TY - GEN
T1 - URBAN-NET
T2 - 4th IEEE International Conference on Big Data, Big Data 2016
AU - Lee, Sangkeun
AU - Chen, Liangzhe
AU - Duan, Sisi
AU - Chinthavali, Supriya
AU - Shankar, Mallikarjun
AU - Prakash, B. Aditya
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016
Y1 - 2016
N2 - Critical Infrastructures (CIs) such as energy, water, and transportation are complex networks that are crucial for sustaining day-to-day commodity flows vital to national security, economic stability, and public safety. The nature of these CIs is such that failures caused by an extreme weather event or a man-made incident can trigger widespread cascading failures, sending ripple effects at regional or even national scales. To minimize such effects, it is critical for emergency responders to identify existing or potential vulnerabilities within CIs during such stressor events in a systematic and quantifiable manner and take appropriate mitigating actions. We present here a novel critical infrastructure monitoring and analysis system named URBAN-NET. The system includes a software stack and tools for monitoring CIs, pre-processing data, interconnecting multiple CI datasets as a heterogeneous network, identifying vulnerabilities through graph-based topological analysis, and predicting consequences based on 'what-if' simulations along with visualization. As a proof-of-concept, we present several case studies to show the capabilities of our system. We also discuss remaining challenges and future work.
AB - Critical Infrastructures (CIs) such as energy, water, and transportation are complex networks that are crucial for sustaining day-to-day commodity flows vital to national security, economic stability, and public safety. The nature of these CIs is such that failures caused by an extreme weather event or a man-made incident can trigger widespread cascading failures, sending ripple effects at regional or even national scales. To minimize such effects, it is critical for emergency responders to identify existing or potential vulnerabilities within CIs during such stressor events in a systematic and quantifiable manner and take appropriate mitigating actions. We present here a novel critical infrastructure monitoring and analysis system named URBAN-NET. The system includes a software stack and tools for monitoring CIs, pre-processing data, interconnecting multiple CI datasets as a heterogeneous network, identifying vulnerabilities through graph-based topological analysis, and predicting consequences based on 'what-if' simulations along with visualization. As a proof-of-concept, we present several case studies to show the capabilities of our system. We also discuss remaining challenges and future work.
KW - critical infrastructure
KW - graph theory
KW - national-scale
KW - network
KW - simulation
KW - vulnerability
UR - http://www.scopus.com/inward/record.url?scp=85015156366&partnerID=8YFLogxK
U2 - 10.1109/BigData.2016.7840902
DO - 10.1109/BigData.2016.7840902
M3 - Conference contribution
AN - SCOPUS:85015156366
T3 - Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016
SP - 2600
EP - 2609
BT - Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016
A2 - Ak, Ronay
A2 - Karypis, George
A2 - Xia, Yinglong
A2 - Hu, Xiaohua Tony
A2 - Yu, Philip S.
A2 - Joshi, James
A2 - Ungar, Lyle
A2 - Liu, Ling
A2 - Sato, Aki-Hiro
A2 - Suzumura, Toyotaro
A2 - Rachuri, Sudarsan
A2 - Govindaraju, Rama
A2 - Xu, Weijia
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 5 December 2016 through 8 December 2016
ER -