Global Positioning System Location Research Articles

Exposure of Neighborhood Racial and Socio-Economic Composition in Activity Space: A New Approach Adjusting for Residential Conditions

Abstract A longstanding urban sociological literature emphasizes the geographic isolation of city dwellers in residence and everyday routines, expecting exposures to neighborhood racial and socio-economic structure driven principally by city-wide segregation and the role of proximity and homophily in mobility. The compelled mobility approach emphasizes the uneven distribution of organizational and institutional resources across urban space, expecting residents of poor Black-segregated neighborhoods to exhibit non-trivial levels of everyday exposure to White, non-poor areas for resource seeking. We use two sets of location data in the hypersegregated Chicago metro to examine these two approaches: Global Positioning System (GPS) location tracking on a sample of older adults from the Chicago Health and Activity Space in Real-Time (CHART) study and travel diaries on a sample of younger adults by the Chicago Metropolitan Agency for Planning (CMAP). We introduce a novel and flexible individual-level method for assessing activity space exposures that accounts for the spatially proximate environment around home. Analyses reveal that activity space contexts mimic the racial/ethnic and socio-economic landscape of respondents’ broad residential environment. However, after residential-based adjustment, Black younger (CMAP) adults from poor Black neighborhoods are disproportionately exposed to Whiter, less Black but less non-poor neighborhoods. Older (CHART) adult activity spaces align more closely with their residential areas; however, activity spaces of poor-Black-neighborhood-residing CHART Blacks are systematically poorer and, less consistently, more Black and less White after local area adjustment. Implications for understanding contextual exposures on well-being and the potential for age or cohort differences in isolation are discussed.

Low-Earth-Orbit Satellites and Robust Theory-Augmented GPS/Inertial-Navigation-System Tight Integration for Vehicle-Borne Positioning

Positioning by means of the Global Positioning System (GPS) is a traditional and widely used method. However, its performance is affected by the user environment, such as multi-path effects and poor anti-interference abilities. Therefore, an Inertial Navigation System (INS) has been integrated with GPS to overcome the disadvantages of GPS positioning. INSs do not rely on any external system information and has strong autonomy and independence from the external environment. However, the performance of GPS/INS is visibly degraded in low-observability GPS environments (tall buildings, viaducts, underground tunnels, woods, etc.). Fortunately, with the emergence of Low-Earth-Orbit (LEO) satellites in recent years, the constellation configuration can be extended with the advantages of lower orbits, greater speeds, and richer geometric structures. LEO improves the geometric structure between users and satellites and provides many more observations. Meanwhile, a robust theory approach is applied that can restrain or remove the impact of low-accuracy observations. In this study, we applied LEO data and a robust theory approach to enhance the GPS/INS tight integration. To verify the effectiveness of this method, a set of vehicles and simulated LEO data were analyzed. The results show that robust Kalman filtering (RKF) provides a visible enhancement in the positioning accuracy of GPS/INS integration. This effectively restrains the mutation error and has a smoothing effect on the positioning results. In addition, the addition of LEO data significantly improves the positioning accuracy of a sole GPS and GPS/INS integration. The GPS/LEO/INS integration has the highest positioning accuracy, with Root-Mean-Square Errors (RMSEs) of the north, east, and vertical positions of 2.38 m, 1.94 m, and 2.49 m, respectively, which corresponds to an improvement of 30.21%, 47.43%, and 34.13% compared to sole GPS-based positioning and 8.60%, 17.24%, and 12.14% when compared to the GPS/INS mode. Simultaneously, the simulation results show that LEO and INSs can improve the positioning performance of GPS under GPS-blocked conditions.

Internet of Things Based Oil Pipeline Spill Detection System Using Deep Learning and LAB Colour Algorithm

Given the role that pipelines play in transporting crude oil, which is considered the basis of the global economy and across different environments, hundreds of studies revolve around providing the necessary protection for it. Various technologies have been employed in this pursuit, differing in terms of cost, reliability, and efficiency, among other factors. Computer vision has emerged as a prominent technique in this field, albeit requiring a robust image-processing algorithm for spill detection. This study employs image segmentation techniques to enable the computer to interpret visual information and images effectively. The research focuses on detecting spills in oil pipes caused by leakage, utilizing images captured by a drone equipped with a Raspberry Pi and Pi camera. These images, along with their global positioning system (GPS) location, are transmitted to the base station using the message queuing telemetry transport Internet of Things (MQTT IoT) protocol. At the base station, deep learning techniques, specifically Holistically-Nested Edge Detection (HED) and extreme inception (Xception) networks, are employed for image processing to identify contours. The proposed algorithm can detect multiple contours in the images. To pinpoint a contour with a black color, representative of an oil spill, the CIELAB color space (LAB) algorithm effectively removes shadow effects. If a contour is detected, its area and perimeter are calculated to determine whether it exceeds a certain threshold. The effectiveness of the proposed system was tested on Iraqi oil pipeline systems, demonstrating its capability to detect spills of different sizes.

DeepGPS: Deep Learning Enhanced GPS Positioning in Urban Canyons

Global Positioning System (GPS) has benefited many novel applications, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , navigation, ride-sharing, and location-based services, in our daily life. Although GPS works well in most places, its performance in urban canyons is well-known poor, due to the signal reflections of non-line-of-sight (NLOS) satellites. Tremendous efforts have been made to mitigate the impacts of NLOS signals, while previous works heavily rely on precise proprietary 3D city models or other third-party resources, which are not easily accessible. In this paper, we present <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DeepGPS</i> , a deep learning enhanced GPS positioning system that can correct GPS estimations by only considering some simple contextual information. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DeepGPS</i> fuses environmental factors, including building heights and road distribution around GPS's initial position, and satellite statuses to describe the positioning context, and exploits an encoder-decoder network model to implicitly learn the complex relationships between positioning contexts and GPS estimations from massive labeled GPS samples. As a result, the well-trained model can accurately predict the correct position for each erroneous GPS estimation given its positioning context. We further improve the model with a novel constraint mask to filter out invalid candidate locations, and enable continuous localization with a simple mobility model. A prototype system is implemented and experimentally evaluated using a large-scale bus trajectory dataset and real-field GPS measurements. Experimental results demonstrate that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DeepGPS</i> significantly enhances GPS performance in urban canyons, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , on average effectively correcting 90.1% GPS estimations with accuracy improvement by 64.6%.

Geometric trust-based secure localization technique for resiliency of GPS outage and location error in vehicular cyber-physical systems (VCPS)

Management of vehicle traffic is a challenging task as it is non-deterministic by nature. Vehicular Cyber-Physical Systems (VCPS) is the emerging field of dynamics of vehicle management. Vehicle localization is considered an important task in VCPS. Many researchers proposed methodologies for this based on the Global Positioning System (GPS) which poses few location identification errors. Also, there are more vulnerabilities to the existing vehicular positioning system due to Zig-Zag attacks and bad-mouth attacks. In this work, an error-free and secure environment for communication between dynamically moving vehicle models has been proposed. In our proposed model a localization technique based on mathematical geometry which is capable of GPS outages and encompasses the dynamism of vehicle and on-road trajectory has been developed. The proposed model includes Extended Kalman filter-based routing to predict the neighbouring vehicle position. To avoid vulnerabilities created by the malicious nodes, a trust-based computation is performed by each node on its neighbours perceiving the authenticity of received messages. To validate the methodology, NS2 tool has been used to simulate the VCPS and to test the efficiency with different scenarios such as erroneous location, GPS outage, and malicious attack. The result shows that the proposed approach is more optimal and secure than the existing methodologies.

Automatic measurement of departing times in smartphone alerting systems: A pilot study

AimSmartphone alerting systems (SAS) alert volunteers in close vicinity of suspected out-of-hospital cardiac arrest. Some systems use sophisticated algorithms to select those who will probably arrive first. Precise estimation of departing times and travel times may help to further improve algorithms. We developed a global positioning system (GPS) based method for automatic measurements of departing times. The aim of this pilot study was to evaluate feasibility and precision of the method. MethodsRegion of Lifesavers alerting app (iOS/ Android, version 3.0, FirstAED ApS, Denmark) was used in this study. 27 experiments were performed with 9 students, who were instructed to stay in their flats during the study days. A geofence was set for each alarm in the alerting system with a radius of 10 m (8 cases), 15 m (10 cases), and 20 m (9 cases) around the GPS position at which the alarm was accepted in the app. The system logged responders as being departed when the smartphone position was registered outside the geofence. The students were instructed to manually start a stopwatch at the time of the alert and to stop the stopwatch once they had entered the street in front of their flat. ResultsThe median difference between automatically and manually retrieved times were −16 seconds [interquartile range IQR 50 seconds] (geofence 10 m), 30 seconds [IQR 25 seconds] (15 m), and 20 seconds [IQR 13 seconds] (20 m), respectively. The 20 m geofence was associated with the smallest interquartile range. ConclusionDeparting times of volunteer responders in SAS can be retrieved automatically using GPS and a geofence.

Performance Analysis of Relative GPS Positioning for Low-Cost Receiver-Equipped Agricultural Rovers.

Global navigation satellite systems (GNSSs) became an integral part of all aspects of our lives, whether for positioning, navigation, or timing services. These systems are central to a range of applications including road, aviation, maritime, and location-based services, agriculture, and surveying. The Global Positioning System (GPS) Standard Position Service (SPS) provides position accuracy up to 10 m. However, some modern-day applications, such as precision agriculture (PA), smart farms, and Agriculture 4.0, have demanded navigation technologies able to provide more accurate positioning at a low cost, especially for vehicle guidance and variable rate technology purposes. The Society of Automotive Engineers (SAE), for instance, through its standard J2945 defines a maximum of 1.5 m of horizontal positioning error at 68% probability (1σ), aiming at terrestrial vehicle-to-vehicle (V2V) applications. GPS position accuracy may be improved by addressing the common-mode errors contained in its observables, and relative GNSS (RGNSS) is a well-known technique for overcoming this issue. This paper builds upon previous research conducted by the authors and investigates the sensitivity of the position estimation accuracy of low-cost receiver-equipped agricultural rovers as a function of two degradation factors that RGNSS is susceptible to: communication failures and baseline distances between GPS receivers. The extended Kalman filter (EKF) approach is used for position estimation, based on which we show that it is possible to achieve 1.5 m horizontal accuracy at 68% probability (1σ) for communication failures up to 3000 s and baseline separation of around 1500 km. Experimental data from the Brazilian Network for Continuous Monitoring of GNSS (RBMC) and a moving agricultural rover equipped with a low-cost GPS receiver are used to validate the analysis.

A review of UAV autonomous navigation in GPS-denied environments

Unmanned aerial vehicles (UAVs) have drawn increased research interest in recent years, leading to a vast number of applications, such as, terrain exploration, disaster assistance and industrial inspection. Unlike UAV navigation in outdoor environments that rely on GPS (Global Positioning System) for localization, indoor navigation cannot rely on GPS due to the poor quality or lack of signal. Although some reviewing papers particularly summarized indoor navigation strategies (e.g., Visual-based Navigation) or their specific sub-components (e.g., localization and path planning) in detail, there still lacks a comprehensive survey for the complete navigation strategies that cover different technologies. This paper proposes a taxonomy which firstly classifies the navigation strategies into Mapless and Map-based ones based on map usage and then, respectively categorizes the Mapless navigation into Integrated, Direct and Indirect approaches via common characteristics. The Map-based navigation is then split into Known Map/Spaces and Map-building via prior knowledge. In order to analyze these navigation strategies, this paper uses three evaluation metrics (Path Length, Deviation Rate and Exploration Efficiency) according to the common purposes of navigation to show how well they can perform. Furthermore, three representative strategies were selected and 120 flying experiments conducted in two reality-like simulated indoor environments to show their performances against the evaluation metrics proposed in this paper, i.e., the ratio of Successful Flight, the Mean time of Successful Flight, the Mean Length of Successful Flight, the Mean time of Flight, and the Mean Length of Flight. In comparison to the CNN-based Supervised Learning (directly maps visual observations to UAV controls) and the Frontier-based navigation (necessitates continuous global map generation), the experiments show that the CNN-based Distance Estimation for navigation trades off the ratio of Successful Flight and the required time and path length. Moreover, this paper identifies the current challenges and opportunities which will drive UAV navigation research in GPS-denied environments.

Effects of Anthropogenic and Climate-Induced Habitat Changes on Adult Female Mule Deer Survival

Increased wildfire size and frequency, invasion of non-native annual grasses, extensive fire suppression, climate change, and widespread juniper expansion have led to dramatic changes in sagebrush ecosystems, which provide habitat for a suite of wildlife species, including mule deer (Odocoileus hemionus). These landscape-level disturbances can cause habitat loss and degradation, which influence the quantity and quality of available forage and can negatively influence adult female mule deer survival. We used Program MARK to estimate survival rates using a known-fate model. Our dataset included 111 radio-collared female mule deer to estimate monthly survival rates and to investigate factors that may affect these rates, including movement behavior (migratory or resident) and habitat characteristics (wildfire and juniper canopy cover) in mule deer habitats supporting sagebrush potential vegetation types (PVTs). The dataset included 116 421 Global Positioning System (GPS) location points from 2015 to 2017 in the John Day Basin, Oregon, United States. Survival averaged 0.78 (95% confidence interval = 0.61–0.82) across the study period but was 0.69 (95% confidence interval = 0.59–0.77) in 2015. Our results indicated survival was positively influenced by the presence of small amounts of recent fire (< 15% of total winter range burned; 1−20 yr postfire) within an individual's winter range. Annual survival (0.78) was low compared with survival rates for adult female mule deer in other parts of their geographic range. These findings demonstrate how postfire vegetation response can have a positive effect on mule deer survival, regardless of their migration strategy. Our findings also indicate how climate change poses a growing threat to mule deer populations as prolonged periods of drought increase the spread of fatal diseases.

Real-Time Monitoring of Grazing Cattle Using LORA-WAN Sensors to Improve Precision in Detecting Animal Welfare Implications via Daily Distance Walked Metrics.

Animal welfare monitoring relies on sensor accuracy for detecting changes in animal well-being. We compared the distance calculations based on global positioning system (GPS) data alone or combined with motion data from triaxial accelerometers. The assessment involved static trackers placed outdoors or indoors vs. trackers mounted on cows grazing on pasture. Trackers communicated motion data at 1 min intervals and GPS positions at 15 min intervals for seven days. Daily distance walked was determined using the following: (1) raw GPS data (RawDist), (2) data with erroneous GPS locations removed (CorrectedDist), or (3) data with erroneous GPS locations removed, combined with the exclusion of GPS data associated with no motion reading (CorrectedDist_Act). Distances were analyzed via one-way ANOVA to compare the effects of tracker placement (Indoor, Outdoor, or Animal). No difference was detected between the tracker placement for RawDist. The computation of CorrectedDist differed between the tracker placements. However, due to the random error of GPS measurements, CorrectedDist for Indoor static trackers differed from zero. The walking distance calculated by CorrectedDist_Act differed between the tracker placements, with distances for static trackers not differing from zero. The fusion of GPS and accelerometer data better detected animal welfare implications related to immobility in grazing cattle.

UNDERGROUND CRUDE OIL PIPELINE LEAKAGE DETECTION USING DEXINED DEEP LEARNING TECHNIQUES AND LAB COLOR SPACE

Computer vision plays a big role in pipeline leakage detection systems and is one of the latest techniques. Still, it requires a powerful image-processing algorithm to detect objects. The purpose of this work is to develop and implement spill detection in oil pipes caused by leakage using images taken by a drone equipped with a Raspberry Pi 4. The acquired images are sent to the base station along with the global positioning system (GPS) location of the captured images via the message queuing telemetry transport Internet of Things (MQTT IoT) protocol. At the base station, images are processed to identify contours by dense extreme inception networks for edge detection(DexiNed) deep learning techniques based on holistically-nested edge detection(HED) and extreme inception (Xception) networks. This algorithm is capable of finding many contours in images. To find a contour with black color, the CIELAB color space (LAB) has been used. The proposed algorithm removes small contours and computes the area of the remaining contours. If the contour is above the threshold value, it is considered a spill; otherwise, it will be saved in a database for further inspection. For testing purposes, three different spill areas were implemented with spill sizes of (1 m^2,2 m^2 ,and 3 m^2). Images have been captured at three different heights (5 m, 10 m, and 15 m) by the drone used to capture the images. The result shows that effective detection has been obtained at 10 meters high. To monitor the entire system, a web application has been integrated into the base station.

The Movement of GPS Positioning Discrepancy Clouds at a Mid-Latitude Region in March 2015

The geomagnetic storm on 17 March 2015 had a strong impact on the global navigation satellite systems (GNSS) positioning results in many GNSS Continuously Operating Reference Stations (CORS) in Europe. The analysis of global positioning system (GPS) observations in Latvian CORS stations discovered a strong impact of this space weather event over the whole country. The impact appeared as a moving cloud of positioning discrepancies across the country. However, the analysis of the days before 17 March revealed other smaller duration ionospheric scintillation events. The objective was to analyze the GPS positioning discrepancy cloud movement, total electron content (TEC), and rate of change of the TEC index (ROTI) relationships, as well as discrepancy statistics. The area of analysis on 16–18 March was increased by including the EGNOS ground-based Ranging and Integrity Monitoring Stations (RIMS): GVLA and GVLB, LAPA and LAPB, and WRSA and WRSB. The conclusion of the study is that each “shot” after 90 s gives a completely new cloud with a new impacted station subset, its configuration, and completely irregular discrepancy values.

Analysis of Ionosphere Day to Day Variability using Parametric Methods on GPS TEC

The development of global positioning systems (GPS) has cleared the way for a better understanding of the globe and the systems surrounding the earth. The GPS signals that are sent out and received by GPS receivers are affected by severe space weather (in the ionosphere) as well as atmospheric weather and lithospheric disturbances. These disturbances not only result in inaccuracies in GPS location but also make it difficult to make the necessary corrections to those faults. The study of these perturbations is helpful in gaining an understanding of the phenomenal changes that occur in the ionosphere as a result of the Sun and its solar effects, which include solar flares, sunspot numbers, solar winds, geomagnetic fields, and natural disasters such as earthquakes and thunderstorms, among other things. In this study, an effort is made to highlight the distinctions between geomagnetic calm days and disturbed days that occurred in January 2022. The research is carried out by using parametric approaches to the GPS vertical total electron content (VTEC) at Hyderabad (17.4°N, 78.5°E), which is a station with a low height. An examination of the power spectral densities (PSDs) of both the tranquil and the disturbed conditions is carried out. The improvement in VTEC data was noted on two days in January 2022 that had low geomagnetic activity; these days were the 6th and 13th. On the sixth of January 2022, enhancements can be seen at the normalised frequencies of 0.4966 and 0.7683, with a power spectral density (PSD) of -10.14dB and -12.94dB, respectively. On the thirteenth of January 2022, enhancements can be seen at the normalised frequencies of 0.5005 and 0.956, with PSDs of -9.264dB and -8.392dB. When compared to the preceding records, it can be seen very clearly that the PSDs of both geomagnetically tranquil and disturbed days are rather comparable to one another.

Optimal GPS Acquisition Algorithm in Severe Ionospheric Scintillation Scene

The Global Positioning System (GPS) plays an important role in navigation and positioning services. When GPS signals propagate through a complex space environment, they are susceptible to interference of ionospheric scintillation. As one of the biggest interference sources on GPS navigation and positioning, ionospheric scintillation will lead to signal intensity decline and carrier phase fluctuation, making signal acquisition of the GPS receiver challenging. Thus, an acquisition algorithm based on differential coherent integration combining accumulation correlation and bit sign transition estimation is proposed. The coherent accumulation is applied to reduce computational loads and contribution by the Gaussian white noise in the signal. Moreover, the differential coherence integration is utilized to eliminate data blocks with bit transition, prolonging the coherence integration time and improving the data utilization rate. Experimental results show that under severe ionospheric scintillation condition, weak GPS signals can be acquired successfully after improving the acquisition algorithm, with the acquisition probability reaching 50% when the signal-to-interference ratio (SIR) drops to −34 dB. Comparing to the differential coherence integration, the complexity of the calculation reduces to only 21.75% effectively after the improvement. The execution time is less than half of the differential coherence integral.

Energy landscapes of Kodiak brown bears: a comparison of accelerometer and global positioning system-derived estimates

Within optimal foraging theory animals should maximize their net energy gain while minimizing energetic costs. Energetic expenditure in wild animals is therefore key to measure proxies of fitness. Accelerometers are an effective tool to study animal movement-based energetics, but retrieval of the device is usually required and often difficult. Accelerometers measure movement across three axes (x, y, and z) and can be calibrated to measures of oxygen consumption from captive animals, providing estimates of overall energy expenditure. Measuring energetic expenditures using a global positioning system (GPS) approach could provide an alternative method to study energetic ecology. This technique uses locomotor speeds across a range of slopes from successive GPS locations, which can be linked to the energy expenditure from captive individuals. We compared accelerometer and GPS methods of energetic expenditures in free-roaming brown bears (Ursus arctos) on the Kodiak Archipelago, Alaska, USA. We then applied the GPS method to examine how multiple factors influenced brown bear movement-based daily energetic expenditures (MDEE). We found that while the two energetic measurements differed (Wilcoxon signed rank test: V = 2116, p < 0.001), they were positively correlated (r = 0.82, p < 0.001). The GPS method on average provided 1.6 times greater energy estimates than the accelerometer method. Brown bears had lower MDEE during periods of high food abundance, supporting optimal foraging theory. Reproductive status and age did not influence MDEE, however movement rates had a positive linear relationship. Energetic ecology is important for understanding drivers of animal movements. Data from GPS collars can provide useful information on energetic expenditures, but should be validated for the specific taxa, ecosystem, and GPS sampling rate used. Additionally, while movement-based estimates of energy expenditure can elucidate the mechanisms driving habitat use decisions, they may not fully reflect an animal’s overall energy demands. Brown bear movement-based energetic expenditure was influenced by food abundance and movement rates, which highlighted the importance of access to prime foraging sites to enhance energetic efficiency.

Fuzzy Authentication System for Online Transaction Fraud Detection Using Geo Location

In the current days, there is an extensive use of online transaction of information from one place to another place and daily, it is incrementing as an exponential growth. Simultaneously, online activities are also performed by the hackers and thus incrementing the online fraud. Therefore, the present research work is an attempt to minimize online fraudulent activities especially for the financial sectors by implementing the different authentication and authorization schemes at the different levels. In the present work authentication scheme has been proposed by implementing the secure One Time Password (OTP) transmission and Personal Identification Number (PIN) which are based on Global Positioning System (GPS) where transaction is performed then GPS has collected the location details from where the transaction has performed and the location of phone in which OTP is received. The concept of distance has calculated with the both GPS locations and used as a authentication factor. Client Financial Control Application (CFCA) is also responsible for authentication for the online transaction which ables to allow or deny transaction based on account status and fuzzy rule based expert system is used to authenticate the distance, transaction time and other parameters while transaction is performed. The main objective of this paper is to provide security for online transactions and to keep track for online fraudulent transaction.

Small-scale location identification in natural environments with deep learning based on biomimetic sonar echoes

Many bat species navigate in complex, heavily vegetated habitats. To achieve this, the animal relies on a sensory basis that is very different from what is typically done in engineered systems that are designed for outdoor navigation. Whereas the engineered systems rely on data-heavy senses such as lidar, bats make do with echoes triggered by short, ultrasonic pulses. Prior work has shown that ‘clutter echoes’ originating from vegetation can convey information on the environment they were recorded in—despite their unpredictable nature. The current work has investigated the spatial granularity that these clutter echoes can convey by applying deep-learning location identification to an echo data set that resulted from the dense spatial sampling of a forest environment. The Global Positioning System (GPS) location corresponding to the echo collection events was clustered to break the survey area into the number of spatial patches ranging from two to 100. A convolutional neural network (Resnet 152) was used to identify the patch associated with echo sets ranging from one to ten echoes. The results demonstrate a spatial resolution that is comparable to the accuracy of recreation-grade GPS operating under foliage cover. This demonstrates that fine-grained location identification can be accomplished at very low data rates.

Feasibility and acceptability of mobile methods to assess home and neighborhood environments related to adolescent sleep

ObjectiveA growing evidence base suggests home and neighborhood environmental exposures may influence adolescent sleep, but few studies have assessed these relationships using methods that account for time-varying, location-specific exposures, or multiple neighborhood contexts. This study aimed to assess the feasibility and acceptability of using smartphone global positioning system (GPS) tracking and ecological momentary assessment (EMA) to assess time-varying home and neighborhood environmental exposures hypothesized to be associated with adolescent sleep. MethodsAdolescents aged 15-17 years in Philadelphia completed 7 days of continuous smartphone GPS tracking, which was used to identify daily levels of exposure to geocoded neighborhood factors (eg, crime, green space). Four daily EMA surveys assessed home sleep environment (eg, noise, light), stress, health behaviors, and neighborhood perceptions. Feasibility and acceptability of GPS tracking and EMA were assessed, and distributions of daily environmental exposures were examined. ResultsAmong 25 teens (mean age 16, 56% male), there was a high level of GPS location data captured (median daily follow-up: 24 hours). Seventy-eight percent of EMA surveys were completed overall. Most participants (96%) reported no privacy concerns related to GPS tracking and minimal burden from EMA surveys. Exposures differed between participants’ home neighborhoods and locations visited outside the home neighborhood (eg, higher crime away from home). Sleep environment disruptions were present on 29% of nights (most common: uncomfortable temperature) and were reported by 52% of adolescents. ConclusionsResults demonstrate the feasibility and acceptability of mobile methods for assessing time-varying home and neighborhood exposures relevant to adolescent sleep for up to 1 week.

A field test of R package GPSeqClus: For establishing animal location clusters

Abstract The ability to track animals with Global Positioning System (GPS) collars opened an enormous potential for studying animal movements and behaviour in their natural environment. One such endeavour is to identify clusters of GPS locations as a way to estimate predator kill rate. Clapp et al. (2021) developed an R package (GPSeqClus) to assess a location dataset based on user‐defined parameters to identify clusters and their characteristics. These characteristics can then help to distinguish resting‐site clusters from kill sites of their large (&gt;50 kg) prey. We identified location clusters of an adult male wolf Canis lupus on Ellesmere Island, Nunavut, Canada in July 2009 and tracked him until he died in April 2010. Identifying location clusters was challenging because the collar only obtained two GPS locations per day (12 h apart). In July 2010, we searched 30 of 52 location‐clusters we identified as kill/scavenge sites and found 17 of them as such, given they had muskox Ovibos moschatus or caribou Rangifer tarandus pearyi remains nearby. We also documented five wolf rendezvous sites, two den sites, and the wolf's death site to total 60 location‐clusters in all. We used a two‐step process in testing the R Package GPSeqClus (hereafter GPSeqClus): (1) compare the number of clusters our method discerned with the number identified by the new algorithm, and (2) compare the number of biologically significant clusters (e.g. den sites, kill/feeding sites) we found with the number the new algorithm located. We made these tests with GPSeqClus by varying the search radius, number of days at a site, and minimum number of locations required for a cluster. GPSeqClus compared well to our technique, with the best sub‐algorithm among the 25 we tested only missing three of our identified clusters and yielding six additional clusters. GPSeqClus identified 16 of the 17 confirmed sites of remains, all wolf home sites, and the wolf's carcass site. Identifying clusters using a 500‐m search radius, a 1.5‐day window, and a minimum of two GPS locations per cluster was suitable for a coarse GPS acquisition rate of two locations per day when prey are large, such as muskox or caribou. Given that GPSeqClus performed well with our coarse location dataset, we expect it will also perform even better with a collar acquiring more than two locations per day. Having a field‐tested utility such as GPSeqClus will enhance carnivore predation studies elsewhere.

Deep-Ensemble-Learning-Based GPS Spoofing Detection for Cellular-Connected UAVs

Unmanned aerial vehicles (UAVs) are an emerging technology in the 5G-and-beyond systems with the promise of assisting cellular communications and supporting IoT deployment in remote and density areas. Safe and secure navigation is essential for UAV remote and autonomous deployment. Indeed, the opensource simulator can use commercial software-defined radio tools to generate fake global positioning system (GPS) signals and spoof the UAV GPS receiver to calculate wrong locations, deviating from the planned trajectory. Fortunately, the existing mobile positioning system can provide additional navigation for cellular-connected UAVs and verify the UAV GPS locations for spoofing detection, but it needs at least three base stations (BSs) at the same time. In this article, we propose a novel deep-ensemble-learning-based, mobile-network-assisted UAV monitoring and tracking system for cellular-connected UAV spoofing detection. The proposed method uses path losses between BSs and UAVs communication to indicate the UAV trajectory deviation caused by GPS spoofing. To increase the detection accuracy, three statistics methods are adopted to remove environmental impacts on path losses. In addition, deep ensemble learning methods are deployed on the edge cloud servers and use the multilayer perceptron (MLP) neural networks to analyze path losses statistical features for making a final decision, which has no additional requirements and energy consumption on UAVs. The experimental results show the effectiveness of our method in detecting GPS spoofing, achieving above 97% accuracy rate under two BSs, while it can still achieve at least 83% accuracy under only one BS.