Title: Computer Science Research Opportunities in Sustainability

A recent workshop organized by the U.S. National Science Foundation and the Computing Community Consortium brought together a number of researchers to explore ways in which fundamental advances in computer science could help put the world on a path to /sustainability/: managing our use of natural and human-made resources in such a way that these resources can meet the needs of future generations. Sustainability spans a number of natural and built systems, and includes the many factors affecting climate, our energy generation and distribution systems, and our transportation networks.

From this workshop, several themes emerged that cut across all areas of sustainability:

• All of our systems, including those for electricity, transportation, and the heating and cooling of buildings, must be much more adaptive, self-optimizing, and able to automatically diagnose and repair malfunctions
• System control must be implemented by loosely-coupled, autonomous agents, in order to achieve high levels of robustness and to preserve the privacy of individual users.
• People play important roles in all of these systems. Systems must be able to automatically learn the needs of preferences of users, and they must guide people to make wise choices in their use of resources.
• Better modeling and simulation tools must be created that can predict the effect of different choices and policies in ways that are understandable to technical experts, as well as to policy makers and the general public.

Title: Energy-Efficient Computing and Applications

Title: Can machine learning help translate the science of climate change to information relevant for preparedness and policy?

Climate extremes may be defined inclusively as changes in the attributes of severe weather or hydrological events as well as relatively large changes in local to regional scale hydrometeorological patterns, that may be caused or exacerbated by natural climate variability or climate change, and which may in turn have significant impacts on natural, engineered and human systems. The largest knowledge gap in the physical science basis of climate change, which is relevant for informing adaptation and policy, is arguably our lack of understanding of climate extremes. The machine learning discipline offers a suite of methods and techniques which may be brought to bear on this immense societal problem. However, the inherent challenges, ranging from non-stationarity and long lead times to nonlinear spatiotemporal processes and complex dependence structures, motivate the development of novel and transformative data-intensive approaches. A combination of physics-based models, conceptual understanding and data-intensive approaches may be our best bet to solve the grand challenge of climate change which has been called the defining problem of our age.

Title: Computer Science and Engineering at the Nexus of Energy and Environment: A View from UCSD Microgrid

This talk examines the evolution of microelectronics-based embedded sensing, processing and its integration with the societal scale systems -- in particular, the electrical grid -- that can be used as testing grounds for the prototyping and testing of smart grid technologies. Using the prototype of a microgrid at the campus of the University of California at San Diego, we present energy data that points to promising methods for operation of various types of buildings and data center enclosures. These leverage coordinated use of sensing, information processing, and building HVAC systems to improve quality of energy use and decrease its cost. We examine the emerging computer science problems arising from energy arbitration, alternative energy sourcing and capacity provisioning for computational resources through dynamic deferral of energy loads.

Title: Design for Livable Environment in China

This talk presents the urgent need for computing to help design sustainable urban systems in China's modernization efforts. After a short review of the time-proved concept of "livable environment" from Chinese history, this talk summarizes key principles in ancient China to find and build livable environment for human habitat that can sustain for a long time. However, in China today, modern design and construction reduced livability of many Chinese cities. Flood, heat and traffic jam become common in many built areas in China. To solve these problems, the speaker will offer her suggestions how computer science can provide tools to help people make their environment livable.

Title: GreenOrbs: Lessons Learned from Extremely Large Scale Sensor Network Deployment

The world has just ten years to bring greenhouse gas emissions under control before the damage they cause becomes irreversible." This is a famous prediction raised by climate scientists and environmentalists recently. It reflects the increasing attention in the past decade from human beings on global climate change and environmental pollution. On the other hand, forest, which is regarded as the earth's lung, is a critical component in global carbon cycle. It is able to absorb 10%~30% of CO2 from industrial emissions. Moreover, it has large capacity of water conservation, preventing water and soil loss, and hence reducing the chance of nature disasters like mud-rock flows and floods. Forestry applications usually require long-term, large-scale, continuous, and synchronized surveillance of huge measurement areas with diverse creatures and complex terrains. The state-of-arts forestry techniques, however, support only small-scale, discontinuous, asynchronous, and coarse-grained measurements, which at the same time incur large amount of cost with respect to human resource and equipments. WSNs have great potential in resolving the challenges in forestry. Under such circumstances, GreenOrbs is launched. The information GreenOrbs offers can be used as evidences, references, and scientific tools for human beings in the battle against global climate changes and environmental pollution.

The prototype system is deployed in the campus woodland of Zhejiang Forestry University. The deployment area is around 40,000 square meters. The deployment started in May 2009 and included 50 nodes. In November 2009 it was expanded to include 330 nodes. The system scale reaches 400 in April 2010. The duty cycle of nodes is set at 8%. The network diameter is 12 hops. The sensor data are published online via the official GreenOrbs website.The Tianmu Mountain deployment includes 200 nodes and has been in continuous operation since August 2009. The deployment area is around 200,000 square meters. The duty cycle of nodes is set at 5%. The network diameter is 20 hops.

We learned a lot of lessons during the deployment of GreenOrbs. This experiment results in several publications, including ACM Sensys 2009, 2010, ACM Sigmetrics 2010, ICNP 2010, INFOCOM 2010, etc. In this discussion, we will focus on several open issues for extremely large scale deployment of sensor networks including routing, diagnosis, localization, link quality, and etc.

Title: Smart Green Building Initiative at HKUST

Buildings are key to achieving future sustainable development. Many existing green building projects are trying to achieve the goal of "green net zero" in terms of energy, carbon, water, and waste. The objective of our smart green building initiative is to construct a showcase to quantitatively demonstrate various innovative technologies from our researchers in different disciplines toward the goal of "green net zero", such as water management system, personalized ventilation system, solar adsorption cooling system, LED lighting system, smart wall materials, battery storage and management system, and biomass energy production. I will address the role IT can play to make green buildings smart. Green buildings are sustainable only if occupants can enjoy it without much effort and without sacrificing the comfort. With various sensing and control technologies making a smart resource monitoring and management system, the goal of smart green buildings will no longer be a luxury, but an imperative.

Title: Sustaining a Global Shared Software Infrastructure

Scientific discovery and innovation are advancing along fundamentally new pathways opened by development of increasingly sophisticated software. Software is also directly responsible for increased scientific productivity, the significant enhancement of research capabilities, and plays a central role in addressing important issues related to sustainability including questions of climate change, energy alternatives, and ecological sensing. Nurturing, accelerating and sustaining this critical modality for scientific investigation in the 21st century requires significant long-term coordinated investments across funding agencies, research communities and industry. For example, NSF recently established the Software Infrastructure for Sustained Innovation (SI2) program, with the overarching goal of transforming innovations in research and education into sustained software resources that are an integral part of a national and international cyberinfrastructure. SI2 envisions a softwa! re ecosystem built on vibrant partnerships among academia, government laboratories and industry, including international entities, for the development and stewardship of sustainable software. In this talk I will focus on the "software crisis" and will highlight NSF's efforts towards addressing this crisis as part of its SI2 program. I will also discuss international dimensions of the SI2 program, including an upcoming US-China workshop focused on a shared software infrastructure.

Title: Harnessing Wind in China: Controlling Variability through Location and Regulation

The need to make the transition from fossil fuels to renewables is a global one, reflecting the awareness of the problems of CO2 emissions from fossil fuels and the attractiveness of energy from renewable sources, especially wind. As with the U.S., China has a tremendous amount of potential energy from wind, but faces challenges similar to the U.S. in harnessing the wind. The areas of highest wind energy potential are localized and far from population centers. At the same time, the variability and uncertainty of wind complicate the problem of producing a consistent and reliable source of energy to power the grid. In this talk, I will summarize the results of two studies that study the potential for reducing variability from wind through the careful choice of locations, and the use of large hydroelectric facilities as a source of regulation to reduce short term variability. Using wind data from China, we solve the problem of optimal location of wind farms with Markowitz portfolio theory, which captures correlations in wind energy from different locations. We solve the problem of optimal regulation of energy from a hydroelectricity facility using dynamic programming, programmed to minimize deviations from the planned outflow.

Title: Algorithmic Methods in Conservation Biology

Conservation biologists make frequent use of models to understand and predict ecosystem dynamics and species distributions. They use mathematical optimization to plan and manage protected areas in order to best conserve biodiversity in the face of limited budgets. I will describe some of these modeling and optimization problems, and algorithmic solutions that have been successfully applied.

Title: Software Requirements Engineering for Environmental Sustainability

As computer scientists, we must work alongside other environmental scientists and others to improve the environmental sustainability for humankind by protecting our living space for future generations. Indeed, the use of information and communications technology (ICT) contributes significantly to the exploitation of our planet's resources. On the other hand, ICT also generates considerable potential for "greening through IT". Yet one thing that is missing is a comprehensive understanding of how software engineering can help in this endeavor.

We are just beginning a project, "Environmental Sustainability in Software Engineering" (EnviroSiSE), which will analyze what and how software engineering (SE) can contribute to the improving the environmental sustainability of ICT and the development of ICT systems for environmental sustainability (ICT4ES) in other domains. This interdisciplinary aim links the fields of software engineering and environmental informatics, but will need to reach beyond to other environmental scientists and industries for a full understanding of sustainability issues.

To achieve this aim, we have set forth the following preliminarty research questions:

• RQ1: What are the implications for SE of ICT4ES ? that is, when making environmental sustainability a first-class citizen among the quality objectives for system development?
• RQ2: How can the necessities resulting from ICT4ES be implemented in a software engineering approach?
• RQ3: How can we assess the impacts of a given software-intensive system for environmental sustainability, including both direct and indirect effects and considering different groups of stakeholders?

Our goal is to support the development of ICT4ES with an adequate software engineering approach that integrates knowledge of environmental informatics. Software systems are among the most powerful tools currently in use by humankind; providing the designers of these systems with conceptual techniques to infuse their process with environmental sustainability promises to improve different facets of human civilizations.

Title: Optimization Models and Algorithms in Natural Resource Planning

Computational Sustainability is a newly emerging interdisciplinary research field with the overall goal of developing computational models, methods, and tools to help manage the balance between environmental, economic, and societal needs for a sustainable future. In this talk, we will focus on a two examples of computational sustainability problems, in issues of wildlife preservation and for models for preventative treatment of forests to limit the impact of damage from fires. Both examples give rise to new and challenging optimization problems that require new techniques for scalable algorithmic methods. Motivated by issues in allocating limited preventative resources to protect a landscape against the spread of a wildfire from a stochastic ignition point, we give approximation algorithms for a new family of stochastic optimization problems. We study several models in which we are given a graph with edge costs and node values, a budget, and a probabilistic distribution over ignition nodes: the goal is to find a budget-limited set of edges whose removal protects the largest expected value from being reachable from a stochastic ignition node. In particular, 2-stage stochastic models capture the tradeoffs between preventative treatment and real-time response. The resulting stochastic cut problems are interesting in their own right, and capture a number of related interdiction problems, both in the domain of computational sustainability, and beyond. We start by designing approximation algorithms in the case that underlying metric is specified by a tree, and then apply more general tools so as to give bicriteria approximation results (i.e., both approximately optimal and approximately respecting the budget constraints) in more general settings.

This is joint work with Gwen Spencer.

Title: Understanding Climate Change: A Data Driven Approach

Climate change and its impact will play a key role in developing strategies for a sustainable future. Indeed, changes in climate, such as global warming, have been projected to have potentially dramatic consequences, including increased occurrence of extreme weather events, shocks in food and water supplies, rising sea levels, etc. These consequences, real and potential, have created an urgent need to characterize the Earth's climate and its evolution to its present state, and to predict future changes in response to natural and anthropogenic causes. Fortunately, the climate and earth sciences have recently undergone a rapid transformation from a data-poor to a data-rich environment. In particular, climate and ecosystem related observations from remote sensors on satellites, as well as outputs of climate or earth system models from large-scale computational platforms, provide massive amounts of data. The application of data driven approaches, such as machine learning, data mining, and statistics, to these information rich data sets offers new opportunities for understanding and predicting the behavior of the Earth's ecosystem and for advancing the science of climate change. This talk will discuss some of the challenges in analyzing such data sets and our recent research in discovering interesting patterns and relationships from them.

Title: Ternary Computing for a Human-Cyber-Physical Universe

This talk presents an overview of the newly initiated Frontier Research Project of Chinese Academy of Sciences called FIT (Future Information Technology utilizing human-cyber-physical resources). This 10-year basic research project targets computing applications and markets of 2020-2030, and helps China's sustainable development by increasing the utilization of IT and human resources to reduce the use of energy and physical recourses. The talk then highlights open problems related to four of the FIT main components: functional sensing, customizable internet, cloud-sea computing, and end-to-end ecosystems.