Indoor Data Research Articles

PFIN: An Efficient Particle Filter-Based Indoor Navigation Framework for UAVs

The utilization of Unmanned Aerial Vehicles (UAVs) like drones for indoor data gathering or sensing applications have gained popularity over the last decade. Indoor UAV navigation is a complex process, which involves several tasks such as mapping, localization, and path planning with obstacle avoidance. In this paper, a Particle Filter-based Indoor Navigation (PFIN) framework is proposed for the drone navigation process. In PFIN, Quadcopter Mapping Algorithm (QMA) is proposed using particle filter analysis to aid in mapping for indoor navigation. In addition, particle filter-based Optimized Localization Algorithm (OLA) and Adaptive Velocity Procedure (AVP) are proposed for the purpose of enhancing the precision in localization and to improve the velocity estimation for collision avoidance, respectively. Thus, the proposed PFIN framework is experimented using Software-In-The-Loop (SITL) tools such as Robot Operating System (ROS) and Gazebo for visualizing its behavior, and Crazyflie 2.0 drone assisted Hardware-In-The-Loop (HITL) simulation in verifying the correctness of the algorithms in a laboratory setup. The PFIN framework reduces position error on an average by 14% than the conventional Extended Kalman Filter (EKF) model. The SITL and HITL simulations demonstrate the efficiency of the algorithms through improved precision in UAV exploration.

Improved Indoor Positioning by Means of Occupancy Grid Maps Automatically Generated from OSM Indoor Data

In recent years, there is a growing interest in indoor positioning due to the increasing amount of applications that employ position data. Current approaches determining the location of objects in indoor environments are facing problems with the accuracy of the sensor data used for positioning. A solution to compensate inaccurate and unreliable sensor data is to include further information about the objects to be positioned and about the environment into the positioning algorithm. For this purpose, occupancy grid maps (OGMs) can be used to correct such noisy data by modelling the occupancy probability of objects being at a certain location in a specific environment. In that way, improbable sensor measurements can be corrected. Previous approaches, however, have focussed only on OGM generation for outdoor environments or require manual steps. There remains need for research examining the automatic generation of OGMs from detailed indoor map data. Therefore, our study proposes an algorithm for automated OGM generation using crowd-sourced OpenStreetMap indoor data. Subsequently, we propose an algorithm to improve positioning results by means of the generated OGM data. In our study, we used positioning data from an Ultra-wideband (UWB) system. Our experiments with nine different building map datasets showed that the proposed method provides reliable OGM outputs. Furthermore, taking one of these generated OGMs as an example, we demonstrated that integrating OGMs in the positioning algorithm increases the positioning accuracy. Consequently, the proposed algorithms now enable the integration of environmental information into positioning algorithms to finally increase the accuracy of indoor positioning applications.

VLC-based Data Transfer and Energy Harvesting Mobile System

This paper explores a low-cost portable visible light communication (VLC) system to support the increasing needs of lightweight mobile applications. VLC grows rapidly in the past decade for many applications (e.g., indoor data transmission, human sensing, and visual MIMO) due to its RF interference immunity and inherent high security. However, most existing VLC systems heavily rely on fixed infrastructures with less adaptability to emerging lightweight mobile applications. This work proposes Light Storage, a portable VLC system takes the advantage of commercial smartphone flashlights as the transmitter and a solar panel equipped with both data reception and energy harvesting modules as the receiver. Light Storage can achieve concurrent data transmission and energy harvesting from the visible light signals. It develops multi-level light intensity data modulation to increase data throughput and integrates the noise reduction functionality to allow portability under various lighting conditions. The system supports synchronization together with adaptive error correction to overcome both the linear and non-linear signal offsets caused by the low time-control ability from the commercial smartphones. Finally, the energy harvesting capability in Light Storage provides sufficient energy support for efficient short range communication. Light Storage is validated in both indoor and outdoor environments and can achieve over 98% data decoding accuracy, demonstrating the potential as an important alternative to support low-cost and portable short range communication.

Generative multiview inpainting for object removal in large indoor spaces

As interest in image-based rendering increases, the need for multiview inpainting is emerging. Despite of rapid progresses in single-image inpainting based on deep learning approaches, they have no constraint in obtaining color consistency over multiple inpainted images. We target object removal in large-scale indoor spaces and propose a novel pipeline of multiview inpainting to achieve color consistency and boundary consistency in multiple images. The first step of the pipeline is to create color prior information on masks by coloring point clouds from multiple images and projecting the colored point clouds onto the image planes. Next, a generative inpainting network accepts a masked image, a color prior image, imperfect guideline, and two different masks as inputs and yields the refined guideline and inpainted image as outputs. The color prior and guideline input ensure color and boundary consistencies across multiple images. We validate our pipeline on real indoor data sets quantitatively using consistency distance and similarity distance, metrics we defined for comparing results of multiview inpainting and qualitatively.

Analyzing the Relationship between Indoor Temperatures and 911 Calls through a Case-Control Study in New York City, NY

The frequency and intensity of extreme heat events are expected to increase with climate change. Understanding the ways in which heat influences health outcomes is critical to mitigating its effect both now and in the future through targeted interventions and adaptations. However, few studies have been able to analyze the role of indoor conditions in influencing health therefore missing a critical component of the environment. In addition, previous studies have not focused on heat-specific health outcomes. This study will fill this information gap through a case-control study design and regression analyses. Working with the New York City Fire Department Emergency Medical Services we compiled the Patient Care Reports and indoor temperatures for patients receiving emergency care during the summer of 2016. In order to collect indoor data, paramedics carried portable sensors into buildings while responding to 911 calls which passively measured indoor conditions. We compared 55 heat-specific health outcomes and 1611 controls through a case-control study and propensity score matching. Indoor temperatures over 28℃ resulted in a significant increase (OR=3.88 CI[1.83, 8.25]) in chance of heat-related 911 calls. Current heat warnings are for outdoor temperatures are issued at two or more days between 35-37℃ or any length of time above ~38℃. However, our results indicate the threshold at which people become sick due to indoor exposure is actually lower. Therefore these results could inform policy regarding indoor temperature safety standards.

Autonomous Indoor Scanning System Collecting Spatial and Environmental Data for Efficient Indoor Monitoring and Control

As various activities related to entertainment, business, shopping, and conventions are done increasingly indoors, the demand for indoor spatial information and indoor environmental data is growing. Unlike the case of outdoor environments, obtaining spatial information in indoor environments is difficult. Given the absence of GNSS (Global Navigation Satellite System) signals, various technologies for indoor positioning, mapping and modeling have been proposed. Related business models for indoor space services, safety, convenience, facility management, and disaster response, moreover, have been suggested. An autonomous scanning system for collection of indoor spatial and environmental data is proposed in this paper. The proposed system can be utilized to collect spatial dimensions suitable for extraction of a two-dimensional indoor drawing and obtainment of spatial imaging as well as indoor environmental data on temperature, humidity and particulate matter. For these operations, the system has two modes, manual and autonomous. The main function of the systems is autonomous mode, and the manual mode is implemented additionally. It can be applied in facilities without infrastructure for indoor data collection, such as for routine indoor data collection purposes, and it can also be used for immediate indoor data collection in cases of emergency (e.g., accidents, disasters).

INDOOR LOCALIZATION FOR 3D MOBILE CADASTRAL MAPPING USING MACHINE LEARNING TECHNIQUES

Abstract. With the rapid global urbanization, several multi-dimensional complex infrastructures have emerged, introducing new challenges in the management of the vertically stratified buildings spaces. 3D indoor cadastral spaces consist a zestful research topic as their complexity and geometry alterations during time, prevents the assignment of the corresponding Rights, Restrictions and Responsibilities (RRR). In the absence of the necessary horizontal spatial data infrastructure/floor plans their determination is weak. In this paper a fit-for-purpose technical framework and a crowdsourced methodology for the implementation of 3D cadastral surveys focused on indoor cadastral spaces, is proposed and presented. As indoor data capturing tool, an open-sourced cadastral mobile application for Android devices, is selected and presented. An Indoor Positioning System based on Bluetooth technology is established while an innovative machine learning architecture is developed, in order to explore its potentials to automatically provide the position of the mobile device within an indoor environment, aiming to add more intelligence to the proposed 3D crowdsourced cadastral framework. A practical experiment for testing the examined technical solution is conducted. The produced results are assessed to be quite promising.

Secure indoor traffic modeling in long‐term evolution femtocell networks using efficient Markov decision process

AbstractMobile users spent most of their time in an indoor environment, therefore it is crucial to develop indoor traffic models for next‐generation networks. The significance of this paper is related to the predominant femtocell technology as it is anticipated to be a very crucial part of the modern mobile networks such as 5G and beyond networks. The Femtocell network can satisfy the increased bandwidth demand for video and data traffic with improved network capacity and security. Indoor data traffic exhibits different characteristics that need to be dealt with distinct criteria of the Poisson process and existing classifying techniques. Indoor traffic models, proposed in the literature do not consider long‐range dependent characteristics for traffic modeling. Moreover, these traffic models have the ability to capture large flows only. In this paper, we have provided an efficient quality of service‐based classifier for downlink transmission in long‐term evolution (LTE)‐femtocell network. Our proposed algorithm models the indoor traffic by Poisson Lomax Burst Process. Modeled traffic streams, after prioritization, are classified using Markov Decision Process (MDP) classifier. Furthermore, a detailed comparison of our proposed MDP algorithm with the state‐of‐the‐art algorithms in the literature has also been presented. The network performance parameters have been assessed in terms of throughput, delay, and jitter. Simulation results depict a significant improvement in throughput in contrast with Round Robin (RR), which is approximately 8%, 6%, 6%, and 7% for voice, video, BE, and BG services, respectively, while modified least weighted delay first (MLWDF) gives least performance among them. MDP also provides an improvement in delay and average jitter of four different traffic flows and mixed traffic users when compared with RR and MLWDF algorithms.

An Optimization Technique of the 3D Indoor Map Data Based on an Improved Octree Structure

The construction and retrieval of indoor maps are important for indoor positioning and navigation. It is necessary to ensure a good user experience while meeting real-time requirements. Unlike outdoor maps, indoor space is limited, and the relationship between indoor objects is complex which would result in an uneven indoor data distribution and close relationship between the data. A data storage model based on the octree scene segmentation structure was proposed in this paper initially. The traditional octree structure data storage model has been improved so that the data could be backtracked. The proposed method will solve the problem of partition lines within the range of the object data and improve the overall storage efficiency. Moreover, a data retrieval algorithm based on octree storage structure was proposed. The algorithm adopts the idea of “searching for a point, points around the searched point are within the searching range.” Combined with the octree neighbor retrieval methods, the closure constraints are added. Experimental results show that using the improved octree storage structure, the retrieval cost is 1/8 of R-tree. However, by using the neighbor retrieval, it improved the search efficiency by about 27% on average. After adding the closure constraint, the retrieval efficiency increases by 25% on average.

3D Geometry-Based Indoor Network Extraction for Navigation Applications Using SFCGAL

This study is focused on indoor navigation network extraction for navigation applications based on available 3D building data and using SFCGAL library, e.g. simple features computational geometry algorithms library. In this study, special attention is given to 3D cadastre and BIM (building information modelling) datasets, which have been used as data sources for 3D geometric indoor modelling. SFCGAL 3D functions are used for the extraction of an indoor network, which has been modelled in the form of indoor connectivity graphs based on 3D geometries of indoor features. The extraction is performed by the integration of extract transform load (ETL) software and the spatial database to support multiple data sources and provide access to SFCGAL functions. With this integrated approach, the current lack of straightforward software support for complex 3D spatial analyses is addressed. Based on the developed methodology, we perform and discuss the extraction of an indoor navigation network from 3D cadastral and BIM data. The efficiency and performance of the network analyses were evaluated using the processing and query execution times. The results show that the proposed methodology for geometry-based navigation network extraction of buildings is efficient and can be used with various types of 3D geometric indoor data.

Deep Spatial–Temporal Model Based Cross-Scene Action Recognition Using Commodity WiFi

With the popularization of Internet-of-Things (IoT) systems, passive action recognition on channel state information (CSI) has attracted much attention. Most conventional work under the machine-learning framework utilizes handcrafted features (e.g., statistic features) that are unable to sufficiently describe the sequence data and heavily rely on designers’ experiences. Therefore, how to automatically learn abundant spatial–temporal information from CSI data is a topic worthy of study. In this article, we propose a deep learning framework that integrates spatial features learned from the convolutional neural network (CNN) into the temporal model multilayer bidirectional long short-term memory (Bi-LSTM). Specifically, CSI streams are segmented into a series of patches, from which spatial features are extracted by our designed CNN structure. Considering long-term dependencies between adjacent sequences, the fully connected layer of CNN for each patch is taken as the Bi-LSTM sequential input to further capture temporal features. Our model is appealing in that it can simultaneously learn temporal dynamics and convolutional perceptual representations. To the best of our knowledge, this is the first work to explore deep spatial–temporal features for CSI-based action recognition. Furthermore, in order to solve the problem that the trained model fully fails with environmental changes, we use the off-the-shelf model as the pretrained model and fine-tune it in the new scenario. The transfer method is able to realize cross-scene action recognition with low computational consumption and satisfactory accuracy. We carry out experiments on indoor data and the experimental results validate the effectiveness of our algorithm.

Indoor multiuser visible light communication systems using Hadamard-coded modulation.

Visible light communications (VLC) is a short-range optical wireless communication technology that uses light-emitting diodes (LEDs) as lighting devices and data transmitters. This paper describes a multiuser VLC system using Hadamard-coded modulation (HCM) for indoor data transmission. Considering the peak transmitted power limit of the LEDs, a DC-reduced HCM (DCR-HCM) is used to reduce the nonlinear clipping distortion. Since the Hadamard codewords have different bandwidth requirements for a given symbol rate, they can be assigned to users with varying hardware capabilities. Optimally assigning codewords to users is found to significantly improve the average throughput, up to twice higher than a random assignment for a typical scenario. When the number of active users is less than the size of the Hadamard matrix used, more than one codeword can be assigned per user, which further improves the throughput. This paper also examines a scenario where multiple lamps in an indoor space transmit the same data. Since the time of arrival for the received signals emitted from different lamps is different, the Hadamard codes received are no longer orthogonal, resulting in multiple access interference and inter-chip interference. The number of acceptable codewords is computed based on the specific interference experienced in different parts of the indoor space. The spatial distribution of the maximum throughput is also simulated, showing that the ratio of the maximum to the minimum data rate can be as high as 10 when considering the entire area of a typical indoor room.This article is part of the theme issue ‘Optical wireless communication’.

Indoor positioning-based mobile resource movement data management system for smart factory operations management

The primary role of modern information and communication technology infrastructures in smart factory is to collect a wide range of digital data from manufacturing resources; however, many manufacturing companies still have significant trouble in collecting data relevant for operations management. Especially, it is difficult to collect data about movement of mobile resources, such as human operators and material handling equipment. Moreover, managers of manufacturing companies often have significant troubles in utilizing the raw data collected by information and communication technology infrastructures due to its complexities and vast amount. To fill these gaps, this article proposes indoor positioning-based mobile resource movement data management system, which can be used to collect and process the mobile resource movement data flexibly. The indoor positioning technologies enable to track the positions of physical objects in real time; however, they generate time series data for 3D coordinates of object position not suitable for practical use. Therefore, this article aims to integrate the indoor positioning technology with a specialized user application, which allows the users to define what kinds of data should be collected and how the raw data should be transformed.

Power performance and thermal operation of organic photovoltaic modules in real operating conditions

AbstractPrinted organic photovoltaics (OPVs) have moved beyond the research laboratory phase and have demonstrated their suitability to be upscaled for industrial manufacture. The improved efficiencies and increased lifetime testify the potential for this technology to compete with crystalline silicon–based technologies under some operating conditions. There are companies capable of producing large‐area OPV modules for outdoor installation. Examples of solar cell production on an industrial scale for energy production have been shown in the last 5 years. In this work, we present the performance data of a large‐area OPV module under operating conditions representative for real outdoor operation. This study focuses on the analysis of the thermal behavior of the module and shows how its performance can be affected by the temperatures it can reach outdoor under natural sunlight. A comparison of outdoor measurements with indoor data measured under simulated sunlight at several temperature‐irradiance combinations is also presented.

The significance of occupancy profiles in determining post retrofit indoor thermal comfort, overheating risk and building energy performance

Recently, retrofit of tower blocks has gained momentum particularly in the UK social housing sector due to the increasing rate of fuel poverty coupled with deteriorating indoor living conditions. However, the process of making improvements to the thermal performance of building envelopes can significantly impact on occupants' thermal comfort, increasing overheating risks with the changing climate and associated heat waves. The first phase of the study evaluated the building energy performance of a 1960s social housing tower block prototype in London, pre-retrofit, where the building simulation model was created and calibrated with monitored indoor data and occupants' interviews. The second research phase, the subject of this paper, uses the model to further investigate the impact of improved thermal insulation of the building envelope, based on U-values prescribed by the UK Building Regulations (Part L1B), on the potential risk of overheating. The study investigates the impact of retrofitting on occupants’ thermal comfort and building energy performance in the current and future climate scenarios (2030, 2050 and 2080). Results confirm that improving the U-value of external walls will significantly reduce the heating energy use by 70% under future climate scenarios while the To increases by 15–17% with U-value of 0.5 W/m2K and 0.3 W/m2K in comparison to the base case. The overall results indicate that the different occupancy patterns adopted in the simulation model have a significant impact on the predicted duration of overheating which will, in turn, have an impact on determining appropriate retrofit strategies to reduce overheating risks.

Cadastral data as a source for 3D indoor modelling

Despite the rapid development of indoor spatial data acquisition technology, there are currently no solutions that enable large-scale indoor spatial data acquisition due to several limiting factors that characterize the indoor space. This fact, together with the rapidly growing need for indoor models, is the main motivation for our research. The focus is on the study of the appropriateness of existing cadastral data for 3D indoor modelling. Within the study, a framework for 3D indoor modelling has been developed, comprising a chain of processes, starting from initial cadastral data and ending with the OGC IndoorGML compliant document. The process chain is divided into three parts, which are described individually and supported by UML activity diagrams. The Slovenian Building Cadastre data represents the basis for the framework design and data assessment. The IndoorGML standard is used for final outputs, as it provides a standardized data model for the representation and exchange of indoor spatial information designed for indoor navigation and location-based services. The data storage options using a spatially enabled database are presented for storing 2D and 3D geometries. The stored data enables fully automatic IndoorGML document generation on request, while also taking advantage of all spatial database functionalities. The proposed approach is software independent and can be implemented with various spatially enabled software packages. In addition to 3D indoor data modelling, the framework represents a comprehensive method for assessing the usability of input data for the purpose of 3D indoor modelling. The assessment is done for the case of the Slovenian Building Cadastre. The assessment of the cadastral data suitability for 3D indoor modelling can be used for decisions regarding future steps towards a multi-purpose 3D real property cadastre. The presented concept can be applied in many countries worldwide that have a similar condominium registration system.

Mobile Light Storage: Make the Light Smarter

Abstract This paper explores a low-cost portable visible light communication (VLC) system to support the increasing needs of lightweight mobile applications. VLC grows rapidly in the past decade for many applications (e.g., indoor data transmission, human sensing, and visual MIMO) due to its RF interference immunity and inherent high security. However, most existing VLC systems heavily rely on fixed infrastructures with less adaptability to emerging lightweight mobile applications. This work proposes Light Storage, a portable VLC system takes the advantage of commercial smartphone flashlights as the transmitter and a solar panel equipped with both data reception and energy harvesting modules as the receiver. Light Storage can achieve concurrent data transmission and energy harvesting from the visible light signals. It develops multi-level light intensity data modulation to increase data throughput and integrates the noise reduction functionality to allow portability under various lighting conditions. The system supports synchronization together with adaptive error correction to overcome both the linear and non-linear signal offsets caused by the low time-control ability from the commercial smartphones. Finally, the energy harvesting capability in Light Storage provides sufficient energy support for efficient short range communication. Light Storage is validated in both indoor and outdoor environments and can achieve over 98% data decoding accuracy, demonstrating the potential as an important alternative to support low-cost and portable short range communication.

基于物联网的室内数据采集监控系统

基于物联网的室内数据采集监控系统

Interference Mitigation Using Angular Diversity Receiver With Efficient Channel Estimation in MIMO VLC

Visible Light Communication (VLC) is a promising solution to meet the ever increasing demand for indoor data connectivity with high bit rates. VLC uses the license-free bands and provides high-speed connections unlike RF wireless communication. However; indoor VLC suffers from performance degradation due to either co-channel interference (CCI) or inter-symbol interference (ISI). In this paper, an interference management scheme is proposed for VLC Multi-Input Multi-Output (MIMO) based systems. The scheme reduces the number of interfered signals by using a constrained field-of-view angular diversity receiver CFOV-ADR and then uses least-square (LS) channel estimation with maximum-likelihood (ML) equalizer to resolve the interfered signals. The proposed interference mitigation scheme using constrained-FOV-ADR with channel estimation IM-CFOV-CE enables frequency reuse of one. The bit-error-rate (BER) is calculated at various room positions with different receiver’s heights and rotation angles. The performance evaluation results reveal that the proposed system can achieve a higher number of downlink channels and superior BER performance than that of without CCI management. Also, the proposed scheme has been compared with Time Division Multiple Access (TDMA) technique, and achieves enhanced performance at all positions and orientations of the ADR.

Adaptive spatial-layout selection for massive multi-color visible light communications.

Massive multi-color visible light communications (mMC-VLC) sufficiently utilizing space and color domain resources is proposed to satisfy high-spectral-efficiency, high-speed, and high-density-coverage requirements of next-generation indoor data connections. However, the gap between the number of LEDs and photodiodes (PDs) and the high correlation among different channels limit the multiplexing of mMC-VLC. Also, the mobility of the receiver is the bottleneck of mMC-VLC. So, adaptive spatial-layout selection (ASLS) is proposed to settle the above problems, which selects $ N $N sets $ n $n-color LEDs from the transmitter to form an approximate optimal closed-circle layout adapting to the receiver position. First, the optimal parameter problems to minimize the ill condition of the activated system under layout constraints of the closed circle and linear types are formulated for a fixed receiver position. Second, to achieve adaptivity, the fitting curves of the optimal layout parameter and $ {D_v} $Dv under both constraints are researched; $ {D_v} $Dv is the vertical distance between the transmitter and receiver planes. Finally, the closest layout-selected principle (CLSP) is proposed to solve the problem of the LEDs perhaps not perfectly forming the optimal parameter layout for mMC-VLC. The bit-error ratio (BER) performances and application scopes of ASLS under both layout constraints are compared to determine that the constraint layout is a closed circle; meanwhile the available maximal $ N $N corresponding to the receiver position is obtained. The optimal parameter of ASLS is linearly related to the receiver position and not related to $ N $N. The ASLS always achieves better BER performance than optical multi-stream spatial modulation.