We present insights obtained from conducting a year-long, 455 meter deployment of wireless plug-load electric meters in a large commercial building. We develop a stratified sampling methodology for surveying the energy use of Miscellaneous Electric Loads (MELs) in commercial buildings, and apply it to our study building. Over the deployment period, we collected over nine hundred million individual readings. Among our findings, we document the need for a dynamic, scalable IPv6 routing protocol which supports point-to-point routing and multiple points of egress. Although the meters are static physically, we find that the set of links they use is dynamic; not using such a dynamic set results in paths that are twice as long. Finally, we conduct a detailed survey of the accuracy possible with inexpensive AC metering hardware. Based on a 21-point automated calibration of a population of 500 devices, we find that it is possible to produce nearly utility-grade metering data.

We present A-MAC, a receiver-initiated link layer for low-power wireless networks that supports several services under a unified architecture, and does so more efficiently and scalably than prior approaches. A-MAC's versatility stems from layering unicast, broadcast, wakeup, pollcast, and discovery above a single, flexible synchronization primitive. A-MAC's efficiency stems from optimizing this primitive and with it the most consequential decision that a low-power link makes: whether to stay awake or go to sleep after probing the channel. Today's receiver-initiated protocols require more time and energy to make this decision, and they exhibit worse judgment as well, leading to many false positives and negatives, and lower packet delivery ratios. A-MAC begins to make this decision quickly, and decides more conclusively and correctly in both the negative and affirmative. A-MAC's scalability comes from reserving one channel for the initial handshake and different channels for data transfer. Our results show that: (i) a unified implementation is possible; (ii) A-MAC's idle listening power increases by just 1.12× under interference, compared to 17.3× for LPL and 54.7× for RI-MAC; (iii) A-MAC offers high single-hop delivery ratios; (iv) network wakeup is faster and more channel efficient than LPL; and (v) collection routing performance exceeds the state-of-the-art.

Interoperability is essential for the commercial adoption of wireless sensor networks. However, existing sensor network architectures have been developed in isolation and thus interoperability has not been a concern. Recently, IP has been proposed as a solution to the interoperability problem of low-power and lossy networks (LLNs), considering its open and standards-based architecture at the network, transport, and application layers. We present two complete and interoperable implementations of the IPv6 protocol stack for LLNs, one for Contiki and one for TinyOS, and show that the cost of interoperability is low: their performance and overhead is on par with state-of-the-art protocol stacks custom built for the two platforms. At the same time, extensive testbed results show that the ensemble performance of a mixed network with nodes running the two interoperable stacks depends heavily on implementation decisions and parameters set at multiple protocol layers. In turn, these results argue that the current industry practice of interoperability testing does not cover the crucial topic of the performance and motivate the need for generic techniques that quantify the performance of such networks and configure their run-time behavior.

Interoperability is key to widespread adoption of sensor network technology, but interoperable systems have traditionally been difficult to develop and test. We demonstrate an interoperable system development and performance diagnosis environment in which different systems, different software, and different hardware can be simulated in a single network configuration. This allows both development, verification, and performance diagnosis of interoperable systems. Estimating the performance is important since even when systems interoperate, the performance can be sub-optimal, as shown in our companion paper that has been conditionally accepted for SenSys 2011.

We present the architecture, specification, and implementations of a simple monitoring and action profile (sMAP), optimized for sensors, meters, and actuators in building environments. Our architecture is built on HTTP/REST and uses JSON as the object format for interoperability. We implement sMAP on a variety of resource monitors and actuators inside a commercial building, including mote-based wireless sensors and meters running IPv6/6LowPAN, Modbus based panel meters, and external data sources. We show that sMAP is widely implementable and efficient, and our API and schema definitions are expressive and concise. We demonstrate that our architecture is well suited for resource constrained devices using compressed JSON and proxies.

As more and more physical information becomes available, a critical problem is enabling the simple and efficient exchange of this data. We present our design for a simple RESTful web service called the Simple Measuring and Actuation Profile (sMAP) which allows instruments and other producers of physical information to directly publish their data. In our design study, we consider what information should be represented, and how it fits into the RESTful paradigm. To evaluate sMAP, we implement a large number of data sources using this profile, and consider how easy it is to use to build new applications. We also design and evaluate a set of adaptations made at each layer of the protocol stack which allow sMAP to run on constrained devices.

We present A-MAC, a receiver-initiated link layer for low-power wireless networks that supports several services under a unified architecture, and does so more efficiently and scalably than prior approaches. A-MAC's versatility stems from layering unicast, broadcast, wakeup, pollcast, and discovery above a single, flexible synchronization primitive. A-MAC's efficiency stems from optimizing this primitive and with it the most consequential decision that a low-power link makes: whether to stay awake or go to sleep after probing the channel. Today's receiver-initiated protocols require more time and energy to make this decision, and they exhibit worse judgment as well, leading to many false positives and negatives, and lower packet delivery ratios. A-MAC begins to make this decision quickly, and decides more conclusively and correctly in both the negative and affirmative. A-MAC's scalability comes from reserving one channel for the initial handshake and different channels for data transfer. Our results show that: (i) a unified implementation is possible; (ii) A-MAC's idle listening power increases by just 1.12x under interference, compared to 17.3x for LPL and 54.7x for RI-MAC; (iii) A-MAC offers high single-hop delivery ratios, even with multiple contending senders; (iv) network wakeup is faster and far more channel efficient than LPL; and (v) collection routing performance exceeds the state-of-the-art.

A home where every major appliance can be monitored for energy consumption and individually controlled wirelessly has long been a dream of gadgeteers and the green-conscious alike. Research has shown that real-time, per-appliance electricity usage feedback can induce behavior changes that lead to 10% to 20% reduction in usage [2].

An emerging class of sensor networks focuses on reliable collection of high-resolution signals from across the network. In these applications, the system is capable of acquiring more data than can be delivered to the base station, due to severe limits on radio bandwidth and energy. Moreover, these systems are unable to take advantage of conventional approaches to in-network data aggregation, given the high data rates and need for raw signals. These systems face an important challenge: how to maximize the overall value of the collected data, subject to resource constraints.

We report on investigations, conducted at the 2006 Johns Hopkins Workshop, into the use of articulatory features (AFs) for observation and pronunciation models in speech recognition. In the area of observation modeling, we use the outputs of AF classifiers both directly, in an extension of hybrid HMM/neural network models, and as part of the observation vector, an extension of the "tandem" approach. In the area of pronunciation modeling, we investigate a model having multiple streams of AF states with soft synchrony constraints, for both audio-only and audio-visual recognition. The models are implemented as dynamic Bayesian networks, and tested on tasks from the small-vocabulary switchboard (SVitchboard) corpus and the CUAVE audio-visual digits corpus. Finally, we analyze AF classification and forced alignment using a newly collected set of feature-level manual transcriptions.