Massachusetts Green High Performance Computing Center

The goal of this study is to describe a design architecture for a self-powered IoT (Internet of Things) sensor network that is currently being deployed at various locations throughout the Dallas-Fort Worth metroplex to measure and report on Particulate Matter (PM) concentrations. This system leverages diverse low-cost PM sensors, enhanced by machine learning for sensor calibration, with LoRaWAN connectivity for long-range data transmission. Sensors are GPS-enabled, allowing precise geospatial mapping of collected data, which can be integrated with urban air quality forecasting models and operational forecasting systems. To achieve energy self-sufficiency, the system uses a small-scale solar-powered solution, allowing it to operate independently from the grid, making it both cost-effective and suitable for remote locations. This novel approach leverages multiple operational modes based on power availability to optimize energy efficiency and prevent downtime. By dynamically adjusting system behavior according to power conditions, it ensures continuous operation while conserving energy during periods of reduced supply. This innovative strategy significantly enhances performance and resource management, improving system reliability and sustainability. This IoT network provides localized real-time air quality data, which has significant public health benefits, especially for vulnerable populations in densely populated urban environments. The project demonstrates the synergy between IoT sensor data, machine learning-enhanced calibration, and forecasting methods, contributing to scientific understanding of microenvironments, human exposure, and public health impacts of urban air quality. In addition, this study emphasizes open source design principles, promoting transparency, data quality, and reproducibility by exploring cost-effective sensor calibration techniques and adhering to open data standards. The next iteration of the sensors will include edge processing for short-term air quality forecasts. This work underscores the transformative role of low-cost sensor networks in urban air quality monitoring, advancing equitable policy development and empowering communities to address pollution challenges.

Distributed cyberinfrastructures (CI) pose opportunities and challenges for the execution of scientific workflows, especially in the context of Earth science applications. They provide heterogeneous resources that can meet the needs of the applications that are part of the scientific workflows and provide the necessary performance and scalability to achieve scientific goals. However, the challenge with distributed CI is that it is difficult to find the right resources for the applications and to orchestrate the workflow execution from resource provisioning to job execution to delivering the final results. In some cases, poor choice of resources may result in slow execution or outright failure. In this paper, we present Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) Pegasus, a CI solution built as part of the U.S. National Science Foundation ACCESS program that provides automated execution of scientific applications. We demonstrate Pegasus's capabilities with SOil MOisture SPatial Inference Engine (SOMOSPIE), an earth science multi-component application for fine-grained soil moisture predictions. We identify a roadmap to migrate applications such as SOMOSPIE on ACCESS resources with the support of ACCESS Pegasus, outlining both strengths and weaknesses of this approach.

poster Share on ACCESS Pegasus: Bringing Workflows to the ACCESS Masses Authors: Mats Rynge USC Information Sciences Institute, USA USC Information Sciences Institute, USA 0000-0002-1779-7189View Profile , Karan Vahi USC Information Sciences Institute, USA USC Information Sciences Institute, USA 0000-0001-8622-2082View Profile , Mohammad Zaiyan Alam USC Information Sciences Institute, USA USC Information Sciences Institute, USA 0009-0006-5705-2639View Profile , Ewa Deelman USC Information Sciences Institute, USA USC Information Sciences Institute, USA 0000-0001-5106-503XView Profile , Todd Miller University of Wisconsin-Madison, USA University of Wisconsin-Madison, USA 0009-0003-0683-3062View Profile , Miron Livny University of Wisconsin-Madison, USA University of Wisconsin-Madison, USA 0000-0001-5444-7439View Profile , Shelley Knuth Research Computing, University of Colorado, USA Research Computing, University of Colorado, USA 0000-0001-7249-4773View Profile , James Griffioen University of Kentucky, USA University of Kentucky, USA 0000-0002-5105-096XView Profile , John Goodhue Massachusetts Green High Performance Computing Center, USA Massachusetts Green High Performance Computing Center, USA 0009-0003-8344-9945View Profile , David Hudak Ohio Supercomputer Center, USA Ohio Supercomputer Center, USA 0000-0002-9043-0850View Profile , Julie Ma Massachusetts Green High Performance Computing Center, USA Massachusetts Green High Performance Computing Center, USA 0000-0001-6220-4314View Profile , Andrew Pasquale Massachusetts Green High Performance Computing Center, USA Massachusetts Green High Performance Computing Center, USA 0000-0002-7501-367XView Profile , Lissie Fein Massachusetts Green High Performance Computing Center, USA Massachusetts Green High Performance Computing Center, USA 0009-0000-9419-9150View Profile Authors Info & Claims PEARC '23: Practice and Experience in Advanced Research ComputingJuly 2023Pages 478–480https://doi.org/10.1145/3569951.3597590Published:10 September 2023Publication History 0citation22DownloadsMetricsTotal Citations0Total Downloads22Last 12 Months22Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

The Ecosystem for Research Networking (ERN) CryoEM Remote Instrument Pilot Project was launched in response to feedback and survey data collected from hundreds of participants of the ERN series of NSF (OAC-2018927) funded community outreach events revealing that Structural Biology instrument driven science is being forced to transition from self-contained islands to federated wide-area internet accessible instruments. Its goal is to facilitate multi-institutional collaboration at the interface of computing and electron microscopy through the implementation of the ERN Federated OpenCI Lab's Instrument CI Cloudlet design. The conclusion will be a web-based portal leveraging federated access to the instrument, workflows utilizing edge computing in conjunction with cloud computing and real-time monitoring for experimental parameter adjustments and decisions. The intention is to foster team science and scientific innovation, with emphasis on under-represented and under-resourced institutions, through the democratization of these scientific instruments. This paper discusses the latest Phase 1 deployment efforts

Incorporating equity and inclusion in the effort toward access for everyone.

1 st in the Northeast) First multi-university HPC facility of its kind in