Demo: An Interoperability Development and Performance Diagnosis Environment

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

FireDeX: a Prioritized IoT Data Exchange Middleware for Emergency Response

International audienceReal-time event detection and targeted decision making for emerging mission-critical applications, e.g. smart fire fighting, requires systems that extract and process relevant data from connected IoT devices in the environment. In this paper, we propose FireDeX, a cross-layer middleware that facilitates timely and effective exchange of data for coordinating emergency response activities. FireDeX adopts a publish-subscribe data exchange paradigm with brokers at the network edge to manage prioritized delivery of mission-critical data from IoT sources to relevant subscribers. It incorporates parameters at the application, network, and middleware layers into a data exchange service that accurately estimates end-to-end performance metrics (e.g. delays, success rates). We design an extensible queueing theoretic model that abstracts these cross-layer interactions as a network of queues, thereby making it amenable for rapid analysis. We propose novel algorithms that utilize results of this analysis to tune data exchange configurations (event priorities and dropping policies) while meeting situational awareness requirements and resource constraints. FireDeX leverages Software-Defined Networking (SDN) methodologies to enforce these configurations in the IoT network infrastructure. We evaluate its performance through simulated experiments in a smart building fire response scenario. Our results demonstrate significant improvement to mission-critical data delivery under a variety of conditions. Our application-aware prioritization algorithm improves the value of exchanged information by 36% when compared with no prioritization; the addition of our network-aware drop rate policies improves this performance by 42% over priorities only and by 94% over no prioritization

Using Wireless Power Meters to Measure Energy Use of Miscellaneous and Electronic Devices in Buildings

Miscellaneous and electronic devices consume about one-third of the primary energy used in U.S. buildings, and their energy use is increasing faster than other end-uses. Despite the success of policies, such as Energy Star, that promote more efficient miscellaneous and electronic products, much remains to be done to address the energy use of these devices if we are to achieve our energy and carbon reduction goals. Developing efficiency strategies for these products depends on better data about their actual usage, but very few studies have collected field data on the long-term energy used by a large sample of devices due to the difficulty and expense of collecting device-level energy data. This paper describes the development of an improved method for collecting device-level energy and power data using small, relatively inexpensive wireless power meters. These meters form a mesh network based on Internet standard protocols and can form networks of hundreds of metering points in a single building. Because the meters are relatively inexpensive and do not require manual data downloading, they can be left in the field for months or years to collect long time-series energy use data. In addition to the metering technology, we also describe a field protocol used to collect comprehensive, robust data on the miscellaneous and electronic devices in a building. The paper presents sample results from several case study buildings, in which all the plug-in devices for several homes were metered, and a representative sample of several hundred plug-in devices in a commercial office building were metered for several months

Articulatory feature-based methods for acoustic and audio-visual speech recognition: Summary from the 2006 JHU Summer Workshop.

We report on investigations, conducted at the 2006 Johns HopkinsWorkshop, 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 classiers 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 classication and forced alignment using a newly collected set of feature-level manual transcriptions

Building operating systems services: An architecture for programmable buildings.

Commercial buildings use 73% of all electricity consumed in the United States [30], and numerous studies suggest that there is a significant unrealized opportunity for savings [69, 72, 81]. One of the many reasons this problem persists in the face of financial incentives is that owners and operators have very poor visibility into the operation of their buildings. Making changes to operations often requires expensive consultants, and the technological capacity for change is unnecessarily limited. Our thesis is that some of these issues are not simply failures of incentives and organization but failures of technology and imagination: with a better software framework, many aspects of building operation would be improved by innovative software applications.To evaluate this hypothesis, we develop an architecture for implementing building applications in a flexible and portable way, called the Building Operating System Services. BOSS allows software to reliability and portably collect, process, and act on the large volumes of data present in a large building. The minimal elements of this architecture are hardware abstraction, data management and processing, and control design; in this thesis we present a detailed design study for each of these components and consider various tradeoffs and findings. Unlike previous systems, we directly tackle the challenges of opening the building control stack at each level, providing interfaces for programming and extensibility while considering properties like scale and fault-tolerance.Our contributions consist of a principled factoring of functionality onto an architecture which permits the type of application we are interested in, and the implementation and evaluation of the three key components. This work has included significant real-world experience, collecting over 45,000 streams of data from a large variety of instrumentation sources in multiple buildings, and taking direct control of several test buildings for a period of time. We evaluate our approach using focused benchmarks and case studies on individual architectural components, and holistically by looking at applications built using the framework