Mani B. Srivastava - US grants

Time varying environments and limited battery energy are two key hurdles on the route to distributed multimedia systems with wireless network links. This project is exploring architectures, protocols, and algorithms to overcome these hurdles for mobile users. In particular, the research is exploring the use reconfigurability in wireless nodes to allow algorithms and protocols to adapt to evolving environments. The reconfigurable wireless node leads to interesting system-level concepts such as hardware applets, in which hardware datapaths can be downloaded and instantiated on demand from network servers. The educational component of the project is based on tightly integrating the proposed research with new graduate and undergraduate courses. The research topics will be explored in project-oriented graduate courses on mobile multimedia information systems and wireless terminal design. Selected prototyping tasks will be used as projects in undergraduate design courses.

Despite enormous progress in networking and computing technologies, their application has remained restricted to conventional person-to-person and person-to-computer communication. However, the Moore's Law driven continual reduction in cost and form factor is now making it possible to imbed networking - even wireless networking - and computing capabilities not just in our PCs and laptops but also other objects. Further, a marriage of these ever tinier and cheaper processors and wireless network interfaces with emerging micro-sensors based on MEMS technology is allowing cheap sensing, processing, and communication capabilities to be unobtrusively embedded in familiar physical objects. The result is an emerging paradigm shift where the primary role of information technology would be to enhance or assist in "person to physical world" communication via familiar physical objects with embedded (a) micro-sensors to react to external stimuli, and (b) wireless networking and computing engines for tetherless communication with compute servers and other networked embedded objects.

The proposed research seeks to explore wireless networking, middleware, and data management technologies for realizing the above vision. The problems of ad hoc structure, distributed nature, unreliable sensing, large scale/density, and novel sensor data types are characteristic of such deeply instrumented physical environments with inter-networked physical objects. This requires one to rethink current architectures, protocols, algorithms, and formalisms that were developed for different needs. Further, to provide a concrete problem domain, we propose to use and evaluate our technologies in a "smart kindergarten" driver application targeted at developmental problem-solving environments for early childhood education. This is a natural application as young children learn by exploring and interacting with objects such as toys in their environment. Our envisioned system would enhance the education process by providing a childhood learning environment that is individualized to each child, adapts to the context, coordinates activities of multiple children, and allows unobtrusive evaluation of the learning process by the teacher. This would be done by wirelessly-networked, sensor-enhanced toys with back-end middleware services and database techniques.

The main information technology contributions of this research would be:
Wireless protocols for networks using short-range radios, with focus on highly unstructured, dynamic, and dense networks of embedded devices, and problems of energy efficiency and quality of service needs of sensor data.
Network architectures designed for naming, addressing, and routing by object capabilities and attributes, as opposed to id based approaches in conventional networks.
Efficient techniques and algorithms for identifying, locating, and tracking users and objects in instrumented environments, particularly indoors.
Middleware architecture providing services such as special communication patterns, context-aware network resource allocation and scheduling under attribute and capacity constraints, power-aware operation, media processing using shared background servers, and context discovery, tracking, and change notification.
Data management methods to handle data from multiple heterogeneous, unreliable, noisy sensors in a highly dynamic environment, with support for real-time sensor data interpretation and fusion, and off-line mining.
Automated mining of user profiles from sensor data, and their use in task planning and execution of actions in the instrumented environment
Techniques for sensor-assisted automatic speech recognition of children's speech.

Complementing the above will be the driver application where a Smart Kindergarten for developmental problem solving will be prototyped based on the above ideas, and evaluated in a real classroom setting. Various objects, particularly toys, will be wirelessly networked and have sensing and perhaps actuator capabilities. A wireless network, with radios and protocols suitable for handling a high density of proximate objects, will interconnect the toys to each other and to database and compute servers using a toy network middleware API. Sensors embedded in toys and worn by children will allow the database servers to discover and track context and configuration information about the children and the toys, and also orchestrate aural, visual, motion, tactile and other feedback. The system will enhance the developmental process by providing a problem-solving environment that is individualized, context adaptive, and coordinated among multiple children. It will also allow monitoring and logging for unobtrusive paper-free assessment by teacher or parent.

The project team is interdisciplinary, with researchers from UCLA's CS and EE Departments for the technology component of the project, and from UCLA's Graduate School of Education and Information Sciences (GSE&IS) for the application component. GSE&IS operates a reputed laboratory elementary school on campus, which will be used for real-life evaluation of

Because of the relentless march of the silicon-based electronics technology as predicted by Moore's Law, computation, storage, and communication are now woven into the fabrics of our lives. The emerging technology of flexible electronics, where electronics components such as transistors and wires are built on a thin flexible material, offers a similar opportunity to weave computation, storage, and communication into the fabric of the very clothing that we wear. The implications of seamlessly integrating a large number of communicating computation and storage resources, mated with sensors and actuators, in close proximity to the human body will transform many aspects of biomedical research and practice. For example, one can imagine biomedical applications where biometric and ambient sensors are woven into the garment of a patient or a person in a medically-critical or hazardous environment to trigger or modulate the delivery of a drug.
To realize this vision outside the laboratory, radical innovation is required in the area of system-level information technology. These systems will not scale to widespread use if they are viewed simply as traditional chips or motherboards based on a different, flexible form factor. Rather, a rethinking of the architecture and the design methodology for all layers of these systems is needed. The reasons are two-fold. First, the underlying technology of electronics in flexible materials has characteristics and computation-communication cost trade-offs that are very different from that of silicon and PCB-based electronics. Second, the natural applications of these systems have environmental dynamics, physical coupling, resource constraints, infrastructure support, and robustness requirements that are very different from those faced by traditional systems.
One of the challenges in developing the needed information technology architecture and design methodology for these systems is that one needs to both conduct experimental work and develop a conceptual understanding of the problem domain. This research studies:
Application: Use as a driver application capability, reconfigurable fabric (R-Fabric) based on a combination of (i) the technology of flexible electronics using organic materials, and (ii) computing, communication, and sensing elements implemented as E-Buttons.
Architecture: Develop the general architecture concepts and cost/performance optimization techniques. The issues that we will focus on will include (i) appropriate primitives for composing the architecture, (ii) system interconnect network optimized for the electrical characteristics of the organic electronics, (iii) techniques to cope with the high ration of communication to computation cost, and (iv) architecture level self-configuration and re-configuration for robust operation.
Programming: Develop techniques and primitives for programming a system composed of hundreds of computation, storage, sensing, and actuation elements that are individually resource constrained and are connected by a structured but fault-prone high-cost interconnect network.
Processors: Develop domain-specific processor architecture optimized for these power-constrained, physically coupled applications.

Design Methodology: Develop techniques and hybrid emulation platform for systematic architecture exploration, simulation, optimization, and reconfiguration of these systems.

Cheap and tiny processors, radios, sensors, and actuators resulting from progress in microelectronics are leading to a new class of embedded systems, often called Wireless Sensor Networks, that consist of a large number of individual nodes that are physically-coupled, energy-constrained, spatially-distributed (often in an ad hoc fashion), and wirelessly-networked. Such systems provide information about the physical environment at an unprecedented level of detail, and to manipulate the physical environment based on this information, in diverse applications such as security and surveillance, monitoring of wildlife habitats, smart sensor-instrumented environments, and condition-based maintenance of complex systems. This research involves the study of techniques to systematically design, optimize, and manage Wireless Sensor Networks so as to meet applications requirements such as how long the system should last, what space should it cover, and how accurately and how rapidly should it sense the environment.

Wireless Sensor Networks are autonomous, self-configuring, and adaptive distributed systems that perform collaborative computation among energy-constrained nodes to produce the desired information about the physical world. The study is developing design-time resource allocation and run-time resource management methods for such systems while exploiting the inter-play of energy, space, time, and accuracy dimensions that underlies the notion of quality of service in these systems. The focus is on trade-offs that manifest themselves at the level of the entire "sensor field' as opposed to the individual sensor nodes. This study is important both for understanding the fundamental performance limits of wireless sensor networks, as well as for developing practical methods to systematically deploy and operate sensor networks for specific applications.

WHYNET: Scalable Testbed for Next Generation Mobile Wireless Networking Technologies
The next generation of wireless communication technology is likely to rely on cross-layer interactions
that extend from the application layer down to the physical devices. This project proposes to design and
develop WHYNET, a Wireless HYbrid NETwork testbed to facilitate detailed study of such interactions and
their impact on application level performance in heterogeneous wireless systems. The eventual technical
impact of this testbed will be to redefine how specific innovations in wireless communication technologies are
evaluated in terms of their potential to improve application-level performance as well as how alternative
approaches are compared with each other. Its broader impact will be to redefine how students are trained in
wireless technologies by providing a multi-disciplinary 'hands on' environment to complement purely
theoretical classroom training.

WHYNET is envisaged as a hybrid testbed that combines the realism of physical testing with the
scalability and flexibility of simulations. The hybrid testbed will be a networked federation of geographically
distributed, heterogeneous wireless physical testbeds with multiple protocol stacks (CDMA 2000 cellular and IP), next generation physical technologies including UWB (Ultra Wide Band), MIMO (Multiple Inputs,
Multiple Outputs) and SDR (Software Defined Radios), and a parallel & distributed multi-tool simulation
framework. Beyond providing a more accurate & flexible evaluation framework, the hybrid testbed will
facilitate a smooth transition from an abstract simulation model to an operational implementation within a
single framework. For instance, protocol prototypes can communicate with simulated lower layers for
repeatable results, or receive and process variable rate real multimedia application inputs for perceptual
evaluation. Once the physical hardware devices are ready for testing, a portion of the target network system
can be configured with real devices while the rest of the network can still reside in the simulated hardware
domain. The effort will also generate a repository of wireless networking scenarios, measurements, models
and implementations. A representative set of studies will be used to demonstrate the unique contributions of
WHYNET for cross-layer optimization studies in particular, and mobile wireless networking in general. These include sensor networks, energy-aware networking, protocols & middleware for multi-access networking, and adaptive transport and security protocols. The testbed itself will be accessible by the research community via a web-based mechanism that will allow remote uploading of models, implementations, and configurations.

The proposed research is likely to have a broader impact on two fronts: the training of future generation
of wireless engineers and wireless technology standards. Wireless engineers will need significant technical
depth to contribute to a rapidly developing technology and significant technical breadth to understand how this
technology fits into a market driven economy. The latter category requires engineers who are trained in
insystemslt aspects with an in-depth understanding of trade-offs and interactions across layers of a wireless
communication system. The current course structure is not designed to produce well-trained engineers of the
second type. The project team feels strongly that broad systems training can only be accomplished in i.hands-
onlo experimental courses or projects where the students see the tradeoffs involved in real system design. The proposed testbed can enable these types of courses across the curriculum. Even though today wireless is a
vertical technology, 4-5 years from now, the most interesting and challenging problems will be those related to wireless systems, so we believe that an inter-disciplinary yet closely-knit engineering program such as ours is well suited for the training of wireless engineer of tomorrow. By providing a scalable platform, methodology, and tools to support objective and accurate evaluation of protocol and technology alternatives, we expect that the testbed will also play an important role in shaping standards activity in IETF and related bodies.

A multi-disciplinary, multi-institution team has been formed to achieve the ambitious objectives of the
WHYNET project. The team members have substantial expertise in design and management of physical and
simulation testbeds (Bagrodia, Gerla, Rao, Takai), development of novel radio technology (Daneshrad, Fitz,
Mitra), wireless systems (Mitra, Rao, Srivastava), protocol design (Gerla, Krishnamurthy, Mohapatra, Royer, Shen, Srivastava, Tripathi) and performance evaluation (Bagrodia, Gerla, Molle, Rao, Tripathi). Many of thePIs have successfully worked together on previous collaborative projects. We have also received strong support from a number of companies that play a critical role in this space including Microsoft, Hughes
Research Laboratories (now part of Boeing), ST Electronics, HP, Ericsson, Intel, and Xtreme Spectrum.

Wireless sensor networks with their ability of in situ and dense spatiotemporal sampling of physical phenomena have rapidly emerged as a valuable new class of instrument for the basic sciences. Like any instrument, however, sensor networks suffer from impairments that adversely impact the integrity of the sensed information about the physical phenomena. The causes of reduced integrity in sensor networks are many, and include various internal and external uncertainties in the sensing, processing, and communication elements of the system. Examples include calibration errors, faults, sensing channel degradation and obstructions, bio-fouling etc. This project is comprehensively addressing the integrity problem by studying its causes, understanding fundamental limits, developing algorithms and system support, and validating the approach in real-life. The two key elements of the overall approach are autonomous detection of integrity compromise, and resilient estimation and aggregation. Underlying these two are novel statistical and signal-processing techniques that exploit learned models of the physical phenomenon, the measurement process, and the faults which result in corrupted or missed data. In addition, the project is investigating approaches for guiding remediation of the causes of integrity problems, and for integrity-driven deployment of sensor network to achieve desired resilience. The research would result in a thorough understanding of integrity failure in sensor networks, and result in a toolkit of design tools and run-time software for ensuring high-integrity operation. These would be validated via terrestrial, under-soil, and aquatic ecological observation application, and also disseminated for broader use via the project web site at http://nesl.ee.ucla.edu/projects/integrity.

Wireless embedded systems are invigorating CISE research areas from operating systems, distributed embedded computing, architecture, and networking to signal processing, algorithms, and data management, and opening up new broad-impact applications ranging from wide area environmental management to biomedical monitoring. These systems are increasingly focused on a particularly powerful and exciting class of deployment: the heterogeneous or tiered sensor network. A heterogeneous sensor network contains nodes with different capabilities, such as tiny, low-power "motes" and higher-powered, "microservers". Motes are inexpensive and require no infrastructure for long-term deployments, but also extremely constrained in memory, CPU power, and communication. Microservers, in contrast, are more efficient than motes at many computation- and memory-intensive tasks, and more readily interfaced to high-bandwidth peripherals, such as high-rate ADCs and network interfaces; but their higher energy consumption requires power infrastructure, such as solar panels, in long-term deployments. A heterogeneous system containing both motes and microservers can combine the advantages of both devices, using motes to achieve the desired spatial sensing density and microservers to achieve the desired processing power.

Wireless embedded sensor systems present the CISE community with an array of intertwined research challenges: real time sensing of complex and diverse phenomena, embedded computing constrained in bandwidth, energy, memory, and storage, controlled mobility, and the autonomous coordination of vast numbers of network nodes. But implementing and testing sensor network applications is daunting even aside from these challenges: many of the constraints that yield low-power, long-lifetime systems also undermine traditional methods for instrumenting and understanding program behavior. Coordinated community infrastructure can thus act as a tremendous research accelerator, and without shared infrastructure, research in the field will be significantly hampered. This project addresses a pressing need: wireless sensor network research demands a concentrated effort to develop a community resource for heterogeneous sensor systems.

The investigators will develop a community resource based on Emstar, a highly resilient application methodology for microservers and general heterogeneous deployments. Emstar smoothly combines simulation, emulation, and deployment, leading to qualitatively easier debugging and application analysis. An EmTOS component seamlessly integrates motes and microservers; the EmView visualizer provides unprecedented visibility into wireless sensor network communication patterns.

This will build on the investigators Emstar prototype which has proven its value in deployments of heterogeneous networks.
With expanded functionality, integration, hardening, enhanced usability, and longer-term support, its broad array of tools will become accessible to the computer and information science and engineering community. The project will expand Emstar's flexibility, completeness, robustness, documentation, and programmability; extend its functionality by learning from targeted deployments such as seismic arrays, mobile environmental sensing, and medical informatics; and develop a robust, active Emstar community through workshops, tutorials, and mailing lists. The proposed community resource will act as a tremendous accelerator for research into heterogeneous wireless sensor networks, enabling quick and thorough exploration of socially important applications including environmental monitoring, medical and public health systems, and industrial and civic infrastructure development and management. The project will also support and develop undergraduate and graduate level project courses using Emstar, and explicitly involve an undergraduate research program targeting underrepresented minorities.

This project is investigating the impact on the network architecture of a new class of applications involving embedded sensing technology as it moves from scientific, engineering, defense, and industrial contexts to the wider personal, social and urban contexts. These applications draw on sensed information about people, objects, and physical spaces to enable new kinds of social exchange and offer new and unexpected views of our communities. They require new algorithms and software mechanisms because unlike scientific applications of distributed sensing, a single system is widely distributed, intermittently connected, and privately administered; and unlike traditional Internet applications the physical inputs are critical to the behavior. The project is developing principles and abstractions that are vital for the future Internet to incorporate such applications, to identify the network fabric architecture options, and to develop the key components and services to realize them. These include network services for context verification and resolution control to enable privacy-aware and verifiable sensing, and application services for naming, dissemination, and aggregation of sensed data. In addition, the project is developing concrete instances of personal, social and urban embedded networked sensing applications to act as design drivers for the broader community of researchers architecting the new Internet core, as well tools to assist in application authoring.
Broader Impact: Ultimately, this research will yield fundamental understanding of software architectures, networking models and data processing techniques that are needed to support a citizenry actively participating in collection, sharing, and interpretation of physical sensor data in the public sphere at multiple scales.

Abstract:

This collaborative project studies dynamical systems characterized by a combination of hybrid and probabilistic behavior. Hybrid behavior is characterized by discrete switching between system modes and continuous evolution within a mode. Such systems frequently arise in a wide range of applications, from power electronics and communication networks to economics and biology. In this research, a new modeling framework for such systems is developed, which supports external variables, compositional reasoning, and nondeterministic as well as probabilistic transitions. New stability criteria for such probabilistic hybrid systems are obtained. In contrast with existing results, they are formulated in terms of two independent components: a family of Lyapunov functions (one for each continuous mode) and a slow-switching condition of an average-dwell-time type. This modularity has the benefit of decoupling the search for Lyapunov functions from the verification of the desired properties of the discrete dynamics. The latter task is the focus of the project, and is treated using two complementary methods: one based on proving an invariant property, and another based on solving an optimization problem. These theoretical results are supported by development of new software tools.

Proposal Number: 0820061/0820034/0820230

TITLE: Design and Run-time Techniques for Physically Coupled Software

PIs: Ramesh Govindan (USC), Rajesh Gupta (UCSD), Mani Srivastava (UCLA), and Paulo Tabuada (UCLA)

ABSTRACT:

Many real-world systems are deeply embedded in the physical world and their operational behavior is determined in large part by a tight coupling between the system components and the physical environment. This project seeks to establish the scientific principles governing software for such physically-coupled systems by focusing on four challenges in the context of distributed sensing and control applications: 1) Support for physical context in the form of programming structures that enable application software to explicitly capture the state of the physical world as an observable in an embedded computation; 2) Formal methods for composing software modules that indirectly interact with each other through the physical world, and a run-time safety supervisor that provably enforces correctness of composition; 3) Programming structures to enable design and verification of applications with resource provisioning that is driven by and adapts to physical-world dynamics; 4) System software support for sharing physically-coupled sensor and actuator resources in distributed settings. In addition, educational techniques targeting the teaching of topics in physically-coupled computational systems are being explored by creating shared educational content in the form of self-contained reusable modules.

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).

Obtaining physiological/behavioral data from human subjects in their natural environments is essential to conducting ecologically valid social and behavioral research. While several body area wireless sensor network (BAWSN) systems exist today for physiological data collection, their use has been restricted to controlled settings (laboratories, driving/flying scenarios, etc.); significant noise, motion artifacts, and existence of other uncontrollable confounding factors are the often cited reasons for not using physiological measurements from natural environments. In order to provide scientifically valid data from natural environments, a BAWSN system must meet several unique requirements (1) Stringent data quality without sensing redundancy, (2) Personalization to account for wide between person differences in physiological measurements, and (3) Real-time inferencing to allow for subject confirmation and timely intervention.

Intellectual Merit: In this project, a multidisciplinary team of researchers spanning various computing disciplines and behavioral sciences are developing a general purpose framework called FieldStream that will make it possible for BAWSN systems to provide long term unattended collection of objective, continuous, and reliable physiological/behavioral data from natural environments that can be used for conducting population based scientific studies. FieldStream is being incorporated in two real-life projects ? NIH sponsored AutoSense at Memphis and NSF sponsored Urban Sensing at UCLA, to help validate the assumptions, establish the feasibility of developed solutions, and to uncover new requirements.

Broader Impact: By making it possible to obtain scientifically valid objective data from the field, FieldStream promises to help solve several behavioral problems of critical importance to human society that have remained unanswered for lack of such data.

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).

This project focuses on understanding the principles and methods for the design of green networks at the edge of the Internet. The total power consumption of edge networks is estimated to be quite significant, so even moderate improvements in energy-usage in an individual device can result in non-trivial savings overall. Obtaining these moderate improvements in the energy-efficiency of edge networks is challenging for two reasons: the diversity of edge networks and the dynamics in their workload.

Intellectual Merit. Leveraging the researchers' combined expertise in low-power electronics, link-layer technologies, and energy-efficient network subsystem design and architecture, the project will: a) devise a deep energy-inspection architecture that encompasses a broad range of edge devices and networking technologies, and incorporates innovative hardware designs for subsystem-level monitoring and control of energy usage; b) explore run-time energy adaptation at various levels of the network, enabled by this inspection architecture; c) examine coordination mechanisms for controlling edge network energy usage which will allow coordinated energy management across components and devices, enabling more aggressive energy savings.

Broad Impacts. The project can have significant societal benefit, targeted as it is on sustainable technologies. Moreover, the techniques it develops for energy efficiency can be broadly applied to other areas of computing: large server systems, mobile devices, and consumer appliances. Beyond its impact on technology, the project will also contribute to workforce development by training EE and CS students in sustainability.

Abstract: The Variability Expedition
Project: Variability-Aware Software for Efficient Computing with Nanoscale Devices

As semiconductor manufacturers build ever smaller components, circuits and chips at the nano scale become less reliable and more expensive to produce ? no longer behaving like precisely chiseled machines with tight tolerances. Modern computing is effectively ignorant of the variability in behavior of underlying system components from device to device, their wear-out over time, or the environment in which the computing system is placed. This makes them expensive, fragile and vulnerable to even the smallest changes in the environment or component failures. We envision a computing world where system components -- led by proactive software -- routinely monitor, predict and adapt to the variability of manufactured systems. Changing the way software interacts with hardware offers the best hope for perpetuating the fundamental gains in computing performance at lower cost of the past 40 years. The Variability Expedition fundamentally rethinks the rigid, deterministic hardware-software interface, to propose a new class of computing machines that are not only adaptive but also highly energy efficient. These machines will be able to discover the nature and extent of variation in hardware, develop abstractions to capture these variations, and drive adaptations in the software stack from compilers, runtime to applications. The resulting computer systems will work and continue working while using components that vary in performance or grow less reliable over time and across technology generations. A fluid software-hardware interface will thus mitigate the variability of manufactured systems and make machines robust, reliable and responsive to the changing operating conditions.

The Variability Expedition marshals the resources of researchers at the California Institute for Telecommunications and Information Technology (Calit2) at UC San Diego and UC Irvine, as well as UCLA, University of Michigan, Stanford and University of Illinois at Urbana-Champaign. With expertise in process technology, architecture, and design tools on the hardware side, and in operating systems, compilers and languages on the software side, the team also has the system implementation and applications expertise needed to drive and evaluate the research as well as transition the research accomplishments into practice via application drivers in wireless sensing, software radio and mobile platforms.

A successful Expedition will dramatically change the computing landscape. By re-architecting software to work in a world where monitoring and adaptation are the norm, it will achieve more robust, efficient and affordable systems that are able to predict and withstand not only hardware failures, but other kinds of software bugs or even attacks. The new paradigm will apply across the entire spectrum of embedded, mobile, desktop and server-class computing machines, yielding particular gains in sensor information processing, multimedia rendering, software radios, search, medical imaging and other important applications. Transforming the relationship between hardware and software presents valuable opportunities to integrate research and education, and this Expedition will build on established collaborations with educator-partners in formal and informal arenas to promote interdisciplinary teaching, training, learning and research. The team has built strong industrial and community outreach ties to ensure success and reach out to high-school students through a combination of tutoring and summer school programs. The Variability Expedition will engage undergraduate and graduate students in software, hardware and systems research, while promoting participation by underrepresented groups at all levels and broadly disseminating results within academia and industry.

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).

This project involves the renovation of sections of Boelter Hall, part of UCLA's School of Engineering and Applied Science. The building houses the departments of Chemical and Biomolecular Engineering, Civil and Environmental Engineering, and Computer Science. The unifying theme of the space to be renovated is its use for "sustainability research." Four research "collaboratories" will be created and core mechanical, electrical and plumbing infrastructure will be renovated to support these. The collaboratories are: a Structural Sustainability Collaboratory, a Bio-Sustainability Collaboratory, an Energy and Water Sustainability Collaboratory, and a Sustainable Enviro-Bio-Nano-Technology Collaboratory.

The renovated facility will be used for research in technologies for renewable and alternative energy production and storage, and environmental engineering. Some of the research goals include: the development of the biosynthesis of pharmaceuticals to replace current processes involving organic solvents and to convert renewable resources into pharmaceuticals; a study of the effect of biofuel combustion products on mammalian cells; the discovery, development and optimization of new methods for designing metabolic pathways, new enzymes for biosensors, and new biodegradable polymers; understanding microbial processes at the sub-cellular level; the biotransformation of pollutants, nanoparticles, and pathogens to solve hazardous waste problems and improve public health; and understanding how site-specific physiological and hydrogeochemical conditions and engineered manipulations affect biodegradation activities, microbial community structures, and the fate and transport of pollutants.

In addition to providing infrastructure for research, the renovated facility will be used by undergraduates, graduate students and post-doctoral researchers for research training. The outcomes of some of the research activities may translate into technologies that industry can commercialize and that society can use to provide new energy streams, enhance environmental stewardship, and mitigate the adverse consequences of environmental change.

Cyber-physical systems regulating critical infrastructures, such as electrical grids and water networks, are increasingly geographically distributed, necessitating communication between remote sensors, actuators and controllers. The combination of networked computational and physical subsystems leads to new security vulnerabilities that adversaries can exploit with devastating consequences. A synchronized attack on the interdependent network components and physical plants can create complex and new security vulnerabilities that cannot be addressed by securing the constituent systems individually.

This project takes a holistic view by utilizing the properties of physical systems to design new secure protocols and architectures for cyber-physical systems (CPS) through a unified conceptual framework, which uses models for the physical system and the communication/computation network to define precise attack models and vulnerabilities. These mathematical models are used to design algorithms and protocols with provable operational security guarantees, thus enabling the design of more trustworthy architectures and components. The algorithms, protocols, and architectures are validated on CPS testbeds targeting building, automobile, and smart-grid applications. Additionally, the research is being integrated into the curriculum via the creation of novel coursework combining the underlying control, information theory, cryptography, and embedded system concepts.

By improving the protection of critical cyber-physical infrastructure against emerging threats, this research is expected to provide direct socio-economic benefits, ranging from individual organizations to a national scale. The inter-disciplinary team of this project will integrate teaching and curriculum development with the research, contributing to the training of a new generation of engineers well versed in the design of trustworthy cyber-physical systems.

This award provides funding for a collaborative project between University of California, Los Angeles and Indian Institute of Information Technology, New Delhi, India. Buildings are among the largest consumers of energy, both directly in the form of electricity, gas etc. as well as indirectly through consumption of water as part of the critical energy-water nexus that one must consider for true sustainability. The objective of this project is to develop the foundations of low-cost and easy-to-deploy sensing methods that provide observability into patterns and causes of energy and water consumption in a building, and run-time methods that use the sensory information for intelligent control of various buildings systems to minimize the direct and indirect energy use. The project team is collaborating with international academic and industrial collaborators who offer access to complementary experimental opportunities and unique opportunities to develop low-cost technologies that scale across different climatic and socioeconomic contexts. Key elements of the research include (i) Low-cost self-calibrating sensors that infer energy and water usage indirectly from side-band signals, (ii) Methods to reduce overall energy and water footprint by better management of building subsystems, by timely identification and repair of energy and water wasting physical degradations, and by providing information feedback and incentives to influence occupant behaviors, and (iii) Study of the impact of human, cultural and societal factors on privacy, safety, and user interaction mechanisms. The project has the potential of significant socioeconomic benefits by facilitating assessment of efficacy of conservation measures, targeting of incentives, auditing compliance with regulations, and facilities maintenance. The project also contributes to workforce development and training of students on energy challenges in a global socioeconomic context. This project is a part of pervasive communications and computing collaboration (PC3) initiative.

Pervasive computing, such as sensors in smartphones, buildings, automobiles and cities, result in increased sharing of sensor data, whether initiated by users or by other authorities such as service providers, government entities, interest groups, and individuals. Embedded in this data is information which others, even using sophisticated data mining algorithms, can fuse to construct a virtual biography of our activities, revealing private behaviors and lifestyle patterns. Researchers in this project are devising computational methods to let users exercise privacy control over their personal sensory data that is shared.

Intellectual Merit: The project is developing a user-configurable cryptographically-secure ?privacy shield? to run on smartphones and act upon sensor information flowing to other users, apps, and services. To make privacy understandable, the user is presented with a higher level abstraction for expressing privacy and sharing in terms of rich inferences and contexts drawn from sensor measurements. The user can designate some inferences and contexts as private. To provide privacy while ensuring the quality of service provided by the recipients of the sensory information, the system also incorporates algorithms which, over time, learn a personalized model of the privacy risk from sharing an inference. The theoretical concepts and the system realization are being validated via user studies in mobile health and personal sensing.

Broader Impacts: By providing better understanding of the behavioral privacy problem and risks inherent in sharing seemingly innocuous data, results from this project will lead to a more educated and informed citizenry, regulators, and policy makers, and provide effective tools for privacy management to those who share sensory information.

Accurate and reliable knowledge of time is fundamental to cyber-physical systems for sensing, control, performance, and energy efficient integration of computing and communications. This statement underlies the proposal. Emerging CPS applications depend on precise knowledge of time to infer location and control communication. There is a diversity of semantics used to describe time, and quality of time varies as we move up and down the system stack. System designs tend to overcompensate for these uncertainties and the result is systems that may be over designed, inefficient, and fragile.

The intellectual merit derives from the new and fundamental concept of time and the holistic measure of quality of time (QoT) that captures metrics including resolution, accuracy, and stability. The proposal builds a system stack ("ROSELINE") that enables new ways for clock hardware, operating system, network services, and applications to learn, maintain and exchange information about time, influence component behavior, and robustly adapt to dynamic QoT requirements, as well as to benign and adversarial changes in operating conditions. Application areas that will benefit from Quality of Time will include: smart grad, networked and coordinated control of aerospace systems, underwater sensing, and industrial automation.

The broader impact of the proposal is due to the foundational nature of the work which builds a robust and tunable quality of time that can be applied across a broad spectrum of applications that pervade modern life. The proposal will also provide valuable opportunities to integrate research and education in graduate, undergraduate, and K-12 classrooms. There will be extensive outreach through publications, open sourcing of software, and participation in activities such as the Los Angeles Computing Circle for pre-college students.

The MetroInsight project is building an end to end system for knowledge discovery from real-time data streams collected through a variety of sensors, data collection and aggregation methods. These data streams are highly dimensional with multiple sensors observing same or similar phenomena over multiple sensory spectrums and scales. These are also sometimes real-time and/or have strong timing relationships that is necessary to support metropolitan infrastructure through effective analytics and policy support. The project brings together a diverse number of partners utilities, universities, companies and cities with the ability to contribute novel tools and urban sensor data and to translate knowledge into actions. MetroInsight's unique combination of tools, data and partnerships, in part with the MetroLab Network , makes it well poised to set an example for the MetroLab programs across the nation as well as the rest of the municipal governments. The project will explore connections between multimodal datasets and urban infrastructure management to build a practical system consisting of integrated tools, as well as training a new generation of metropolitan workforce. As part of an ambitious plan for community building and workforce development, the project includes creation of new learning modules, certification programs on energy and sustainability, an online courses on sensor data analytics and new capstone projects in a new Data Science master's degree program.

To achieve project goals, MetroInsight is building infrastructure for managing data, networks and processing that will support design of new algorithms and tools in the project. Specifically, the project is developing algorithms to transform multimodal urban data to a lower dimensional data that reflects underlying physical and social phenomena. These low dimensional data may consist of population level data suitable for dynamic processing to support real time monitoring and visualization by cityscale operators of various lifelines from transportation, communications to emergency response. To address technical challenges in complex and subtle spatiotemporal dynamics of interdependent urban networks, MetroInsight will develop metadata methods and tools that support discovery of operational interdependencies, quantification of uncertainties for decision support and to provide assurances related to integrity and security of data, compliance related to ethical and legal privacy expectations.

Every year, 3.5 million people in the US experience homelessness, with 1 in 30 children becoming homeless. Despite numerous government-sponsored programs and efforts by nonprofit organizations, many homeless people live in abject conditions. This research re-envisions smart city technologies to best serve those in need of access to basic resources including food, shelter and medical services. The proposed infrastructure will connect the currently disjoint efforts of public services, NGOs and private citizens, and use population-modeling and planning algorithms to match the varying and unpredictable supply with those who need it. In pursuit of the overarching goal of collecting and delivering services to maximize social welfare, this research will make advances in the science of population modeling, the analysis and design of human-centered planning algorithms, and technological challenges including secure and privacy-aware sensing modalities and mobile technologies.

As part of a human-centered design approach, interviews and observations will be conducted to understand user needs, and design a system that multiple stakeholders can use to report their needs and extra supply. This collected data will be used by non-profit organizations to strategically distribute resources. The real-world stakeholders such as food banks, food pantries, shelters, street medicine teams, and food rescue organizations will be closely involved in the design and evaluation process.

This research is high-risk and high-reward, and appropriate for EAGER. Failure means that the resulting planning algorithms will make unfair decisions and prioritize a few organizations or donors, or will make fair, but inefficient allocation decisions, which will endanger social justice and community well-being. Success will improve both efficiency of resource distribution and the quality of life of underserved populations in the United States. The completion of the project will produce 1) algorithms for optimal resource allocation that are both efficient and aware of human-in-the-loop concerns, and which can be used for other functions including disaster-response, and 2) communication infrastructure for non-profit organizations, volunteers, and populations in need, to coordinate other service activities. The project has potential for great societal impact: it will make charitable donations convenient and inexpensive for those with supply power, increasing the volume of donations and thereby reducing wastage. The outcome will be an improved realization of the philanthropic potential of the increasingly sharing nature of the American economy.

The Internet of Things (IoT) includes a variety of devices such as smart appliances, cars, and other physical systems that are deeply embedded in our everyday lives and that are at risk from new kinds of threats to security and privacy from hackers or state actors. In IoT systems, sensors are used to probe the physical state of the system (e.g., temperature in a building or rotational speed of a wheel of a car) and then software control systems use algorithms to determine appropriate adjustments to the system (e.g., run the air conditioning for 5 minutes or apply the brakes). The project is focused on protecting those control systems and algorithms to ensure security and privacy for users.

The research addresses trustworthy and privacy-aware control architectures for IoT through mechanisms drawn from control, cryptography, software, and hardware. These include: (i) A framework for formally reasoning about safety and privacy properties of control software in conjunction with dynamical models of the physical world and associated sensing and actuation channels; (ii) Lightweight domain-specific mechanisms, for policing flow of information through software applications, while leveraging the semantics of machine learning and control algorithms, physics of the system, and numerical properties; (iii) Enforcing desired safety and information leakage properties via a combination of principled sensor data perturbation, control algorithms optimized for efficient computation over encrypted data, and a hardware-supported trusted computing base tailored to protecting sensed data and control algorithm parameters; (iv) A resilient control and timing infrastructure that protects against attacks on timing information through a hybrid use of edge and cloud resources and physical models. The success of the mechanisms is being assessed on experimental testbeds for smart home, industrial automation and smart vehicles, but have broader applicability to many other IoT applications. The project team is also creating a new graduate class on IoT security and developing educational material on IoT security for high-schoolers through the Los Angeles Computing Circle initiative at the University of California - Los Angeles.

The Center of Excellence for Mobile Sensor Data-to-Knowledge (MD2K) has developed open-source software for smart phones and cloud. Scientists use MD2K software to develop and test algorithms to monitor health, wellness, and work productivity via wearable sensors. The mResearch project is aimed at assisting Computer and Information Science and Engineering (CISE) researchers. The mResearch project will significantly enhance MD2K software and integrate Internet-of-Things (IoT) devices. The enhanced MD2K software will accelerate research in sensors design, mobile computing, privacy, analytics (especially machine learning and deep learning), and visualization. mResearch will enable CISE researchers to easily deploy their contributed software in scientific studies for health, smart homes, and workplace. The resulting discoveries and tools will help individuals improve their health, wellness, and work productivity.

MD2K has developed open-source mobile sensor big data software platforms mCerebrum for smartphones and Cerebral Cortex for the cloud. This scalable and generalizable infrastructure is used for collecting, analyzing, and sharing high-frequency, mobile sensor data and associated labels in the context of scientific field studies. In particular, it supports the development and validation of models and algorithms for inferring markers of health, wellness, and productivity, and their associated risk factors. It has already been used at eleven sites across the country to collect over 300 terabytes of mobile sensor data in the field setting from over 2,000 participants. It has resulted in new computational models for the detection of conversation, smoking, eating, craving, stress, and cocaine use. The mResearch project is making five significant infrastructure enhancements to the MD2K infrastructure to assist CISE researchers in mobile sensor development, mobile computing, privacy, analytics, visualization, and participant engagement. First, it will enable data analytic workflow management across multiple layers of the system to enable reproducible and extensible experimentation. Second, it will allow encapsulation of data sources to provide convenient and responsible access to them in data analytic workflows. Third, it will facilitate cloud-assisted complex, real-time analytics for personalizing mobile interventions and improving engagement. Fourth, simulators will be developed with the ability to feed stored data into the platform at various points to enable research on system components and properties such as data compression, transfer and storage, as well as the scalability of data analytics. Finally, Internet-of-Things (IoT) devices and services will be integrated. With these five enhancements, the MD2K software will provide a complete, open, and modularized architecture. It will include all aspects of sensor data collection, data processing algorithms, cloud-based machine learning, and IoT integration. The enhanced MD2K software will facilitate reproducible and extensible CISE research with high-frequency mobile sensor data.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

The rapid growth of Internet-of-Things (IoT) devices points to a future with trillions of devices deployed at a planetary scale for use in diverse application domains, ranging from healthcare, to transportation, to energy, to manufacturing, to environmental sensing, and more. Such growth poses significant challenges. First, due to the relatively slower growth of cloud infrastructure, the current practice of offloading data from IoT devices for computing at cloud servers will not be sustainable due to scaling challenges. Second, future IoT devices will operate in environments where they will host multiple applications simultaneously and move across physical spaces with different owners, thus requiring flexible sharing across trust domains of sensing, processing, communication, and action resources present in the devices and the physical spaces. Addressing these emerging challenges requires a radical rethinking of the current device-edge-cloud architecture of IoT systems. In this project, the researchers are exploring a new architecture approach called the “Internet of Collaborating Things” (IoCT). In IoCT, instead of offloading device data to edge and cloud servers, the application computation is distributed across an amorphous collection of interconnected IoT devices that produce and consume data, while harnessing emerging on-device compute accelerators. The project will address three key challenges associated with the IoCT vision: (i) secure multitenancy on resource-constrained IoT devices; (ii) secure collaboration among IoT devices; and (iii) resilient management of resources distributed across IoT devices and the spaces they are operating in. <br/><br/>The societal benefits of the project are twofold. First, it will provide the foundations for a massive-scale IoT infrastructure, without the large cost and energy footprint associated with using traditional server infrastructure. Second, it will allow IoT applications involving rich high-dimensional sensors and sophisticated physical control to be run anywhere by securely harnessing resources present in nearby devices and physical spaces. This includes autonomous urban robots interacting in complex manners with social spaces and built environments. Additionally, the project team will undertake specific broader impact activities to advance education, transition technology, and broaden participation. In particular, for creating and nurturing an IoCT community of researchers and practitioners, the project team will release open-source software, create experimental testbeds, organize workshops, and contribute to broadening participation in computing through outreach to local high schools using two pre-college programs: the Los Angeles Computing Circle at UCLA and the Summer Turing Institute at UMass Amherst.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.