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.

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 entire 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.

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.

Today, most energy harvesting sensors rely on capacitors to buffer small amounts of energy 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. 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 and lifetime of energy harvesting sensors.

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.

City-scale sensing holds the promise of enabling deeper insight into how our urban environments function. Applications such as observing air quality and measuring traffic flows can have powerful impacts, allowing city planners and citizen scientists alike to understand and improve their world. However, the path from conceiving applications to implementing them is fraught with difficulty. A successful city-scale deployment requires physical installation, power management, and communications-all challenging tasks standing between a good idea and a realized one. The Signpost platform, presented at IPSN 2018, has been created to address these challenges. Signpost enables easy deployment by relying on harvested, solar energy and wireless networking rather than their wired counterparts. To further lower the bar to deploying applications, the platform provides the key resources necessary to support its pluggable sensor modules in their distributed sensing tasks. In this demo, we present the Signpost hardware and several applications running on a deployment of Signposts on UC Berkeley's campus, including distributed, energy-adaptive traffic monitoring and fine grained weather reporting. Additionally we show the cloud infrastructure supporting the Signpost deployment, specifically the ability to push new applications and parameters down to existing sensors, with the goal of demonstrating that the existing deployment can serve as a future testbed.

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.

City-scale sensing holds the promise of enabling deeper insight into how our urban environments function. Applications such as observing air quality and measuring sources of noise pollution can have powerful impacts, allowing city planners and citizen scientists alike to understand and improve their world. However, the path from conceiving applications to implementing them is fraught with many challenges. A successful city-scale deployment requires physical installation, power management, and communications---all challenging tasks standing between a good idea and a realized one, suggesting the need for a platform that enables easy deployment and experimentation of city-scale sensing applications. To address these basic challenges, we present Signpost, a modular platform for city-scale sensing. Signpost simplifies deployment and installation in cities by removing the need for connection to wired infrastructure and instead harvesting energy from an integrated solar panel. The platform provides the key resources necessary for its pluggable sensor modules to support city-scale applications. Signpost stores excess energy for later use, distributes energy between modules, and provides communication through multiple wireless protocols. It also offers local storage for sensor data and allows for additional processing in a duty-cycled Linux environment.

The era of city-scale sensing is dawning. Supported by new sensing capabilities, the capability to detect and measure phenomena throughout a large area will allow deeper insight and understanding into how cities work. The challenge of city-scale sensing is not limited to developing new sensing applications, however. A sensor must be installed in a location. It must be provided power, storage, and communications. All these tasks stand aside from the desired sensing effort, but are necessary nevertheless.

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 program 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 cessation 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 between 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.

Building connected, pervasive, human-facing, and responsive applications that incorporate local sensors, smartphone interactions, device actuation, and cloud-based learning--the promised features of the Internet of Things (IoT)---requires a complete suite of tools spanning both hardware and software. We present a set of these pieces, including a gateway, four hardware building blocks, multiple sensor platforms, an indoor localization system, and software for connecting users and devices. Each piece plays an integral role towards enabling applications, from facilitating rapid development of wireless smart devices to composing data streams and services from a diverse set of components. By providing layered interoperable systems, our toolkit offers cohesive support for moving beyond single-device, cloud-centric applications---typical in today's IoT landscape--and towards richer applications that incorporate multiple data streams, human interaction, cloud processing, location awareness, multiple communication protocols, historical data, access control, and on-demand user interfaces. To show how the pieces in the toolkit cooperate, we demonstrate a location-based access control application where a user's smartphone can control a room's lighting, but only from within the room. Further, data streams from the phone and nearby sensors are used to provide a constant lighting service which attempts to maintain a user-set brightness under variable external lighting conditions.

We demonstrate PolyPoint, a high-fidelity RF-based indoor localization system that achieves 28~cm accuracy indoors and tracks a fast-moving quadcopter with only 56~cm average error. PolyPoint uses ultra-wideband signals to achieve high precision RF time-of-flight estimates between nodes. To further improve accuracy, PolyPoint exploits two forms of diversity: frequency diversity, which leverages several ultra-wideband channels to improve channel response, and antenna diversity, which adds three antennas at 120 degree offsets to mitigate the effects of antenna polarization and nulls. PolyPoint introduces an efficient, novel ranging protocol that maximizes these diversity sources with a minimal number of packets.

We are becoming increasingly surrounded by smart and connected devices, popularly known as the Internet of Things. The emerging user interface paradigm for many such things eschews physical buttons, knobs, and displays in favor of virtual interfaces that are downloaded from the web and rendered on remote platforms---like smartphones. However, such smartphone app-based interfaces often require tedious discovery and installation, as well as device discovery, pairing, and configuration before a user can interact with a nearby device. Requiring an explicit app install for each new device type scales poorly with device growth, and particularly hinders casual interactions with ambient devices. Instead of the high-friction, walled-garden approach now taking root, we propose name, a physical web browser that provides a seamless, scalable approach to browsing and interacting with nearby things. name leverages multiple network patterns and modern web technologies to provide users with rich device interfaces, even for devices under network or power constraints. We argue that this approach scales better and that it provides more intuitive and natural functionality for both users and developers. This demo presents the basic concept, allows others to experience our preliminary implementation, and raises several open research questions.