Sophisticated Batteryless Sensing

Wireless embedded sensing systems have revolutionized scientific, industrial, and consumer applications. Sensors have become a fixture in our daily lives, as well as the scientific and industrial communities by allowing continuous monitoring of people, wildlife, plants, buildings, roads and highways, pipelines, and countless other objects. Recently a new vision for sensing has emerged---known as the Internet-of-Things (IoT)---where trillions of devices invisibly sense, coordinate, and communicate to support our life and well being. However, the sheer scale of the IoT has presented serious problems for current sensing technologies---mainly, the unsustainable maintenance, ecological, and economic costs of recycling or disposing of trillions of batteries. This energy storage bottleneck has prevented massive deployments of tiny sensing devices at the edge of the IoT. This dissertation explores an alternative---leave the batteries behind, and harvest the energy required for sensing tasks from the environment the device is embedded in. These sensors can be made cheaper, smaller, and will last decades longer than their battery powered counterparts, making them a perfect fit for the requirements of the IoT. These sensors can be deployed where battery powered sensors cannot---embedded in concrete, shot into space, or even implanted in animals and people. However, these batteryless sensors may lose power at any point, with no warning, for unpredictable lengths of time. Programming, profiling, debugging, and building applications with these devices pose significant challenges. First, batteryless devices operate in unpredictable environments, where voltages vary and power failures can occur at any time---often devices are in failure for hours. Second, a device\u27s behavior effects the amount of energy they can harvest---meaning small changes in tasks can drastically change harvester efficiency. Third, the programming interfaces of batteryless devices are ill-defined and non- intuitive; most developers have trouble anticipating the problems inherent with an intermittent power supply. Finally, the lack of community, and a standard usable hardware platform have reduced the resources and prototyping ability of the developer. In this dissertation we present solutions to these challenges in the form of a tool for repeatable and realistic experimentation called Ekho, a reconfigurable hardware platform named Flicker, and a language and runtime for timely execution of intermittent programs called Mayfly

Waldo: Batteryless Occupancy Monitoring with Reflected Ambient Light

Reliable and accurate room-level occupancy-tracking systems can enable many new advances in sensors and applications of modern smart buildings. This allows buildings to be more capable of adapting to the needs of their occupants in their day-to-day activities and better optimize certain resources, such as power and air conditioning, to do so. Unfortunately, existing occupancy-tracking systems are plagued by large size, high energy consumption, and, unsurprisingly, short battery lifetimes. In this paper, we present Waldo, a batteryless, room-level occupancy monitoring sensor that harvests energy from indoor ambient light reflections, and uses changes in these reflections to detect when people enter and exit a room. Waldo is mountable at the top of a doorframe, allowing for detection of a person and the direction they are traveling at the entry and exit point of a room. We evaluated the Waldo sensor in an office-style setting under mixed lighting conditions (natural and artificial) on both sides of the doorway with subjects exhibiting varying physical characteristics such as height, hair color, gait, and clothing. 651 number of controlled experiments were ran on 6 doorways with 12 individuals and achieved a total detection accuracy of 97.38%. Further, it judged the direction of movement correctly with an accuracy of 95.42%. This paper also evaluates and discusses various practical factors that can impact the performance of the current system in actual deployments. This work demonstrates that ambient light reflections provide both a promising low-cost, long-term sustainable option for monitoring how people use buildings and an exciting new research direction for batteryless computing

CARTOS: A Charging-Aware Real-Time Operating System for Intermittent Batteryless Devices

This paper presents CARTOS, a charging-aware real-time operating system designed to enhance the functionality of intermittently-powered batteryless devices (IPDs) for various Internet of Things (IoT) applications. While IPDs offer significant advantages such as extended lifespan and operability in extreme environments, they pose unique challenges, including the need to ensure forward progress of program execution amidst variable energy availability and maintaining reliable real-time time behavior during power disruptions. To address these challenges, CARTOS introduces a mixed-preemption scheduling model that classifies tasks into computational and peripheral tasks, and ensures their efficient and timely execution by adopting just-in-time checkpointing for divisible computation tasks and uninterrupted execution for indivisible peripheral tasks. CARTOS also supports processing chains of tasks with precedence constraints and adapts its scheduling in response to environmental changes to offer continuous execution under diverse conditions. CARTOS is implemented with new APIs and components added to FreeRTOS but is designed for portability to other embedded RTOSs. Through real hardware experiments and simulations, CARTOS exhibits superior performance over state-of-the-art methods, demonstrating that it can serve as a practical platform for developing resilient, real-time sensing applications on IPDs

Scheduling Tasks on Intermittently-Powered Real-Time Systems

Batteryless systems go through sporadic power on and off phases due to intermittently available energy; thus, they are called intermittent systems. Unfortunately, this intermittence in power supply hinders the timely execution of tasks and limits such devices’ potential in certain application domains, e.g., healthcare, live-stock tracking. Unlike prior work on time-aware intermittent systems that focuses on timekeeping [1, 2, 3] and discarding expired data [4], this dissertation concentrates on finishing task execution on time. I leverage the data processing and control layer of batteryless systems by developing frameworks that (1) integrate energy harvesting and real-time systems, (2) rethink machine learning algorithms for an energy-aware imprecise task scheduling framework, (3) develop scheduling algorithms that, along with deciding what to compute, answers when to compute and when to harvest, and (4) utilize distributed systems that collaboratively emulate a persistently powered system. Scheduling Framework for Intermittently Powered Computing Systems. Batteryless systems rely on sporadically available harvestable energy. For example, kinetic-powered motion detector sensors on the impalas can only harvest energy when the impalas are moving, which cannot be ascertained in advance. This uncertainty poses a unique real-time scheduling problem where existing real-time algorithms fail due to the interruption in execution time. This dissertation proposes a unified scheduling framework that includes both harvesting and computing. Imprecise Deep Neural Network Inference in Deadline-Aware Intermittent Systems. This dissertation proposes Zygarde- an energy-aware and outcome-aware soft-real-time imprecise deep neural network (DNN) task scheduling framework for intermittent systems. Zygarde leverages the semantic diversity of input data and layer-dependent expressiveness of deep features and infers only the necessary DNN layers based on available time and energy. Zygarde proposes a novel technique to determine the imprecise boundary at the runtime by exploiting the clustering classifiers and specialized offline training of the DNNs to minimize the loss of accuracy due to partial execution. It also proposes a single metric, η to represent a system’s predictability that measures how close a harvesterâs harvesting pattern is to a constant energy source. Besides, Zygarde consists of a scheduling algorithm that takes available time, available energy, impreciseness, and the classifier's performance into account. Scheduling Mutually Exclusive Computing and Harvesting Tasks in Deadline-Aware Intermittent Systems. The lack of sufficient ambient energy to directly power the intermittent systems introduces mutually exclusive computing and charging cycles of intermittently powered systems. This introduces a challenging real-time scheduling problem where the existing real-time algorithms fail due to the lack of interruption in execution time. To address this, this dissertation proposes Celebi, which considers the dynamics of the available energy and schedules when to harvest and when to compute in batteryless systems. Using data-driven simulation and real-world experiments, this dissertation shows that Celebi significantly increases the number of tasks that complete execution before their deadline when power was only available intermittently. Persistent System Emulation with Distributed Intermittent System. Intermittently-powered sensing and computing systems go through sporadic power-on and off periods due to the uncertain availability of energy sources. Despite the recent efforts to advance time-sensitive intermittent systems, such systems fail to capture important target events when the energy is absent for a prolonged time. This event miss limits the potential usage of intermittent systems in fault- intolerant and safety-critical applications. To address this problem, this dissertation proposes Falinks, a framework that allows a swarm of distributed intermittently powered nodes to collaboratively imitate the sensing and computing capabilities of a persistently powered system. This framework provides power-on and off schedules for the swamp of intermittent nodes which has no communication capability with each other.Doctor of Philosoph

Hardware Architectures for Low-power In-Situ Monitoring of Wireless Embedded Systems

As wireless embedded systems transition from lab-scale research prototypes to large-scale commercial deployments, providing reliable and dependable system operation becomes absolutely crucial to ensure successful adoption. However, the untethered nature of wireless embedded systems severely limits the ability to access, debug, and control device operation after deployment—post-deployment or in-situ visibility. It is intuitive that the more information we have about a system’s operation after deployment, the better/faster we can respond upon the detection of anomalous behavior. Therefore, post-deployment visibility is a foundation upon which other runtime reliability techniques can be built. However, visibility into system operation diminishes significantly once the devices are remotely deployed, and we refer to this problem as a lack of post-deployment visibility

Attacking and Defending Emerging Computer Systems Using The Memory Remanence Effect

In computer systems, manufacturing variances and hardware effects are typically abstracted away by the software layer. This dissertation explores how these effects, specifically memory remanence, can be used both as an attack vector and a tool to defend emerging computing systems. To achieve this, we show how time-keeping, anonymity, and authenticity can be affected by memory remanence. In terms of attacks, we explore the deanonymizing effect of approximate computing in the context of approximate memory in Probable Cause. We show how data passing through an approximate memory is watermarked with a device specific tag that points the attacker back to the device. In terms of defenses, we first present TARDIS: an approach to provide a notion of time for transiently powered embedded devices without requiring any hardware modification using remanence effect of SRAM. TARDIS allows these devices to keep a coarse-grained notion of time without the need for a running clock. Second, we propose data retention voltage of memory cells as a new type of physical unclonable function that allows for low-cost authentication and counterfeit resistance in computer systems.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/136985/1/rahmati_1.pd

Energy-Efficient System Architectures for Intermittently-Powered IoT Devices

Various industry forecasts project that, by 2020, there will be around 50 billion devices connected to the Internet of Things (IoT), helping to engineer new solutions to societal-scale problems such as healthcare, energy conservation, transportation, etc. Most of these devices will be wireless due to the expense, inconvenience, or in some cases, the sheer infeasibility of wiring them. With no cord for power and limited space for a battery, powering these devices for operating in a set-and-forget mode (i.e., achieve several months to possibly years of unattended operation) becomes a daunting challenge. Environmental energy harvesting (where the system powers itself using energy that it scavenges from its operating environment) has been shown to be a promising and viable option for powering these IoT devices. However, ambient energy sources (such as vibration, wind, RF signals) are often minuscule, unreliable, and intermittent in nature, which can lead to frequent intervals of power loss. Performing computations reliably in the face of such power supply interruptions is challenging

SCALING UP TASK EXECUTION ON RESOURCE-CONSTRAINED SYSTEMS

The ubiquity of executing machine learning tasks on embedded systems with constrained resources has made efficient execution of neural networks on these systems under the CPU, memory, and energy constraints increasingly important. Different from high-end computing systems where resources are abundant and reliable, resource-constrained systems only have limited computational capability, limited memory, and limited energy supply. This dissertation focuses on how to take full advantage of the limited resources of these systems in order to improve task execution efficiency from different aspects of the execution pipeline. While the existing literature primarily aims at solving the problem by shrinking the model size according to the resource constraints, this dissertation aims to improve the execution efficiency for a given set of tasks from the following two aspects. Firstly, we propose SmartON, which is the first batteryless active event detection system that considers both the event arrival pattern as well as the harvested energy to determine when the system should wake up and what the duty cycle should be. Secondly, we propose Antler, which exploits the affinity between all pairs of tasks in a multitask inference system to construct a compact graph representation of the task set for a given overall size budget. To achieve the aforementioned algorithmic proposals, we propose the following hardware solutions. One is a controllable capacitor array that can expand the system’s energy storage on-the-fly. The other is a FRAM array that can accommodate multiple neural networks running on one system.Doctor of Philosoph