A Latent Source Model to Detect Multiple Spatial Clusters With Application in a Mobile Sensor Network for Surveillance of Nuclear Materials

Cheng, Jerry; Xie, Minge; Chen, Rong; Roberts, Fred S.

doi:10.1080/01621459.2013.808945

Cited by 5 publications

(10 citation statements)

References 39 publications

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“…In this application field as well as others (e.g., surveillance [11], crowdsensing through smartphones [12] and dynamic coverage [13]) there is a growing trend towards mobile sensing platforms. For air pollution monitoring in particular, innovative sensing strategies such as wearable air quality sensing nodes [14] and smart-phones used as mobile air quality sensors [15] are proposed.…”

Section: A Mobile Sensingmentioning

confidence: 99%

High Resolution Air Pollution Maps in Urban Environments Using Mobile Sensor Networks

Marjovi

Arfire

Martinoli

2015

2015 International Conference on Distributed Computing in Sensor Systems

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Abstract-We propose three modeling methods using a mobile sensor network to generate high spatio-temporal resolution air pollution maps for urban environments. In our deployment in Lausanne (Switzerland), dedicated sensing nodes are anchored to the public buses and measure multiple air quality parameters including the Lung Deposited Surface Area (LDSA), a state of the art metric for quantifying human exposure to ultrafine particles. In this paper, our focus is on generating LDSA maps. In particular, since the sensor network coverage is spatially and temporally dynamic, we leverage models to estimate the values for the locations and times where the data are not available. We first discretize the area topologically based on the street segments in the city and we then propose the following three prediction models: i) a log-linear regression model based on nine meteorological (e.g., temperature and precipitations) and gaseous (e.g., NO2 and CO) explanatory variables measured at two static stations in the city, ii) a novel network-based log-linear regression model that takes into account the LDSA values of the most correlated streets and also the nine explanatory variables mentioned above, iii) a novel Probabilistic Graphical Model (PGM) in which each street segment is considered as one node of the graph, and inference on conditional joint probability distributions of the nodes results in estimating the values in the nodes of interest. More than 44 millions of geo-and time-stamped LDSA measurements (i.e., more than 14 months of real data) are used in this paper to evaluate the proposed modeling approaches in various time resolutions (hourly, daily, weekly and monthly). The results show that the three approaches bring significant improvements in R 2 , RMSE and FAC metrics compared to a baseline KNearest Neighbor method.

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Section: A Mobile Sensingmentioning

confidence: 99%

High Resolution Air Pollution Maps in Urban Environments Using Mobile Sensor Networks

Marjovi

Arfire

Martinoli

2015

2015 International Conference on Distributed Computing in Sensor Systems

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“…A mobile network can also possibly have blind spots in areas that cannot be reached by any vehicles, although this may be mitigated by installing some fixed sensors at these locations. Cheng et al . provides additional discussions on this type of mobile network.…”

Section: Introductionmentioning

confidence: 99%

“…Cheng et al . studied nuclear detection procedure under the mobile sensor network we adopt here but used a static approach rather than a dynamic approach.…”

Section: Introductionmentioning

confidence: 99%

“…studied nuclear detection procedure under the mobile sensor network we adopt here but used a static approach rather than a dynamic approach. Specifically, Cheng et al . assumes a model with latent variables, and the estimation is performed with a Monte Carlo expectation maximization algorithm, extending that in Xie et al …”

Section: Introductionmentioning

confidence: 99%

“…However, as the source is more likely to be moving (being carried by a walking person, or in a moving vehicle), simple data combination is not appropriate. Cheng et al . proposed to estimate the trajectory of the source as the concatenation of single‐time period location estimates.…”

Section: Introductionmentioning

confidence: 99%

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A dynamic fusion system for fast nuclear source detection and localization with mobile sensor networks

Grelaud

Mitra

Xie

et al. 2017

Appl Stoch Models Bus & Ind

Self Cite

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This paper proposes a dynamic system, with an associated fusion learning inference procedure, to perform real-time detection and localization of nuclear sources using a network of mobile sensors. This is motivated by the need for a reliable detection system in order to prevent nuclear attacks in major cities such as New York City. The approach advocated here installs a large number of relatively inexpensive (and perhaps relatively less accurate) nuclear source detection sensors and GPS devices in taxis and police vehicles moving in the city. Sensor readings and GPS information are sent to a control center at a high frequency, where the information is immediately processed and fused with the earlier signals. We develop a real-time detection and localization method aimed at detecting the presence of a nuclear source and estimating its location and power. We adopt a Bayesian framework to perform the fusion learning and use a sequential Monte Carlo algorithm to estimate the parameters of the model and to perform real-time localization. A simulation study is provided to assess the performance of the method for both stationary and moving sources. The results provide guidance and recommendations for an actual implementation of such a surveillance system.Appl. Stochastic Models Bus. Ind. 2018, 34 4-19 GRELAUD ET AL. t ), of all the sensors in the system and the binary signals the sensors send to the control center t = (d (1) t , · · · , d (n t ) t ). Here, n t is the number of active sensors. The locations, t , are assumed to be accurate, but the signals received by the control center could possibly be false.This model assumes the presence of one source. It is compared with the baseline model, where no source is present, for the purpose of detecting the presence of a source. Details of the comparison are presented in Section 3.2. The extension to multiple source detection is discussed in Section 6.State-space models have been widely used in many applications, including source tracking [29,30,[36][37][38][39][40][41][42][43]. However, our approach is different from standard source tracking problems, mainly because in our case, both negative and positive binary signals are observed and used. The use of state-space formation allows the data collected at time t to be used for the estimation of the location of the source x t with the information observed in the past, through an updating of the previous estimation of x t−1 , instead of performing an independent analysis at each time point. A source's motion possesses 6

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Bayesian Scan Statistics

Neill

2024

Handbook of Scan Statistics

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A Latent Source Model to Detect Multiple Spatial Clusters With Application in a Mobile Sensor Network for Surveillance of Nuclear Materials

Cited by 5 publications

References 39 publications

High Resolution Air Pollution Maps in Urban Environments Using Mobile Sensor Networks

High Resolution Air Pollution Maps in Urban Environments Using Mobile Sensor Networks

A dynamic fusion system for fast nuclear source detection and localization with mobile sensor networks

Bayesian Scan Statistics

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