STEP: toward a semantics-aware framework for monitoring community-scale infrastructure
Urban communities rely on built utility infrastructures as critical lifelines that provide essential services such as water, gas, and power, to sustain modern socioeconomic systems. These infrastructures consist of underground and surface-level assets that are operated and geo-distributed over large...
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2024-01-01
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| Series: | Data-Centric Engineering |
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| Online Access: | https://www.cambridge.org/core/product/identifier/S2632673624000327/type/journal_article |
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| author | Andrew Chio Jian Peng Nalini Venkatasubramanian |
| author_facet | Andrew Chio Jian Peng Nalini Venkatasubramanian |
| author_sort | Andrew Chio |
| collection | DOAJ |
| description | Urban communities rely on built utility infrastructures as critical lifelines that provide essential services such as water, gas, and power, to sustain modern socioeconomic systems. These infrastructures consist of underground and surface-level assets that are operated and geo-distributed over large regions where continuous monitoring for anomalies is required but challenging to implement. This article addresses the problem of deploying heterogeneous Internet of Things sensors in these networks to support future decision-support tasks, for example, anomaly detection, source identification, and mitigation. We use stormwater as a driving use case; these systems are responsible for drainage and flood control, but act as conduits that can carry contaminants to the receiving waters. Challenges toward effective monitoring include the transient and random nature of the pollution incidents, the scarcity of historical data, the complexity of the system, and technological limitations for real-time monitoring. We design a SemanTics-aware sEnsor Placement framework (STEP) to capture pollution incidents using structural, behavioral, and semantic aspects of the infrastructure. We leverage historical data to inform our system with new, credible instances of potential anomalies. Several key topological and empirical network properties are used in proposing candidate deployments that optimize the balance between multiple objectives. We also explore the quality of anomaly representation in the network through new perspectives, and provide techniques to enhance the realism of the anomalies considered in a network. We evaluate STEP on six real-world stormwater networks in Southern California, USA, which shows its efficacy in monitoring areas of interest over other baseline methods. |
| format | Article |
| id | doaj-art-00e26c045b4e45a8a6ab281a0d4eb671 |
| institution | Kabale University |
| issn | 2632-6736 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Data-Centric Engineering |
| spelling | doaj-art-00e26c045b4e45a8a6ab281a0d4eb6712024-12-20T09:06:38ZengCambridge University PressData-Centric Engineering2632-67362024-01-01510.1017/dce.2024.32STEP: toward a semantics-aware framework for monitoring community-scale infrastructureAndrew Chio0https://orcid.org/0000-0001-8920-2749Jian Peng1Nalini Venkatasubramanian2Department of Computer Science, University of California, Irvine, California, USAOrange County Public Works, Orange, California, USADepartment of Computer Science, University of California, Irvine, California, USAUrban communities rely on built utility infrastructures as critical lifelines that provide essential services such as water, gas, and power, to sustain modern socioeconomic systems. These infrastructures consist of underground and surface-level assets that are operated and geo-distributed over large regions where continuous monitoring for anomalies is required but challenging to implement. This article addresses the problem of deploying heterogeneous Internet of Things sensors in these networks to support future decision-support tasks, for example, anomaly detection, source identification, and mitigation. We use stormwater as a driving use case; these systems are responsible for drainage and flood control, but act as conduits that can carry contaminants to the receiving waters. Challenges toward effective monitoring include the transient and random nature of the pollution incidents, the scarcity of historical data, the complexity of the system, and technological limitations for real-time monitoring. We design a SemanTics-aware sEnsor Placement framework (STEP) to capture pollution incidents using structural, behavioral, and semantic aspects of the infrastructure. We leverage historical data to inform our system with new, credible instances of potential anomalies. Several key topological and empirical network properties are used in proposing candidate deployments that optimize the balance between multiple objectives. We also explore the quality of anomaly representation in the network through new perspectives, and provide techniques to enhance the realism of the anomalies considered in a network. We evaluate STEP on six real-world stormwater networks in Southern California, USA, which shows its efficacy in monitoring areas of interest over other baseline methods.https://www.cambridge.org/core/product/identifier/S2632673624000327/type/journal_articleheterogeneous anomaliessemantics-aware modelingcredible anomaly generationsensor deploymentstormwater monitoring |
| spellingShingle | Andrew Chio Jian Peng Nalini Venkatasubramanian STEP: toward a semantics-aware framework for monitoring community-scale infrastructure Data-Centric Engineering heterogeneous anomalies semantics-aware modeling credible anomaly generation sensor deployment stormwater monitoring |
| title | STEP: toward a semantics-aware framework for monitoring community-scale infrastructure |
| title_full | STEP: toward a semantics-aware framework for monitoring community-scale infrastructure |
| title_fullStr | STEP: toward a semantics-aware framework for monitoring community-scale infrastructure |
| title_full_unstemmed | STEP: toward a semantics-aware framework for monitoring community-scale infrastructure |
| title_short | STEP: toward a semantics-aware framework for monitoring community-scale infrastructure |
| title_sort | step toward a semantics aware framework for monitoring community scale infrastructure |
| topic | heterogeneous anomalies semantics-aware modeling credible anomaly generation sensor deployment stormwater monitoring |
| url | https://www.cambridge.org/core/product/identifier/S2632673624000327/type/journal_article |
| work_keys_str_mv | AT andrewchio steptowardasemanticsawareframeworkformonitoringcommunityscaleinfrastructure AT jianpeng steptowardasemanticsawareframeworkformonitoringcommunityscaleinfrastructure AT nalinivenkatasubramanian steptowardasemanticsawareframeworkformonitoringcommunityscaleinfrastructure |