World-scale augmented reality (AR) applications need a ubiquitous 6DoF localization backend to anchor content to the real world consistently across devices. Large organizations such as Google and Niantic are 3D scanning outdoor public spaces in order to build their own Visual Positioning Systems (VPS). These centralized VPS solutions fail to meet the needs of many future AR applications-they do not cover private indoor spaces because of privacy concerns, regulations, and the labor bottleneck of updating and maintaining 3D scans. In this paper, we present OpenFLAME, a federated VPS backend that allows independent organizations to 3D scan and maintain a separate VPS service for their own spaces. This enables access control of indoor 3D scans, distributed maintenance of the VPS backend, and encourages larger coverage. Sharding of VPS services introduces several unique challenges-coherency of localization results across spaces, quality control of VPS services, selection of the right VPS service for a location, and many others. We introduce the concept of federated image-based localization and provide reference solutions for managing and merging data across maps without sharing private data.

Batteryless energy harvesting systems enable a wide array of new sensing, computation, and communication platforms untethered by power delivery or battery maintenance demands. Energy harvesters charge a buffer capacitor from an unreliable environmental source until enough energy is stored to guarantee a burst of operation despite changes in power input. Current platforms use a fixed-size buffer chosen at design time to meet constraints on charge time or application longevity, but static energy buffers are a poor fit for the highly volatile power sources found in real-world deployments: fixed buffers waste energy both as heat when they reach capacity during a power surplus and as leakage when they fail to charge the system during a power deficit.

Increasingly sophisticated low-power microcontrollers are at the heart of millions of IoT and edge computing deployments, with developers pushing large-scale data collection, processing, and inference to end nodes. Advanced workloads on resource-constrained systems depend on emerging technologies to meet performance and lifetime demands. High-performance Non-Volatile RAMs (NVRAMs) are one such technology enabling a new class of systems previously made impossible by memory limitations, including ultra-low-power designs using program state non-volatility and sensing systems storing and processing large blocks of data.

This paper presents an investigation into practical considerations in the minimization of Age of Information (AoI) for the system architectures with and without updating feedback. To study the impact of the critical characteristics of the energy harvesters on the computing accuracy of the average AoI, an RF energy harvester with a half-wavelength patch antenna array along with a 3-stage voltage rectifier is designed and prototyped with the center frequency of 2.655 GHz. A cryptography algorithm is also implemented on a$\mu$-controller unit to imitate the behavior of a practical processing unit. The paper shows measurement results and discusses the impact of the non-idealities in the energy harvester on the average AoI of the system. It also shows that the nonlinear power conversion profile of the energy harvester can increase the average AoI to over 300% compared to an ideal and linear energy harvester.

Energy harvesting systems support the deployment of low-power microcontrollers untethered by constant power sources or batteries, enabling long-lived deployments in a variety of applications previously limited by power or size constraints. However, the limitations of harvested energy mean that even the lowest-power microcontrollers operate intermittently—waiting for the harvester to slowly charge a buffer capacitor and rapidly discharging the capacitor to support a brief burst of computation. The challenges of the intermittent operation brought on by harvested energy drive a variety of hardware and software techniques that first enabled long-running computation, then focused on improving performance. Many of the most promising systems demand dynamic updates of available energy to inform checkpointing and mode decisions.Unfortunately, existing energy monitoring solutions based on analog circuits (e.g., analog-to-digital converters) are ill-matched for the task because their signal processing focus sacrifices power efficiency for increased performance—performance not required by current or future intermittent computation systems. This results in existing solutions consuming as much energy as the microcontroller, stealing energy from useful computation. To create a low-power energy monitoring solution that provides just enough performance for intermittent computation use cases, we design and implement Failure Sentinels, an on-chip, fully-digital energy monitor. Failure Sentinels leverages the predictable propagation delay response of digital logic gates to supply voltage fluctuations to measure available energy. Our design space exploration shows that Failure Sentinels provides 30–50mV of resolution at sample rates up to 10kHz, while consuming less than 2µA of current. Experiments show that Failure Sentinels increases the energy available for software computation by up to 77%, compared to current solutions. We also implement a RISC-V-based FPGA prototype that validates our design space exploration and shows the overheads of incorporating Failure Sentinels into a system-on-chip.

Energy harvesting is a promising solution to power billions of ultra-low-power Internet-of-Things devices to enable ubiquitous computing. However, energy harvesters typically output tiny amounts of energy and, therefore, cannot continuously power devices; this leads to intermittent computing, where the energy harvester periodically charges a capacitor to sufficient voltage to power brief computation, until the capacitor's charge is drained, and the cycle repeats. To retain program state across frequent power failures, prior work proposes checkpointing program state to Non-Volatile Memory (NVM) before a power failure. Unfortunately, the most widely deployed, highest performance, and lowest cost devices employ Flash as their NVM, but the power, time, and endurance limitations of Flash writes are incompatible with the frequent NVM checkpoints of intermittent computation.