More Like this

1. The Streetscape Application Services Stack (SASS): Towards a distributed sensing architecture for urban applications

   Pargoo, N_S; Ghasemi, M; Xia, S; Turkcan, M_K; Ehsan, T; Zang, C; Sun, Y; Ghaderi, J; Zussman, G; Kostic, Z; et al (November 2024, arXiv)

   As urban populations grow, cities are becoming more complex, driving the deployment of interconnected sensing systems to realize the vision of smart cities. These systems aim to improve safety, mobility, and quality of life through applications that integrate diverse sensors with real-time decision-making. Streetscape applications—focusing on challenges like pedestrian safety and adaptive traffic management— depend on managing distributed, heterogeneous sensor data, aligning information across time and space, and enabling real-time processing. These tasks are inherently complex and often difficult to scale. The Streetscape Application Services Stack (SASS) addresses these challenges with three core services: multimodal data synchronization, spatiotemporal data fusion, and distributed edge computing. By structuring these capabilities as clear, composable abstractions with clear semantics, SASS allows developers to scale streetscape applications efficiently while minimizing the complexity of multimodal integration. We evaluated SASS in two real-world testbed environments: a controlled parking lot and an urban intersection in a major U.S. city. These testbeds allowed us to test SASS under diverse conditions, demonstrating its practical applicability. The Multimodal Data Synchronization service reduced temporal misalignment errors by 88%, achieving synchronization accuracy within 50 milliseconds. Spatiotemporal Data Fusion service improved detection accuracy for pedestrians and vehicles by over 10%, leveraging multicamera integration. The Distributed Edge Computing service increased system throughput by more than an order of magnitude. Together, these results show how SASS provides the abstractions and performance needed to support real-time, scalable urban applications, bridging the gap between sensing infrastructure and actionable streetscape intelligence. 

   more » « less
2. The Streetscape Application Services Stack (SASS): Towards a Distributed Sensing Architecture for Urban Applications

   Pargoo, Navid; Ghasemi, Mahshid; Xia, Shuren; Turkcan, Mehmet; Ehsan, Taqiya; Zang, Chengbo; Sun, Yuan; Ghaderi, Javad; Zussman, Gil; Kostic, Zoran; et al (November 2024, arXiv:2411.19714 [cs.NI],)

   As urban populations grow, cities are becoming more complex, driving the deployment of interconnected sensing systems to realize the vision of smart cities. These systems aim to improve safety, mobility, and quality of life through applications that integrate diverse sensors with real-time decision-making. Streetscape applications—focusing on challenges like pedestrian safety and adaptive traffic management—depend on managing distributed, heterogeneous sensor data, aligning information across time and space, and enabling real-time processing. These tasks are inherently complex and often difficult to scale. The Streetscape Application Services Stack (SASS) addresses these challenges with three core services: multimodal data synchronization, spatiotemporal data fusion, and distributed edge computing. By structuring these capabilities as clear, composable abstractions with clear semantics, SASS allows developers to scale streetscape applications efficiently while minimizing the complexity of multimodal integration. We evaluated SASS in two real-world testbed environments: a controlled parking lot and an urban intersection in a major U.S. city. These testbeds allowed us to test SASS under diverse conditions, demonstrating its practical applicability. The Multimodal Data Synchronization service reduced temporal misalignment errors by 88%, achieving synchronization accuracy within 50 milliseconds. Spatiotemporal Data Fusion service improved detection accuracy for pedestrians and vehicles by over 10%, leveraging multicamera integration. The Distributed Edge Computing service increased system throughput by more than an order of magnitude. Together, these results show how SASS provides the abstractions and performance needed to support real-time, scalable urban applications, bridging the gap between sensing infrastructure and actionable streetscape intelligence. 

   more » « less
3. Electromagnetic modeling and science reach of $\text{DMRadio-}{\mathrm{m}}^3$

   https://doi.org/10.1103/bwgd-kxsb  

   AlShirawi, A; Ankel, V; Bartram, C; Begin, J; Bell, C; Benabou, J N; Brouwer, L; Chaudhuri, S; Cho, H-M; Corbin, J; et al (September 2025, Physical review D)

   $\text{DMRadio-}{\mathrm{m}}^3$is an experimental search for dark matter axions. It uses a solenoidal dc magnetic field to convert an axion dark-matter signal to an ac electromagnetic response in a coaxial copper pickup. The current induced by this axion signal is measured by dc SQUIDs. $\text{DMRadio-}{\mathrm{m}}^3$is designed to be sensitive to Kim-Shifman-Vainshtein-Zakharov (KSVZ) and Dine-Fischler-Srednicki-Zhitnisky (DFSZ) QCD axion models in the 10–200 MHz ( $41\mathrm{neV}/c^2–0.83\mathrm{\mu }\mathrm{eV}/c^2$) range, and to axions with $g_{a\gamma \gamma }=g_{a\gamma \gamma ,\mathrm{DFSZ}}\left(30\mathrm{MHz}\right)=1.87\times {10}^{-17}{\mathrm{GeV}}^{-1}$over 5–30 MHz as an extended goal. In this work, we present the electromagnetic modeling of the response of the experiment to an axion signal over the full frequency range of $\text{DMRadio-}{\mathrm{m}}^3$, which extends from the low-frequency, lumped-element limit to a regime where the axion Compton wavelength is only a factor of 2 larger than the detector size. With these results, we determine the live time and sensitivity of the experiment. The primary science goal of sensitivity to DFSZ axions across 30–200 MHz can be achieved with a $3\sigma$live scan time of 2.9 years. 

   more » « less
4. An ethical decision-making framework with serious gaming: a smart water case study on flooding

   https://doi.org/10.2166/hydro.2021.097  

   Ewing, Gregory; Demir, Ibrahim (March 2021, Journal of Hydroinformatics)

   null (Ed.)

   Abstract Sensors and control technologies are being deployed at unprecedented levels in both urban and rural water environments. Because sensor networks and control allow for higher-resolution monitoring and decision making in both time and space, greater discretization of control will allow for an unprecedented precision of impacts, both positive and negative. Likewise, humans will continue to cede direct decision-making powers to decision-support technologies, e.g. data algorithms. Systems will have ever-greater potential to effect human lives, and yet, humans will be distanced from decisions. Combined these trends challenge water resources management decision-support tools to incorporate the concepts of ethical and normative expectations. Toward this aim, we propose the Water Ethics Web Engine (WE)2, an integrated and generalized web framework to incorporate voting-based ethical and normative preferences into water resources decision support. We demonstrate this framework with a ‘proof-of-concept’ use case where decision models are learned and deployed to respond to flooding scenarios. Findings indicate that the framework can capture group ‘wisdom’ within learned models to use in decision making. The methodology and ‘proof-of-concept’ system presented here are a step toward building a framework to engage people with algorithmic decision making in cases where ethical preferences are considered. We share our framework and its cyber components openly with the research community. 

   more » « less
5. Voluntary consensus based geospatial data standards for the global illegal trade in wild fauna and flora

   https://doi.org/10.1038/s41597-022-01371-w  

   Gore, Meredith L.; Schwartz, Lee R.; Amponsah-Mensah, Kofi; Barbee, Emily; Canney, Susan; Carbo-Penche, Maria; Cronin, Drew; Hilend, Rowan; Laituri, Melinda; Luna, David; et al (December 2022, Scientific Data)

   Abstract We have more data about wildlife trafficking than ever before, but it remains underutilized for decision-making. Central to effective wildlife trafficking interventions is collection, aggregation, and analysis of data across a range of source, transit, and destination geographies. Many data are geospatial, but these data cannot be effectively accessed or aggregated without appropriate geospatial data standards. Our goal was to create geospatial data standards to help advance efforts to combat wildlife trafficking. We achieved our goal using voluntary, participatory, and engagement-based workshops with diverse and multisectoral stakeholders, online portals, and electronic communication with more than 100 participants on three continents. The standards support data-to-decision efforts in the field, for example indictments of key figures within wildlife trafficking, and disruption of their networks. Geospatial data standards help enable broader utilization of wildlife trafficking data across disciplines and sectors, accelerate aggregation and analysis of data across space and time, advance evidence-based decision making, and reduce wildlife trafficking. 

   more » « less