Publications from Lab11 Plus demos and posters.

Fine-tuning models on edge devices like mobile phones would enable privacy-preserving personalization over sensitive data. However, edge training has historically been limited to relatively small models with simple architectures because training is both memory and energy intensive. We present POET, an algorithm to enable training large neural networks on memory-scarce battery-operated edge devices. POET jointly optimizes the integrated search search spaces of rematerialization and paging, two algorithms to reduce the memory consumption of backpropagation. Given a memory budget and a run-time constraint, we formulate a mixed-integer linear program (MILP) for energy-optimal training. Our approach enables training significantly larger models on embedded devices while reducing energy consumption while not modifying mathematical correctness of backpropagation. We demonstrate that it is possible to fine-tune both ResNet-18 and BERT within the memory constraints of a Cortex-M class embedded device while outperforming current edge training methods in energy efficiency. POET is an open-source project available at https://github.com/ShishirPatil/poet

Content has been formatted as TeX source. Click to format for web.

Fine-tuning models on edge devices like mobile phones would enable privacy-preserving personalization over sensitive data. However, edge training has historically been limited to relatively small models with simple architectures because training is both memory and energy intensive. We present POET, an algorithm to enable training large neural networks on memory-scarce battery-operated edge devices. POET jointly optimizes the integrated search search spaces of rematerialization and paging, two algorithms to reduce the memory consumption of backpropagation. Given a memory budget and a run-time constraint, we formulate a mixed-integer linear program (MILP) for energy-optimal training. Our approach enables training significantly larger models on embedded devices while reducing energy consumption while not modifying mathematical correctness of backpropagation. We demonstrate that it is possible to fine-tune both ResNet-18 and BERT within the memory constraints of a Cortex-M class embedded device while outperforming current edge training methods in energy efficiency. POET is an open-source project available at https://github.com/ShishirPatil/poet

Content has been formatted for web. Click to view raw TeX

The radio transmitter is the most power-consuming component of a wireless embedded system. We present Judo, a radio transmitter that enables power balance between the wireless transmission, sensing, and processing tasks of a wireless embedded system. Judo transmitters leverage the fact that modern radio transceivers offer high receive sensitivity at low power. Therefore, even if the radio transmitter emits a weak signal, the link budget and transmission range will often remain high. With this key insight, we revisit the radio transmitter architecture by dramatically reducing the radiated power and hence the overall power draws. Specifically, Judo transmitter uses a tunnel diode oscillator to integrate the stages of a radio transmitter into a single energy-efficient step. In this step, baseband signals are generated and mixed using peak power below 100 μW. However, we sacrifice stability of tunnel diode oscillator for low-power consumption. We use the injection-locking phenomenon to stabilise the tunnel diode oscillator with an external carrier signal. Based on this novel architecture, we implement a transmitter that supports frequency-shift keying as a modulation scheme. Judo transmits over distances greater than 100 m at a bit rate of 100 kbps. It does so with an emitter device providing the carrier signal, and located at a distance of more than 100 m from Judo transmitter. In terms of critical link metrics, it outperforms the radio transmitters commonly used in wireless embedded systems.

Content has been formatted as TeX source. Click to format for web.

The radio transmitter is the most power-consuming component of a wireless embedded system. We present Judo, a radio transmitter that enables power balance between the wireless transmission, sensing, and processing tasks of a wireless embedded system. Judo transmitters leverage the fact that modern radio transceivers offer high receive sensitivity at low power. Therefore, even if the radio transmitter emits a weak signal, the link budget and transmission range will often remain high. With this key insight, we revisit the radio transmitter architecture by dramatically reducing the radiated power and hence the overall power draws. Specifically, Judo transmitter uses a tunnel diode oscillator to integrate the stages of a radio transmitter into a single energy-efficient step. In this step, baseband signals are generated and mixed using peak power below 100 μW. However, we sacrifice stability of tunnel diode oscillator for low-power consumption. We use the injection-locking phenomenon to stabilise the tunnel diode oscillator with an external carrier signal. Based on this novel architecture, we implement a transmitter that supports frequency-shift keying as a modulation scheme. Judo transmits over distances greater than 100 m at a bit rate of 100 kbps. It does so with an emitter device providing the carrier signal, and located at a distance of more than 100 m from Judo transmitter. In terms of critical link metrics, it outperforms the radio transmitters commonly used in wireless embedded systems.

Content has been formatted for web. Click to view raw TeX

In many engineering disciplines such as circuit board, chip, and mechanical design, a hardware description language (HDL) approach provides important benefits over direct manipulation interfaces by supporting concepts like abstraction and generator meta-programming. While several such HDLs have emerged recently and promised power and flexibility, they also present challenges – especially to designers familiar with current graphical workflows. In this work, we investigate an IDE approach to provide a graphical editor for a board-level circuit design HDL. Unlike GUI builders which convert an entire diagram to code, we instead propose generating equivalent HDL from individual graphical edit actions. By keeping code as the primary design input, we preserve the full power of the underlying HDL, while remaining useful even to advanced users. We discuss our concept, design considerations such as performance, system implementation, and report on the results of an exploratory remote user study with four experienced hardware designers. 

Content has been formatted as TeX source. Click to format for web.

In many engineering disciplines such as circuit board, chip, and mechanical design, a hardware description language (HDL) approach provides important benefits over direct manipulation interfaces by supporting concepts like abstraction and generator meta-programming. While several such HDLs have emerged recently and promised power and flexibility, they also present challenges – especially to designers familiar with current graphical workflows. In this work, we investigate an IDE approach to provide a graphical editor for a board-level circuit design HDL. Unlike GUI builders which convert an entire diagram to code, we instead propose generating equivalent HDL from individual graphical edit actions. By keeping code as the primary design input, we preserve the full power of the underlying HDL, while remaining useful even to advanced users. We discuss our concept, design considerations such as performance, system implementation, and report on the results of an exploratory remote user study with four experienced hardware designers.

Content has been formatted for web. Click to view raw TeX

In much of the world, electricity grids are not instrumented at the customer level, limiting insights into the power quality experienced by utility customers. Moreover, to understand grid performance, regulators and investors must depend on utilities to self-report reliability data. To address these challenges, we introduce PowerWatch, an agile methodology to directly measure customer experience and aggregated grid performance without relying on the utility for deployment or management. PowerWatch employs a system of distributed sensors coupled with cloud-based analytics. We evaluate the PowerWatch methodology by deploying 462 sensors in homes and businesses in Accra, Ghana for over a year, yielding the largest open-source data set on electricity reliability at the customer-level in the region. We describe the architecture, design, and performance of PowerWatch, as well as the data that are collected, explaining how we determine the accuracy and coverage of our methodology without ground truth. Finally, we report on grid performance issues, finding nearly twice as many outages as the utility observed, suggesting a need for better grid performance monitoring.

Content has been formatted as TeX source. Click to format for web.

In much of the world, electricity grids are not instrumented at the customer level, limiting insights into the power quality experienced by utility customers. Moreover, to understand grid performance, regulators and investors must depend on utilities to self-report reliability data. To address these challenges, we introduce PowerWatch, an agile methodology to directly measure customer experience and aggregated grid performance without relying on the utility for deployment or management. PowerWatch employs a system of distributed sensors coupled with cloud-based analytics. We evaluate the PowerWatch methodology by deploying 462 sensors in homes and businesses in Accra, Ghana for over a year, yielding the largest open-source data set on electricity reliability at the customer-level in the region. We describe the architecture, design, and performance of PowerWatch, as well as the data that are collected, explaining how we determine the accuracy and coverage of our methodology without ground truth. Finally, we report on grid performance issues, finding nearly twice as many outages as the utility observed, suggesting a need for better grid performance monitoring.

Content has been formatted for web. Click to view raw TeX

Mainstream board-level circuit design tools work at the lowest level of design --- schematics and individual components. While novel tools experiment with higher levels of design, abstraction often comes at the expense of the fine-grained control afforded by low-level tools. In this work, we propose a hardware description language (HDL) approach that supports users at multiple levels of abstraction from broad system architecture to subcircuits and component selection. We extend the familiar hierarchical block diagram with polymorphism to include abstract-typed blocks (e.g., generic resistor supertype) and electronics modeling (i.e., currents and voltages). Such an approach brings the advantages of reusability and encapsulation from object-oriented programming, while addressing the unique needs of electronics designers such as physical correctness verification. We discuss the system design, including fundamental abstractions, the block diagram construction HDL, and user interfaces to inspect and fine-tune the design; demonstrate example designs built with our system; and present feedback from intermediate-level engineers who have worked with our system.

Content has been formatted as TeX source. Click to format for web.

Mainstream board-level circuit design tools work at the lowest level of design — schematics and individual components. While novel tools experiment with higher levels of design, abstraction often comes at the expense of the fine-grained control afforded by low-level tools. In this work, we propose a hardware description language (HDL) approach that supports users at multiple levels of abstraction from broad system architecture to subcircuits and component selection. We extend the familiar hierarchical block diagram with polymorphism to include abstract-typed blocks (e.g., generic resistor supertype) and electronics modeling (i.e., currents and voltages). Such an approach brings the advantages of reusability and encapsulation from object-oriented programming, while addressing the unique needs of electronics designers such as physical correctness verification. We discuss the system design, including fundamental abstractions, the block diagram construction HDL, and user interfaces to inspect and fine-tune the design; demonstrate example designs built with our system; and present feedback from intermediate-level engineers who have worked with our system.

Content has been formatted for web. Click to view raw TeX

Social scientists, psychologists, and epidemiologists use empirical human interaction data to research human behaviour, social bonding, and disease spread. Historically, systems measuring interactions have been forced to choose between deployability and measurement fidelity---they operate only in instrumented spaces, under line-of-sight conditions, or provide coarse-grained proximity data. We introduce SociTrack, a platform for autonomous social interaction tracking via wireless distance measurements. Deployments require no supporting infrastructure and provide sub-second, decimeter-accurate ranging information over multiple days. The key insight that enables both deployability and fidelity in one system is to decouple node mobility and network management from range measurement, which results in a novel dual-radio architecture. SociTrack leverages an energy-efficient and scalable ranging protocol that is accurate to 14.8 cm (99th percentile) in complex indoor environments and allows our prototype to operate for 12 days on a 2000 mAh battery. Finally, to validate its deployability and efficacy, SociTrack is used by early childhood development researchers to capture caregiver-infant interactions.

Content has been formatted as TeX source. Click to format for web.

Social scientists, psychologists, and epidemiologists use empirical human interaction data to research human behaviour, social bonding, and disease spread. Historically, systems measuring interactions have been forced to choose between deployability and measurement fidelity—they operate only in instrumented spaces, under line-of-sight conditions, or provide coarse-grained proximity data. We introduce SociTrack, a platform for autonomous social interaction tracking via wireless distance measurements. Deployments require no supporting infrastructure and provide sub-second, decimeter-accurate ranging information over multiple days. The key insight that enables both deployability and fidelity in one system is to decouple node mobility and network management from range measurement, which results in a novel dual-radio architecture. SociTrack leverages an energy-efficient and scalable ranging protocol that is accurate to 14.8 cm (99th percentile) in complex indoor environments and allows our prototype to operate for 12 days on a 2000 mAh battery. Finally, to validate its deployability and efficacy, SociTrack is used by early childhood development researchers to capture caregiver-infant interactions.

Content has been formatted for web. Click to view raw TeX

Modern electronic design automation (EDA) tooling tends to focus on either the system-level design or the low-level electrical connectivity between physical components on a printed circuit board (PCB). We believe that a usable and functional system for circuit design needs to be able to interleave both levels of abstraction seamlessly and allow designers to transition between them freely. Existing work has experimented with approaches like circuit synthesis, functional characterization, or fine grained physical modeling. Each of these approaches augment the design process as it exists today, with its fundamental split between various levels of abstraction. We notice that hierarchical block diagrams can capture both high-level system structure as well as fine grained physical connectivity, and use that symmetry to construct a model for electronic circuits that can span the entire design process. Additionally, we construct user interfaces for our model that can support users of different skill levels throughout a design task. We discuss the design of our system, detailing both fundamental abstractions and usability trade-offs, and demonstrate its current capabilities through the design of example electronics projects.

Content has been formatted as TeX source. Click to format for web.

Modern electronic design automation (EDA) tooling tends to focus on either the system-level design or the low-level electrical connectivity between physical components on a printed circuit board (PCB). We believe that a usable and functional system for circuit design needs to be able to interleave both levels of abstraction seamlessly and allow designers to transition between them freely. Existing work has experimented with approaches like circuit synthesis, functional characterization, or fine grained physical modeling. Each of these approaches augment the design process as it exists today, with its fundamental split between various levels of abstraction. We notice that hierarchical block diagrams can capture both high-level system structure as well as fine grained physical connectivity, and use that symmetry to construct a model for electronic circuits that can span the entire design process. Additionally, we construct user interfaces for our model that can support users of different skill levels throughout a design task. We discuss the design of our system, detailing both fundamental abstractions and usability trade-offs, and demonstrate its current capabilities through the design of example electronics projects.

Content has been formatted for web. Click to view raw TeX

As the number of Internet of Things devices continue to
grow, a pattern of off-loading user control and interaction to
individuals’ mobile devices has emerged. The user experience,
however, is burdened by high-friction tasks, including device
discovery, app installation, scanning, pairing, and configuration. 
Additionally, the tools and systems currently employed
to facilitate interaction vary in degree of usefulness, provide
inconsistent support, and fail to mesh together in a meaningful way. 
This user experience model scales poorly with the
increasing population of “things”, and significantly hinders
casual interactions with ambient devices, as well as regular
interactions with persistent devices. To break away from this
restrictive paradigm, we propose an architecture that provides
a seamless, scalable approach to discovering and interacting
with nearby “things” in both short- and long-term contexts.
The system takes advantage of multiple network patterns and
modern web technologies to supply users with rich device
interfaces that can interact directly over local networking protocols. 
A mobile app-based implementation of this system is
tested with several embedded wireless devices. In our analysis, 
we find that our method scales better than current popular
models and that it enables powerful and complex functionality
while remaining natural, intuitive, and secure for users.

Content has been formatted as TeX source. Click to format for web.

As the number of Internet of Things devices continue to grow, a pattern of off-loading user control and interaction to individuals’ mobile devices has emerged. The user experience, however, is burdened by high-friction tasks, including device discovery, app installation, scanning, pairing, and configuration. Additionally, the tools and systems currently employed to facilitate interaction vary in degree of usefulness, provide inconsistent support, and fail to mesh together in a meaningful way. This user experience model scales poorly with the increasing population of “things”, and significantly hinders casual interactions with ambient devices, as well as regular interactions with persistent devices. To break away from this restrictive paradigm, we propose an architecture that provides a seamless, scalable approach to discovering and interacting with nearby “things” in both short- and long-term contexts. The system takes advantage of multiple network patterns and modern web technologies to supply users with rich device interfaces that can interact directly over local networking protocols. A mobile app-based implementation of this system is tested with several embedded wireless devices. In our analysis, we find that our method scales better than current popular models and that it enables powerful and complex functionality while remaining natural, intuitive, and secure for users.

Content has been formatted for web. Click to view raw TeX

Low-power wide-area networks (LPWANs) are a compelling answer to the
networking challenges faced by many Internet of Things devices. Their
combination of low power, long range, and deployment ease has motivated a
flurry of research, including exciting results on backscatter and
interference cancellation that further lower power budgets and increase
capacity. But despite the interest, we argue that unlicensed LPWAN
technologies can only serve a narrow class of Internet of Things
applications due to two principal challenges: capacity and coexistence.

We propose a metric, bit flux, to describe networks and applications in
terms of throughput over a coverage area. Using bit flux, we find that the
combination of low bit rate and long range restricts the use case of LPWANs
to sparse sensing applications. Furthermore, this lack of capacity leads
networks to use as much available bandwidth as possible, and a lack of
coexistence mechanisms causes poor performance in the presence of multiple,
independently-administered networks. We discuss a variety of techniques and
approaches that could be used to address these two challenges and enable
LPWANs to achieve the promise of ubiquitous connectivity.

Content has been formatted as TeX source. Click to format for web.

Low-power wide-area networks (LPWANs) are a compelling answer to the networking challenges faced by many Internet of Things devices. Their combination of low power, long range, and deployment ease has motivated a flurry of research, including exciting results on backscatter and interference cancellation that further lower power budgets and increase capacity. But despite the interest, we argue that unlicensed LPWAN technologies can only serve a narrow class of Internet of Things applications due to two principal challenges: capacity and coexistence.

We propose a metric, bit flux, to describe networks and applications in terms of throughput over a coverage area. Using bit flux, we find that the combination of low bit rate and long range restricts the use case of LPWANs to sparse sensing applications. Furthermore, this lack of capacity leads networks to use as much available bandwidth as possible, and a lack of coexistence mechanisms causes poor performance in the presence of multiple, independently-administered networks. We discuss a variety of techniques and approaches that could be used to address these two challenges and enable LPWANs to achieve the promise of ubiquitous connectivity.

Content has been formatted for web. Click to view raw TeX

The vision of sensor systems that collect critical and previously ungathered
information about the world is often only realized when sensors, students,
and subjects move outside the academic laboratory. However, deployments at
even the smallest scales introduce complexities and risks that can be
difficult for a research team to anticipate. Over the past year, our
interdisciplinary team of engineers and economists has been designing,
deploying, and operating a large sensor network in Accra, Ghana that
measures power outages and quality at households and firms. This network
consists of 457 custom sensors, over 3,000 mobile app instances,
thousands of participant surveys, and custom user incentive and
deployment management systems. In part, this deployment supports an
evaluation of the impacts of investments in the grid on reliability and
the subsequent effects of improvements in reliability on
socioeconomic well-being. We report our experiences as we move from
performing small pilot deployments to our current scale, attempting to
identify the pain points at each stage of the deployment. Finally, we
extract high-level observations and lessons learned from our
deployment activities, which we wish we had originally known when
forecasting budgets, human resources, and project timelines. These
insights will be critical as we look toward scaling our deployment to the
en-tire city of Accra and beyond, and we hope that they will encourage and
support other researchers looking to measure highly granular information
about our world’s critical systems.

Content has been formatted as TeX source. Click to format for web.

The vision of sensor systems that collect critical and previously ungathered information about the world is often only realized when sensors, students, and subjects move outside the academic laboratory. However, deployments at even the smallest scales introduce complexities and risks that can be difficult for a research team to anticipate. Over the past year, our interdisciplinary team of engineers and economists has been designing, deploying, and operating a large sensor network in Accra, Ghana that measures power outages and quality at households and firms. This network consists of 457 custom sensors, over 3,000 mobile app instances, thousands of participant surveys, and custom user incentive and deployment management systems. In part, this deployment supports an evaluation of the impacts of investments in the grid on reliability and the subsequent effects of improvements in reliability on socioeconomic well-being. We report our experiences as we move from performing small pilot deployments to our current scale, attempting to identify the pain points at each stage of the deployment. Finally, we extract high-level observations and lessons learned from our deployment activities, which we wish we had originally known when forecasting budgets, human resources, and project timelines. These insights will be critical as we look toward scaling our deployment to the en-tire city of Accra and beyond, and we hope that they will encourage and support other researchers looking to measure highly granular information about our world’s critical systems.

Content has been formatted for web. Click to view raw TeX

Printed Circuit Board (PCB) design tools are critical in helping users build non-trivial electronics devices. 
While recent work recognizes deficiencies with current tools and explores novel methods, little has been done to understand modern designers and their needs.
To gain better insight into their practices, we interview fifteen electronics designers of a variety of backgrounds. 
Our open-ended, semi-structured interviews examine both overarching design flows and details of individual steps. 
One major finding was that most creative engineering work happens during system architecture, yet current tools operate at lower abstraction levels and create significant tedious work for designers.
From that insight, we conceptualize abstractions and primitives for higher-level tools and elicit feedback from our participants on clickthrough mockups of design flows through an example project. 
We close with our observation on opportunities for improving board design tools and discuss generalizability of our findings beyond the electronics domain.

Content has been formatted as TeX source. Click to format for web.

Printed Circuit Board (PCB) design tools are critical in helping users build non-trivial electronics devices. While recent work recognizes deficiencies with current tools and explores novel methods, little has been done to understand modern designers and their needs. To gain better insight into their practices, we interview fifteen electronics designers of a variety of backgrounds. Our open-ended, semi-structured interviews examine both overarching design flows and details of individual steps. One major finding was that most creative engineering work happens during system architecture, yet current tools operate at lower abstraction levels and create significant tedious work for designers. From that insight, we conceptualize abstractions and primitives for higher-level tools and elicit feedback from our participants on clickthrough mockups of design flows through an example project. We close with our observation on opportunities for improving board design tools and discuss generalizability of our findings beyond the electronics domain.

Content has been formatted for web. Click to view raw TeX

Today, most sensors that harvest energy from indoor solar, ambient RF, or
thermal gradients buffer small amounts of energy in capacitors as they
intermittently work through a sensing task. While the utilization of
capacitors for energy storage affords these systems indefinite lifetimes,
their low energy capacity necessitates complex intermittent programming models
for state retention and energy management. However, recent advances in
battery technology lead us to reevaluate the impact that increased energy
storage capacity may have on the necessity of these programming models
and the reliability of energy harvesting sensors. In this paper, we
propose a capacity-based framework to help structure energy harvesting
sensor design, analyze the impact of capacity on key reliability metrics
using a data-driven simulation, and consider how backup energy storage
alters the design space. We find that for many designs that utilize solar
energy harvesting, increasing energy storage capacity to 1-10 mWh can
obviate the need for intermittent programming techniques, augment the
total harvested energy by 1.4-2.3x, and improve the availability of a
sensor by 1.3-2.6x. We also show that a hybrid design using energy
harvesting with a secondary-cell battery and a backup primary-cell
battery can achieve 2-4x the lifetime of primary-cell only designs while
eliminating the failure modes present in energy harvesting systems.
Finally, we implement an indoor, solar energy harvesting sensor based on
our analysis and find that its behavior aligns with our simulation’s
predictions.

Content has been formatted as TeX source. Click to format for web.

Today, most sensors that harvest energy from indoor solar, ambient RF, or thermal gradients buffer small amounts of energy in capacitors as they intermittently work through a sensing task. While the utilization of capacitors for energy storage affords these systems indefinite lifetimes, their low energy capacity necessitates complex intermittent programming models for state retention and energy management. However, recent advances in battery technology lead us to reevaluate the impact that increased energy storage capacity may have on the necessity of these programming models and the reliability of energy harvesting sensors. In this paper, we propose a capacity-based framework to help structure energy harvesting sensor design, analyze the impact of capacity on key reliability metrics using a data-driven simulation, and consider how backup energy storage alters the design space. We find that for many designs that utilize solar energy harvesting, increasing energy storage capacity to 1-10 mWh can obviate the need for intermittent programming techniques, augment the total harvested energy by 1.4-2.3x, and improve the availability of a sensor by 1.3-2.6x. We also show that a hybrid design using energy harvesting with a secondary-cell battery and a backup primary-cell battery can achieve 2-4x the lifetime of primary-cell only designs while eliminating the failure modes present in energy harvesting systems. Finally, we implement an indoor, solar energy harvesting sensor based on our analysis and find that its behavior aligns with our simulation’s predictions.

Content has been formatted for web. Click to view raw TeX

The Internt of Things (IoT) is a rapidly evolving application space. One
of the fascinating new fields in IoT research is mm-scale sensors, which
make up a myriad of new application domains. Enabled by the unique
characteristics of cyber-physical systems and recent advances in low-power
design and bare-die 3D chip stacking, mm-scale sensors are rapidly becoming
a reality. In this paper, we will survey the challenges and solutions to
3D-stacked mm-scale design, highlighting low-power circuit issues ranging
from low-power SRAM and miniature neural network accelerators to radio
communication protocols and analog interfaces. We will discuss system-level
challenges and illustrate several complete systems and their merging
applicaiton spaces.

Content has been formatted as TeX source. Click to format for web.

The Internt of Things (IoT) is a rapidly evolving application space. One of the fascinating new fields in IoT research is mm-scale sensors, which make up a myriad of new application domains. Enabled by the unique characteristics of cyber-physical systems and recent advances in low-power design and bare-die 3D chip stacking, mm-scale sensors are rapidly becoming a reality. In this paper, we will survey the challenges and solutions to 3D-stacked mm-scale design, highlighting low-power circuit issues ranging from low-power SRAM and miniature neural network accelerators to radio communication protocols and analog interfaces. We will discuss system-level challenges and illustrate several complete systems and their merging applicaiton spaces.

Content has been formatted for web. Click to view raw TeX

Incentives are a key facet of human studies research, yet the state-of-the-art
often designs and implements incentive systems in an ad-hoc, on-demand manner.
We introduce the first vocabulary for formally describing incentive
systems and develop a software infrastructure that enables UI-based graphical
generation of complex, auditable, reliable, and reproducible incentive systems.
We call this infrastructure the Open INcentive Kit (OINK).
A review of recent literature from several communities finds that of the one hundred and twenty-one publications that incorporate
incentives, only thirty-one describe their incentive system in detail, and all of these could be implemented using OINK. We evaluate OINK in practice by using it for an active energy monitoring deployment in Ghana and find that OINK successfully facilitates thousands of individual incentive payments. Finally, we describe our efforts to generalize OINK for different research communities, specifically focusing on architectural decisions around extensibility to support unanticipated use cases. OINK is free and open-source software.

Content has been formatted as TeX source. Click to format for web.

Incentives are a key facet of human studies research, yet the state-of-the-art often designs and implements incentive systems in an ad-hoc, on-demand manner. We introduce the first vocabulary for formally describing incentive systems and develop a software infrastructure that enables UI-based graphical generation of complex, auditable, reliable, and reproducible incentive systems. We call this infrastructure the Open INcentive Kit (OINK). A review of recent literature from several communities finds that of the one hundred and twenty-one publications that incorporate incentives, only thirty-one describe their incentive system in detail, and all of these could be implemented using OINK. We evaluate OINK in practice by using it for an active energy monitoring deployment in Ghana and find that OINK successfully facilitates thousands of individual incentive payments. Finally, we describe our efforts to generalize OINK for different research communities, specifically focusing on architectural decisions around extensibility to support unanticipated use cases. OINK is free and open-source software.

Content has been formatted for web. Click to view raw TeX

Smart and connected devices offer enormous potential to enable
context-aware, localized, and multi-device orchestrations that could
substantially increase the reach and utility of computing.  The growth
of these applications has been hampered, however, as devices, their
data, and their control have been largely sequestered to their own
vendor-specific APIs, clouds, and applications---a largely stove-piped
state of affairs.  In instances where barriers between devices have
been pierced, the connections often occur between vendor clouds,
affecting the latency, privacy, and reliability of the original
application, while simultaneously making them more complex.  Locally
executing applications have not materialized as devices with
incompatible communication protocols, inconsistent APIs, and
incongruent data models rarely communicate. We claim that what is
needed to unlock the application potential is an architecture tailored
to facilitating applications composed of networked devices.

Our proposed architecture addresses this by providing a port-based abstraction
for devices using a small wrapper layer. This device abstraction
provides a consistent view of devices, and embeddable runtimes provide existing
applications straightforward access to devices. The architecture also supports
device discovery, shared interfaces between devices, and an application
specification interface that promotes creating device-agnostic applications
capable of operating even when devices change.  We demonstrate the efficacy of
our architecture with two application case studies that highlight the
abstraction layers between applications and devices and employ the
embeddability of our system to add new functionality to existing systems.

Content has been formatted as TeX source. Click to format for web.

Smart and connected devices offer enormous potential to enable context-aware, localized, and multi-device orchestrations that could substantially increase the reach and utility of computing. The growth of these applications has been hampered, however, as devices, their data, and their control have been largely sequestered to their own vendor-specific APIs, clouds, and applications—a largely stove-piped state of affairs. In instances where barriers between devices have been pierced, the connections often occur between vendor clouds, affecting the latency, privacy, and reliability of the original application, while simultaneously making them more complex. Locally executing applications have not materialized as devices with incompatible communication protocols, inconsistent APIs, and incongruent data models rarely communicate. We claim that what is needed to unlock the application potential is an architecture tailored to facilitating applications composed of networked devices.

Our proposed architecture addresses this by providing a port-based abstraction for devices using a small wrapper layer. This device abstraction provides a consistent view of devices, and embeddable runtimes provide existing applications straightforward access to devices. The architecture also supports device discovery, shared interfaces between devices, and an application specification interface that promotes creating device-agnostic applications capable of operating even when devices change. We demonstrate the efficacy of our architecture with two application case studies that highlight the abstraction layers between applications and devices and employ the embeddability of our system to add new functionality to existing systems.

Content has been formatted for web. Click to view raw TeX

Network connectivity is often one of the most challenging aspects of deploying
sensors. In many countries, cellular networks provide the most reliable,
highest bandwidth, and greatest coverage option for internet access. While this makes smartphones a seemingly ideal platform to serve as a gateway between sensors and the cloud,
we find that a device designed for
multi-tenant operation and frequent human interaction becomes unreliable
when tasked to continuously run a single application with no human
interaction, a seemingly counter-intuitive result. Further, we find that economy phones cannot physically
withstand continuous operation, resulting in a surprisingly high rate of
permanent device failures in the field. If these observations hold more broadly, they would make mobile phones
poorly suited to a range of sensing applications for which they have been
rumored to hold great promise.

Content has been formatted as TeX source. Click to format for web.

Network connectivity is often one of the most challenging aspects of deploying sensors. In many countries, cellular networks provide the most reliable, highest bandwidth, and greatest coverage option for internet access. While this makes smartphones a seemingly ideal platform to serve as a gateway between sensors and the cloud, we find that a device designed for multi-tenant operation and frequent human interaction becomes unreliable when tasked to continuously run a single application with no human interaction, a seemingly counter-intuitive result. Further, we find that economy phones cannot physically withstand continuous operation, resulting in a surprisingly high rate of permanent device failures in the field. If these observations hold more broadly, they would make mobile phones poorly suited to a range of sensing applications for which they have been rumored to hold great promise.

Content has been formatted for web. Click to view raw TeX

The U.S. Federal Government and commercial partners have identified a critical gap in today's measurement technology---the ability to accurately, inexpensively, and wirelessly submeter building electricity usage at the circuit-level. Such metering technology would enable building owners, operators, and occupants to characterize and curtail electricity use in buildings---a major cost and source of carbon emissions today. Existing circuit-level metering systems are too costly to deploy, due to difficult installation or cumbersome calibration processes, too inaccurate, due to an inability to faithfully calculate power from synchronized current and voltage channels, or too unreliable, due to a strong dependence on a frequently lossy wireless channel. We propose Triumvi, a standalone, self-powered, non-contact, true-power metering system to help make circuit-level metering affordable, accurate, and reliable---in short, usable. In a splitcore current transformer form factor, Triumvi harvests energy to power itself, monitors current and voltage, calculates power, encrypts data, and wirelessly transmits the results. Our prototype can sustain a sample rate of nearly 0.5\,Hz when the load draws at least 360\,W and exhibits an average error of 4.3\% over a load power draw range of 150--600\,W. Triumvi also supports rapid installation, incremental upgrades, metering three phase and high current loads, charge sharing between between meters, and current waveform analysis, creating a highly flexible metering system capable of energy audits, industrial equipment monitoring, and many applications in-between.

Content has been formatted as TeX source. Click to format for web.

The U.S. Federal Government and commercial partners have identified a critical gap in today’s measurement technology—the ability to accurately, inexpensively, and wirelessly submeter building electricity usage at the circuit-level. Such metering technology would enable building owners, operators, and occupants to characterize and curtail electricity use in buildings—a major cost and source of carbon emissions today. Existing circuit-level metering systems are too costly to deploy, due to difficult installation or cumbersome calibration processes, too inaccurate, due to an inability to faithfully calculate power from synchronized current and voltage channels, or too unreliable, due to a strong dependence on a frequently lossy wireless channel. We propose Triumvi, a standalone, self-powered, non-contact, true-power metering system to help make circuit-level metering affordable, accurate, and reliable—in short, usable. In a splitcore current transformer form factor, Triumvi harvests energy to power itself, monitors current and voltage, calculates power, encrypts data, and wirelessly transmits the results. Our prototype can sustain a sample rate of nearly 0.5 Hz when the load draws at least 360 W and exhibits an average error of 4.3% over a load power draw range of 150–600 W. Triumvi also supports rapid installation, incremental upgrades, metering three phase and high current loads, charge sharing between between meters, and current waveform analysis, creating a highly flexible metering system capable of energy audits, industrial equipment monitoring, and many applications in-between.

Content has been formatted for web. Click to view raw TeX

Ultra wideband technology has shown great promise for providing
high-quality location estimation, even in complex indoor multipath
environments, but existing ultra wideband systems require tens to hundreds of
milliwatts during operation.
%
Backscatter communication has demonstrated the viability of astonishingly
low-power tags, but has thus far been restricted to
narrowband systems with low localization resolution.
%
The challenge to combining these complimentary technologies is that they
share a compounding limitation, constrained transmit power. Regulations limit
ultra wideband transmissions to just -41.3\,dBm/MHz, and a backscatter device
can only reflect the power it receives.
%
The solution is long-term integration of this limited power, lifting the
initially imperceptible signal out of the noise.
%
This integration only works while the target is stationary. However, stationary
describes the vast majority of objects, especially lost ones.
% especially things you are trying to find.
%
With this insight, we design Slocalization, a sub-microwatt,
decimeter-accurate localization system that opens a new tradeoff space in
localization systems and realizes an energy, size, and cost point that invites
the localization of every thing.
%
To evaluate this concept,
we implement an energy-harvesting Slocalization tag and find that
Slocalization can recover ultra wideband backscatter
in under fifteen minutes across thirty meters of space
and localize tags with a mean 3D Euclidean error of only 30\,cm.

Content has been formatted as TeX source. Click to format for web.

Ultra wideband technology has shown great promise for providing high-quality location estimation, even in complex indoor multipath environments, but existing ultra wideband systems require tens to hundreds of milliwatts during operation. % Backscatter communication has demonstrated the viability of astonishingly low-power tags, but has thus far been restricted to narrowband systems with low localization resolution. % The challenge to combining these complimentary technologies is that they share a compounding limitation, constrained transmit power. Regulations limit ultra wideband transmissions to just -41.3 dBm/MHz, and a backscatter device can only reflect the power it receives. % The solution is long-term integration of this limited power, lifting the initially imperceptible signal out of the noise. % This integration only works while the target is stationary. However, stationary describes the vast majority of objects, especially lost ones. % especially things you are trying to find. % With this insight, we design Slocalization, a sub-microwatt, decimeter-accurate localization system that opens a new tradeoff space in localization systems and realizes an energy, size, and cost point that invites the localization of every thing. % To evaluate this concept, we implement an energy-harvesting Slocalization tag and find that Slocalization can recover ultra wideband backscatter in under fifteen minutes across thirty meters of space and localize tags with a mean 3D Euclidean error of only 30 cm.

Content has been formatted for web. Click to view raw TeX

City-scale sensing holds the promise of enabling a deeper understanding of our
urban environments.
However, a city-scale deployment requires physical installation,
power management, and communications---all challenging tasks standing between a
good idea and a realized one.
This indicates the need for a platform that enables easy deployment and
experimentation for applications operating at city scale.
To address these challenges, we present Signpost, a modular, energy-harvesting platform for city-scale sensing.
Signpost simplifies deployment by eliminating the need
for connection to wired infrastructure and instead harvesting energy from an
integrated solar panel. The platform furnishes the key resources necessary to
support multiple, pluggable sensor modules while providing
fair, safe, and reliable sharing in the face of dynamic energy constraints.
We deploy Signpost with several sensor modules, showing the viability
of an energy-harvesting, multi-tenant, sensing system, and evaluate its
ability to support sensing applications.
We believe Signpost reduces the difficulty inherent in city-scale deployments,
enables new experimentation, and provides improved insights into urban health.

Content has been formatted as TeX source. Click to format for web.

City-scale sensing holds the promise of enabling a deeper understanding of our urban environments. However, a city-scale deployment requires physical installation, power management, and communications—all challenging tasks standing between a good idea and a realized one. This indicates the need for a platform that enables easy deployment and experimentation for applications operating at city scale. To address these challenges, we present Signpost, a modular, energy-harvesting platform for city-scale sensing. Signpost simplifies deployment by eliminating the need for connection to wired infrastructure and instead harvesting energy from an integrated solar panel. The platform furnishes the key resources necessary to support multiple, pluggable sensor modules while providing fair, safe, and reliable sharing in the face of dynamic energy constraints. We deploy Signpost with several sensor modules, showing the viability of an energy-harvesting, multi-tenant, sensing system, and evaluate its ability to support sensing applications. We believe Signpost reduces the difficulty inherent in city-scale deployments, enables new experimentation, and provides improved insights into urban health.

Content has been formatted for web. Click to view raw TeX

Low-power microcontrollers lack some of the hardware features and
memory resources that enable multiprogrammable systems.  Accordingly,
microcontroller-based operating systems have not provided important
features like fault isolation, dynamic memory allocation, and
flexible concurrency. However, an emerging class of embedded
applications are software platforms, rather than single purpose
devices, and need these multiprogramming features.  Tock, a new
operating system for low-power platforms, takes advantage of limited
hardware-protection mechanisms as well as the type-safety features of the
Rust programming language to provide a multiprogramming environment
for microcontrollers.  Tock isolates software faults, provides
memory protection, and efficiently manages memory for dynamic
application workloads written in any language. It achieves this while
retaining the dependability requirements of long-running applications.

Content has been formatted as TeX source. Click to format for web.

Low-power microcontrollers lack some of the hardware features and memory resources that enable multiprogrammable systems. Accordingly, microcontroller-based operating systems have not provided important features like fault isolation, dynamic memory allocation, and flexible concurrency. However, an emerging class of embedded applications are software platforms, rather than single purpose devices, and need these multiprogramming features. Tock, a new operating system for low-power platforms, takes advantage of limited hardware-protection mechanisms as well as the type-safety features of the Rust programming language to provide a multiprogramming environment for microcontrollers. Tock isolates software faults, provides memory protection, and efficiently manages memory for dynamic application workloads written in any language. It achieves this while retaining the dependability requirements of long-running applications.

Content has been formatted for web. Click to view raw TeX

As personal fabrication becomes increasingly accessible and popular,
a larger number of makers, many without formal training,
are dabbling in embedded and electronics design. However, existing
general-purpose, board-level circuit design techniques do not
share desirable properties of modern software development, like
rich abstraction layers and automated compiler checks, which facilitate
powerful tools that ultimately lower the barrier to entry for
programming, by allowing a higher level of design—separating specification
from implementation—and providing automated guidance
and feedback. In this paper, we present a novel methodology for
embedded design generation that allows the generation of complete
designs from high-level specifications. We present an implementation
capable of synthesizing a variety of examples to show that
our approach is viable. Starting from user-specified requirements
and a library of available components, our tool encodes the design
space as a system of constraints. Off-the-shelf solvers then reason
over these constraints to produce a block diagram containing sufficient
information to generate the finalized device firmware, bill of
materials, and circuit netlist.

Content has been formatted as TeX source. Click to format for web.

As personal fabrication becomes increasingly accessible and popular, a larger number of makers, many without formal training, are dabbling in embedded and electronics design. However, existing general-purpose, board-level circuit design techniques do not share desirable properties of modern software development, like rich abstraction layers and automated compiler checks, which facilitate powerful tools that ultimately lower the barrier to entry for programming, by allowing a higher level of design—separating specification from implementation—and providing automated guidance and feedback. In this paper, we present a novel methodology for embedded design generation that allows the generation of complete designs from high-level specifications. We present an implementation capable of synthesizing a variety of examples to show that our approach is viable. Starting from user-specified requirements and a library of available components, our tool encodes the design space as a system of constraints. Off-the-shelf solvers then reason over these constraints to produce a block diagram containing sufficient information to generate the finalized device firmware, bill of materials, and circuit netlist.

Content has been formatted for web. Click to view raw TeX

We present a method for calibration-free, infrastructure-free localization
in sensor networks. Our strategy is to estimate node positions and
noise distributions of all links in the network \emph{simultaneously}
-- a strategy that has not been attempted thus far. In particular, we
account for biased, non-line-of-sight (NLOS) range measurements from
ultra-wideband (UWB) devices that lead to multi-modal noise
distributions, for which few solutions exist to date. Our approach
circumvents cumbersome a-priori calibration, allows for rapid
deployment in unknown environments, and facilitates adaptation to
changing conditions. Our first contribution is a generalization of the
classical multidimensional scaling algorithm to account for
measurements that have multi-modal error distributions. Our second
contribution is an online approach that iterates between node
localization and noise parameter estimation. We validate our method in
3-dimensional networks, (i) through simulation to test the sensitivity
of the algorithm on its design parameters, and (ii) through physical
experimentation in a NLOS environment. Our setup uses UWB devices that
provide time-of-flight measurements, which can lead to positively
biased distance measurements in NLOS conditions. We show that our
algorithm converges to accurate position estimates, even when initial
position estimates are very uncertain, initial error models are
unknown, and a significant proportion of the network links are in
NLOS.

Content has been formatted as TeX source. Click to format for web.

We present a method for calibration-free, infrastructure-free localization in sensor networks. Our strategy is to estimate node positions and noise distributions of all links in the network simultaneously – a strategy that has not been attempted thus far. In particular, we account for biased, non-line-of-sight (NLOS) range measurements from ultra-wideband (UWB) devices that lead to multi-modal noise distributions, for which few solutions exist to date. Our approach circumvents cumbersome a-priori calibration, allows for rapid deployment in unknown environments, and facilitates adaptation to changing conditions. Our first contribution is a generalization of the classical multidimensional scaling algorithm to account for measurements that have multi-modal error distributions. Our second contribution is an online approach that iterates between node localization and noise parameter estimation. We validate our method in 3-dimensional networks, (i) through simulation to test the sensitivity of the algorithm on its design parameters, and (ii) through physical experimentation in a NLOS environment. Our setup uses UWB devices that provide time-of-flight measurements, which can lead to positively biased distance measurements in NLOS conditions. We show that our algorithm converges to accurate position estimates, even when initial position estimates are very uncertain, initial error models are unknown, and a significant proportion of the network links are in NLOS.

Content has been formatted for web. Click to view raw TeX

We present SurePoint, a system for drop-in, high-fidelity indoor localization.
SurePoint builds on recently available commercial ultra-wideband radio hardware.
While ultra-wideband radio hardware can provide the timing primitives necessary
for a simple adaptation of two-way ranging, we show that with the addition of
frequency and spatial diversity, we can achieve a 53\% decrease in median
ranging error.
Because this extra diversity requires many additional packets for each range
estimate, we next develop an efficient broadcast ranging protocol for
localization that ameliorates this overhead.
We evaluate the performance of this ranging protocol in stationary and
fast-moving environments and find that it achieves up to 0.08\,m median error
and 0.53\,m 99th percentile error.
As ranging requires the tag to have exclusive access to the channel, we next
develop a protocol to coordinate the localization of multiple tags in space.
This protocol builds on recent work exploiting the constructive interference
phenomenon. The ultra-wideband PHY uses a different modulation scheme
compared to the narrowband PHY used by previous work, thus we first explore the
viability and performance of constructive interference with ultra-wideband
radios.
Finally, as the ranging protocol requires careful management of the
ultra-wideband radio and tight timing, we develop TriPoint, a dedicated
``drop-in'' ranging module that provides a simple \iic interface. We show that
this additional microcontroller demands only marginal energy overhead while
facilitating interoperability by freeing the primary microcontroller to
handle other tasks.

Content has been formatted as TeX source. Click to format for web.

We present SurePoint, a system for drop-in, high-fidelity indoor localization. SurePoint builds on recently available commercial ultra-wideband radio hardware. While ultra-wideband radio hardware can provide the timing primitives necessary for a simple adaptation of two-way ranging, we show that with the addition of frequency and spatial diversity, we can achieve a 53% decrease in median ranging error. Because this extra diversity requires many additional packets for each range estimate, we next develop an efficient broadcast ranging protocol for localization that ameliorates this overhead. We evaluate the performance of this ranging protocol in stationary and fast-moving environments and find that it achieves up to 0.08 m median error and 0.53 m 99th percentile error. As ranging requires the tag to have exclusive access to the channel, we next develop a protocol to coordinate the localization of multiple tags in space. This protocol builds on recent work exploiting the constructive interference phenomenon. The ultra-wideband PHY uses a different modulation scheme compared to the narrowband PHY used by previous work, thus we first explore the viability and performance of constructive interference with ultra-wideband radios. Finally, as the ranging protocol requires careful management of the ultra-wideband radio and tight timing, we develop TriPoint, a dedicated “drop-in” ranging module that provides a simple I²C interface. We show that this additional microcontroller demands only marginal energy overhead while facilitating interoperability by freeing the primary microcontroller to handle other tasks.

Content has been formatted for web. Click to view raw TeX

We present Monoxalyze, a keychain-sized, Bluetooth-based, carbon
monoxide breathalyzer that aims to enable mobile, scalable smoking
cessation intervention programs. These intervention programs have
been shown to greatly increase the rate of a quit attempt, which in
turn decreases the rate of smoking, a major public health problem
that still affects over one billion people around the world.
Currently, intervention programs verify cessation compliance by
requiring pro- gram participants to periodically visit clinics and
exhale through large, expensive carbon monoxide breathalyzers—a
practice that cannot scale to one billion smokers. Monoxalyze
enables mobile ces- sation verification by working with a user’s
smartphone to establish a ring of spatio-temporal transitive trust
between the Monoxalyze device, the user, and the smartphone, a
concept that can be applied to many third-party monitoring
applications. In Monoxalyze, the links of this trust are
represented by simultaneous exhalation verification, facial
recognition, and device-to-phone visible light authentication. In
our evaluation, we show that Monoxalyze lasts over 80 days be-
tween charges, and has the ability to verify a Monoxalyze user.
With a small user study we show that Monoxalyze determines smoking
cessation with 92% accuracy, a level comparable with commercial CO
breathalyzers. Further contributions describe the design
decisions behind creating a low-power BLE device.

Content has been formatted as TeX source. Click to format for web.

We present Monoxalyze, a keychain-sized, Bluetooth-based, carbon monoxide breathalyzer that aims to enable mobile, scalable smoking cessation intervention programs. These intervention programs have been shown to greatly increase the rate of a quit attempt, which in turn decreases the rate of smoking, a major public health problem that still affects over one billion people around the world. Currently, intervention programs verify cessation compliance by requiring pro- gram participants to periodically visit clinics and exhale through large, expensive carbon monoxide breathalyzers—a practice that cannot scale to one billion smokers. Monoxalyze enables mobile ces- sation verification by working with a user’s smartphone to establish a ring of spatio-temporal transitive trust between the Monoxalyze device, the user, and the smartphone, a concept that can be applied to many third-party monitoring applications. In Monoxalyze, the links of this trust are represented by simultaneous exhalation verification, facial recognition, and device-to-phone visible light authentication. In our evaluation, we show that Monoxalyze lasts over 80 days be- tween charges, and has the ability to verify a Monoxalyze user. With a small user study we show that Monoxalyze determines smoking cessation with 92% accuracy, a level comparable with commercial CO breathalyzers. Further contributions describe the design decisions behind creating a low-power BLE device.

Content has been formatted for web. Click to view raw TeX

We introduce \emph{Harmonium}, a novel ultra-wideband RF localization architecture
that achieves decimeter-scale accuracy indoors.
Harmonium strikes a balance between tag simplicity and processing complexity to
provide fast and accurate indoor location estimates.
Harmonium uses only commodity components and consists of a small, inexpensive,
lightweight, and FCC-compliant ultra-wideband transmitter or \emph{tag}, fixed
infrastructure \emph{anchors} with known locations, and centralized processing
that calculates the tag's position.
Anchors employ a new frequency-stepped narrowband receiver architecture that
rejects narrowband interferers and
extracts high-resolution timing information without the cost or complexity of
traditional ultra-wideband approaches.
In a complex indoor environment, 90\% of position estimates obtained with
Harmonium exhibit less than 31\,cm of error with an average 9\,cm of inter-sample
noise. In non-line-of-sight conditions (i.e. through-wall), 90\% of position error is less than
42\,cm.
The tag draws 75\,mW when actively transmitting, or 3.9\,mJ per location fix
at the 19\,Hz update rate.  Tags weigh 3\,g and cost \$4.50\,USD at modest
volumes.
Harmonium introduces a new design point for indoor localization
and enables localization of small, fast objects such as micro quadrotors,
devices previously restricted to expensive optical motion capture
systems.

Content has been formatted as TeX source. Click to format for web.

We introduce Harmonium, a novel ultra-wideband RF localization architecture that achieves decimeter-scale accuracy indoors. Harmonium strikes a balance between tag simplicity and processing complexity to provide fast and accurate indoor location estimates. Harmonium uses only commodity components and consists of a small, inexpensive, lightweight, and FCC-compliant ultra-wideband transmitter or tag, fixed infrastructure anchors with known locations, and centralized processing that calculates the tag’s position. Anchors employ a new frequency-stepped narrowband receiver architecture that rejects narrowband interferers and extracts high-resolution timing information without the cost or complexity of traditional ultra-wideband approaches. In a complex indoor environment, 90% of position estimates obtained with Harmonium exhibit less than 31 cm of error with an average 9 cm of inter-sample noise. In non-line-of-sight conditions (i.e. through-wall), 90% of position error is less than 42 cm. The tag draws 75 mW when actively transmitting, or 3.9 mJ per location fix at the 19 Hz update rate. Tags weigh 3 g and cost $4.50 USD at modest volumes. Harmonium introduces a new design point for indoor localization and enables localization of small, fast objects such as micro quadrotors, devices previously restricted to expensive optical motion capture systems.

Content has been formatted for web. Click to view raw TeX

We present PowerBlade, the smallest, lowest cost, and lowest power
AC plug-load meter that measures real, reactive and apparent power, and
reports this data, along with cumulative energy consumption, over an
industry-standard Bluetooth Low Energy radio.  Achieving this design
point requires revisiting every aspect of conventional power meters: a
new method of acquiring voltage; a non-invasive, planar method of
current measurement; an efficient and accurate method of computing
power from the voltage and current channels; a radio interface that
leverages nearby smart phones to display data and report it to the
cloud; and a retro power supply re-imagined with vastly lower current
draw, allowing extreme miniaturization.  PowerBlade occupies a mere 1"
$\times$ 1" footprint, offers a 1/16" profile, draws less
than 180~mW itself, offers 1.13\% error on unity power factor loads in
the 2-1200~W range and slightly worse for non-linear and reactive
loads, and costs \$11 in modest quantities of about 1,000 units.  This
new design point enables affordable large-scale studies of plug-load
energy usage---an area of growing national importance.

Content has been formatted as TeX source. Click to format for web.

We present PowerBlade, the smallest, lowest cost, and lowest power AC plug-load meter that measures real, reactive and apparent power, and reports this data, along with cumulative energy consumption, over an industry-standard Bluetooth Low Energy radio. Achieving this design point requires revisiting every aspect of conventional power meters: a new method of acquiring voltage; a non-invasive, planar method of current measurement; an efficient and accurate method of computing power from the voltage and current channels; a radio interface that leverages nearby smart phones to display data and report it to the cloud; and a retro power supply re-imagined with vastly lower current draw, allowing extreme miniaturization. PowerBlade occupies a mere 1" × 1" footprint, offers a 1/16" profile, draws less than 180 mW itself, offers 1.13% error on unity power factor loads in the 2-1200 W range and slightly worse for non-linear and reactive loads, and costs $11 in modest quantities of about 1,000 units. This new design point enables affordable large-scale studies of plug-load energy usage—an area of growing national importance.

Content has been formatted for web. Click to view raw TeX

As we show in this paper, I/O has become the limiting
factor in scaling down size and power toward the goal of
invisible computing. Achieving this goal will require
composing optimized and specialized---yet
reusable---components with an interconnect that permits
tiny, ultra-low power systems.  In contrast to today's
interconnects which are limited by power-hungry
pull-ups or high-overhead chip-select lines, our
approach provides a superset of common bus features but
at lower power, with fixed area and pin count, using
fully synthesizable logic, and with surprisingly low
protocol overhead.

We present \textbf{MBus}, a new 4-pin,
22.6\,pJ/bit/chip chip-to-chip interconnect made of two
``shoot-through'' rings.  MBus facilitates ultra-low
power system operation by implementing automatic
power-gating of each chip in the system, easing the
integration of active, inactive, and activating
circuits on a single die.  In addition, we introduce a
new bus primitive: power oblivious communication, which
guarantees message reception regardless of the
recipient's power state when a message is sent.  This
disentangles power management from communication,
greatly simplifying the creation of viable, modular,
and heterogeneous systems that operate on the order of
nanowatts.

To evaluate the viability, power, performance,
overhead, and scalability of our design, we build both
hardware and software implementations of MBus and show
its seamless operation across two FPGAs and twelve
custom chips from three different semiconductor
processes.  A three-chip, 2.2\,mm$^3$ MBus system draws
8\,nW of total system standby power and uses only
22.6\,pJ/bit/chip for communication.  This is the
lowest power for any system bus with MBus's feature
set.

Content has been formatted as TeX source. Click to format for web.

As we show in this paper, I/O has become the limiting factor in scaling down size and power toward the goal of invisible computing. Achieving this goal will require composing optimized and specialized—yet reusable—components with an interconnect that permits tiny, ultra-low power systems. In contrast to today’s interconnects which are limited by power-hungry pull-ups or high-overhead chip-select lines, our approach provides a superset of common bus features but at lower power, with fixed area and pin count, using fully synthesizable logic, and with surprisingly low protocol overhead.

We present MBus, a new 4-pin, 22.6 pJ/bit/chip chip-to-chip interconnect made of two “shoot-through” rings. MBus facilitates ultra-low power system operation by implementing automatic power-gating of each chip in the system, easing the integration of active, inactive, and activating circuits on a single die. In addition, we introduce a new bus primitive: power oblivious communication, which guarantees message reception regardless of the recipient’s power state when a message is sent. This disentangles power management from communication, greatly simplifying the creation of viable, modular, and heterogeneous systems that operate on the order of nanowatts.

To evaluate the viability, power, performance, overhead, and scalability of our design, we build both hardware and software implementations of MBus and show its seamless operation across two FPGAs and twelve custom chips from three different semiconductor processes. A three-chip, 2.2 mm³ MBus system draws 8 nW of total system standby power and uses only 22.6 pJ/bit/chip for communication. This is the lowest power for any system bus with MBus’s feature set.

Content has been formatted for web. Click to view raw TeX

Power meters are critical for submetering loads in residential and commercial settings, but high installation cost and complexity hamper their broader adoption. Recent approaches address installation burdens by proposing non-invasive meters that easily clip onto a wire, or stick onto a circuit breaker, to perform contactless metering. Unfortunately, these designs require regular maintenance (e.g.\ battery replacement) or reduce measurement accuracy (e.g.\ work poorly with non-unity power factors). This paper presents Gemini, a new design point in the power metering space. Gemini addresses the drawbacks of prior approaches by decoupling and distributing the AC voltage and current measurement acquisitions, and recombining them wirelessly using a low-bandwidth approach, to offer non-invasive real, reactive, and apparent power metering. Battery maintenance is eliminated by using an energy-harvesting design that enables the meter to power itself using a current transformer. Accuracy is substantially improved over other non-invasive meters by virtualizing the voltage channel---effectively allowing the meter to calculate power as if it could directly measure voltage (since true power requires sample-by-sample multiplication of current and voltage measurements acquired with tight timing constraints). Collectively, these improvements result in a new design point that meters resistive loads with 0.6\,W average error and a range of reactive and switching loads with 2.2\,W average error---matching commercial, mains-powered solutions.

Content has been formatted as TeX source. Click to format for web.

Power meters are critical for submetering loads in residential and commercial settings, but high installation cost and complexity hamper their broader adoption. Recent approaches address installation burdens by proposing non-invasive meters that easily clip onto a wire, or stick onto a circuit breaker, to perform contactless metering. Unfortunately, these designs require regular maintenance (e.g. battery replacement) or reduce measurement accuracy (e.g. work poorly with non-unity power factors). This paper presents Gemini, a new design point in the power metering space. Gemini addresses the drawbacks of prior approaches by decoupling and distributing the AC voltage and current measurement acquisitions, and recombining them wirelessly using a low-bandwidth approach, to offer non-invasive real, reactive, and apparent power metering. Battery maintenance is eliminated by using an energy-harvesting design that enables the meter to power itself using a current transformer. Accuracy is substantially improved over other non-invasive meters by virtualizing the voltage channel—effectively allowing the meter to calculate power as if it could directly measure voltage (since true power requires sample-by-sample multiplication of current and voltage measurements acquired with tight timing constraints). Collectively, these improvements result in a new design point that meters resistive loads with 0.6 W average error and a range of reactive and switching loads with 2.2 W average error—matching commercial, mains-powered solutions.

Content has been formatted for web. Click to view raw TeX

Understanding building usage patterns and resource consumption,
particularly for existing buildings, requires a sensing infrastructure
for the building. Often, deploying these sensors and obtaining
real-time information is hindered by installation and maintenance
difficulties resulting from scaling down and powering these devices.
Devices that rely on batteries are limited by the scale of the
batteries and the maintenance cost of replacing them while AC mains
powered sensors incur high upfront installation costs. To mitigate
these burdens, we present a new architecture for designing
building-monitoring focused energy-harvesting sensors. The key to this
architecture is masking the inevitable inter-mittency provided by
energy-harvesting with a trigger abstraction that activates the device
only when there is useful work to be done. In this paper, we describe
our architecture and demonstrate how it supports existing
energy-harvesting sensor designs. Further, we realize three additional
design points within the architecture and demonstrate how the sensors
are effective at building monitoring and event detection. The sensors,
however, are classically disruptive: they improve ease of installation
and maintenance, but to do so, they sacrifice some fidelity and
reliability. Whether this trade-off is acceptable remains to be
explored, but the technology needed to do so is now here.

Content has been formatted as TeX source. Click to format for web.

Understanding building usage patterns and resource consumption, particularly for existing buildings, requires a sensing infrastructure for the building. Often, deploying these sensors and obtaining real-time information is hindered by installation and maintenance difficulties resulting from scaling down and powering these devices. Devices that rely on batteries are limited by the scale of the batteries and the maintenance cost of replacing them while AC mains powered sensors incur high upfront installation costs. To mitigate these burdens, we present a new architecture for designing building-monitoring focused energy-harvesting sensors. The key to this architecture is masking the inevitable inter-mittency provided by energy-harvesting with a trigger abstraction that activates the device only when there is useful work to be done. In this paper, we describe our architecture and demonstrate how it supports existing energy-harvesting sensor designs. Further, we realize three additional design points within the architecture and demonstrate how the sensors are effective at building monitoring and event detection. The sensors, however, are classically disruptive: they improve ease of installation and maintenance, but to do so, they sacrifice some fidelity and reliability. Whether this trade-off is acceptable remains to be explored, but the technology needed to do so is now here.

Content has been formatted for web. Click to view raw TeX

Currently, researchers study face-to-face interactions using wearable sensors and
smartphones which provide 2 to 5~m proximity sensing every 20 to 300~s.
However, studying interaction distance, which is known to
impact disease spread, communication behavior, and other phenomenon, has
proven challenging.
Smartphones are limited by their inaccurate and/or
impractical ranging capabilities, while wearable sensors
are limited by their need for
infrastructure nodes, bulkiness, and/or inaccurate ranging.
To address these challenges, we present Opo, a 14~cm$^2$, 11.4~g ``lapel pin''
built from commercial components.
Opo sensors range neighbors every 2~s up to 2~m away with 5\% average
error, all while requiring zero
infrastructure and improving upon current wearable sensors' accuracy and power usage.
The cornerstone of Opo is an ultrasonic wakeup circuit that draws 19~\uA
when no neighbors are present. This enables Opo sensors to discover and range
neighbors without the need for infrastructure nodes and slow or power-hungry
RF discovery protocols.
Thus, Opo is able to sense interaction distance with high accuracy (5~cm)
and temporal fidelity (2~s) on a limited power budget.

Content has been formatted as TeX source. Click to format for web.

Currently, researchers study face-to-face interactions using wearable sensors and smartphones which provide 2 to 5 m proximity sensing every 20 to 300 s. However, studying interaction distance, which is known to impact disease spread, communication behavior, and other phenomenon, has proven challenging. Smartphones are limited by their inaccurate and/or impractical ranging capabilities, while wearable sensors are limited by their need for infrastructure nodes, bulkiness, and/or inaccurate ranging. To address these challenges, we present Opo, a 14 cm², 11.4 g “lapel pin” built from commercial components. Opo sensors range neighbors every 2 s up to 2 m away with 5% average error, all while requiring zero infrastructure and improving upon current wearable sensors’ accuracy and power usage. The cornerstone of Opo is an ultrasonic wakeup circuit that draws 19 μA when no neighbors are present. This enables Opo sensors to discover and range neighbors without the need for infrastructure nodes and slow or power-hungry RF discovery protocols. Thus, Opo is able to sense interaction distance with high accuracy (5 cm) and temporal fidelity (2 s) on a limited power budget.

Content has been formatted for web. Click to view raw TeX

We propose an ultra-low power interconnect bus for
millimeter-scale wireless sensor nodes. Using only 4~IO pads,
the bus minimizes the required chip real estate, enabling
ultra-small form factors in modular sensor node
designs. Low power is achieved using a ``clockless''
design of member nodes while aggressive power gating
allows an ultra-low power standby mode with only
53~gates powered on. An integrated wakeup scheme is
compatible with PMUs that have a special low power
standby mode. The MBus is fully synthesizable and uses
robust timing. Implemented in a 3~module system in
180~nm technology, Mbus achieves 8~nW of standby power
and 17.5~pJ/bit/chip.

Content has been formatted as TeX source. Click to format for web.

We propose an ultra-low power interconnect bus for millimeter-scale wireless sensor nodes. Using only 4 IO pads, the bus minimizes the required chip real estate, enabling ultra-small form factors in modular sensor node designs. Low power is achieved using a “clockless” design of member nodes while aggressive power gating allows an ultra-low power standby mode with only 53 gates powered on. An integrated wakeup scheme is compatible with PMUs that have a special low power standby mode. The MBus is fully synthesizable and uses robust timing. Implemented in a 3 module system in 180 nm technology, Mbus achieves 8 nW of standby power and 17.5 pJ/bit/chip.

Content has been formatted for web. Click to view raw TeX

We explore the indoor positioning problem with unmodified smartphones
and slightly-modified commercial LED luminaires.  The
luminaires---modified to allow rapid, on-off keying---transmit their
identifiers and/or locations encoded in human-imperceptible optical
pulses.  A camera-equipped smartphone, using just a single image frame
capture, can detect the presence of the luminaires in the image,
decode their transmitted identifiers and/or locations, and determine
the smartphone's location and orientation relative to the luminaires.
Continuous image capture and processing enables continuous position
updates.
The key insights underlying this work are (i) the driver circuits of
emerging LED lighting systems can be easily modified to transmit data
through on-off keying; (ii) the rolling shutter effect of CMOS imagers
can be leveraged to receive many bits of data encoded in the optical
transmissions with just a single frame capture, (iii) a camera is
intrinsically an angle-of-arrival sensor, so the projection of
multiple nearby light sources with known positions onto a camera's
image plane can be framed as an instance of a sufficiently-constrained
angle-of-arrival localization problem, and (iv) this problem can be
solved with optimization techniques.  We explore the feasibility of
the design through an analytical model, demonstrate the viability of
the design through a prototype system, discuss the challenges to a
practical deployment including usability and scalability, and
demonstrate decimeter-level accuracy in both carefully controlled and
more realistic human mobility scenarios.

Content has been formatted as TeX source. Click to format for web.

We explore the indoor positioning problem with unmodified smartphones and slightly-modified commercial LED luminaires. The luminaires—modified to allow rapid, on-off keying—transmit their identifiers and/or locations encoded in human-imperceptible optical pulses. A camera-equipped smartphone, using just a single image frame capture, can detect the presence of the luminaires in the image, decode their transmitted identifiers and/or locations, and determine the smartphone’s location and orientation relative to the luminaires. Continuous image capture and processing enables continuous position updates. The key insights underlying this work are (i) the driver circuits of emerging LED lighting systems can be easily modified to transmit data through on-off keying; (ii) the rolling shutter effect of CMOS imagers can be leveraged to receive many bits of data encoded in the optical transmissions with just a single frame capture, (iii) a camera is intrinsically an angle-of-arrival sensor, so the projection of multiple nearby light sources with known positions onto a camera’s image plane can be framed as an instance of a sufficiently-constrained angle-of-arrival localization problem, and (iv) this problem can be solved with optimization techniques. We explore the feasibility of the design through an analytical model, demonstrate the viability of the design through a prototype system, discuss the challenges to a practical deployment including usability and scalability, and demonstrate decimeter-level accuracy in both carefully controlled and more realistic human mobility scenarios.

Content has been formatted for web. Click to view raw TeX

The Internet of Things (IoT) is a rapidly emerging
application space, poised to become the largest
electronics market for the semiconductor industry. IoT
devices are focused on sensing and actuating of our
physical environment and have a nearly unlimited
breadth of uses. In this paper, we explore the IoT
application space and then identify two common
challenges that exist across this space: ultra-low
power operation and system design using modular,
composable components. We survey recent low power
techniques and discuss a low power bus that enables
modular design. Finally, we conclude with three example
ultra-low power, millimeter-scale IoT systems.

Content has been formatted as TeX source. Click to format for web.

The Internet of Things (IoT) is a rapidly emerging application space, poised to become the largest electronics market for the semiconductor industry. IoT devices are focused on sensing and actuating of our physical environment and have a nearly unlimited breadth of uses. In this paper, we explore the IoT application space and then identify two common challenges that exist across this space: ultra-low power operation and system design using modular, composable components. We survey recent low power techniques and discuss a low power bus that enables modular design. Finally, we conclude with three example ultra-low power, millimeter-scale IoT systems.

Content has been formatted for web. Click to view raw TeX

We present a $2\times4\times4$~mm$^3$ imaging system
complete with optics, wireless communication, battery, power
management, solar harvesting, processor and memory. The system
features a 160$\times$160 resolution CMOS image sensor with 304~nW
continuous in-pixel motion detection mode.  System components
are fabricated in five different IC layers and die-stacked for
minimal form factor.  Photovoltaic (PV) cells face the opposite
direction of the imager for optimal illumination and generate
456~nW at 10~klux to enable energy autonomous system operation.

Content has been formatted as TeX source. Click to format for web.

We present a 2×4×4 mm³ imaging system complete with optics, wireless communication, battery, power management, solar harvesting, processor and memory. The system features a 160×160 resolution CMOS image sensor with 304 nW continuous in-pixel motion detection mode. System components are fabricated in five different IC layers and die-stacked for minimal form factor. Photovoltaic (PV) cells face the opposite direction of the imager for optimal illumination and generate 456 nW at 10 klux to enable energy autonomous system operation.

Content has been formatted for web. Click to view raw TeX

Current submetering systems suffer from prohibitive device costs, invasive
installations, and burdensome maintenance.  In this paper we present
Deltaflow, a submetering system that can estimate the power draw of individual
loads by augmenting aggregate measurements with very simple sensors. The key
insight is that we can drastically reduce sensor complexity by encoding
information in the mere existence of a radio transmission, rather than the
contents of that transmission. A sensor consisting simply of a radio and an
energy-harvesting power supply tuned to harvest a side-channel emission of
energy consumption (e.g. light, heat, magnetic field, vibration) will
exhibit an activation frequency that is correlated with the power draw of the
load to which it is affixed. These sensors report their activations to the
data-processing backend, which can determine the actual power draw by
incorporating ground truth aggregate measurements such as those provided by
utility meters. The server maps sensor activations to energy consumption by observing
when the aggregate measurement and the sensor activation frequency change simultaneously.
The server iteratively partitions the system history into discrete states which are
used to construct and solve instances of a linear optimization problem. Solutions
to the problem reveal the mapping from pulse frequencies to individual load power draw.
This systems approach to submetering results in deployments that are easy to
install and maintain, while contributing zero additional load, enabling building
owners and occupants to simply affix tags to energy consumers and automatically begin
receiving real-time power draw readings.

Content has been formatted as TeX source. Click to format for web.

Current submetering systems suffer from prohibitive device costs, invasive installations, and burdensome maintenance. In this paper we present Deltaflow, a submetering system that can estimate the power draw of individual loads by augmenting aggregate measurements with very simple sensors. The key insight is that we can drastically reduce sensor complexity by encoding information in the mere existence of a radio transmission, rather than the contents of that transmission. A sensor consisting simply of a radio and an energy-harvesting power supply tuned to harvest a side-channel emission of energy consumption (e.g. light, heat, magnetic field, vibration) will exhibit an activation frequency that is correlated with the power draw of the load to which it is affixed. These sensors report their activations to the data-processing backend, which can determine the actual power draw by incorporating ground truth aggregate measurements such as those provided by utility meters. The server maps sensor activations to energy consumption by observing when the aggregate measurement and the sensor activation frequency change simultaneously. The server iteratively partitions the system history into discrete states which are used to construct and solve instances of a linear optimization problem. Solutions to the problem reveal the mapping from pulse frequencies to individual load power draw. This systems approach to submetering results in deployments that are easy to install and maintain, while contributing zero additional load, enabling building owners and occupants to simply affix tags to energy consumers and automatically begin receiving real-time power draw readings.

Content has been formatted for web. Click to view raw TeX

Conventional AC power meters perform at least two distinct functions:
power conversion, to supply the meter itself, and energy metering, to
measure the load consumption.  This paper presents Monjolo, a new
energy-metering architecture that combines these two functions to
yield a new design point in the metering space.  The key insight
underlying this work is that the output of a current transformer --
nominally used to measure a load current -- can be harvested and used
to {\em intermittently} power a wireless sensor node.  The hypothesis
is that the node's activation frequency increases monotonically with
the primary load's draw, making it possible to estimate load power
from the interval between activations, assuming the node consumes a
fixed energy quanta during each activation.  This paper explores this
thesis by designing, implementing, and evaluating the Monjolo
metering architecture.  The results demonstrate that it is possible to
build a meter that draws zero-power under zero-load conditions, offers
high accuracy for near-unity power factor loads, works with non-unity
power factor loads in combination with a whole-house meter, wirelessly
reports readings to a data aggregator, is resilient to communication
failures, and is parsimonious with the radio channel, even under heavy
loads.  Monjolo eliminates the high-voltage AC-DC power supply and AC
metering circuitry present in earlier designs, enabling a smaller,
simpler, safer, and lower-cost design point that supports novel
deployment scenarios like non-intrusive circuit-level metering.

Content has been formatted as TeX source. Click to format for web.

Conventional AC power meters perform at least two distinct functions: power conversion, to supply the meter itself, and energy metering, to measure the load consumption. This paper presents Monjolo, a new energy-metering architecture that combines these two functions to yield a new design point in the metering space. The key insight underlying this work is that the output of a current transformer – nominally used to measure a load current – can be harvested and used to intermittently power a wireless sensor node. The hypothesis is that the node’s activation frequency increases monotonically with the primary load’s draw, making it possible to estimate load power from the interval between activations, assuming the node consumes a fixed energy quanta during each activation. This paper explores this thesis by designing, implementing, and evaluating the Monjolo metering architecture. The results demonstrate that it is possible to build a meter that draws zero-power under zero-load conditions, offers high accuracy for near-unity power factor loads, works with non-unity power factor loads in combination with a whole-house meter, wirelessly reports readings to a data aggregator, is resilient to communication failures, and is parsimonious with the radio channel, even under heavy loads. Monjolo eliminates the high-voltage AC-DC power supply and AC metering circuitry present in earlier designs, enabling a smaller, simpler, safer, and lower-cost design point that supports novel deployment scenarios like non-intrusive circuit-level metering.

Content has been formatted for web. Click to view raw TeX

We present AudioDAQ, a new platform for continuous data acquisition
using the headset port of a mobile phone.  AudioDAQ differs from
existing phone peripheral interfaces by drawing all necessary power
from the microphone bias voltage, encoding all data as analog audio,
and leveraging the phone's built-in voice memo application (or a
custom application) for continuous data collection.
These properties make the AudioDAQ design more universal, so it works
across a broad range of phones including sophisticated smart phones
and simpler feature phones, enables simple analog peripherals without
requiring a microcontroller, requires no hardware or software
modifications on the phone itself, uses significantly less power than
prior approaches, and allows continuous data capture over an extended
period of time. The AudioDAQ design is efficient because it draws all
necessary power from the microphone bias voltage, and it is general
because this voltage and a voice memo application are present on most
mobile phones in use today.  We show the viability of our architecture
by evaluating an end-to-end system that can capture EKG signals
continuously for hours and send the data to the cloud for storage,
processing, and visualization.

Content has been formatted as TeX source. Click to format for web.

We present AudioDAQ, a new platform for continuous data acquisition using the headset port of a mobile phone. AudioDAQ differs from existing phone peripheral interfaces by drawing all necessary power from the microphone bias voltage, encoding all data as analog audio, and leveraging the phone’s built-in voice memo application (or a custom application) for continuous data collection. These properties make the AudioDAQ design more universal, so it works across a broad range of phones including sophisticated smart phones and simpler feature phones, enables simple analog peripherals without requiring a microcontroller, requires no hardware or software modifications on the phone itself, uses significantly less power than prior approaches, and allows continuous data capture over an extended period of time. The AudioDAQ design is efficient because it draws all necessary power from the microphone bias voltage, and it is general because this voltage and a voice memo application are present on most mobile phones in use today. We show the viability of our architecture by evaluating an end-to-end system that can capture EKG signals continuously for hours and send the data to the cloud for storage, processing, and visualization.

Content has been formatted for web. Click to view raw TeX

Most modern software-defined radios are large, expensive, and
power-hungry, and this diminishes their utility in low-power,
size-constrained settings like sensor networks and mobile computing.
We explore the viability of scaling down the software radio in size,
cost, and power, and show that an index card-sized, sub-\$150, `AA'
battery-powered system is possible using off-the-shelf components.
Key to our approach is that we leverage an integrated, reconfigurable,
flash-based FPGA with a hard ARM Cortex-M3 microprocessor which
simultaneously enables lower power and tighter hardware/soft\-ware
integration than prior designs.  This architecture allows us to
implement timing-critical MAC protocols and validate the speculated
performance of several recent MAC/PHY primitives and protocols
including Backcast, A-MAC, and Glossy using an IEEE 802.15.4-compliant
radio implementation that interoperates with commercial radios.  The
work also identifies several enhancements in the underlying hardware
components that could improve power, performance, and flexibility.

Content has been formatted as TeX source. Click to format for web.

Most modern software-defined radios are large, expensive, and power-hungry, and this diminishes their utility in low-power, size-constrained settings like sensor networks and mobile computing. We explore the viability of scaling down the software radio in size, cost, and power, and show that an index card-sized, sub-$150, ‘AA’ battery-powered system is possible using off-the-shelf components. Key to our approach is that we leverage an integrated, reconfigurable, flash-based FPGA with a hard ARM Cortex-M3 microprocessor which simultaneously enables lower power and tighter hardware/soft-ware integration than prior designs. This architecture allows us to implement timing-critical MAC protocols and validate the speculated performance of several recent MAC/PHY primitives and protocols including Backcast, A-MAC, and Glossy using an IEEE 802.15.4-compliant radio implementation that interoperates with commercial radios. The work also identifies several enhancements in the underlying hardware components that could improve power, performance, and flexibility.

Content has been formatted for web. Click to view raw TeX

We study the problem of augmenting battery-powered sensornet trees
with energy-harvesting leaf nodes.  Our results show that leaf nodes
that are smaller in size than today's typical battery-powered sensors
can harvest enough energy from ambient sources to acquire and transmit
sensor readings every minute, even under poor lighting conditions.
However, achieving this functionality, especially as leaf nodes scale
in size, requires new platforms, protocols, and programming.
Platforms must be designed around low-leakage operation, offer a
richer power supply control interface for system software, and employ
an unconventional energy storage hierarchy.  Protocols must not only
be low-power, but they must also become low-energy, which affects
initial and ongoing synchronization, and periodic communications.
Systems programming, and especially bootup and communications, must
become low-latency, by eliminating conservative timeouts and startup
dependencies, and embracing high-concurrency.  Applying these
principles, we show that robust, indoor, perpetual sensing is viable
using off-the-shelf technology.

Content has been formatted as TeX source. Click to format for web.

We study the problem of augmenting battery-powered sensornet trees with energy-harvesting leaf nodes. Our results show that leaf nodes that are smaller in size than today’s typical battery-powered sensors can harvest enough energy from ambient sources to acquire and transmit sensor readings every minute, even under poor lighting conditions. However, achieving this functionality, especially as leaf nodes scale in size, requires new platforms, protocols, and programming. Platforms must be designed around low-leakage operation, offer a richer power supply control interface for system software, and employ an unconventional energy storage hierarchy. Protocols must not only be low-power, but they must also become low-energy, which affects initial and ongoing synchronization, and periodic communications. Systems programming, and especially bootup and communications, must become low-latency, by eliminating conservative timeouts and startup dependencies, and embracing high-concurrency. Applying these principles, we show that robust, indoor, perpetual sensing is viable using off-the-shelf technology.

Content has been formatted for web. Click to view raw TeX

Wireless sensor nodes have many compelling applications such
as smart buildings, medical implants, and surveillance
systems. However, existing devices are bulky, measuring
$>$~1\,cm$^3$, and they are hampered by short lifetimes and
fail to realize the ``smart dust'' vision of~[1]. Smart
dust requires a mm$^3$-scale, wireless sensor node with
perpetual energy harvesting. Recently two
application-specific implantable microsystems~[2][3]
demonstrated the potential of a mm$^3$-scale system in
medical applications. However, [3]~is not programmable
and~[2] lacks a method for re-programming or
re-synchronizing once encapsulated. Other practical
issues remain unaddressed, such as a means to protect
the battery during the time period between system
assembly and deployment and the need for flexible
design to enable use in multiple application domains.
To this end, we propose a 1.0\,mm$^3$ general-purpose
heterogeneous sensor node platform with a stackable
multi-layer structure that includes a new, ultra-low
power I$^2$C (Inter-Integrated Circuit) interface for
inter-layer communication. The system has an
ultra-low-power optical wakeup receiver and GOC (Global
Optical Communication), which enables
re-programming and synchronization. It also includes an
ultra-low power PMU (Power Management Unit) with BOD
(Brown-Out Detector) to prevent processor malfunctions
and battery damage. The BOD also controls a POR
(Power-On Reset) module in other layers to enable a
proper reset sequence. Image and temperature sensors
are implemented, but the modularity of the system
allows end-users to easily replace or add layers to
incorporate specific circuits in appropriate
technologies as needed.

Content has been formatted as TeX source. Click to format for web.

Wireless sensor nodes have many compelling applications such as smart buildings, medical implants, and surveillance systems. However, existing devices are bulky, measuring > 1 cm³, and they are hampered by short lifetimes and fail to realize the “smart dust” vision of [1]. Smart dust requires a mm³-scale, wireless sensor node with perpetual energy harvesting. Recently two application-specific implantable microsystems [2][3] demonstrated the potential of a mm³-scale system in medical applications. However, [3] is not programmable and [2] lacks a method for re-programming or re-synchronizing once encapsulated. Other practical issues remain unaddressed, such as a means to protect the battery during the time period between system assembly and deployment and the need for flexible design to enable use in multiple application domains. To this end, we propose a 1.0 mm³ general-purpose heterogeneous sensor node platform with a stackable multi-layer structure that includes a new, ultra-low power I²C (Inter-Integrated Circuit) interface for inter-layer communication. The system has an ultra-low-power optical wakeup receiver and GOC (Global Optical Communication), which enables re-programming and synchronization. It also includes an ultra-low power PMU (Power Management Unit) with BOD (Brown-Out Detector) to prevent processor malfunctions and battery damage. The BOD also controls a POR (Power-On Reset) module in other layers to enable a proper reset sequence. Image and temperature sensors are implemented, but the modularity of the system allows end-users to easily replace or add layers to incorporate specific circuits in appropriate technologies as needed.

Content has been formatted for web. Click to view raw TeX

We endow the mobile phone with a low-cost, open interface that can
parasitically power external peripherals, and transfer data to and
from them, using analog, digital, and serial signaling, using
only the existing headset audio port. This interface, called
HiJack, allows the mobile phone to easily integrate with a
range of external sensors, opening the door to new
phone-centric sensing applications. In this paper, we
characterize the signaling and power delivery capability of
the audio jack, design circuits and software to transfer data
and harvest energy, and evaluate the performance of our
designs. We also use the mobile phone's audio channel to cre-
ate a layered communications stack that supports low-level,
analog signaling and high-level, multiplexed data
communications with external devices. Our design supports a
single, bi-directional communications channel at a data rate
of 8.82 kbps over a Manchester-encoded serial stream, using
just a few discrete components and the hardware peripherals
found in almost any microcontroller. Our harvester delivers
7.4 mW to a load with 47\% efficiency using components that
cost \$2.34 in 10K volume. Integrating the pieces, we present a
combined system for delivering data and power over audio, and
demonstrate its use by turning an iPhone into an inexpensive
oscilloscope, EKG monitor, and soil moisture sensor, all at
price points accessible to most consumers in developing
regions. 

Content has been formatted as TeX source. Click to format for web.

We endow the mobile phone with a low-cost, open interface that can parasitically power external peripherals, and transfer data to and from them, using analog, digital, and serial signaling, using only the existing headset audio port. This interface, called HiJack, allows the mobile phone to easily integrate with a range of external sensors, opening the door to new phone-centric sensing applications. In this paper, we characterize the signaling and power delivery capability of the audio jack, design circuits and software to transfer data and harvest energy, and evaluate the performance of our designs. We also use the mobile phone’s audio channel to cre- ate a layered communications stack that supports low-level, analog signaling and high-level, multiplexed data communications with external devices. Our design supports a single, bi-directional communications channel at a data rate of 8.82 kbps over a Manchester-encoded serial stream, using just a few discrete components and the hardware peripherals found in almost any microcontroller. Our harvester delivers 7.4 mW to a load with 47% efficiency using components that cost $2.34 in 10K volume. Integrating the pieces, we present a combined system for delivering data and power over audio, and demonstrate its use by turning an iPhone into an inexpensive oscilloscope, EKG monitor, and soil moisture sensor, all at price points accessible to most consumers in developing regions.

Content has been formatted for web. Click to view raw TeX