CPS: Synergy: Collaborative Research: Mapping and Querying Underground Infrastructure Systems

    Project: Research project

    Project Details


    CPS: Synergy: Collaborative Research: Mapping and Querying Underground Infrastructure Systems
    Project Summary
    The general realm of smart cities is gaining significant momentum and the emergence of the Internet of Things (IoT) promises enable effective solution to many of the problems they are common in urban settings, and to improve the quality of life of the residents. However, at present, one of the main challenges is the status of the existing infrastructure. Of a particular relevance for improving the current state of the affairs are the civil infrastructure systems (CIS). Indeed, in a smart city, maintenance crews should be able to detect leaks in water and gas systems more easily; sewer systems should rarely overflow, even during heavy rains; the power grid is expected to be much more reliable; traffic can be redirected when needed; and, most importantly, that the various CIS can effectively communicate with one another. Although large cities like Chicago or New York may have some sort of reliable GIS data for particular thematic layers in their CIS, the overwhelming majority of municipalities do not. What is more, CAD drawings are seldom validated and the infrastructure elements shown on the drawings are often located in the wrong place. Just as importantly, the drawings are rarely complete and significant portions of a CIS can easily be missing, rendering any significant GIS analysis nearly meaningless.
    Intellectual Merits: This proposal will address the following challenges towards enabling a smarter urban infrastructure management:
    • Develop systematic approaches to generate GIS-based data from heterogeneous sources (paper maps; CAD drawings) and enable web based access to multiple thematic layers of such data. Part of the proposed work will focus on devising novel ontology-approaches.
    • Develop comprehensive type-systems for combining spatial, spatio-temporal and graph data, incorporating the uncertainty as a “first-class citizen”. Novel categories of queries and corresponding processing algorithms will also be developed.
    • Generate network science-based algorithm to flag missing elements (e.g., a node with a number of connections of 1 can only exist if it is an end-node, and therefore two nodes directly connected with two number of connections of 1 are likely wrong).
    • Generate an algorithm to connect flagged end-points from task 1 based purely on distance in the absence of a supporting layer, or based on topological distance in the presence of a support layer (e.g., roads and buildings).
    • Provide methodologies for context-aware access of heterogeneous data from multiple sources, along with a context-aware methodologies for displaying such data (e.g., a crew dealing with gas pipes should not see too many details about water pipes, unless strictly necessary).
    • Provide novel analytics methods that will enable reasoning and prediction about duration of particular milestones depending on variety of context dimensions (e.g., working crew, weather, emergency level, etc.).
    Broader Impact: One of the broader impacts of the proposed research is that it will reveal the connections between physical flows and environmental, social, health, and economic impacts of effective maintenance of smart cities infrastructure. The research plans are geared towards the need for a systematic approaches, instead of focusing on the end-of-pipe and post-crisis management. We believe that the findings related to the interplay of geo-spatial domains and behavioral domains will have impact in many other smart city maintenance scenarios like,
    Effective start/end date9/1/168/15/17


    • National Science Foundation (CNS-1646107)


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