{"help":"Return the metadata of a dataset (package) and its resources. :param id: the id or name of the dataset :type id: string","success":true,"result":[{"id":"7cebefde-a25c-4760-816b-3ca9d2650981","name":"data-lost-woods-forest-vegetation-and-not-topography-most-affects-connectivity-mesh-radio","title":"Data from: Lost in the woods: Forest vegetation, and not topography, most affects the connectivity of mesh radio networks for public safety","author":"Eloise G. Zimbelman and Robert F. Keefe","author_email":"eloisez@uidaho.edu","maintainer":"RCDS Data Repository","maintainer_email":"rcds-web@uidaho.edu","license_title":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/","notes":"\u003Cp\u003EReal-time data- and location-sharing using mesh networking radios paired with smartphones may improve situational awareness and safety in remote environments lacking communications infrastructure. Despite being increasingly used for wildland fire and public safety applications, there has been little formal evaluation of the network connectivity of these devices. The objectives of this study were to 1) characterize the connectivity of mesh networks in variable forest and topographic conditions; 2) evaluate the abilities of lidar and satellite remote sensing data to predict connectivity; and 3) assess the relative importance of the predictive metrics. A large field experiment was conducted to test the connectivity of a network of one mobile and five stationary goTenna Pro mesh radios on 24 Public Land Survey System sections approximately 260 ha in area in northern Idaho. Dirichlet regression was used to predict connectivity using 1) both lidar- and satellite-derived metrics (LIDSAT); 2) lidar-derived metrics only (LID); and 3) satellite-derived metrics only (SAT). On average the full network was connected only 32.6% of the time (range: 0% to 90.5%) and the mobile goTenna was disconnected from all other devices 18.2% of the time (range: 0% to 44.5%). RMSE for the six connectivity levels ranged from 0.101 to 0.314 for the LIDSAT model, from 0.103 to 0.310 for the LID model, and from 0.121 to 0.313 for the SAT model. Vegetation-related metrics affected connectivity more than topography. Developed models may be used to predict the connectivity of real-time mesh networks over large spatial extents using remote sensing data in order to forecast how well similar networks are expected to perform for wildland firefighting, forestry, and public safety applications. However, safety professionals should be aware of the impacts of vegetation on connectivity.\u003Cbr \/\u003E\n\u003Cstrong\u003EData Use\u003C\/strong\u003E\u003Cbr \/\u003E\n\u003Cem\u003ELicense\u003C\/em\u003E: \u003Ca href=\u0022https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode\u0022\u003ECC-BY\u003C\/a\u003E\u003Cbr \/\u003E\n\u003Cem\u003ERecommended Citation\u003C\/em\u003E: Zimbelman, E., \u0026amp; Keefe, R. (2022). Data from: Lost in the woods: Forest vegetation, and not topography, most affects the connectivity of mesh radio networks for public safety (Version 1.0) [Data set]. University of Idaho. \u003Ca href=\u0022https:\/\/doi.org\/10.7923\/6XRT-QB81\u0022\u003Ehttps:\/\/doi.org\/10.7923\/6XRT-QB81\u003C\/a\u003E\u003C\/p\u003E\n","url":"https:\/\/data.nkn.uidaho.edu\/dataset\/data-lost-woods-forest-vegetation-and-not-topography-most-affects-connectivity-mesh-radio","state":"Active","log_message":"Edited by awchild.","private":true,"revision_timestamp":"Mon, 12\/12\/2022 - 11:22","metadata_created":"Thu, 05\/19\/2022 - 09:00","metadata_modified":"Mon, 12\/12\/2022 - 11:22","creator_user_id":"6cc16f2c-77c2-4d12-ac06-56bbb86b535b","type":"Dataset","resources":[{"id":"a6376e8c-93cb-4c41-8668-deb467d83cc4","revision_id":"","url":"https:\/\/data.nkn.uidaho.edu\/sites\/default\/files\/Zimbelman_Keefe_2022_LostInTheWoods_1.zip","description":"\u003Cp\u003EThe datasets are described in the associated manuscript submitted to PLOS ONE. The LIDSAT, LID, and SAT files are structured the same way, with each row representing a Public Land Survey System (PLSS) section and each column representing a response variable or remote sensing predictor. The first column (\u201csection_id\u201d) indicates the PLSS section ID. The next six columns (\u201creceived_6\u201d to \u201creceived_1\u201d) represent the number of transmitted signals received by 5, 4, 3, 2, 1, and 0 stationary goTennas, respectively, and the \u201ctot_trans\u201d column represents the total number of signals transmitted by the mobile goTenna in the section. The next six columns (\u201cCon_6_obs\u201d to \u201cCon_1_obs\u201d) represent the proportion of transmitted signals received by 5, 4, 3, 2, 1, and 0 stationary goTennas (i.e., the six connectivity levels). These were calculated by dividing the respective \u201creceived\u201d columns by the \u201ctot_trans\u201d column (e.g., Con_6_obs = received_6\/tot_trans, etc.). Because Dirichlet regression cannot handle zero values, zeroes were imputed as described in the manuscript in order to derive the next six columns (\u201cCon_6\u201d to \u201cCon_1\u201d). These columns correspond to the compositional response variables used to develop the Dirichlet regression models and represent the proportion of time 5, 4, 3, 2, 1, and 0 stationary goTennas were connected to the mobile goTenna, respectively. All remaining columns after \u201cCon_1\u201d correspond to either a lidar- or satellite-derived metric calculated for each section, according to the descriptions and variable keys located in the manuscript. The LIDSAT, LID, and SAT datasets have identical response variables and the only difference between them is the inclusion of different remote sensing predictors. The LIDSAT dataset contains all of the lidar- and satellite-derived predictors, the LID dataset only contains the lidar-derived predictors, and the SAT dataset only contains the satellite-derived predictors. The ATAK_Full_RS_Metrics_MaxMinValues dataset contains the maximum and minimum values for each remote sensing predictor variable which were used to normalize the variables as described in the manuscript. The first column contains the remote sensing predictor variable name and matches the remote sensing variable names in the LIDSAT, LID, and SAT datasets. The next two columns list the minimum and maximum values of the corresponding predictor.\u003C\/p\u003E\n\u003Cp\u003E\u003Cstrong\u003EFile Directory\u003C\/strong\u003E:\u003Cbr \/\u003E\nLIDSAT.csv:\u003Cbr \/\u003E\nLID.csv:\u003Cbr \/\u003E\nSAT.csv:\u003Cbr \/\u003E\nATAK_Full_RS_Metrics_MaxMinValues.csv: Contains the maximum and minimum values for each remote sensing predictor variable which were used to normalize the variables as described in the manuscript.\u003Cbr \/\u003E\n\u003Cem\u003EHeader Key\u003C\/em\u003E:\u003Cbr \/\u003E\n[Column 1]: Contains the remote sensing predictor variable name and matches the remote sensing variable names in the LIDSAT, LID, and SAT datasets.\u003Cbr \/\u003E\nmin: Minimum values of the corresponding predictor.\u003Cbr \/\u003E\nMax: Maximum values of the corresponding predictor.\u003Cbr \/\u003E\nreadme.txt\u003C\/p\u003E\n","format":"zip","state":"Active","revision_timestamp":"Mon, 12\/12\/2022 - 08:33","name":"Data Access| Lost in the woods: Forest vegetation, and not topography, most affects the connectivity of mesh radio networks for public safety (Compressed data package)","mimetype":"application\/zip","size":"518.91 KB","created":"Thu, 05\/19\/2022 - 09:18","resource_group_id":"89a02fc8-c98e-44bb-9d3e-2d3d0849fbca","last_modified":"Date changed Mon, 12\/12\/2022 - 08:33"},{"id":"b5570500-325f-4c3c-bcdd-b86e2c66d6cc","revision_id":"","url":"https:\/\/data.nkn.uidaho.edu\/sites\/default\/files\/readme_2.txt","description":"","format":"txt","state":"Active","revision_timestamp":"Mon, 12\/12\/2022 - 11:22","name":"Metadata Access | Lost in the woods: Forest vegetation, and not topography, most affects the connectivity of mesh radio networks for public safety (txt document)","mimetype":"text\/plain","size":"7.6 KB","created":"Mon, 12\/12\/2022 - 11:22","resource_group_id":"89a02fc8-c98e-44bb-9d3e-2d3d0849fbca","last_modified":"Date changed Mon, 12\/12\/2022 - 11:22"}],"tags":[{"id":"d0f2120c-38ee-45ac-805d-fae9701718e9","vocabulary_id":"2","name":"wildland firefighting"},{"id":"63a0c5ee-35d9-453c-9a08-cfd69fb3d0a9","vocabulary_id":"2","name":"public safety"},{"id":"60c15779-de63-4cf1-8918-f6560a9158c1","vocabulary_id":"2","name":"mesh network"},{"id":"b01e0836-7f64-4e0f-a65b-65035ff4ba0c","vocabulary_id":"2","name":"location sharing"},{"id":"2c4e82c0-3e04-41fe-9c0d-ac89452e7d99","vocabulary_id":"2","name":"Dingell Act"},{"id":"86ea782a-51d3-4181-8b15-9e0ccb1eb862","vocabulary_id":"2","name":"resource tracking"}],"groups":[{"description":"\u003Cp\u003ESince 1889, the University of Idaho has provided motivated students with a transformative higher education experience that prepares them to solve real-world problems and achieve success in their lives and careers.\u003C\/p\u003E\n\u003Cp\u003ELearn more at \u003Ca href=\u0022https:\/\/www.uidaho.edu\u0022\u003Ehttps:\/\/www.uidaho.edu\u003C\/a\u003E\u003C\/p\u003E\n","id":"89a02fc8-c98e-44bb-9d3e-2d3d0849fbca","image_display_url":"https:\/\/data.nkn.uidaho.edu\/sites\/default\/files\/UI_Main_horizontal_4c.jpg","title":"University of Idaho","name":"group\/university-idaho"}]}]} Error | RCDS Data Repository

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