GPS Tracking Research Articles

ТЕХНОЛОГИИ ЦИФРОВОЙ ЭКОНОМИКИ В АГРОТЕХНОЛОГИЧЕСКОЙ МОДЕЛИ

The article examines the application of modern technologies, in particular blockchain, artificial intelligence (AI) and the Internet of Things (IoT), in agriculture. The authors emphasize the importance of digitalization of the agricultural sector to improve efficiency, sustainability and food security. The article analyzes the advantages and disadvantages of blockchain technology, such as security, cost reduction, speed and versatility, as well as uncertain regulatory status, high energy dependence and scalability. Examples of blockchain use in the agricultural sector are described, for example, the Grain Discovery platform in Canada, to improve the transparency and efficiency of supply chains. Then, the possibilities of AI in agriculture are considered, including plant disease identification, weed classification, water management, weather and crop yield forecasting. The authors distinguish four types of AI and note the importance of standardization and regulation of this technology. A SWOT analysis of AI application in the agricultural sector is provided, where the strengths are increased productivity and efficiency of management decisions, and the weaknesses are the need for significant investments and the length of time it takes for technologies to enter the market. The final part of the article discusses the Internet of Things (IoT) technology, its components and application in agriculture. The authors provide statistics on the growth of the IoT market in Russia and note the importance of developing national standards in this area. Examples of IoT use in agriculture are described, such as GPS trackers, animal activity sensors, precision farming systems and RFID technologies. The authors conclude that agricultural technologies are a key tool for the development and transformation of agriculture.

Diverse foraging strategies of breeding Swinhoe's Storm-petrel in the productive marginal sea of the Northwest Pacific

Understanding the foraging behavior is essential for investigating seabird ecology and conservation, as well as monitoring the well-being of the marine environment. Breeding seabirds adopt diverse foraging strategies to maximize energy gains and cope with the intensified challenges of parenting and self-maintenance. Such trade-off may stem from the heterogeneity of food resources and the constraints of central place foraging. Nevertheless, abundant marine productivity could alleviate the energy limitation for seabirds, resulting in a consistent foraging approach. Here, we investigated the foraging strategy during the breeding season of a cryptic small-sized seabird, Swinhoe's Storm-petrel (Hydrobates monorhis), in the Yellow Sea, a productive marginal sea of the Northwest Pacific. Using GPS tracking, we evaluated habitat preference, quantified the foraging strategy, and tested if environmental conditions and individual traits influence foraging trips. We found that Swinhoe's Storm-petrels preferred nearshore areas with shallow water and engaged in primarily short foraging trips. Distinctive southeastward and southwestward strategies emerged when combining trip metrics, including foraging direction, duration, and maximum distance. The bathymetry, proximity to the coastline, and sea surface temperature differed in two foraging strategies. Foraging strategies exhibited flexibility between individuals, potentially explained by wing morphology, in which longer-winged birds are more likely to embark on longer-distance foraging trips. These findings highlight the impact of environmental factors and individual traits on seabirds' foraging decisions in productive marginal sea ecosystems. Our study also provides valuable insights into the foraging ecology of this Asian endemic storm-petrel.

Mitigating Risk: Predicting H5N1 Avian Influenza Spread with an Empirical Model of Bird Movement

Understanding timing and distribution of virus spread is critical to global commercial and wildlife biosecurity management. A highly pathogenic avian influenza virus (HPAIv) global panzootic, affecting ~600 bird and mammal species globally and over 83 million birds across North America (December 2023), poses a serious global threat to animals and public health. We combined a large, long‐term waterfowl GPS tracking dataset (16 species) with on‐ground disease surveillance data (county‐level HPAIv detections) to create a novel empirical model that evaluated spatiotemporal exposure and predicted future spread and potential arrival of HPAIv via GPS tracked migratory waterfowl through 2022. Our model was effective for wild waterfowl, but predictions lagged HPAIv detections in poultry facilities and among some highly impacted nonmigratory species. Our results offer critical advance warning for applied biosecurity management and planning and demonstrate the importance and utility of extensive multispecies tracking to highlight potential high‐risk disease spread locations and more effectively manage outbreaks.

ГЕОЛОКАЦИОННЫЕ СЕРВИСЫ НОВОГО ПОКОЛЕНИЯ: РАЗРАБОТКА СИСТЕМЫ УМНЫХ ОПОВЕЩЕНИЙ И ПОСТРОЕНИЯ МАРШРУТОВ ПОИСКА ДЛЯ ПОТЕРЯВШИХСЯ ЖИВОТНЫХ

The concept of a new generation of geolocation services takes a step towards an effective search for missing animals. In modern society, when traditional methods of searching for missing animals have low efficiency, and the available online services do not function fully, there is an urgent need to develop fundamentally new solutions. The purpose of this article is to present the concept of a new generation of geolocation services that can radically change the approach to finding missing animals and significantly increase the chances of their successful return home. Unlike the existing disparate approaches, the new concept is based on the integrated application of modern technologies. The article discusses the main technologies: GPS tracking, LBS positioning, smart alerts, algorithms for building search routes; the functionality of new-generation geolocation services; describes the advantages and prospects of introducing a new concept; and also considers the development and implementation of new-generation geolocation services.

Futuristic smart AI powered backpack

The Smartbag revolutionizes independent mobility for the visually impaired by combining cutting-edge technology with intuitive design. Functioning as both a navigational aid and an interactive assistant, it utilizes a Raspberry Pi 4 and Arduino UNO to power its array of features. Equipped with GPS tracking, ultrasonic sensors, and a facial recognition camera, it detects obstacles, charts safe routes, and captures images upon command. Integrated health monitoring and fall detection systems prioritize user safety, while a built-in chatbot offers companionship and support during travel. With voice command capabilities and accessible interfaces, the Smartbag ensures seamless interaction and navigation. It represents a comprehensive solution for individuals with visual impairments, empowering them to navigate the world confidently and independently.

Smart Sensors for Real-time Monitoring in a Sustainable Logistics

The new logistics landscape needs openness, efficiency, and agility. Conventional tracking techniques are unable to keep up with the complex dance of items traveling across countries. Intelligent and communicative smart sensors have changed the game by allowing for real-time process monitoring and revolutionizing the logistics process from start to finish. This study explores how smart sensors can revolutionize the logistics industry. First, smart devices were study in-depth and their various features were equally examined, such as GPS tracking, RFID scanning, temperature and humidity sensors, package integrity assessment, vibration and acoustic sensors, and environmental monitoring. By conducting a comprehensive investigation, this research paper aims to scrutinize the data collecting ability of these sensors. The study will focus on their potential for gathering an abundance of information regarding cargo whereabouts, surrounding environment conditions, physical soundness, and equipment efficiency. This stream of real-time current data helps in active problem-solving, and imparts all the relevant insights one needs to know about the complete sustainable logistics operation. Their critical role toward improving productivity, transparency, and environmental sustainability in the dynamic logistics environment. For this reason, they can offer all perspectives needed (which are the most complete) to foster a proactive analysis and enhance any aspect over time.

INVESTIGATION OF ROCKFALL HAZARDS IN UNDERGROUND MINES AND DEVELOPMENT OF A PREDICTIVE MODEL

The project aims to design a safety system for underground mine workers. Underground mining presents significant safety challenges due to hazardous environmental conditions, structural instability, and the risk of accidents. A comprehensive Underground Mining Safety Monitoring and Alerting System is crucial to protect miners' lives and improve operational efficiency. This system integrates advanced sensors to monitor key parameters, including gas concentrations, temperature, humidity, seismic activity, and water levels. Wearable devices track the location and health metrics of miners, providing real-time visibility of personnel. A wireless communication network transmits data to a central monitoring system, where potential hazards are identified and alerts are triggered through audible, visual, and mobile notifications. The system's automated response features further enhance safety by initiating preventative actions. The underground mine safety monitoring and alerting system is a comprehensive solution designed to improve safety, minimize accidents, and enhance operational efficiency in underground mines. This system combines advanced sensors, wireless communication networks, and data analytics to offer realtime monitoring and alerting capabilities. Environmental sensors continuously measure critical parameters such as air quality, temperature, humidity, and noise levels. Personnel tracking systems, using RFID, GPS, or Wi-Fi technologies, monitor the location of workers within the mine. Data analytics software processes sensor data to identify potential safety risks, allowing for proactive actions to prevent accidents. The system also triggers automated alerts and notifications to both personnel and control rooms when safety hazards are detected, facilitating quick emergency responses.

ECONOMIC BENEFITS OF ROUTE MONITORING: GPS TRACKING IN KAZAKHSTAN’S RAILWAYS

In the context of Kazakhstan's railway operations, this study explores the economic advantages derived from the implementation of GPS tracking technology focused on route monitoring. Employing Extreme Programming (XP) principles, a case study within a national railway company details the process, challenges, and outcomes of integrating GPS trackers on mobile phones for workforce location tracking and geo-fencing. Results demonstrate the significant improvement in monitoring workforce activities and precise payroll calculation based on actual working hours. Emphasizing the role of the XP methodology, the study showcases enhanced stakeholder communication, iterative development, and continuous system improvement. Technical aspects of the GPS tracking system, including architecture, data flow, and integration with existing railway management systems, are examined. Moreover, the study delves into encountered challenges such as privacy concerns, data security, and user acceptance during implementation. This case study presents valuable insights for transportation organizations aiming to implement location-tracking and geo-fencing technologies, providing a blueprint for effective project management, iterative development, and stakeholder engagement in pursuit of economic benefits through route monitoring.

Combining video and GPS-tracking to study the spatial foraging distribution of a single-prey loading seabird

Seabirds are valuable indicators of marine ecosystem processes and studying seabird diets can shed light on natural or human-induced variability in food-web composition. Specifically single-prey loading seabird species such as terns have the potential to act as visual sentinels of prey availability offshore. However, obtaining diet information from remote bird colonies is often challenging and time consuming. In this pilot study we present a novel approach to combine two established methods to study seabird foraging ecology, providing a powerful and cost-effective tool to study the distribution of prey items available to seabirds. We combined GPS tracking data of Sandwich Terns (Thalasseus sandvicensis) with prey-observations from a hide in 2012 and 2013, and from semi-continuously recorded camera footage in 2017. By doing so, we identified 115 approximate catch locations of prey (86 herring/sprat Clupeidae, 29 sandeel Ammodytidae). Combining GPS-data and prey observations yielded detailed knowledge on the movements and chick diets of tracked birds as well as the spatial origin and lengths of captured prey items. Further catch distances of both Clupeidae and Ammodytidae resulted in deliveries of larger prey items and thus higher energy yield per trip, but also a higher energy expenditure per trip. We discuss the limitations and potential of our methodological approach to study foraging energetics during chick-provisioning of seabirds that carry prey items visible in their beaks.

The Mobility Interview: Triangulating Interview and Global Positioning System Data to Explore the Role of Mobility in Everyday Life

This article presents a mixed-methods research protocol suitable for studying everyday mobility and its effects on life chances for a wide range of populations. We argue that it can address, among other issues, two important methodological challenges in the study of mobility: addressing pre-reflexivity and habits, as well as differentiating between voluntary and forced movements. The research protocol combines a problem-centered interview, two weeks of GPS tracking, and a subsequent mobility interview. It has been shaped by its application to more than 60 socially disadvantaged residents of rural peripheries as part of an international research project. It contributes to existing qualitative methods of spatial analysis because its design incorporates not only theoretical considerations but also insights of real-world application from conducting it in varied contexts and with different populations, which are typically underrepresented in more elaborate research designs. Nevertheless, due to its thematic openness, the design is not only interesting for mobility research, but also for other research focusing on spatial phenomena at the individual level. We provide a detailed account of the method’s implementation, contextualize it in relation to previous approaches, and discuss the analytical potential of the data obtained. We suggest that by triangulating movement data with interview data, perceptions and meanings of everyday mobility can be uncovered that would otherwise remain at a pre-reflexive level.

Mopsi location-based service

<p>Mopsi is a location-based platform for storing photos and GPS tracks of the users. It allowed user to share their data on-line with real-time user location, communicate with other users, share the data in Facebook, browse the collected photos and tracks on map, perform searches and ask recommendations. Mopsi was operational from 2009 to 2020. This paper documents the history of Mopsi, its main functionalities, research achievements, and the collected data.</p>

Vehicle Access Authentication and Driver Safety

Abstract: This paper aims with the rise in vehicle theft cases, the need for reliable anti-theft devices has become crucial. Existing vehicle lock system, including mechanical locks, car alarms, and GPS tracking, have not been able to effectively reduce theft rates. This project proposes an innovative solution using face detection technology to design and develop an advanced vehicle locking system in real time. The system allows the car module to be initiated either through face detection or by sending a status message from a cell phone. Upon receiving the message, the car module verifies user authentication. In the event of unauthorized access, a message will be forwarded to the authorized person or the owner cell phone with their photo, if the authorized person gives the permission then the ignition will be switched on. After the face detection for driver safety purpose the Alcohol content will be checked and the seat belt detection will be done. If the accident occurs while driving a car, then the message will be forwarded to the authorized people with the current location and also to the nearest police stations or to the hospitals.

Improving last mile distribution systems through the Internet of Things: a South African case

The study investigates the impact of the IoT on the cost and quality of last-mile logistics, the critical and often challenging final phase of the supply chain. With the relentless growth of e-commerce and the increasing demands for efficient and reliable delivery services, understanding how IoT affects last-mile logistics is increasingly becoming important. Through the integration of IoT technologies such as GPS tracking, RFID systems, and real-time data analytics, the study aimed to assess the extent to which the application of the IoT affects the cost and quality of last-mile distribution in South Africa. As a result, the study provides valuable insights into the potential cost savings or increases associated with IoT adoption, quality challenges and areas of improvement, customer satisfaction and the overall performance of last-mile distribution systems. The methodology involves the collection of primary data from selected distribution companies in South Africa. Statistical software was used to analyse the data to shed light on the tangible benefits and challenges associated with IoT adoption in last-mile logistics as they relate to cost and quality. The research findings indicate that implementing IoT in last-mile distribution systems in South Africa can significantly improve efficiency and effectiveness. The IoT integration with existing infrastructure enables seamless communication, proactive decision-making, and reduced delivery delays. Overall, by leveraging IoT technologies and real-time data analysis, organisations can optimise their distribution processes, reduce costs, improve quality, and enhance overall customer experience.

An online blockchain based sustainable logistics management system (OBSLMS) enabled by the Internet of Things for the textile industry

Blockchain technology and IoT have been beneficial for information sharing, supply-chain management, product tracking and device control in multiple fields. Now, their combined potential has become an emerging concept, one that can be implemented in the textile industry. In most cases, textile industries are the backbone of the economy of a country. The major textile fibres are cotton, jute, wool, silk, bamboo, linen, etc. In theory, the fibre producers’ profit and their products’ market price may vary depending on the fibre’s demand, aggregate supply and the current market structure. But, in real-life textile markets, intermediaries between the producers and buyers lead to significant decreases in the producers’ profit. This results in the producer incurring losses and not being able to get back their initial investments as revenue. This research paper aims to provide a solution to the above-stated critical issue. In this paper, we have proposed an Online Blockchain-based Sustainable Logistics Management System (OBSLMS) for textile businesses. The implementation mechanism of the OBSLMS consists of blockchain based business transactions, an IoT based GPS tracking system and a unique QR (Quick Response) code verification for the endpoint delivery of textile fibres from producers to buyers. This sustainable solution also has a bidding feature. Moreover, OBSLMS has been programmed, implemented and tested. The OBSLMS uses the availability and efficiency of the openly available online platform, blockchain technology and IoT to help both the textile fibre producers and the buyers gain a fair profit.

CORRIGENDUM: Thermal soaring over the North Sea and implications for wind farm interactions

Seabirds use several flight modes at sea, including thermal soaring, in which thermal uplift is used to gain altitude and save energy. An increase in flight altitude may have consequences for wind farm interactions if it results in birds spending more time within the rotor-swept zone (RSZ). To understand conditions under which thermal soaring occurs and potential implications for wind farm interactions, we investigated thermal soaring in relation to atmospheric conditions in June and July at 2 study areas in the North Sea, west and north of the Dutch coast. We developed algorithms that identified thermal soaring in GPS tracks of lesser black-backed gulls Larus fuscus and radar tracks of seabirds. By combining species-specific 3-dimensional information on flight behaviour from bio-logging with the continuous spatiotemporal coverage of radar positioned at wind parks, we obtained a more comprehensive overview of thermal soaring at sea than either method would obtain alone. Our results showed that birds flew at higher altitudes during thermal soaring than non-soaring flight, increasing the proportion of flight time within the RSZ. Thermal soaring occurred inside offshore wind farms to a similar degree as outside. Thermal soaring was positively correlated with positive temperature differences (ΔT) between sea surface and air (at 2 m above sea level), and north and north-westerly winds. We show that the probability of thermal soaring over the North Sea, inside and outside wind farms, increases with larger temperature differences, resulting in increased time spent within the RSZ and an increased collision risk for seabirds.

Investigation of optimized observation periods for estimating a representative home range of free-roaming domestic dogs

Free-roaming domestic dogs (FRDD), as vectors of zoonotic diseases, are of high relevance for public health. Understanding roaming patterns of dogs can help to design disease control programs and disease transmission simulation models. Studies on GPS tracking of dogs report stark differences in recording periods. So far, there is no accepted number of days required to capture a representative home range (HR) of FRDD. The objective of this study was to evaluate changes in HR size and shape over time of FRDD living in Chad, Guatemala, Indonesia and Uganda and identify the period required to capture stable HR values. Dogs were collared with GPS units, leading to a total of 46 datasets with, at least, 19 recorded days. For each animal and recorded day, HR sizes were estimated using the Biased Random Bridge method and percentages of daily change in size and shape calculated and taken as metrics. The analysis revealed that the required number of days differed substantially between individuals, isopleths, and countries, with the extended HR (95% isopleth value) requiring a longer recording period. To reach a stable HR size and shape values for 75% of the dogs, 26 and 21 days, respectively, were sufficient. However, certain dogs required more extended observational periods.

Improved prediction of hiking speeds using a data driven approach.

Hikers and hillwalkers typically use the gradient in the direction of travel (walking slope) as the main variable in established methods for predicting walking time (via the walking speed) along a route. Research into fell-running has suggested further variables which may improve speed algorithms in this context; the gradient of the terrain (hill slope) and the level of terrain obstruction. Recent improvements in data availability, as well as widespread use of GPS tracking now make it possible to explore these variables in a walking speed model at a sufficient scale to test statistical significance. We tested various established models used to predict walking speed against public GPS data from almost 88,000 km of UK walking / hiking tracks. Tracks were filtered to remove breaks and non-walking sections. A new generalised linear model (GLM) was then used to predict walking speeds. Key differences between the GLM and established rules were that the GLM considered the gradient of the terrain (hill slope) irrespective of walking slope, as well as the terrain type and level of terrain obstruction in off-road travel. All of these factors were shown to be highly significant, and this is supported by a lower root-mean-square-error compared to existing functions. We also observed an increase in RMSE between the GLM and established methods as hill slope increases, further supporting the importance of this variable.

Movement behaviour of the common frog (Rana temporaria) in an anthropized mountain landscape: integrated step selection analysis of GPS tracking data

Abstract Movement is a key aspect of amphibian ecology as the life cycle of many species involves seasonal dispersal and migration. However, there is still a lack of knowledge about how amphibians move across landscapes in relation to landscape structure and resource availability. Modern lightweight GPS loggers and associated statistical tools offer new perspectives to fill this gap. We tracked the movements of 20 adult European common frogs (Rana temporaria) in a human-transformed mountain landscape during summer using GPS tags programmed with a constant 4-hour time interval between fixes. Using integrated step-selection analysis, we demonstrate firstly that frogs strongly selected aquatic habitats over open grassland. Second, ski runs have a negative effect on frogs, indicated by longer movement steps. Weather (air temperature and rain) had no effect on frog movements. These results constitute a promising first step towards studying anuran movement in relation to landscape structure at fine spatial and temporal scales.

Research on the Spatial and Temporal Dynamics of Crowd Activities in Commercial Streets and Their Relationship with Formats—A Case Study of Lao Men Dong Commercial Street in Nanjing

Crowd activity is an important indicator of commercial streets’ attractiveness and developmental potential. The development of positioning technologies such as GPS and mobile signal tracking has provided a large amount of trajectory data for studying crowd activities on commercial streets. These data can not only be used for the statistics, extraction, and visualization of crowd information, but they also facilitate the exploration of deeper insights into dynamic behaviors, choices, trajectories, and other details of crowd activities. Based on this, this article proposes a new framework for analyzing crowd activities to explore the spatial activity patterns of crowds and understand the dynamic spatial needs of people by analyzing their correlations with local formats. Specifically, we analyze the spatial activity characteristics of a crowd in the Lao Men Dong Commercial Street area by identifying the stay points and trajectory clusters of the crowd, and we establish a regression analysis model by selecting commercial street format variables to evaluate their impact on crowd activities. Through case analysis of the Lao Men Dong Commercial Street, this study confirms that our method is feasible and suitable for spatial research at different scales, thereby providing relevant ideas for format location selection, spatial layout, and other planning types, and for promoting the sustainable development of urban spaces.

Identifying Interesting Sceneries and Objects in A Tropic Forest Through Visitors Employed Photography

Tropical Forest is a unique forest landscape character composed of vegetation with various altitude strata combined with other biotic and abiotic landscape elements. The combination of landscape objects produces a landscape character that is visually attractive to visitors. There are few references to forest sceneries and objects of interest to forest visitors, even though this information is important for forest tourism. This study identified tropical forest sceneries and objects of interest to forest visitors through photographs taken by visitors. The method used is Visitors' Employed Photography (VEP) combined with GPS tracking and artificial intelligence Google Clouds Vision API. In this VEP method, we invited 41 respondents to walk in the "Situ Gede Forest " and directly photograph interesting objects in the field. We obtained 1,206 photos from respondents containing the scenery and objects of the "Situ Gede Forest" and the location of the photo-taking points. The photos were labeled via Google Cloud's Vision API and clustered based on the Photo's label into 15 photo clusters using R Statistics software. The grouping results identified three sceneries and objects that interested the respondents: "View to the standing of trees inside the forest, woodland, or jungle," "Forest scenery with recreational activities and zoo," and "Body of water, edge of the body of water, waterway, and trees." Information on the three characteristics of tropical forest landscapes is beneficial in tropical forest management activities, where landscapes with these three types must be conserved and preserved in the interest of sustainable tourism forest.