The OSU Internet-of-Things Research-to-Market Showcase, held on January 15, 2015, was a great first step for sharing our vision toward building a collaborative IoT Research-to-Market Research Center. We received valuable input from key industry and government partners and our academic collaborators. In this short video, Terri Fiez provides the context and sets the stage for the event and our future plans.
This area presents some very challenging and exciting opportunities for creating novel integrated solutions that will change the way we live. We are currently working on the next steps while developing an action plan to create an end-to-end IoT open test bed. This test bed includes sensing technologies, capability to accommodate various IoT systems, the software infrastructure to handle the IoT system data and the data intelligence and visualization to extract information and act on it. Security is a critical part of the system at all levels.
If you're interested in working with us on this exciting effort that will help propel us all into the future, please contact Terri Fiez at email@example.com. To be included in future informational mailings, please click here.
Use the tabs above to get more details on the IoT Showcase.
Internet of Things Research-to-Market Showcase
|8:00 - 8:30||Morning Break – Memorial Union Ballroom (Lower Level)|
|8:30 - 8:45||Welcome
Ron Adams, OSU Interim Vice President for Research
|8:45 - 9:15||Internet of Things Research-to-Market – Stage Setting (Video)
Terri Fiez, Professor & Director of Strategic Initiatives, School of EECS
|9:15 - 9:25||Demonstration Project: Apple's New Retina Display Research-to-Market through OSU-HP Collaboration (Video)
John Wager, EECS Professor
|9:30 - 10:30||
IoT Flash Talks – Sensory and IoT Systems
|10:30 - 10:45||Break|
|10:45 - 10:55||Demonstration Project: Simple and Accurate Indoor Location Research-to-Market through OSU-Intel Collaboration (in progress)
Christian Le, Intel (Rahul Khanna, Intel and Huaping Liu, OSU) (Video) (Demonstration Video) (Overview slide) (Presentation slides)
|11:00 - 12:00||
IoT Flash Talks – Software and Data Intelligence
|12:00 - 12:50||Luncheon|
|12:50 - 1:20||Plenary Address "Analytics for IoT: From Sensors to Decisions" (Video) (Presentation slides)
Tom Dietterich, Distinguished Professor, School of Electrical Engineering & Computer Science
|1:20 -1:30||Walk to the Kelley Engineering Center (KEC)|
|1:30 - 2:40||
Breakout Session #1
|2:40 - 2:45||
Move to Second Breakout Session
|2:50 - 4:00||
Breakout Session #2
|4:00 - 4:30||Wrap Up and Next Steps, KEC 1001|
|4:30||Conclude and Lab Tours|
Software Systems and Infrastructure for IoT
Asynchronous programming is in demand today because responsiveness is especially important on mobile and wearable devices. One contemporary development task is refactoring long-running, blocking synchronous code (e.g., accessing the web, cloud, database, or file system) into non-blocking asynchronous code. In this talk I present our research on program analysis and transformation together with our useful toolset that enables app developers to retrofit asynchrony.
Advanced Metering Infrastructures (AMI) which have thousands to millions of limited capability smart meters connected by local and wide-area networks are a pre-cursor to IoT with many of the same challenges. In this talk we discuss some of the security challenges and proposed solutions in the AMI context, and then look ahead to upcoming challenges in IoT which will be much larger and heterogenous.
The Internet of Things is inherently about people — helping to improve their safety or their experiences in their homes, jobs, and in the world. For example, we can warn people of dangerous situations near their current location, recommend the next painting to look at in a museum, or enable them to remotely "program" objects in their smart homes to cooperate with each other. My work will focus on two aspects of this: (1) enabling ordinary users to do this sort of "programming," and (2) doing so in ways that allow IoT systems to be inclusive to both males and females.
In order for the promise of IoT to be realized, devices produced by different vendors must be able to communicate reliably and seamlessly with each other. And while vendors can test that their own devices correctly support one or more of the rapidly evolving IoT protocols, it is impractical for them to build a test bed with a wide array of other reference devices that support the same protocol. The IoT Test & Compatibility lab will be a membership driven organization where members send their IoT devices for testing in a variety of situations containing a wide array of other products and ensure that their product interacts correctly and as expected. This talk will give an overview of our plans to build the IoT Test & Compatibility lab and make it an effective partner in supporting the vision of an interconnected world.
This joint university-industry lab develops innovative projects designed by and targeted to millennials. Spidey Sense, a wearable haptic feedback device, allows navigation indoors without seeing. Music Sense senses emotional reaction to music for analyzing how music affects people. The Smart Helmet developed in partnership with Intel performs the function of the airplane black box for bicyclists.
Today, creating an IoT system requires writing multiple different programs, such as those that run on sensors, mobile devices, web servers, and other cloud-based backend servers. To simplify the creation and reuse of IoT systems, we are collaborating with National Instruments on a new approach supporting system-level programming. An engineer uses our new visual programming language to draw a picture of the entire system as a whole, from which a compiler generates the collaborating programs and deploys the resulting binaries in a single step across distributed and cloud-based machines.
Tools from cryptography allow mutually distrusting parties to carry out complex computations on data while at the same time keeping the inputs to the computation private. In the Internet of things, these secure computation techniques can be used for achieving consensus in an ad-hoc network of devices, or for computing aggregate sensor data while keeping the individual measurements private. Intense work over the past decade has reduced the overhead of these these cryptographic techniques by many orders of magnitude, finally making this powerful technology practical for many applications.
Attila A. Yavuz
Modern vehicles are being equipped with advanced sensing and communication technologies, which enable them to support innovative services in the Internet of Vehicles (IoV) era such as autonomous driving. These services can be effective through the spatial and temporal synchronization of the vehicle with the other entities in the environment. Hence, the communication in IoVs must be delay-aware, reliable, scalable and secure to (a) prevent an attacker from injecting/manipulating messages; (b) minimize the impact (e.g., delay, communication overhead) introduced by crypto operations. However, existing crypto mechanisms introduce significant computation and bandwidth overhead, which creates critical safety problems. To address this need, we are developing a new suite of cryptographic mechanisms, supported with time-valid framework and hardware-acceleration, to ensure secure and reliable operation IoVs.
Flash memories are nonvolatile, high density and low cost memories. Flash memories find applications in cell phones, digital cameras, embedded systems, data centers, etc. In order to improve the density of flash memories, multi-level (q-levels) memory cells are used so that each cell stores log_2(q) bits. After many rewrites the flash is prone to errors, mostly of limited magnitude errors. We are developing some efficient new classes of error correcting codes applicable to flash memories.
Software developed for the IoT must run on a wide variety of devices with different hardware and feature sets. To avoid repeating work and decrease time-to-market for new devices, developers create and maintain massively configurable software systems. Our research focuses on languages, algorithms, and methods for effectively managing the complexity inherent in massively configurable software. For example, we have developed verification techniques for statically ensuring that all configurations of a software system are type correct, and editing models that minimize the complexity and clarify the impact of programmers working on a particular subset of configurations.
Glencora Borradaile and Amir Nayyeri
The optimization problems that arise from an IoT will push our computational resources. We will need to relay messages faster, more reliably and with lower power and computational overhead. The information we are able to derive from an IoT will be bound by the computational complexity of the network optimization problems, but we can push beyond these limitations by designing algorithms that are specific to the localized, mesh structure that an IoT will have.
Data Intelligence and Visualization
Sensors drift and break. We need automatic methods for detecting this and either compensating in the downstream data analysis (e.g., ignoring bad sensor values) or dispatching a technician to replace/repair the sensor. I have been developing methods to do this for a large existing sensor networks — namely, weather station networks.
Sensors produce a tremendous amount of data that must be interpreted by some end user. Presenting data to end users in an appropriate manner is critical. We are studying the effects of matching visual feedback to people in a personalized manner, taking into account their tasks, context, and individuality.
Database analytics algorithms leverage quantifiable structural properties of the data to extract interesting insights. The same information, however, are represented using many different forms and the structural properties observed over particular representations do not necessarily hold for alternative forms. Enterprises spend a great deal of time and resources to convert databases to the representations over which their analytics algorithms are effective. Thus, current data analytics algorithms cannot handle the volume, variety, and the velocity of the data generated in the Internet of things. In order to make database analytics scalable and usable, we develop database analytics algorithms that are effective over a wide range of choices of structural organizations.
Xiaoli Fern (in collaboration with Raviv Raich, Mathew Betts)
Birds are widely used as biological indicators of the health of our ecosystem. Our project aims to develop a rich set of capabilities that allows us to extract ecologically meaningful information and knowledge from bio-acoustic data continuously collected with in-situ field recorders. In addition to the standard species recognition problem, our efforts also address the problem of detecting rare species and estimating the abundancy of the detected species based on their song activities. Collaborating with ecologists, we also hope to gain a better understanding of the singing behavior of birds by studying their song activities in relation to different aspects of their habitats.
Advanced sports analytics hinges on the availability of detailed, high-quality data. While the technological and human cost of collecting such data is manageable for teams at elite levels, it is beyond the means of most teams at non-elite levels, e.g. high school athletics. Our research aims at lowering the cost of producing the type of data necessary for advanced sports analytics at non-elite levels. Due to the fact that the vast majority of teams regularly record video of their sporting events, the focus of our work is to push the limits of reliably extracting data directly from video. The presentation will overview our past and recent work on applying computer vision to video of American football and basketball.
Recognizing functional objects in images is a long-standing open problem in computer vision. This problem is challenging, because functional objects are not characterized by their appearance and shape (which can be directly observed in the image), but by their function (e.g., sittable surfaces, looking glass) or affordance (e.g., slots are for inserting things into). In our work, we focus on objects whose function (and affordance) can be defined in relation to people and their activities in the scene. Given a video, our goal is to detect functional objects in the scene that can be viewed as attracting people to approach them — called dark matter — as well as functional objects that repel people to move away from them. To this end, we analyze noisy behavior of people in the scene using agent-based, probabilistic Lagrangian mechanics.
The Internet of Things has enabled the general public to participate in scientific research in a paradigm known as citizen science. Many citizen science projects allows participants to submit observations of interest, such as sightings of a particular bird species. A major concern with this approach is the quality of the observations submitted by the general public. My work will highlight the use of machine learning to extract the signal from the noise in the context of the eBird project, which is one of the largest citizen science projects in existence.
Internet of Things offers the unique possibility of tightly integrating sensing, planning, and control to optimize the performance of a whole system. We have been working on different applications of IoT including fire and medical emergency response, wild fire and invasive species management, and endangered species protection. These problems are characterized by many interacting distributed entities, highly control-sensitive costs, random exogenous events, noisy sensors, and the need to control multiple parallel activities. Our research develops near-optimal planning and control algorithms for such domains using techniques in artificial intelligence and machine learning.
Patient information from personal genotyping, "omics" profiling, and mobile diagnostic devices promises to usher in the era of precision medicine, in which intelligent systems analyze patient data to provide tailored treatment recommendations; but such systems can only be built on a foundation of predictive models that span from organ systems to cells to molecular pathways. Our lab uses a data-driven approach to map the molecular networks that underlie atherosclerosis, a condition that causes most of the 1.7 million heart attacks and strokes that occur each year. We develop and use statistical machine learning and network analysis to pinpoint the molecules that control gene expression changes in cells in the plaque, with the eventual goal of identifying new molecular targets for inducing atherosclerosis regression or beneficial plaque remodeling in humans.
Data is being collected every moment, everywhere, on everything. Without analysis and interpretation, data remains just that, data. While automatic data analysis is highly desirable, it is often difficult to know which analysis tools to use, or even what questions to ask of the analysis, given a new data application domain. Visualization helps the domain experts and data stakeholders to explore their data sets visually for patterns that can lead to proper choice of analysis tools as well as additional questions. We have been working with civil engineers on visualizing the rating of bridges based on the load data collected from sensors located at various parts of the bridges as well as data from simulation.
Electric supply and demand is becoming a source of concern due to the increase in home power consumption. Home energy management can provide further flexibility in energy demand management. A system which can learn, monitor, and control home energy usage patterns is of interest. We illustrate how voltage signatures can be learned and used to provide online detection of home appliance activations.
IoT Systems for Envisioning the Future
Simple, accurate, indoor location (SAiL) technology we have developed resolves one of the major technical challenges of current time-based positioning systems: precise timing synchronization requirements of the observation points. Using WiFi only, SAiL achieves a location accuracy better than 1m, that will enable a number of applications waiting for it: indoor mapping, incentivized shopping, retail analytics, healthcare, inventory management, sports analytics/training, robot guidance, just to name a few. We are collaborating with Intel to develop a commercial solution.
In the context of smart grid infrastructure, power system protection devices lie at the intersection of bulk equipment and modern networked sensing and control. Secure and reliable operation of these interdependent systems requires exhaustive theoretical and applied research both on the energy systems and computer science domains. Oregon State University is advancing real-time demand estimation techniques by building a synchrophasor network that renders our campus into a living power systems laboratory.
FDVSP uses a combination of TCP and UDP for video streaming to improve visual quality and real-time responsiveness of VoD, and peer-to-peer video streaming, and video conferencing. The basic idea is to transmit important parts of a video stream via TCP, and the rest using UDP. In addition, the proportion of video data transmitted using TCP versus UDP is dynamically adjusted depending on the network condition. FDVSP results in less rebuffering instances than TCP, which reduces the occurrence of freeze frames, and less packets drops than UDP, which improves visual quality of video frames.
Jointly designed based on the latest LED communication and RF technologies, the WiFO communication system can provide order of magnitude improvement in bandwidth over current WiFi systems.
Energy consumption in datacenters has recently been a matter of great concern to cloud service providers. For instance, it is estimated that US datacenters consumed about 2% of the 2010 total US energy use. By 2020, it is estimated that there will be 26 Billion IoT units, generating all together massive amounts of data that needs to be stored and managed by datacenters. With the emergence of IoT, datacenters energy consumption is then expected to rise to much higher levels in coming years. Our research focuses on developing new resource management techniques that can limit/reduce the energy use of these next-generation datacenters.
Advancing renewable power integration, grid stability, and grid efficiency depends on load flexibility. In every home, factory, and commercial building there are many opportunities to regulate power usage and store energy. For example, electric hot water heaters in the Pacific Northwest represent a collective energy storage capability of more than 5,000 MWh. These demand response opportunities can be exploited through increased connectivity and control of hot water heaters, heating and cooling systems, compressors, lighting, refrigeration, and other appliances.
Annette von Jouanne
OSU is the headquarters of the Northwest National Marine Renewable Energy Center (NNMREC), a US Department of Energy (USDOE) Center facilitating the integration of marine renewables onto the utility grid. This research includes developing testing solutions including OSU’s unique wave energy linear test bed, wave tanks and Ocean Sentinel instrumentation buoy, in addition to a grid emulator for the ocean testing of full-scale WECs to allow developers to ocean-test their devices on a simulated electric grid.
The key feature of Smart Grid is automated monitoring, control, and decision making with the help of massive data collection and advanced data processing techniques. However, heavy reliance on data exposes a grid to cyber data attacks which modify part of the data according to the attackers' objective. We investigate potential impacts of data attacks on the grid control and electricity market operations. In addition, we study economic countermeasures and how to make reliable control decisions even when some part of the data are corrupt.
An enabling component to process various data and make intelligent decisions in all IoT systems is the microprocessor, or the “brain” of the systems. With the pervasive use of IoT devices in various applications such as healthcare, home automation, energy systems, transportation and civil infrastructure, we are facing an unprecedented challenge of designing microprocessors that have orders of magnitude higher performance-to-power ratio. At the System Technology and Architecture Research (STAR) Lab here at OSU, one of our main research efforts is to develop next generation processors that utilize the flexibility and power-efficiency of multi-core architectures. We have been developing techniques and schemes that target increased energy proportionality, enhanced security, reduced form factor, and richer set of functionalities for IoT systems.
Julia Zhang and Yue Zhang
Additive manufacturing has enabled innovations in low-volume production, due to the advantages of faster and cheaper prototyping, reduced lead times, and shorter supply chains. An increasing number of manufacturers has started 3d print high power electric machines for automobile traction and renewable power generators. It becomes crucial to evaluate how the additive manufacturing process affects the electromagnetic and mechanical performance of electric machines, particularly the torque capability, core loss, residual stress and deformation. We employ numerical modeling methods such as finite element analysis to achieve the evaluation and to inspire optimal designs.
The number of large complex systems composed of many interacting subsystems has skyrocketed over the last decade. Coordinating thousands of subsystems in dynamic and stochastic environments, an idea that a mere decade ago would have been outlandish, is not only possible, but imperative today. Today, the technological bottlenecks stem from the lack of mathematics and algorithms to coordinate such systems, rather than difficulties associated with building them. My research focus is on developing the objectives, utilities and incentives that ensure ensure such systems function properly. Applications to date include air traffic management, hybrid power system management, sensor coordination, and multi-robot coordination.
Sensory Systems for IoT
John F. Wager
The transparent transistor was invented at OSU late 2001, and HP licensed this technology from Oregon State University in 2002. Joint development between HP and OSU led to a large number of patents related to the use of amorphous oxide semiconductors for flat-panel display applications. These developments are key to the success of Apple’s new Retina display.
This research addresses reliable and maintenance-free sensing of building and environment conditions. Present day wireless sensors are costly and many potential sensing applications are limited by battery life. Our approach has a comprehensive focus that combines low power circuitry with specialized network protocols. The resulting system-level design improves robustness and enhances battery lifetime.
We are building extremely small (max. dimension < 1cm) battery-less sensor tags that harvest energy from dedicated or ambient RF transmitters. We’ve already demonstrated state-of-the-art rectifier sensitivity in sensor tag SoC, with tag operation at > 10m at 2.4GHz. The sensor tag SoC includes a low-power 6Ghz-10GHz UWB transmitter that can provide sub-10cm spatial resolution and/or 28Mb/s data transmission with a payload of ~ 1000 bits. Our goal is to build a tag that can be used to track insects such as bumblebees without affecting their flight.
Energy-efficiency is one of the most critical constraints for next-generation wearables, as the battery size, cost, weight, and capacity determine user acceptability. We are developing both circuits and systems that attempt to minimize the power consumption for various building blocks of a futuristic micro-powered sensor-on-a-chip: low-noise amplifiers, ADCs, CPUs, radios, and energy harvesting. Finally, we are incorporating our technical prototypes into real-world clinical applications, such as a vitamin clinical trial with the Linus Pauling Institute, and a USDA-funded project for addressing adolescent obesity.
Control of blood sugar remains a challenge for those who suffer from type 1 diabetes. Continuous monitoring of glucose levels can reduce the risk of common complications of this disease. Working in collaboration with Pacific Diabetes Technologies, we have developed a flexible glucose sensing strip that has been integrated with an insulin delivery catheter. These devices are currently being tested in pigs. The goal of future work is to develop an artificial endocrine pancreas by additionally incorporating the delivery of glucagon into this device. It is hoped that this technology will one day enhance the lifestyle and reduce the risks of those living with type 1 diabetes.
The Applied Magnetics Laboratory at Oregon State University, in collaboration with Hewlett Packard Company and University of Oregon, is investigating processes to inkjet print 3-dimensional magnetic components that may be customized and integrated during manufacturing to enable smart systems capable of sensing, actuation, transaction, communication and computation.
Pushing the limits of targeted performance and power efficiency in a communications interface, such as the analog-to-digital converter (ADC), routinely presents itself as the design bottleneck in advanced applications. Present day and future mobile communication systems, for example, increasingly demand higher data rates while being forced to scale back power consumption due to limited battery life. Our research is paving a new path where performance and efficiency would jointly find success through architectural and topological innovations.
Many sensor devices rely on rationally designed structures to enhance the detection sensitivity, which requires expensive top-down semiconductor fabrication processes. In this research, we explored bioenabled nanophotonic sensors using diatoms, which are photosynthetic marine micro-organisms that create their own skeletal shells of hydrated amorphous silica, called frustules, which possess photonic crystal-like hierarchical micro- nano-scale features. Our research shows that such bioenabled sensors formed by low-cost and eco-friendly bottom-up processes can improve the detection limit by several orders of magnitude. We expect this technology to offer significant engineering potentials for biological and chemical applications, including cancer diagnostics, early disease detection, defense threat reduction, homeland security, pollution monitoring, and environmental protection.
Merging chemical and biological sensors with modern integrated circuits has the potential to push complex electronics into low-cost, point-of-care detection applications. We are building non-optical sensor platforms through monolithic fabrication of MEMS sensors on integrated circuits substrates. Recent work includes integrated piezoelectric sensors for handheld environmental monitoring – quantifying organic solvents and other industrial byproducts in air. We are also developing sensor platforms for antibody mediated medical diagnostics and exploring applications in wearable, implantable, and broadly deployable sensor platforms.
We are developing technologies for low cost sensors using magnetic nanoparticles to detect biological molecules. At sufficiently low cost, such sensors will become ubiquitous, monitoring our homes, offices, bodies, food and the environment for pathogens, pollutants or terror agents. In our approach, the inductive sensor is built using only established integrated circuit processing techniques. The biological molecules are labeled with microscopic magnetic nanoparticles, functionalized to attach to specific biomolecular targets, to give them a unique and easily detected magnetic signature. By taking advantage of the low cost of integrated circuit manufacturing, we hope to endow a wide range of devices with a chemical sense.
Our group develops novel algorithms and circuitry for the interface between sensors and the digital signal processors. We specialize in micro-power and high-accuracy circuits for biomedical applications. Our most recent results are a continuous-time ΔΣ modulator for ultrasound beamforming receiver, and a micro-power multiplexed incremental data converter for multi-channel sensor systems.
Advanced semiconductor technologies are key enabling technologies for the Internet of Things (IoT) for realizing cost-effective future generation electronic IoT devices, such as wearables, with higher integration density, increased functionality and performance, and ultra-low power wireless connectivity. Our research addresses the need for modeling and design of on-chip interconnections and passives in advanced semiconductor technologies to support first-pass design of future IoT devices with reduced time-to-market, and cost. Examples include broadband modeling of spiral inductors and compact modeling of micro-fluxgates for integrated eCompass applications.
Mobility in human and unstructured environments is a key challenge that must be addressed before autonomous systems can achieve their full potential in the physical world. Legged locomotion can solve this problem. The Dynamic Robotics Laboratory seeks to discover fundamental principles of legged locomotion, and demonstrate them with walking and running robots. The long-term goal is to meet or exceed the agility, energy economy, and robustness of walking and running animals.
The Personal Robotics Laboratory has been looking make assistive robots and internet-enabled devices practically useful for persons with severe motor disabilities, such as quadriplegia and ALS. We have been looking at context-sensitive interfaces, projected directly into the world, that enable persons with very limited movement to regain control over their environments, and interact easily, and intuitively with the infrastructure around them, either through a sophisticated mobile manipulation robot, or through internet-enabled household devices.
Using biological inspiration, Yigit Menguc designs mechanisms that are as soft as skin and muscle, then manufactures them with techniques in 3D printing, laser machining, and soft lithography. Two classes of instruments that he has invented so far are controllable gecko-inspired adhesives and soft wearable sensors.