As latency-sensitive IoT applications proliferate, edge computing becomes critical to sustaining real-time performance. Yet, limited edge infrastructure and reliance on costly static task profiling constrain its potential. This paper presents fReeLoaders, an IoT ecosystem addressing both challenges through opportunistic offloading and adaptive scheduling. fReeLoaders uses nearby idle smart devices to augment edge availability and uses a deadline-driven reinforcement learning scheduler to learn task behavior on the fly, eliminating expensive a priori profiling. Evaluation on real hardware shows it improves quality of service by 11.4% over a state-of-the-art profiling scheduler and adapts to dynamic workloads.

Today's computational devices are overwhelmingly wireless. To realize wireless communication, today's devices use a grab bag of protocols (Bluetooth, WiFi, 4G/5G, LoRa, NFC, etc.) and no one universal standard has emerged. This diversity presents a ripe pedagogical opportunity to introduce students to the fundamental tradeoffs and design decisions inherent to wireless communication and networking. Furthermore, many wireless protocols are accessible to study in a classroom (in fact, many we all use daily), which lends to a very hands-on course.

The rise of the Internet of Things (IoT) has increased standby energy consumption due to the growing number of smart devices in homes. Existing approaches use real-time energy data and machine learning to identify and minimize standby energy for residential energy management but rely on cloud-based data aggregation and collaborative training due to limited edge device data. However, such an approach incurs extra cloud service costs, risks personal data leakage, and fails to capture residence diversity, resulting in suboptimal energy management performance.

The Internet of Things (IoT) boom has revolutionized almost every corner of people’s daily lives: healthcare, environment, transportation, manufacturing, supply chain, and so on. With the recent development of sensor and communication technology, IoT artifacts, including smart wearables, cameras, smartwatches, and autonomous systems can accurately measure and perceive their surrounding environment. Continuous sensing generates massive amounts of data and presents challenges for machine learning. Deep learning models (e.g., convolution neural networks and recurrent neural networks) have been extensively employed in solving IoT tasks by learning patterns from multi-modal sensory data. Graph neural networks (GNNs), an emerging and fast-growing family of neural network models, can capture complex interactions within sensor topology and have been demonstrated to achieve state-of-the-art results in numerous IoT learning tasks. In this survey, we present a comprehensive review of recent advances in the application of GNNs to the IoT field, including a deep dive analysis of GNN design in various IoT sensing environments, an overarching list of public data and source codes from the collected publications, and future research directions. To keep track of newly published works, we collect representative papers and their open-source implementations and create a Github repository at GNN4IoT.

Edge computing enables scalability and privacy improvements for Internet of Things (IoT) systems, by shifting applications from the cloud to edge servers closer to IoT devices. Conceptually, IoT devices communicate directly with the edge, but in real-world IoT deployments often IoT gateways are needed to bridge devices and edge servers. Design decisions at this gateway layer directly contribute to the responsiveness of edge applications and scalability of the platform, yet these gateways are often overlooked and under-explored. IoT gateways have a compelling mix of features, including reasonable compute capabilities, low cost, direct contact with devices, and spatial distribution in deployments. We hypothesize that a new management layer that organizes already existing gateways can replace expensive edge servers while enabling the privacy, reliability, and performance benefits of executing IoT applications on the edge. We utilize a decentralized architecture that creates a nexus among disjoint gateways using out-of-band discovery, low-overhead abstraction layers, and runtime application scheduling. This platform supports heterogeneous devices, minimizes configuration overhead, executes applications, and provides resiliency to failure. We develop a prototype of the architecture, NexusEdge, and deploy it across several gateways and hundreds of low-power and energy-harvesting devices. When compared to Amazon's AWS IoT Greengrass, NexusEdge shows a 10x improvement in application latency, and a 2.5x reduction in network traffic, indicating better scalability and responsiveness. We demonstrate how NexusEdge supports applications without cloud support, and envision future extensions of this platform.

The trend of Internet of Things is bringing in millions of new smart devices into homes to increase the quality of human life. However, this enormous number of new devices have also brings in an increasing energy consumption in standby for awaiting wire-less communication or status change. To reduce standby energy, existing approaches use real-time consumption data and machine learning techniques to identify standby energy, but aggregate data or intermediate model training updates in the cloud to collaboratively perform load forecasting, which could directly or indirectly cause personal data leakage, alongside with significant communication bandwidth and extra cloud service monetary cost. On the other hand, such a global collaborative model yields unsatisfactory en-ergy management performance as they fail to capture the diversity of each residence. In this paper, we propose a privacy-preserved, communication-efficient, personalized and cloud-service-free resi-dential energy management system (EMS) with personalized feder-ated deep reinforcement learning (PFDRL) framework to tackle the standby energy reduction in residential building.

Energy-harvesting designs typically include highly entangled app-lication-level and energy-management subsystems that span both hardware and software. This tight integration makes developing sophisticated energy-harvesting systems challenging, as developers have to consider both embedded system development and intermit-tent energy management simultaneously. Even when successful, solutions are often monolithic, produce suboptimal performance, and require substantial effort to translate to a new design. Instead, we propose a new energy-harvesting power management architecture, Altair that offloads all energy-management operations to the power supply itself while making the power supply programmable. Altair introduces an energy supervisor and a standard interface to enable an abstraction layer between the power supply hardware and the running application, making both replaceable and recon-figurable. To ensure minimal resource conflict on the application processor, while running resource-hungry optimization techniques in the supervisor, we implement the Altair design in a lower power microcontroller that runs in parallel with the application. We also develop a programmable power supply module and a software library for seamless application development with Altair. We evaluate the versatility of the proposed architecture across a spectrum of IoT devices and demonstrate the generality of the plat-form. We also design and implement an online energy-management technique using reinforcement learning on top of the platform and compare the performance against fixed duty-cycle baselines. Results indicate that sensors running the online energy-manager perform similar to continuously powered sensors, have a l0x higher event generation rate than the intermittently powered ones, 1.8-7x higher event detection accuracy, experience 50% fewer power failures, and are 44% more available than the sensors that maintain a constant duty-cycle.

Commercial Internet of Things (IoT) deployments are mostly closed-source systems that offer little to no flexibility to modify the hardware and software of the end devices. Once deployed, retrofitting such systems to an upgraded functionality requires replacing all the devices, which can be extremely time and cost prohibitive. End users cannot generally leverage deployed infrastructure to add their own sensors or custom data. However, we observe that IoT systems sometimes report battery voltage information to the cloud, and batteries are often user-serviceable. This indicates that perturbing the battery voltage to encode customized information could be a minimally invasive method to retrofit existing IoT devices.

Recent studies have shown that, experiencing the appropriate lighting environment in our day-to-day life is paramount, as different types of light sources impact our mental and physical health in many ways. Researchers have intercon-nected daylong exposure of natural and artificial lights with circadian health, sleep and productivity. That is why having a generalized system to monitor human light exposure and recommending lighting adjustments can be instrumental for maintaining a healthy lifestyle. At present methods for collecting daylong light exposure information and source identification contain certain limitations. Sensing devices are expensive and power consuming and methods of classifications are either inac-curate or possesses certain limitations. In addition, identifying the source of exposure is challenging for a couple of reasons. For example, spectral based classification can be inaccurate, as different sources share common spectral bands or same source can exhibit variation in spectrum. Also irregularities of sensed information in real world makes scenario complex for source identification. In this work, we are presenting a Low Power BLE enabled Color Sensing Board (LPCSB) for sensing background light parameters. Later, utilizing Machine learning and Neural Network based architectures, we try to pinpoint the prime source in the surrounding among four dissimilar types: Incandescent, LED, CFL and Sunlight. Our experimentation includes 27 distinct bulbs and sunlight data in various weather/time of the day/spaces. After tuning classifiers, we have investigated best parameter settings for indoor deployment and also analyzed robustness of each classifier in several imperfect situations. As observed performance degraded significantly after real world deployment, we include synthetic time series examples and filtered data in the training set for boosting accuracy. Result shows that our best model can detect the primary light source type in the surroundings with accuracy up to 99.30% in familiar and up to 90.25% in unfamiliar real world settings with enlarged training set, which is much elevated than earlier endeavors.

Recent years have seen increasing traction to enable new applications that can localize sensors on the screen of an Augmented Reality (AR) device (e.g. smartphone, tablet) so that sensors can be controlled more intuitively. Despite recent advances in this area, both wireless signal dependent and computer vision based localization solutions have seen a slow acceptance due to signal noise, multipath effect, and limited AR device-sensor interactivity. In this paper, we propose a novel solution to combine the complementary advantages of wireless signal based localization solution with the computer vision based solution to track IoT devices and sensors more accurately. Experimental result shows that our system can accurately track IoT devices with an average pixel error of 34 pixels in a 1024 × 768 pixels image, which is a 75.8% improvement from the state-of-the-art model.

Recent advancements in deep learning techniques have shown great potential for smart Internet of Things (IoT) applications. However, the edge devices of IoT applications often collect and store only limited data, which is insufficient for training modern deep learning models. Collaborative training methods such as cloud computing and federated learning set steps to build robust models for IoT applications, yet these methods bring the concern of data privacy (e.g., untrusted central server, model inversion). On the other hand, directly applying privacy-preserving techniques such as differential privacy can dramatically degrade the performance of IoT applications. Inspired by the development of model personalization, we aim to design a federated learning framework in a personalized fashion to reduce the accuracy loss caused by privacy-preserving techniques. In this paper, we present PFed-LDP, a private and accurate federated local differential privacy (LDP) framework for IoT sensing data. We first design a dynamic layer sharing mechanism to separate the local model into global layers and personalized layers. Second, we apply LDP noise to the global layers and transmit them to the federated learning framework for aggregation. Third, each local client updates their model with local personalized layers and aggregated global layers to perform IoT tasks. Our experiments on real-world datasets show that we only sacrifice 1.6% of accuracy to achieve privacy-preserving IoT applications. We also observe that our method has the smallest accuracy range, which means we can achieve the best performance for the worst performed client.

Occupant identification is paramount for many building applications. Regardless, several practical concerns limit existing solutions to be ubiquitously deployed. Current systems are either intrusive, privacy-invasive, or require obtrusive, maintenance-heavy, and special-purpose infrastructure. As an alternative, the shadow pattern of a person reflected in the output voltage of a photovoltaic harvester power supply in many energy-harvesting devices can be used as a unique person identifying feature. In this paper, we present the first dataset containing the time-series open circuit output voltage traces of indoor photovoltaic cell corresponding to occupant door crossing events to perform occupant identification in smart homes. We collect shadow patterns of five participants from two different doors in two rooms of a building. The dataset consists of a total of 900 door entry and exit events during different hours of the day. We sample the voltage at 50 hz and provide the raw timestamped data. We also pre-process the data to filter the event of interest and label the data with associated occupant id and type of door events. Moreover, we provide insights into future research directions using the dataset. The dataset is available at https://doi.org/10.5281/zenodo.7195748

The emergence of radio frequency (RF) dependent device-free indoor occupancy detection has seen slow acceptance due to its high fragility. Experimentation shows that an RF-dependent occupancy detector initially performs well in the room to be sensed. However, once the physical arrangement of objects changes in the room, the performance of the classifier degrades significantly. To address this issue, we propose BLECS, a Bluetooth-dependent indoor occupancy detection system which can adapt itself in the dynamic environment. BLECS uses a reinforcement learning approach to predict the occupancy of an indoor environment and updates its decision policy by interacting with existing IoT devices and sensors in the room. We tested this system in five different rooms for 520 hours in total, involving four occupants. Results show that, BLECS achieves 21.4% performance improvement in a dynamic environment compared to the state-of-the-art supervised learning algorithm with an average F1 score of 86.52%. This system can also predict occupancy with a maximum 89.23% F1 score in a completely unknown environment with no initial trained model.

The fast-growing trend of Internet of Things (IoT) has provided its users with opportunities to improve user experience such as voice assistants, smart cameras, and home energy management systems. Such smart home applications often require large numbers of diverse training data to accomplish a robust model. As single user may not have enough data to train such a model, users intent to collaboratively train their collected data in order to achieve better performance in such applications, which raise the concern of data privacy protection. Existing approaches for collaborative training need to aggregate data or intermediate model training updates in the cloud to perform load forecasting, which could directly or indirectly cause personal data leakage, alongside with significant communication bandwidth and extra cloud service monetary cost.

Edge computing for the Internet of Things prescribes executing applications on server machines closer to devices rather than depending on the cloud. However, server machines are expensive, are not flexible to adapt to varying application requirements, require gateways to interact with IoT devices, and follow a centralized model which increases traffic and application latency. Special-purpose hardware for the edge is becoming increasingly sophisticated, with support for machine learning, secure enclaves etc., and this work is an attempt to leverage such hardware to cooperatively execute edge applications, rather than relying on expensive edge servers. To do so, our design relies on a distributed middleware which can seamlessly scale up with new hardware, and a task scheduler which best matches application requirements with the hardware capabilities available. We have built a prototype middleware that operates on multiple gateways in our testbed of 250 IoT devices, and we plan to further improve our platform to support more varying use cases.

Battery-less energy-harvesting systems have widened the landscape of Internet-of-Things (IoT) applications by taking computation to hard-to-reach places. Energy-harvesting sensors are perpetual, environment-friendly, cost-effective, and maintenance-free. Despite having such lucrative characteristics, battery-powered devices hold majority share of today's IoT market, since developing energy-harvesting applications require more expert knowledge, careful implementation, and rigorous debugging than applications with stable power. In this paper, we argue that development becomes easier, faster, efficient, and scalable with a standard, re-usable, general purpose platform that ensures the platform's versatility across various application with proper balance between abstraction and accessibility in hardware and software. Such platforms would provide flexibility across both hardware and software layers, at the same time, producing reliable performance. However, realizing this design point pose several research challenges that need to be identified and addressed. We identify the limitations in existing systems, articulate the challenges and provide guidelines for the community to work towards a general purpose platform that would enable new diversified battery-less applications in the future.

While relying on energy harvesting to power Internet of Things (IoT) devices eliminates the maintenance burden of battery replacement, energy generation fluctuation constitutes a major source of uncertainty to design reliable self-powered IoT devices. To characterize spatial-temporal variability of energy harvesting, data acquisition campaigns are needed across the range of potential harvesting sources. In this work we present a dataset to characterize thermal energy sources in residential settings by measuring thermoelectric generator (TEG) operating conditions over 16 deployment locations for periods ranging from 19 to 53 days. We present our easy-to-use thermal energy measurement platform built from off-the-shelf component modules and a custom TEG interface circuit. We demonstrate how the collected measurements can inform the design of energy harvesting IoT devices by deriving the TEG's maximum power output and estimating the available energy at each harvesting location.

Rust, an emerging programming language with explosive growth, provides a robust type system that enables programmers to write memory-safe and data-race free code. To allow access to a machine's hardware and to support low-level performance optimizations, a second language, Unsafe Rust, is embedded in Rust. It contains support for operations that are difficult to statically check, such as C-style pointers for access to arbitrary memory locations and mutable global variables. When a program uses these features, the compiler is unable to statically guarantee the safety properties Rust promotes. In this work, we perform a large-scale empirical study to explore how software developers are using Unsafe Rust in real-world Rust libraries and applications. Our results indicate that software engineers use the keyword unsafe in less than 30% of Rust libraries, but more than half cannot be entirely statically checked by the Rust compiler because of Unsafe Rust hidden somewhere in a library's call chain. We conclude that although the use of the keyword unsafe is limited, the propagation of unsafeness offers a challenge to the claim of Rust as a memory-safe language. Furthermore, we recommend changes to the Rust compiler and to the central Rust repository's interface to help Rust software developers be aware of when their Rust code is unsafe.

Nowadays, smart building infrastructures are equipped with hundreds of sensors to monitor building environments and provide smart solutions for occupant comfortability and energy efficiency. Ideally, an automated system can predict and adjust the physical features (e.g., lighting, air quality, temperature, and so on) in a person’s office based on his/her personalized preferences and activities. However, since the data is from one person, there may not be sufficient data for machine learning model training, and the data’s quality may be low (e.g., with noises). Then, it is a challenge to conduct accurate predictions to provide personalized environment adjustment. To handle this problem, in this paper, we propose a smart building assistance system consisting of different sensor data analysis approaches and a deep neural network (DNN)-based prediction model to make a more accurate prediction despite low-quality sensor data. First, we collected a year-long smart building dataset from four different data sources (i.e., sensors, calendar, weather, and survey). Second, we perform different feature engineering approaches (i.e., concretization, one-hot encoding, and multiple feature combination) on the data as inputs for the prediction models. Third, we identify a support vector regression-based prediction model and propose a hybrid DNN model consisting of several recurrent neural network blocks and a feed-forward DNN block to predict different preferred physical features considering different activities of a person (e.g., meeting, lunch, research activities). Finally, we conduct experimental studies to evaluate the performance of the proposed prediction models compared to other existing machine learning models in terms of accuracy. Our predicted preferred physical features match the occupant’s preferred ranges of different physical features during a specific activity. We also open-sourced our code on GitHub.

Internet of Things applications typically run on the cloud and away from the end devices, leading to potential privacy and security risks, lower latency, and reduced reliability. Such deployments commonly use gateways to aggregate and send device data to the cloud. We hypothesize that these gateways are underutilized and can perform more than just packet forwarding. This work is an attempt to build a distributed platform over such gateways, with an objective to push applications to the edge of the network to overcome the shortcomings of the cloud. Our solution enables heterogeneous gateways to discover each other, and provides programming interfaces for developers to run applications on the platform without having to deal with the underlying network and device complexities. We showcase two applications that use our APIs, and also demonstrate how end users can interact with the platform via their personal computers (smartphones, laptops, etc.).

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.

Low-power microcontrollers lack some of the hardware features and most of the memory resources that usually enable multiprogrammable systems. Accordingly, operating system software for these platforms has not provided important features like memory isolation, dynamic memory allocation, and flexible concurrency. However, an emerging class of embedded applications are software platforms, rather than single purpose devices. Tock, a new operating system for low-power platforms, takes advantage of the limited hardware-protection mechanisms available on recent microcontrollers and the type-safety features of the Rust programming language to provide a multiprogramming environment that offers isolation of software faults, memory protection, and efficient memory management for dynamic application workloads written in any language while retaining the dependability requirements of long-running devices.

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.

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 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.

For the last fifteen years, research explored the hardware, software, sensing, communication abstractions, languages, and protocols that could make networks of small, embedded devices---motes---sample and report data for long periods of time while unattended. Today, the application and technological landscapes have shifted, introducing new requirements and new capabilities. Hardware has evolved past 8 and 16 bit microcontrollers: there are now 32 bit processors with lower energy budgets and greater computing capability. New wireless link layers have emerged, creating protocols that support direct interaction with users, but introduce novel limitations that systems must consider. Programming language advances have led to the ability to write system kernels that guarantee safety and reliability while maintaining low overhead. The time has come to look beyond optimizing networks of motes. We look towards new technologies such as Bluetooth Low Energy, Cortex M processors, and capable multi-process operating systems, with new application spaces such as personal area networks, and new capabilities and requirements in security and privacy to inform contemporary hardware and software platforms. It is time for a new, open experimental platform in this post-mote era.

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.

Rust, a new systems programming language, provides compile-time memory safety checks to help eliminate runtime bugs that manifest from improper memory management. This feature is advantageous for operating system development, and especially for embedded OS development, where recovery and debugging are particularly challenging. However, embedded platforms are highly event-based, and Rust's memory safety mechanisms largely presume threads. In our experience developing an operating system for embedded systems in Rust, we have found that Rust's ownership model prevents otherwise safe resource sharing common in the embedded domain, conflicts with the reality of hardware resources, and hinders using closures for programming asynchronously. We describe these experiences and how they relate to memory safety as well as illustrate our workarounds that preserve the safety guarantees to the largest extent possible. In addition, we draw from our experience to propose a new language extension to Rust that would enable it to provide better memory safety tools for event-driven platforms.

Power meters are critical for sub metering 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 contact less 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.

For the last fifteen years, research explored the hardware, software, sensing, communication abstractions, languages, and protocols that could make networks of small, embedded devices---motes---sample and report data for long periods of time unattended. Today, the application and technological landscapes have shifted, introducing new requirements and new capabilities. Hardware has evolved past 8 and 16 bit microcontrollers: there are now 32 bit processors with lower energy budgets and greater computing capability. New wireless link layers have emerged, creating protocols that support rapid and efficient setup and teardown but introduce novel limitations that systems must consider. The time has come to look beyond optimizing networks of motes. We look towards new technologies such as Bluetooth Low Energy, Cortex M processors, and capable energy harvesting, with new application spaces such as personal area networks, and new capabilities and requirements in security and privacy to inform contemporary hardware and software platforms. It is time for a new, open experimental platform in this post-mote era.

Obtaining a detailed, whole-house breakdown of energy usage would allow for homeowners to better understand their energy consumption and opportunities for energy savings. Current solutions are either too course-grained, too difficult to deploy, not networked, or offer incomplete coverage of hard to meter items, such as ceiling lights. We demonstrate a wirelessly networked, energy-harvesting power metering system that draws zero standby power and is power proportional to the load it is metering.

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.

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.

We present a battery-free sensornet edge node design that operates on ambient indoor lighting, drawing just microwatts, but still delivers readings several times per minute.