is important to detect a faulty sensor and isolate the cause of the fault. Existing fault detection techniques demand sensor domain knowledge along with the contextual information and historical data from similar near-by sensors. However, detecting a sensor fault by analyzing just the sensor data is non-trivial since a faulty sensor reading could mimic non-faulty sensor data. This paper presents a novel primitive, which we call the Fall-curve - a sensor's voltage response when the power is turned off - that can be used to characterize sensor faults. The Fall-curve constitutes a unique signature independent of the phenomenon being monitored which can be used to identify the sensor and determine whether the sensor is correctly operating.

The proliferation of Internet of Things (IoT) devices has led to the deployment of various types of sensors in the homes, offices, buildings, lawns, cities, and even in agricultural farms. Due to the diverse nature of IoT deployments and the likelihood of sensor failures in-the-wild, a key challenge in the design of IoT systems is ensuring the integrity, accuracy, and fidelity of sensor data.