실내공간에서 계층 구조를 이용한 K-익명화

With recent progress of mobile devices and indoor positioning technologies, it becomes possible to provide location-based services in indoor space as well as outdoor space. It is in a seamless way between indoor and outdoor spaces or in an independent way only for indoor space. However, we cannot simply apply spatial models developed for outdoor space to indoor space due to their differences. For example, coordinate reference systems are employed to indicate a specific position in outdoor space, while the location in indoor space is rather specified by cell number such as room number. Unlike outdoor space, the distance between two points in indoor space is not determined by the length of the straight line but the constraints given by indoor components such as walls, stairs, and doors. For this reason, we need to establish a new framework for indoor space from fundamental theoretical basis, indoor spatial data models, and information systems to store, manage, and analyse indoor spatial data. In order to provide this framework, an international standard, called IndoorGML has been developed and published by OGC (Open Geospatial Consortium). This standard is based on a cellular notion of space, which considers an indoor space as a set of non-overlapping cells. It consists of two types of modules; core module and extension module. While core module consists of four basic conceptual and implementation modeling components (geometric model for cell, topology between cells, semantic model of cell, and multi-layered space model), extension modules may be defined on the top of the core module to support an application area. As the first version of the standard, we provide an extension for indoor navigation.

Yards of schools and educational environments while having different abilities, are considered as an effective factor in education and learning. But in nowadays schools of Iran, the yards are generally free of educational role, while traditional schools in combination with outdoor space and using the yard have previously had a dynamic function. This research studies on the role of these spaces and manner of their relationships with indoor spaces in traditional and contemporary schools of Iran in addition to the effect of outdoor spaces of school as an effective factor on learning improvement in children. This article has been fulfilled based on a Contents Analysis research methodology, and the data has been collected based on documentary and field studies. The results of the research indicate that outdoor spaces of schools consisting of natural and artificial landscapes will have important role on children training and learning through providing the possibility of active partnership, social partnership, deep understanding and conception, natural growth and readiness, decrease of stress and producing interest and motivation. Also, green spaces facilitate the children’s learning process through providing a suitable location for different domains of education such as: experience of environmental sciences, creativity and presentation of artistic aspects. In this way, we can improve the designing operation for children’s training spaces through putting into operation of the direct (spatial relationship without mediator) and indirect (situation of mediator spaces such as public and private semi-outdoor or public indoor or private spaces) spatial relationships between indoor and outdoor spaces. One of the other factors for improvement of schools efficiency is establishment of different states of relationship such as availability, visual and audio relationships between indoor and outdoor educational spaces.

The purpose of this study is to propose the environmental planning guidelines of elementary school settings for a crime safety. The research methodologies To achieve the goal, the literature review analysis and the survey were used as main research methodologies. The survey is organized as follows. First, elementary education facilities were divided into 20 spaces based on the major space. Second, after analyzing the domestic and foreign CPTED Guidelines, elements of environmental planning were classified to fit in the space. Based on this, the expert survey was conducted. The results of this study are as follows; First, it is necessary to consider specific places such as 'toilets', 'parking lot', 'in-between space', 'main access road', 'sub-access road' and 'harmful facilities around school' for safer school environment. Second, it is significant to plan 'equipment facilities' and 'outdoor space in the school setting' with priority for elementary education environment. Third, environmental planning elements for safer elementary schools can be classified into 9 factors(types) including 'natural surveillance planning', 'territoriality reinforcement planning', 'mechanical surveillance planning', 'access control planning' and 'neighborhood reinforcement planning'. Forth, regarding 'indoor space', crime-free elementary school environment can be build through 'natural surveillance planning' and 'territoriality reinforcement planning'. Finally, regarding 'outdoor space', the crime can be prevented through 'natural surveillance planning' and 'access control planning'.

Abstract. With the fast development of urbanization, the complexity of built environments has dramatically increased, driving a need for assistance in seamless indoor-outdoor navigation. This requires integration of spatial information of indoor and outdoor environments from heterogeneous data sources. While outdoor road network data is largely available from many sources (such as OpenStreetMap), indoor spatial information is either inexistent or is inconsistently represented using several different standards. Among these standards, IndoorGML is a well-developed standard with the focus on indoor location-based services. This standard has already been accepted by Open Geospatial Consortium (OGC) and is now under active development. Although in IndoorGML some mechanisms have been defined to enable integration of indoor and outdoor networks, there is still a lack of concrete guidelines for determination of indoor-outdoor connections. It also lacks solid scientific foundations and efficient tools to extract the connecting nodes and edges that link indoor and outdoor spaces. To address this gap, in this study we focus on the connection of indoor and outdoor spaces and aim to provide a tool, which can automatically construct navigation graphs of the indoor-outdoor transitional space to support seamless integration of indoor-outdoor navigation. To this end, voxel-based modeling approaches are used to model the connecting space between indoor and outdoor environments. Based on Python, we develop the intended tool, which can generate voxel models from point clouds, identify navigable space by taking into account the characteristics of agents (such as pedestrians, wheelchairs, and vehicles), and automatically build navigation graphs linking IndoorGML networks with outdoor street networks. It is expected that the methodology and tools developed from this project will benefit the IndoorGML ecosystem and greatly advance the capability of IndoorGML in representing navigable space to support location-based services.

PurposeWith the rapid development of the indoor spaces positioning technologies such as the radio-frequency identification (RFID), Bluetooth and WI-FI, the locations of indoor spatial objects (static or moving) constitute an important foundation for a variety of applications. However, there are many challenges and limitations associated with the structuring and querying of spatial objects in indoor spaces. The purpose of this study is to address the current trends, limitations and future challenges associated with the structuring and querying of spatial objects in indoor spaces. Also it addresses the related features of indoor spaces such as indoor structures, positioning technologies and others.Design/methodology/approachIn this paper, the author focuses on understanding the aspects and challenges of spatial database managements in indoor spaces. The author explains the differences between indoor spaces and outdoor spaces. Also examines the issues pertaining to indoor spaces positioning and the impact of different shapes and structures within these spaces. In addition, the author considers the varieties of spatial queries that relate specifically to indoor spaces.FindingsMost of the research on data management in indoor spaces does not consider the issues and the challenges associated with indoor positioning such as the overlapping of Wi-Fi. The future trend of the indoor spaces includes included different shapes of indoors beside the current 2D indoor spaces on which the majority of the data structures and query processing for spatial objects have focused on. The diversities of the indoor environments features such as directed floors, multi-floors cases should be considered and studied. Furthermore, indoor environments include many special queries besides the common ones queries that used in outdoor spaces such as KNN, range and temporal queries. These special queries need to be considered in data management and querying of indoor environments.Originality/valueTo the best of the author’s knowledge, this paper successfully addresses the current trends, limitations and future challenges associated with the structuring and querying of spatial objects in indoor spaces.

Smart phones and Location Based Services (LBSs) are getting very popular in recent years. To this end, locating the positions of smart phones becomes inevitable. To date, there exist some systems in some specific areas. For instance, GPS can be used only in the outdoor space. However, people always live in large scale environments like campus, urban and tourist areas, including both indoor and outdoor space. For large scale environment, a single location technique is not always available everywhere. Therefore, we propose a heterogeneous algorithm which combines GPS, WiFi and Internet Of Things (IOT) localizations. We also introduce the localization possibility for each method to estimate the most (possible) one. Besides, the more reliable nearby users provide their information, the further enhancement of the accuracy will get. By measuring the relative WiFi signal strength, it helps especially those users who have no any available methods. We call this concept cooperative localization. Finally, we evaluate the accuracy of our algorithms by simulation. Because signal strength fluctuates from minute to minute, we measure empirical data and put into the simulator to do our experiments. Finally, we also verify that our idea can be implemented on smart phones and work feasibly.

Fungi are one of the bioaerosols in indoor air of hospitals. They have adverse effects on staff and patients. The aim of this study was to investigate the effects of three incubation temperature on the density and composition of airborne fungi in an indoor and outdoor space of hospital. Sabouraud dextrose agar was used for culture the fungi. For improvement of aseptic properties, chloramphenicol was added to this medium. The density of airborne fungi was less than 282CFU/m3. The highest density was detected in emergency room and the lowest of them was in neonatal intensive care unit (NICU) and operation room (OR). Results showed that fungi levels at 25°C were higher than 37 and 15°C (p = 0.006). In addition, ten different genera of fungi were identified in all departments. The predominant fungi were Fusarium spp., Penicillium spp., Paecilomyces spp., and Aspergillus niger. Moreover, the density and trend of distribution of Fusaruim spp. in the indoor space was directivity to outdoor space by ventilation system. The present study has provided that incubation temperature had effect on airborne fungi remarkably. We are suggested that more studies would be conducted on incubation temperature and other ambient factors on airborne fungi.

대부분의 멀티미디어 데이터는 위치정보를 가지고 있다. 따라서 각 멀티미디어 데이터에 적절한 위치정보를 추가하면, 다양한 용도로 활용이 가능하다. 특히 멀티미디어의 위치검색이 다양한 검색조건으로 제공될 수 있다. 지금까지 멀티미디어의 위치는 주로 실외공간의 (x, y, z) 좌표공간을 기반으로 정의된다. 최근 실내공간 정보기술이 발달하면서 실외뿐 아니라, 실내공간에서도 다양한 위치기반 서비스가 가능하며, 멀티미디어의 실내공간 안에서의 위치를 정의할 수 있게 되었다. 그러나 실내공간의 위치는 실외공간의 위치참조체계와 다르다. 예를 들어, 실내공간에서 위치를 지정할 때는 좌표를 이용하지 않고, 층과 방의 번호를 이용한다. 방 번호와 같은 기호를 이용하여 위치를 지정하는 공간을 기호공간이라고 한다. 본 논문에서는 기호공간을 기반으로 실내공간에서 만들어진 멀티미디어 자료의 위치를 지정하는 방법을 제안한다. 이 방법은 특히 현재 진행 중인 OGC의 실내공간정보 표준인 IndoorGML에서 제시하는 데이터모델을 이용한다. 따라서 본 방법은 실내공간에서 만들어진 멀티미디어의 위치를 효과적으로 표현하며, 동시에 여러 시스템이나 서비스간의 호환성을 높이는데 유용하다. Most multimedia contains location information whether they are implicit or explicitly, and which are very useful for several purposes. In particular, we may use location information in defining query conditions to retrieve relevant multimedia. For this reason, a number of works have been done to organize and retrieve geo-referenced multimedia data. However, they mostly focus on outdoor space where position is identified by (x, y, z) coordinates. In this paper, we focus on multimedia in an alternative space, indoor space, which differs from outdoor space in several aspects. First indoor space is considered as symbolic space, where location is identified by a symbolic code such as room number rather than coordinates. Second, topological information is a crucial element in providing indoor spatial information services. Third, indoor space is in more micro-scale than outdoor space, which influences on determining the visibility of cameras. Based on these different characteristics of indoor space, we survey the requirements of management systems of indoor geo-referenced multimedia. Then we propose a geo-coding scheme for multimedia in indoor space as an extension of IndoorGML, an OGC(Open Geospatial Consortium) candidate standard for indoor spatial information. We also present a prototype system called, IngC (INdoor Geo-Coding) developed to store and manage indoor geo-referenced multimedia.

With the development of indoor positioning methods, such as Wi-Fi positioning, geomagnetic sensor positioning, Ultra-Wideband positioning, and pedestrian dead reckoning, the area of location-based services (LBS) is expanding from outdoor to indoor spaces. LBS refers to the geographic location information of moving objects to provide the desired services. Most Wi-Fi-based indoor positioning methods provide two-dimensional (2D) or three-dimensional (3D) coordinates in 1–5 m of accuracy on average approximately. However, many applications of indoor LBS are targeted to specific spaces such as rooms, corridors, stairs, etc. Thus, they require determining a service space from a coordinate in indoor spaces. In this paper, we propose a map matching method to assign an indoor position to a unit space a subdivision of an indoor space, called USMM (Unit Space Map Matching). Map matching is a commonly used localization improvement method that utilizes spatial constraints. We consider the topological information between unit spaces and moving objects’ probabilistic properties, compared to existing room-level mappings based on sensor signals, especially received signal strength-based fingerprinting. The proposed method has the advantage of calculating the probability even if there is only one input trajectory. Last, we analyze the accuracy and performance of the proposed USMM methods by extensive experiments in real and synthetic environments. The experimental results show that our methods bring a significant improvement when the accuracy level of indoor positioning is low. In experiments, the room-level location accuracy improves by almost 30% and 23% with real and synthetic data, respectively. We conclude that USMM methods are helpful to correct valid room-level locations from given positioning locations.

Location-based services (LBSs) have entered into people’s daily lives with the evolution of telecommunication technologies. LBSs are the services that consider the locations of users to provide tailored information. We study a variety of location-based queries in urban environments. Mainly, we focus on processing such queries in the indoor space motivated by the abundance of applications of indoor LBSs and lack of existing studies supporting indoor spatial queries. Although the spatial query processing has been extensively studied for outdoor space, these techniques are not efficient when applied to indoor venues due to inherent differences in the topology of indoor space.

Different indoor representation methods have been studied for their ability to provide indoor location-based services (LBS). Among them, omnidirectional imaging is one of the most typical and simple methods for representing an indoor space. However, a georeferenced omnidirectional image cannot be used for simple attribute searches, spatial queries, and spatial awareness analyses. To perform these functions, topological data are needed to define the features of and spatial relationships among spatial objects including indoor spaces as well as facilities like CCTV cameras considered in patrol service applications. Therefore, this study proposes an indoor space application data model for an indoor patrol service that can implement functions suited to linking indoor space data and service objects. In order to do this, the study presents a method for linking data between omnidirectional images representing indoor spaces and topological data on indoor spaces based on the concept of IndoorGML. Also, we conduct an experimental implementation of the integrated 3D indoor navigation model for patrol service using GIS data. Based on the results, we evaluate the benefits of using such a 3D data fusion method that integrates omnidirectional images with vector-based topological data models based on IndoorGML for providing indoor LBS in built environments.

Influence of architectural design on indoor environment in apartment buildings in Havana

With the development of sensing technologies, various spatial applications have been expanding into indoor spaces. For smooth spatial services, grasping indoor space information is most essential task. However, the indoor spaces is not only becoming increasingly complex, but also frequently changed than outdoor spaces. This makes it hard to provide an accurate location based service in an indoor space. This paper propose a way of managing a dynamic indoor environment by defining a multi-layered indoor model in terms of an object mobility. It allows an indoor space to be managed more elaborate and realistic than up-to-date indoor models which only consider an indoor floor plan. We firstly define a classification of indoor objects based on their characteristic to frequently change location, and propose three-layers indoor model followed by the classified objects with its mobility. Secondly, we design and implement an autonomous scanning system to understand changes of indoor situation quickly and automatically. The system is made up of a combination of IoT devices, including a programmable robot, lidar scanner and single-board computer. Finally, we demonstrate an implementation of the system with constructing the proposed model from a real indoor environment.

Indoor spaces accommodate large parts of people's life. The increasing availability of indoor positioning, driven by technologies like Wi-Fi, RFID, and Bluetooth, enables a variety of indoor location-based services (LBSs). Efficient indoor distance-aware queries on indoor moving objects play an important role in supporting and boosting such LBSs. However, the distance-aware query evaluation on indoor moving objects is challenging because: (1) indoor spaces are characterized by many special entities and thus render distance calculation very complex; (2) the limitations of indoor positioning technologies create inherent uncertainties in indoor moving objects data. In this paper, we propose a complete set of techniques for efficient distance-aware queries on indoor moving objects. We define and categorize the indoor distances in relation to indoor uncertain objects, and derive different distance bounds that can facilitate query evaluation. Existing works often assume indoor floor plans are static, and require extensive pre-computation on indoor topologies. In contrast, we design a composite index scheme that integrates indoor geometries, indoor topologies, as well as indoor uncertain objects, and thus supports indoor distance-aware queries efficiently without time-consuming and volatile distance computation. We design algorithms for range query and k nearest neighbor query on indoor moving objects. The results of extensive experimental studies demonstrate that our proposals are efficient and scalable in evaluating distance-aware queries over indoor moving objects.

The increasing availability of indoor positioning, driven by techniques like RFID, Bluetooth, and smart phones, enables a variety of indoor location-based services (LBSs). Efficient queries based on semantic-constraint in indoor spaces play an important role in supporting and boosting LBSs. However, the existing indoor index techniques cannot support these semantic constraints-based queries. To solve this problem, this paper addresses the challenge of indexing moving objects in indoor spaces, in which both the moving objects and the indoor environments include semantic meanings. We present an indoor semantic-based model, which gives the formal description of semantics of indoor cells and moving objects. Then, a new semantic-based index is proposed for indoor environment, which can support queries under semantic constraint. In addition, we develop efficient algorithms for two new queries, which are the nearest neighbor query based on semantic constraints for static indoor cells and moving objects, respectively. The conducted experiment demonstrates that the proposed index structure is effective, robust, and efficient.