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Register a new userThe Signpost Platform for City-Scale Sensing
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Added by
Branden Ghena
Publication date
2018
fields
Informatics Engineering
and research's language is
English
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City-scale sensing holds the promise of enabling a deeper understanding of our urban environments. However, a city-scale deployment requires physical installation, power management, and communications---all challenging tasks standing between a good idea and a realized one. This indicates the need for a platform that enables easy deployment and experimentation for applications operating at city scale. To address these challenges, we present Signpost, a modular, energy-harvesting platform for city-scale sensing. Signpost simplifies deployment by eliminating the need for connection to wired infrastructure and instead harvesting energy from an integrated solar panel. The platform furnishes the key resources necessary to support multiple, pluggable sensor modules while providing fair, safe, and reliable sharing in the face of dynamic energy constraints. We deploy Signpost with several sensor modules, showing the viability of an energy-harvesting, multi-tenant, sensing system, and evaluate its ability to support sensing applications. We believe Signpost reduces the difficulty inherent in city-scale deployments, enables new experimentation, and provides improved insights into urban health.
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We present HoliCity, a city-scale 3D dataset with rich structural information. Currently, this dataset has 6,300 real-world panoramas of resolution $13312 times 6656$ that are accurately aligned with the CAD model of downtown London with an area of more than 20 km$^2$, in which the median reprojection error of the alignment of an average image is less than half a degree. This dataset aims to be an all-in-one data platform for research of learning abstracted high-level holistic 3D structures that can be derived from city CAD models, e.g., corners, lines, wireframes, planes, and cuboids, with the ultimate goal of supporting real-world applications including city-scale reconstruction, localization, mapping, and augmented reality. The accurate alignment of the 3D CAD models and panoramas also benefits low-level 3D vision tasks such as surface normal estimation, as the surface normal extracted from previous LiDAR-based datasets is often noisy. We conduct experiments to demonstrate the applications of HoliCity, such as predicting surface segmentation, normal maps, depth maps, and vanishing points, as well as test the generalizability of methods trained on HoliCity and other related datasets. HoliCity is available at https://holicity.io.
Despite abundant accessible traffic data, researches on traffic flow estimation and optimization still face the dilemma of detailedness and integrity in the measurement. A dataset of city-scale vehicular continuous trajectories featuring the finest resolution and integrity, as known as the holographic traffic data, would be a breakthrough, for it could reproduce every detail of the traffic flow evolution and reveal the personal mobility pattern within the city. Due to the high coverage of Automatic Vehicle Identification (AVI) devices in Xuancheng city, we constructed one-month continuous trajectories of daily 80,000 vehicles in the city with accurate intersection passing time and no travel path estimation bias. With such holographic traffic data, it is possible to reproduce every detail of the traffic flow evolution. We presented a set of traffic flow data based on the holographic trajectories resampling, covering the whole 482 road segments in the city round the clock, including stationary average speed and flow data of 5-minute intervals and dynamic floating car data.
`Smart cities, urban development projects that design computational systems and sensory technology to monitor activity and regulate energy consumption and resource distribution, are a frontier for the prospective deployment of ICTs for sustainability. Often reduced to technological problems of optimization, these projects have implications far beyond narrow environmental and consumptive frames of sustainability. Studying them requires frameworks that support us in examining technological and environmental sustainability dimensions jointly with social justice perspectives. This paper uses Critical Systems Heuristics (CSH) to examine the design of Sidewalk Toronto, an ongoing smart city development. We explore how the professed values guiding the project are contentiously enacted, and we argue that key stakeholders and beneficiaries in the planning process significantly constrain the emancipatory and transformative potential of the project by marginalizing the role of residents in determining project purposes. This analysis contributes an example that illustrates the relevance of critical systems thinking in ICT4S and offers CSH as a conceptual frame that supports critical reflection on the tensions between the visions and realities of `sustainable ways of organizing human life.
The City of Detroit maintains an active fleet of over 2500 vehicles, spending an annual average of over $5 million on purchases and over $7.7 million on maintenance. Modeling patterns and trends in this data is of particular importance to a variety of stakeholders, particularly as Detroit emerges from Chapter 9 bankruptcy, but the structure in such data is complex, and the city lacks dedicated resources for in-depth analysis. The City of Detroits Operations and Infrastructure Group and the University of Michigan initiated a collaboration which seeks to address this unmet need by analyzing data from the City of Detroits vehicle fleet. This work presents a case study and provides the first data-driven benchmark, demonstrating a suite of methods to aid in data understanding and prediction for large vehicle maintenance datasets. We present analyses to address three key questions raised by the stakeholders, related to discovering multivariate maintenance patterns over time; predicting maintenance; and predicting vehicle- and fleet-level costs. We present a novel algorithm, PRISM, for automating multivariate sequential data analyses using tensor decomposition. This work is a first of its kind that presents both methodologies and insights to guide future civic data research.
Chiral sensitive techniques have been used to probe the fundamental symmetries of the universe, study biomolecular structures, and even develop safe drugs. As chiral signals are inherently weak and often suppressed by large backgrounds, different techniques have been proposed to overcome the limitations of traditionally used chiral polarimetry. Here, we propose an angle-resolved chiral surface plasmon resonance (CHISPR) scheme that can detect the absolute chirality (handedness and magnitude) of a chiral sample and is sensitive to both the real and imaginary part of a chiral samples refractive index. We present analytical results and numerical simulations of CHISPR measurements, predicting signals in the mdeg range for chiral samples of <100nm thickness at visible wavelengths. Moreover, we present a theoretical analysis that clarifies how our far-field measurements elucidate the underlying physics. This CHISPR protocol does not require elaborate fabrication and has the advantage of being directly implementable on existing surface plasmon resonance instrumentation.
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