Augmented Reality and GPS-Based Resource Efficient Navigation System for Outdoor Environments: Integrating Device Camera, Sensors, and Storage

Contemporary navigation systems rely upon localisation accuracy and humongous spatial data for navigational assistance. Such spatial-data sources may have access restrictions or quality issues and require massive storage space. Affordable high-performance mobile consumer hardware and smart software have resulted in the popularity of AR and VR technologies. These technologies can help to develop sustainable devices for navigation. This paper introduces a robust, memory-efficient, augmented-reality-based navigation system for outdoor environments using crowdsourced spatial data, a device camera, and mapping algorithms. The proposed system unifies the basic map information, points of interest, and individual GPS trajectories of moving entities to generate and render the mapping information. This system can perform map localisation, pathfinding, and visualisation using a low-power mobile device. A case study was undertaken to evaluate the proposed system. It was observed that the proposed system resulted in a 29 percent decrease in CPU load and a 35 percent drop in memory requirements. As spatial information was stored as comma-separated values, it required almost negligible storage space compared to traditional spatial databases. The proposed navigation system attained a maximum accuracy of 99 percent with a root mean square error value of 0.113 and a minimum accuracy of 96 percent with a corresponding root mean square value of 0.17

3D indoor modeling and game theory based navigation for pre and post COVID-19 situation

The COVID-19 pandemic has greatly affected human behavior, creating a need for individuals to be more cautious about health and safety protocols. People are becoming more aware of their surroundings and the importance of minimizing the risk of exposure to potential sources of infection. This shift in mindset is particularly important in indoor environments, especially hospitals, where there is a greater risk of virus transmission. The implementation of route planning in these areas, aimed at minimizing interaction and exposure, is crucial for positively influencing individual behavior. Accurate maps of buildings help provide location-based services, prepare for emergencies, and manage infrastructural facilities. There aren’t any maps available for most installations, and there are no proven techniques to categorize features within indoor areas to provide location-based services. During a pandemic like COVID-19, the direct connection between the masses is one of the significant preventive steps. Hospitals are the main stakeholders in managing such situations. This study presents a novel method to create an adaptive 3D model of an indoor space to be used for localization and routing purposes. The proposed method infuses LiDAR-based data-driven methodology with a Quantum Geographic Information System (QGIS) model-driven process using game theory. The game theory determines the object localization and optimal path for COVID-19 patients in a real-time scenario using Nash equilibrium. Using the proposed method, comprehensive simulations and model experiments were done using QGIS to identify an optimized route. Dijkstra algorithm is used to determine the path assessment score after obtaining several path plans using dynamic programming. Additionally, Game theory generates path ordering based on the custom scenarios and user preference in the input path. In comparison to other approaches, the suggested way can minimize time and avoid congestion. It is demonstrated that the suggested technique satisfies the actual technical requirements in real-time. As we look forward to the post-COVID era, the tactics and insights gained during the pandemic hold significant value. The techniques used to improve indoor navigation and reduce interpersonal contact within healthcare facilities can be applied to maintain a continued emphasis on safety, hygiene, and effective space management in the long term. The use of three-dimensional (3D) modeling and optimization methodologies in the long-term planning and design of indoor spaces promotes resilience and flexibility, encouraging the adoption of sustainable and safe practices that extend beyond the current pandemic

Analytical Review of Map Matching Algorithms: Analyzing the Performance and Efficiency Using Road Dataset of the Indian Subcontinent

Precise position information of moving entities on digital road networks is a vital requirement of location-based applications. Location information received from Global Positioning System has some positional error and this inaccurate information generates errors in further processing of navigation and location-based applications. Map matching algorithms are responsible for the prediction of precise location by considering different parameters of the device. Many map matching algorithms were developed by the research community so as to improve performance and accuracy. These algorithms are categorized into different categories. This paper briefly explains the category-wise working of map matching algorithms and also provides analytically reviews of the performance of these algorithms. Five different algorithms from each category were considered in this experiment. The performance of five basic map matching algorithms was further evaluated on the digital road network of the Indian subcontinent. Six separate routes ranging in length from 0.2 to 55 km were used to analyze the efficiency of considered algorithms. This analytical review provides a performance and accuracy comparison of point to point, topological, Kalman filter-based, Hidden Markov Model-based, and Frechet distance-based map matching algorithms. This review concludes that for online map matching, Hidden Markov model-based map matching algorithm provides good accuracy in comparison to other considered algorithms

Fuzzy Logic-Based Approach for Location Identification and Routing in the Outdoor Environment

Finding the precise and accurate location of devices on road networks is challenging in remote areas with poor internet connectivity and Global Positioning System coverage. Navigation applications that completely depend on the internet and reference spatial data for location identification and mapping do not perform well in case of frequent internet disconnection. These reference spatial data sources have many associated challenges like large size, errors in data, and restricted access. To address these challenges, this paper provides an approach for localization and routing using self-generated reference data using likelihood estimation. According to the proposed approach, the trajectory information is used to create the reference data in the format of Comma Separated Values (CSV). This reference data is first analyzed for quality issues and then used for navigation purposes. Further for the localization Sugeno Fuzzy Model is used as a fuzzy inference system for the initial localization and subsequent mapping of the location. The proposed approach is validated using an Android application on seven predefined routes. According to the performed result analysis, the proposed fuzzy logic-based approach is able to provide location identification with 98.9 percent accuracy with a root mean square error value of 3 percent

Development of real-time pipeline management system for the prevention of accidents

Recently the importance of the management technologies for prevention increases. There are many kinds of pipelines under the ground of cities which have pipelines for waterworks, wastewater, oil, gas, electronic power, communications, heat energy, and so on. And these underground pipelines have different roles in supply the essential resources for citizens of cities and will be more important for citizens increasingly. So we call these pipelines as "LifeLine." By the way, these pipelines do not support their core roles to citizens and we can easily see that pipeline accidents have given inconvenient facts or serious man-made disasters to modern citizens as well. For examples, road settlement and sinkholes, waterworks leaks, pollutions by wastewater and oil, explosions by gas, and so on. Nowadays, we live in times using ICT - sensor technologies. So these environments have been advanced and are probably possible to resolve pipeline management problems using them beforehand in real time. Thus, ICT convergences will encourage us to make new technologies and paradigms on the field of pipeline management

Handwritten devanagari manuscript characters recognition using capsnet

Manuscripts serve as a wealth of knowledge for future generations and are a useful source of information for locating material from the Middle Ages. Ancient manuscripts can be found in handwritten form, thus they must be translated into digital form so that computing equipment can access them and additional indexing and search operations can be performed with ease. Manuscript recognition is already possible using a variety of methods. Regional languages like Devanagari, Gurmukhi, Sanskrit, etc., however, have very few methods available. In this study, the Devanagari characters from the manuscripts is recognised using a CapsNet-based method. 33 fundamental characters, 3 conjuncts, and 12 modifiers make up the Devanagari alphabet. The complete dataset is divided into 399 classes for the recognition of basic, modifiers, and conjunct characters. Due to spatial relationship, CapsNet is used to recognize the handwritten characters. The proposed model was run using 10:70, 20:80, and 30:70 as test: train ratio of characters. Also, the number of epochs was varied for better recognition accuracy. The authors observed the best recognition accuracy of 94.6% was achieved to recognize the Devanagari characters using CapsNet

Detection and Mitigation of GNSS Spoofing Attacks in Maritime Environments Using a Genetic Algorithm

Due to the high reliance of daily activities on the Global Navigation Satellite System (GNSS), its security is one of the major concerns for research and industry. Most navigation and mobile-driven location-based services use GNSS to render services. Due to the low power and easy access of GNSS signals, these signals are vulnerable to spoofing and other types of attacks. Recently many GNSS spoofing attacks have been identified in road- and maritime-based environments. This study provides a technique to detect and counter the GNSS spoofing attack in the maritime environment. This technique uses the Receiver Autonomous Integrity Monitoring (RAIM) model with Least Square Estimation (LSE) and Proportional Integral Derivative (PID) Control to detect the spoofing attack. The proposed technique is based on the concept of a genetic algorithm and navigation devices, such as inertial sensors and pilot options for the ship. A case study using the AIS dataset and simulation using MATLAB and NS3 is provided to validate the performance of the proposed approach. Nine different voyages from the AIS dataset were considered to check the accuracy and performance of the proposed algorithm. The accuracy of the proposed technique was analyzed using the correctly identified attack. The result shows that the proposed technique identifies spoofing attacks with an average value of 90 percent. For result analysis the considered nine routes were traversed multiple times. Root mean square error is used to calculate the positional mismatch (error rate). Based on the combined results analysis, the average value of RMSE is 0.28. In a best-case scenario, the proposed approach provides an RMSE value of 0.009

Enhancing Indoor Navigation in Intelligent Transportation Systems with 3D RIF and Quantum GIS

Innovative technologies have been incorporated into intelligent transportation systems (ITS) to improve sustainability, safety, and efficiency, hence revolutionising traditional transportation. The combination of three-dimensional (3D) indoor building mapping and navigation is a groundbreaking development in the field of ITS. A novel methodology, the “Three-Dimensional Routing Information Framework “(3D RIF), is designed to improve indoor navigation systems in the field of ITS. By leveraging the Quantum Geographic Information System (QGIS), this framework can produce three-dimensional routing data and incorporate sophisticated routing algorithms to handle the complexities associated with indoor navigation. The paper provides a detailed examination of how the framework can be implemented in transport systems in urban environments, with a specific focus on optimising indoor navigation for various applications, including emergency services, tourism, and logistics. The framework includes real-time updates and point-of-interest information, thereby enhancing the overall indoor navigation experience. The 3D RIF’s framework boosts the efficiency and effectiveness of intelligent transportation services by optimising the utilisation of internal resources. The research outcomes are emphasised, demonstrating a mean enhancement of around 25.51% in travel. The measurable enhancement highlighted in this statement emphasises the beneficial influence of ITS on the efficiency of travel, hence underscoring the significance of the ongoing progress in this field