We specify and evaluate a new software-defined clock network architecture, Stitch. We use Stitch to derive all subsystem clocks from a single local oscillator (LO) on an embedded platform, and enable efficient radio frequency synchronization (RFS) between two nodes' LOs. RFS uses the complex baseband samples from a low-power low-cost narrowband transceiver to drive the frequency difference between the two devices to less than 3 parts per billion (ppb). Recognizing that the use of a wideband channel to measure clock frequency offset for synchronization purposes is inefficient, we propose to use a separate narrowband radio to provide these measurements. However, existing platforms do not provide the ability to unify the local oscillator across multiple subsystems. We demonstrate Stitch with a reference hardware implementation on a research platform. We show that, with Stitch and RFS, we are able to achieve dramatic efficiency gains in ultra-wideband (UWB) time synchronization and ranging. We demonstrate the same UWB ranging accuracy in state-of-the-art systems but with 59% less utilization of the UWB channel.

We introduce an platform architecture and algorithm to frequency synchronize multiple devices. The platform allows clock unification among oscillator, microcontroller, and radio. The platform accesses complex baseband samples from the radio, estimates the carrier frequency offset, and iteratively drives the main local oscillator (LO) frequency difference between two devices to zero.

A radio tomographic imaging (RTI) system uses the received signal strength (RSS) measured by RF sensors in a static wireless network to localize people in the deployment area, without having them to carry or wear an electronic device. This paper addresses the fact that small-scale changes in the position and orientation of the antenna of each RF sensor can dramatically affect imaging and localization performance of an RTI system. However, the best placement for a sensor is unknown at the time of deployment. Improving performance in a deployed RTI system requires the deployer to iteratively “guess-and-retest”, i.e., pick a sensor to move and then re-run a calibration experiment to determine if the localization performance had improved or degraded. We present an RTI system of servo-nodes, RF sensors equipped with servo motors which autonomously “dial it in”, i.e., change position and orientation to optimize the RSS on links of the network. By doing so, the localization accuracy of the RTI system is quickly improved, without requiring any calibration experiment from the deployer. Experiments conducted in three indoor environments demonstrate that the servo-nodes system reduces localization error on average by 32% compared to a standard RTI system composed of static RF sensors.

We present μSDR, a compact, inexpensive, and battery-powered software-defined radio (SDR) platform built on a single-chip, flash-based FPGA fabric and ARM Cortex-M3 processor, enabling lower power and tighter hardware/software integration than prior commodity SDR platforms. Our architecture, unlike prior designs, is well-suited to hand-held or battery-operated systems and also supports cleaner partitioning since hardware can be easily mapped into the software addresses space, and vice versa. Building on this flexibility, we show how highly time-critical MAC protocols can be implemented on this platform and deployed using just AAA batteries.

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.

The small intestine is the longest part of the gastrointestinal (GI) tract, but it is difficult to examine with traditional endoscopic procedures. To resolve this, swallowable capsules have been created that slowly pass through the entire GI tract taking images. Research is currently being done in magnetic actuation and localization to increase the speed and control of these smart pills. This paper focuses on a technique to more efficiently control a capsule endoscope based on proprioceptive sensing of the magnetic field. This sensing provides knowledge of the applied field in the frame of the capsule, and enables determination of its current state.

Current sensor network nodes are not designed for a rapid succession of large-scale deployments. While hundreds of nodes have been deployed in static networks (e.g. ExScale, GreenOrb) large-scale, mobile, repetitive deployments are difficult to manage. We developed a new platform, the WREN, to be a low-cost, easy to maintain and rapid to deploy, wireless sensing node for human contact network measurements.

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/software 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.

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.

Modern software-defined radios are large, expensive, and power-hungry devices and this, we argue, hampers their more widespread deployment and use, particularly in low-power, size-constrained application settings like mobile phones and sensor networks. To rectify this problem, we propose to put the software-defined radio on a diet by redesigning it around just two core chips -- an integrated RF transceiver and a Flash-based, mixed-signal FPGA. Modern transceivers integrate almost all RF front-end functions while emerging FPGAs integrate nearly all of required signal conditioning and processing functions. And, unlike conventional FPGAs, Flash-based FPGAs offer sleep mode power draws measured in the microamps and startup times measured in the microseconds, both of which are critical for low-power operation. If our platform architecture vision is realized, it will be possible to hold a software-defined radio in the palm of one's hand, build it for $100, and power it for days using the energy in a typical mobile phone battery. This will make software radios deployable in high densities and broadly accessible for research and education.

We present Virtual High-resolution Time (VHT), a power-proportional time-keeping service that offers a baseline power draw of a low-speed clock (e.g. 32 kHz crystal), but provides the time resolution that only a higher frequency clock could offer (e.g. 8 MHz crystal), and scales essentially linearly with access (i.e. the "reading" and "writing" of the clock). We achieve this performance by revisiting a basic assumption in the design of time-keeping systems -- that to achieve a given time-stamping resolution, a free-running timebase of equivalent frequency is needed. We show that this assumption is false and argue that the dependence is not on usage (i.e. whether on or off) but rather on access (i.e. reading and writing). Therefore, it is possible to duty cycle the free-running timebase itself, and augment it with a lower-frequency, temperature-compensated one, which achieves comparable resolution, at a fraction of the power, for typical workloads. The key technical challenge lies in duty cycling the fast clock and synchronizing the fast and slow clocks. To assess the viability of the approach, we explore how VHT could be implemented on several different platform architectures, and to study the power/performance tradeoff, we characterize VHT on one particular architecture in detail. Our results show power-proportional operation with a 10x improvement in average power and a synchronization accuracy exceeding 1 μs at duty cycles below 0.1%.

We introduce the Egs mote platform based on the Cortex M3 microcontroller that focuses on medical sensing applications. Egs uses an Atmel SAM3U microcontroller that runs up to 96 MHz and has up to 52 KB of RAM and 256 KB of Flash. Egs combines this microcontroller with two radios (802.15.4 and Bluetooth), external flash, on board sensors, and a LCD touchscreen to enable a rich set of wireless sensing applications.

The mobile phone is the most pervasive personal communications and computing platform ever created and yet, among its various analog interfaces, only one is open, standardized, and widely accessible: the headset port. In this demo, we augmente the mobile phone with a range of phone-powered peripherals. We show that the mobile phone headset port can be used to efficiently power external peripherals and communicate with them, enabling many new phone-centric applications. But, why use the headset port at all? One reason is that it is an open, simple, and ubiquitous interface with documented electrical and mechanical specifications. Perhaps even more important, the headset interface is backward-and forward-compatible with most mobile phones in use today.

We present our experiences evaluating the power-performance tradeoffs of a sensornet network protocol on a power-aware testbed. We characterize the power draw of the entire network while running the Collection Tree Protocol (CTP), as a function of low-power-listening interval. We find that message transmission counts are poor predictors for energy consumption on the CC2420 radio, that CTP routinely creates energy hotspots in the routing tree, and that conclusions based on protocol evaluation performed without low-power listening enabled provide little insight about the same protocol performance using low-power listening.

To achieve more accurate global time synchronization, this paper argues for decoupling the clock distribution network from the routing tree in a multihop wireless network. We find that both flooding and routing-integrated time synchronization rapidly propagate node-level errors (typically due to temperature fluctuations) across the network. Therefore, we propose that a node chooses synchronization neighbors that offer the greatest frequency stability. We propose two methods to estimate a neighbor’s stability. The first approach selects the neighbor whose Frequency Error Variance, or simply FEV, is smallest with respect to the local clock. The second approach selects the neighbor that reports the lowest FEV relative to its synchronization parent. We also propose the node-level time-variance FEV as an additive metric for selecting more stable clock trees than either naïve flooding or routing-integrated time synchronization can provide. We incorporate these techniques into FTSP, a widelyused time synchronization protocol, and show that the mean error in global time significantly improved (by a factor of five) when some nodes are warmed and others are not.

The efficiency of the time synchronization service in wireless sensor networks is tightly connected to the design of the radio, the quality of the clocking hardware, and the synchronization algorithm employed. While improvements can be made on all levels of the system, over the last few years most work has focused on the algorithmic level to minimize message exchange and in radio architectures to provide accurate time-stamping mechanisms. Surprisingly, the influences of the underlying clock system and its impact on the overall synchronization accuracy has largely been unstudied. In this work, we investigate the impact of the clocking subsystem on the time synchronization service and address, in particular, the influence of changes in environmental temperature on clock drift in highly duty-cycled wireless sensor nodes. We also develop formulas that help the system architect choose the optimal resynchronization period to achieve a given synchronization accuracy. We find that the synchronization accuracy has a two region behavior. In the first region, the synchronization accuracy is limited by quantization error, while int he second region changes in environmental temperature impact the achievable accuracy. We verify our analytic results in simulation and real hardware experiments.

A key prerequisite for residential energy conservation is knowing when and where energy is being spent. Unfortunately, the current generation of energy reporting devices only provide partial and coarse grained information or require expensive professional installation. This limitation stems from the presumption that calculating per-appliance consumption requires per-appliance current measurements. However, since appliances typically emit measurable signals when they are consuming energy, we can estimate their consumption using indirect sensing. This paper presents ViridiScope, a fine-grained power monitoring system that furnishes users with an economical, self-calibrating tool that provides power consumption of virtually every appliance in the home. ViridiScope uses ambient signals from inexpensive sensors placed near appliances to estimate power consumption, thus no in-line sensor is necessary. We use a model-based machine learning algorithm that automates the sensor calibration process. Through experiments in a real house, we show that ViridiScope can estimate the end-point power consumption within 10% error.

In this demonstration, we will present three key technologies we recently developed to improve time synchronization accuracy in sensor networks: (1) Temperature Driven Time Synchronization, (2) Low-Power Sub-μSecond Time Synchronization, and (3) Low-Power FPGA implementation of a High-Low Timer.

Time keeping and synchronization are important services for networked and embedded systems. High quality timing information allows embedded network nodes to provide accurate time-stamping, fast localization, efficient duty cycling schedules, and other basic but essential functions - all of which are required for low power operation.

Water is nature's most precious resource and growing demand is pushing fresh water supplies to the brink of non-renewability. New technological and social initiatives that enhance conservation and reduce waste are needed. Providing consumers with fine-grained real-time information has yielded benefits in conservation of power and gasoline. Extending this philosophy to water conservation, we introduce a novel water monitoring system, NAWMS, that similarly empowers users.

Time synchronization is an essential service in distributed computing and control systems. It is used to enable tasks such as synchronized data sampling and accurate time-of-flight estimation, which can be used to locate nodes. The deviation in nodes' knowledge of time and inter-node resynchronization rate are affected by three sources of time stamping errors: network wireless communication delays, platform hardware and software delays, and environment-dependent frequency drift characteristics of the clock source. The focus of this work is on the last source of error, the clock source, which becomes a bottleneck when either required time accuracy or available energy budget and bandwidth (and thus feasible resynchronization rate) are too stringent. Traditionally, this has required the use of expensive clock sources (such as temperature compensation using precise sensors and calibration models) that are not cost-effective in low-end wireless sensor nodes. Since the frequency of a crystal is a product of manufacturing and environmental parameters, we describe an approach that exploits the subtle manufacturing variation between a pair of inexpensive oscillators placed in close proximity to algorithmically compensate for the drift produced by the environment. The algorithm effectively uses the oscillators themselves as a sensor that can detect changes in frequency caused by a variety of environmental factors. We analyze the performance of our approach using behavioral models of crystal oscillators in our algorithm simulation. Then we apply the algorithm to an actual temperature dataset collected at the James Wildlife Reserve in Riverside County, California, and test the algorithms on a waveform generator based testbed. The result of our experiments show that the technique can effectively improve the frequency stability of an inexpensive uncompensated crystal 5 times with the potential for even higher gains in future implementations.

No abstract available.

No abstract available.