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Internet of Things (IoT) is turning into an essential part of daily life, and numerous IoT-based scenarios will be seen in future of modern cities ranging from small indoor situations to huge outdoor environments. In this era, navigation continues to be a crucial element in both outdoor and indoor environments, and many solutions have been provided in both cases. On the other side, recent smart objects have produced a substantial amount of various data which demands sophisticated data mining solutions to cope with them. This paper presents a detailed review of previous studies on using data mining techniques in indoor navigation systems for the loT scenarios. We aim to understand what type of navigation problems exist in different IoT scenarios with a focus on indoor environments and later on we investigate how data mining solutions can provide solutions on those challenges.

Currently, outdoor navigation systems have widely been used around the world on smartphones. They rely on GPS (Global Positioning System). However, indoor navigation systems are still under development due to the complex structure of indoor environments, including multiple floors, many rooms, steps, and elevators. In this paper, we present the design and implementation of the Indoor Navigation System using Unity and Smartphone (INSUS). INSUS shows the arrow of the moving direction on the camera view based on a smartphone’s augmented reality (AR) technology. To trace the user location, it utilizes the Simultaneous Localization and Mapping (SLAM) technique with a gyroscope and a camera in a smartphone to track users’ movements inside a building after initializing the current location by the QR code. Unity is introduced to obtain the 3D information of the target indoor environment for Visual SLAM. The data are stored in the IoT application server called SEMAR for visualizations. We implement a prototype system of INSUS inside buildings in two universities. We found that scanning QR codes with the smartphone perpendicular in angle between 60∘ and 100∘ achieves the highest QR code detection accuracy. We also found that the phone’s tilt angles influence the navigation success rate, with 90∘ to 100∘ tilt angles giving better navigation success compared to lower tilt angles. INSUS also proved to be a robust navigation system, evidenced by near identical navigation success rate results in navigation scenarios with or without disturbance. Furthermore, based on the questionnaire responses from the respondents, it was generally found that INSUS received positive feedback and there is support to improve the system.

The human sense of vision is a critical tool for interaction and exploration in the physical world. However, this valuable faculty can be impaired by a range of causes, such as congenital disabilities, accidents, or illnesses. Blind or visually impaired persons (BVIP) face considerable challenges in navigation, especially in unfamiliar settings, impeding their autonomy and exposing them to potential hazards. Electronic travel aids (ETAs) have emerged as a solution, providing support for mobility and facilitating autonomous navigation through information on the environment, the user's location, and directional instructions. Despite the existence of these systems, the literature lacks a comprehensive approach to integrating various functions into ETAs to enhance their support of indoor navigation activities for BVIP. This systematic literature review analysed previous ETAs and evaluated them based on the technologies employed and the functions they fulfil. A thorough search was conducted across ten journal databases, focusing on articles published between 2017 and 2022 to capture the most recent advancements in related ETA technologies. The results of this review highlight a promising avenue for future research in developing advanced ETAs for indoor navigation by BVIP.

This paper offers a solution to challenge navigation in the indoor environment by making use of the existing infrastructure. Estimating pedestrian trajectory using pedestrian dead reckoning (PDR) and WiFi is a very popular technique. However, cumulative errors and mismatching are major problems in PDR and WiFi fingerprint matching, respectively. PDR and pedestrian heading are used as the state transition equation, and the step length and WiFi matching results are used as observation equations. A federated particle filter (FPF) based on the principle of information sharing is proposed to fusion PDR and WiFi, which improves pedestrian navigation accuracy. The experimental results show that the average positioning accuracy is 0.94 m and 1.5 m, respectively.

Notably valuable efforts have focused on helping people with special needs. In this work, we build upon the experience from the BlindHelper smartphone outdoor pedestrian navigation app and present Blind MuseumTourer, a system for indoor interactive autonomous navigation for blind and visually impaired persons and groups (e.g., pupils), which has primarily addressed blind or visually impaired (BVI) accessibility and self-guided tours in museums. A pilot prototype has been developed and is currently under evaluation at the Tactual Museum with the collaboration of the Lighthouse for the Blind of Greece. This paper describes the functionality of the application and evaluates candidate indoor location determination technologies, such as wireless local area network (WLAN) and surface-mounted assistive tactile route indications combined with Bluetooth low energy (BLE) beacons and inertial dead-reckoning functionality, to come up with a reliable and highly accurate indoor positioning system adopting the latter solution. The developed concepts, including map matching, a key concept for indoor navigation, apply in a similar way to other indoor guidance use cases involving complex indoor places, such as in hospitals, shopping malls, airports, train stations, public and municipality buildings, office buildings, university buildings, hotel resorts, passenger ships, etc. The presented Android application is effectively a Blind IndoorGuide system for accurate and reliable blind indoor navigation.

Indoor 3D reconstruction and navigation element extraction with point cloud data has become a research focus in recent years, which has important application in community refinement management, emergency rescue and evacuation, etc. Aiming at the problem that the complete wall surfaces cannot be obtained in the indoor space affected by the occluded objects and the existing methods of navigation element extraction are over-segmented or under-segmented, we propose a method to automatically reconstruct indoor navigation elements from unstructured 3D point cloud of buildings with occlusions and openings. First, the outline and occupancy information provided by the horizontal projection of the point cloud was used to guide the wall segment restoration. Second, we simulate the scanning process of a laser scanner for segmentation. Third, we use projection statistical graphs and given rules to identify missing wall surfaces and “hidden doors”. The method is tested on several building datasets with complex structures. The results show that the method can detect and reconstruct indoor navigation elements without viewpoint information. The means of deviation in the reconstructed models is between 0–5 cm, and the completeness and correction are greater than 80%. However, the proposed method also has some limitations for the extraction of “thick doors” with a large number of occluded, non-planar components.

Navigation networks are a common form of indoor map that provide the basis for a wide range of indoor location-based services, intelligent tasks for indoor robots, and three-dimensional (3D) geographic information systems. The majority of current indoor navigation networks are manually modeled, resulting in a laborious and fallible process. Building Information Modeling (BIM) captures design information, allowing for the automated generation of indoor maps. Most existing BIM-based navigation systems for floor-level wayfinding rely on well-defined spatial semantics, and do not adapt well to buildings with irregular 3D shapes, which can make cross-floor path generation difficult. This research introduces an innovative approach to generating 3D indoor navigation networks automatically from BIM data using image thinning, which is referred to as GINIT. Firstly, GINIT extracts grid-based maps for floors from BIM data using only two types of semantics, i.e., slabs and doors. Secondly, GINIT captures cross-floor paths from building components by projecting 3D forms onto a 2D image, thinning the 2D image to capture the 2D projection path, and crossing over the 2D routes with 3D routes to restore the 3D path. Finally, to demonstrate the effectiveness of GINIT, experiments were conducted on three real-world multi-floor buildings, evaluating its performance across eight types of cross-layer architectural component. GINIT overcomes the dependency of space definitions in current BIM-based navigation network generation schemes by introducing image thinning. Due to the adaptability of navigation image thinning to any binary image, GINIT is capable of generating navigation networks from building components with diverse 3D shapes. Moreover, the current studies on indoor navigation network extraction mainly use geometry theory, while this study is the first to generate 3D indoor navigation networks automatically using image thinning theory. The results of this study will offer a unique perspective and foster the exploration of imaging theory applications of BIM.

Evacuation research relies heavily on the efficiency analysis of the study navigation networks, and this principle also applies to indoor scenarios. One crucial type of these scenarios is the attacker and defender topic, which discusses the paralyzing and recovering operations for a specific indoor navigation network. Our approach is to apply the Generative-Adversarial-Neural network (GAN) model to optimize both reduction and increase operations for a specific indoor navigation network. In other words, the proposed model utilizes GAN both in the attacking behavior efficiency analysis and the recovering behavior efficiency analysis. To this purpose, we design a black box of training the generative model and adversarial model to construct the hidden neural networks to mimic the human selection of choosing the critical nodes in the studying navigation networks. The experiment shows that the proposed model could alleviate the selection of nodes that significantly influence network transportation efficiency. Therefore, we could apply this model to disaster responding scenarios like fire evacuation and communication network recovery operations.

The prominence of Indoor Navigation Systems (INS) has been on an upward trajectory in recent years. While the Global Positioning System (GPS) commonly utilizes radio waves from artificial satellites for positioning information, its precision is compromised indoors due to potential radio wave obstruction by buildings. In contrast, pedometers, a critical component of INS, can provide invaluable insights into health, exercise, and user itineraries by detecting the number of steps and pinpointing optimal indoor positions. This paper proposes the development of a high-accuracy pedometer system. The proposed tracking system capitalizes on data harvested from accelerometers, sensors integrated into mobile devices, to furnish indoor tracking predicated on a straightforward pedometer approach. Online measurements and tests were conducted in residential settings, and the recorded tests were subsequently simulated offline via MATLAB. The performance of the system was evaluated in a real-world indoor residential scenario using an iPhone6 mobile device, with the discussion encompassing potential usability aspects of the approach. The devised system mitigated the drift of sensor readings by amalgamating the data from the gyroscope and accelerometer. The experimental results revealed a percentage error of 4.33% for the proposed method, translating to an error of 0.65 meters from an average walking distance of approximately 3 meters, out of 15 meters. Future research endeavors will concentrate on enhancing the accuracy of the approach by implementing data filtering and interference reduction techniques.

Unmanned Aerial Vehicles (UAVs) are increasingly integral to construction site management, supporting monitoring, inspection, and data collection tasks. Effective UAV path planning is essential for maximizing operational efficiency, particularly in complex and dynamic construction environments. While previous BIM-based approaches have explored representation models such as space graphs, grid patterns, and voxel models, each has limitations. Space graphs, though common, rely on predefined spatial spaces, making them less suitable for projects still under construction. Voxel-based methods, considered well-suited for 3D indoor navigation, suffer from three key challenges: (1) a disconnect between the BIM and voxel models, limiting data integration; (2) the computational cost and time required for voxelization, hindering real-time application; and (3) inadequate support for 4D BIM integration during active construction phases. This research introduces a novel framework that bridges the BIM–voxel gap via an enriched voxel map, eliminates the need for repeated voxelization, and incorporates 4D BIM and additional model data such as defined workspaces and safety buffers around fragile components. The framework’s effectiveness is demonstrated through path planning simulations on BIM models from two real-world construction projects under varying scenarios. Results indicate that the enriched voxel map successfully creates a connection between BIM model and voxel model, while covering every timestamp of the project and element attributes during path planning without requiring additional voxel map creation.