Smart and Connected Communities - Emergency Preparedness and Response

Smart and Connected Communities - Emergency Preparedness and Response (SCCEPR)

Integrating Heterogeneous Wide-Area Networks and Advanced Data Science to Bridge the Digital Divide in Rural Emergency Preparedness and Response.

Large-scale emergencies, both man-made and natural, are increasingly incurring devastating losses in terms of human lives as well as financial resources. U.S. losses from weather-related disasters alone exceeded $1 trillion during the three decades between 1980 and 2011, and such events are on the rise due to climate change. Rural communities, with their social and economic composition, are uniquely vulnerable to emergencies both small and large-scale. While the United States critically relies on its rural population for food, water and energy, the available resources and thus effectiveness of emergency preparedness and response (EPR) in rural areas still lag behind their urban counterparts. EPR services increasingly rely on mobile broadband connectivity for timely information collection, integration and dissemination among stakeholders, including responder agencies, local governments and residents. While such technologies are abundant and reliable in the urban context, they are often scarce to non-existent in rural areas. The lack of connectivity coupled with remoteness, rugged terrain, and sparse and predominantly aging population amplify the effect of emergencies in rural areas and collectively constitute the rural EPR digital divide.

While technologically-disadvantaged, rural communities have been shown to have a tradition of collective action, taking charge of their own technological progress. The goal of this project is to analyze the feasibility of EPR information dissemination in disconnected rural areas using TV white space community networks, human and first responder mobility. The project designs a smartphone application (EApp) that leverages a combination of cellular networks, TV white spaces, responder-to-resident and resident-to-resident interactions to maximize the reach of EPR information in rural areas. It also designs technology that allows first responders to be connected as they travel through areas without commercial broadband. In parallel with the technological innovations, the team will work closely with community members and first responders to understand the applicability and usefulness of the approach.

Funding

NSF

This project is supported through a NSF Smart and Connected Communities Award CMMI-1831547.

 

Project Team

Faculty

Mariya Zheleva
Mariya Zheleva
Associate Professor
College of Nanotechnology, Science, and Engineering; Department of Computer Science; Department of Electrical & Computer Engineering
Research Interests: Next generation mobile wireless networks, focusing on autonomous spectrum measurement and dynamic access, architectures and resource allocation in IoT-and-Traditional (IoT and T) networks, measurement infrastructures and networked system design, integration and in-situ deployment
Petko Bogdanov
Petko Bogdanov
Associate Professor
College of Nanotechnology, Science, and Engineering; Department of Computer Science
Research Interests: Data mining, network science, materials informatics
J. Ramon Gil-Garcia
J. Ramon Gil-Garcia
Professor
Department of Public Administration & Policy; International Affairs; Rockefeller College of Public Affairs and Policy
Mila Gascó-Hernandez
Mila Gascó-Hernandez
Associate Professor & Research Director for the Center for Technology in Government
Department of Public Administration & Policy; International Affairs; Rockefeller College of Public Affairs and Policy

Community Partners

Town of Thurman, NY Town Hall sign

 

Warren County Emergency Services logo

Senior Personnel

Theresa A. Pardo
Theresa A. Pardo
Associate Vice President for Research & Economic Development
Division for Research & Economic Development; Department of Public Administration & Policy; International Affairs; Rockefeller College of Public Affairs and Policy; Department of Information Sciences and Technology; Center for Technology in Government (CTG UAlbany); Center for Healthy Aging

Staff

Alessandria Dei portrait

Alessandria Dei

Project Manager

PhD Students

Karyn Doke portrait

 

Qianli Yuan portrait

Undergraduate Students

Tony Comanzo portrait

 

Habib Affinnih portrait

Past Students

Lin Zhang portrait

 

Jason Viviano portrait on the University at Albany podium

 

Matthew Jacobs portrait

 

Ayman Salloum portrait in front of a State Street building

Ayman Salloum

Undergraduate student, Computer Science.
EApp developer.

 

Domenic Recchia portrait

Domenic Recchia

Undergraduate student, Computer Science.
EApp backend developer.

 

Omkar Kulkarni portrait

 

Yenisel Gulatee portrait

 

Nachuan Chengwang

MS student in Computer Science

 

Francisco Cancedda portrait

 

Guangji Yuan

PhD student in Education

Publications

2021
2021

AURORA: A Unified fRamework fOR Anomaly detection on multivariate time series.
Lin Zhang, Wenyu Zhang, Maxwell McNeil, Nachuan Chengwang, David Matteson, and Petko Bogdanov.
Data Mining and Knowledge Discovery (DAMI).

2020
2020

Supporting Resilience in Rural Emergency Preparedness and Response Through Improved Information Access.
Karyn Doke, Qianli Yuan, Mila Gasco-Hernandez, Megan Sutherland-Mitzer, J Ramon Gil-Garcia, Petko Bogdanov, and Mariya Zheleva.
GetMobile: Mobile Computing and Communications 24(2).

Learning Periods from Incomplete Multivariate Time Series.
Lin Zhang, Alexander Gorovits, Wenyu Zhang, and Petko Bogdanov.
Proceedings of the 20th IEEE International Conference on Data Mining (ICDM).

Period Estimation For Incomplete Time Series.
Lin Zhang and Petko Bogdanov.
Proceedings of the 7th IEEE International Conference on Data Science and Advanced Analytics (DSAA).

Exploiting Self-Similarity for Under-Determined MIMO Modulation Recognition.
Wei Xiong, Lin Zhang, Maxwel McNeil, Petko Bogdanov, and Mariya Zheleva.
IEEE International Conference on Computer Communications (IEEE INFOCOM 2020).

Technology adoption for emergency preparedness and response in rural areas: Identifying the main determinants.
Y. Gulatee, Q. Yuan, M. Gasco-Hernandez, R. Gil-Garcia, M. Sutherland-Mitzner, and T Pardo.
International Conference on Theory and Practice of Electronic Governance (ICEGOV), Athens (Greece), September 23rd-25th.

Understanding the role of social, technology, and physical infrastructures in smart communities: The case of rural areas in the US.
G. Yuan, R. Gil-Garcia, M. Sutherland-Mitzner, M. Gasco-Hernandez, and T. Pardo.
Hawaii International Conference on System Sciences 2020 (HICSS-53), Maui, HI (United States), January 7th-10th.

Understanding willingness to share information among citizens and public organizations: The case of emergency preparedness and response.
Y. Gulatee, Gasco-Hernandez, and R. M. & Gil-Garcia.
20th Annual International Conference on Digital Government Research (dg.o 2020), Virtual conference, June 17th-19th.

2019
2019

DSL: Discriminative Subgraph Learning via Sparse Self-Representation.
Lin Zhang and Petko Bogdanov.
Proceedings of SIAM International Conference on Data Mining (SDM).

Robust and Efficient Modulation Recognition Based on Local Sequential IQ Features.
Wei Xiong, Petko Bogdanov, and Mariya Zheleva.
IEEE International Conference on Computer Communications (IEEE INFOCOM 2019).

Towards a Socio-Technical Framework for Bridging the Digital Divide in Rural Emergency Preparedness and Response: Integrating User Adoption, Heterogeneous Wide-Area Networks, and Advanced Data Science.
Mila Gasco, Mariya Zheleva, Petko Bogdanov, and J. Ramon Gil-Garcia.
20th Annual International Conference on Digital Government Research.

DEMO: EApp: Improving Rural Emergency Preparedness and Response.
Karyn Doke, Nachuan Chengwang, Andrew Boggio-Dandry, Petko Bogdanov, and Mariya Zheleva.
25th Annual International Conference on Mobile Computing and Networking (MobiCom'19).

PERCeIDs: Periodic community detection.
Lin Zhang, Alexander Gorovits, and Petko Bogdanov.
Proceedings of the IEEE International Conference on Data Mining (ICDM).

2018
2018

An Efficient System for Subgraph Discovery.
Aparna Joshi, Yu Zhang, Petko Bogdanov, and Jeong-Hyon Hwang.
2018 IEEE International Conference on Big Data (Big Data).

News

Jan. 10, 2021

Our paper “Understanding the Determinants of Adoption and Use of Information and Communication Technologies for Emergency Management: Proposing a Research Agenda based on Existing Academic Literature.” won the best paper runner up in the “Digital Government Track” at HICCS'21.

 

Dec. 10, 2020

Mariya will participate in a panel on 5G and Beyond 5G: Vision and Research Challenges at the N2Women event at GLOBECOM'20.

 

Oct. 1, 2020

New paper on our rural emergency preparedness work appeared in the GetMobile Magazine.

 

Sep. 1, 2020

Mariya gave a talk and participated in a panel on AI for Wireless at WoWMoM'20.

 

Jul. 1, 2020 

UbiNET’s first PhD graduate, Wei Xiong, received the University at Albany Best Dissertation Award. Congrats, Wei!

 

Apr. 24, 2020 

Our paper entitled “Protecting location privacy from untrusted wireless service providers” was accepted to WiSec'20. This is joint work with Keen Sung and Brian Levine from UMass Amherst.

 

Jun. 20, 2019

The UbiNET Lab received the Dynamic Spectrum Alliance 2019 Award for University Research on New Opportunities for Dynamic Spectrum Access.

 

Mar. 28, 2019

Our paper “Towards a Socio-Technical Framework for Bridging the Digital Divide in Rural Emergency Preparedness and Response: Integrating User Adoption, Heterogeneous Wide-Area Networks, and Advanced Data Science.” was accepted to the 20th Annual International Conference on Digital Government Research (Theme: Governance in the Age of Artificial Intelligence).

 

Jan. 15, 2019

Our paper “DSL: Discriminative Subgraph Learning via Sparse Self-Representation.” was accepted to SDM'19. Congrats, Lin!

 

Nov. 20, 2018

Our paper “Robust and Efficient Modulation Recognition Based on Local Sequential IQ Features” was accepted to INFOCOM'19. Congrats, Wei!

 

Sep. 30, 2018

Our paper “An Efficient System for Subgraph Discovery. ” was accepted to BigData'18.