AR Enabled IoT for a Smart and Interactive Environment: A Survey and Future Directions

Jo, Dongsik; Kim, Gerard Jounghyun

doi:10.3390/s19194330

Cited by 61 publications

(36 citation statements)

References 39 publications

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“…User study results demonstrated that for the maintenance purposes, augmenting the view with the actual readings from the sensor could be beneficial to simplify the distinction of the faulty sensors [63]. This is in line with the research of AR potentials in helping to carry out the tasks and interact with IoT objects [69]. The second prototype enriches the actual space with the digital objects that change their properties based on environmental conditions.…”

Section: State Visualizationsupporting

confidence: 63%

“…In addition to the basic dashboard, we have developed AR-based visualization with two prototypes. AR-based solutions promise richer user experience and they are used in various settings, like navigation, tourism, and education [68,69]. The first prototype that we developed demonstrates live sensor measurements when the phone camera is pointing to the particular sensor [63].…”

Section: State Visualizationmentioning

confidence: 99%

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Internet of Things for Smart Spaces: A University Campus Case Study

Gilman

Tamminen

Yasmin

et al. 2020

Sensors

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Advances in technology and data analysis provide rich opportunities for developing intelligent environments assisting their inhabitants, so-called smart environments or smart spaces. Enhanced with technology, sensors, user interfaces, and various applications, such smart spaces are capable of recognizing users and situations they are in, react accordingly, e.g., by providing certain services or changes to the environment itself. Therefore, smart space solutions are gradually coming to different application domains, each with corresponding specific characteristics. In this article, we discuss our experiences and explore the challenges of a long-term real-world Internet of Things (IoT) deployment at a University campus. We demonstrate the technical implementation and data quality issues. We conduct several studies, from data analysis to interaction with space, utilizing the developed infrastructure, and we also share our actions to open the data for education purposes and discuss their outcomes. With this article, we aim to share our experience and provide real-world lessons learned when building an open, multipurpose, publicly used smart space at a University campus.

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Section: State Visualizationsupporting

confidence: 63%

Section: State Visualizationmentioning

confidence: 99%

Internet of Things for Smart Spaces: A University Campus Case Study

Gilman

Tamminen

Yasmin

et al. 2020

Sensors

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“…Firstly, the diversity in the use of AR and the affordance of IoT to provide rich data on agents' contexts form a fertile ground for developing new interaction modalities [16] and, consequently, new MI systems. Secondly, previous research has shown that a complementary combination of AR and IoT enables smart and interactive environments where the agent's interaction with IoT devices can become more intuitive [17], which is essential when designing interaction with complex artifacts and unfamiliar devices [18]. Thirdly, AR and IoT enable otherwise unperceivable information (e.g., readings from sensors, ratings of a service from other users) to be expressed through perceivable modalities (e.g., visualizations, sounds, vibrations) [16].…”

Section: Input Modalitiesmentioning

confidence: 99%

Multimodal Interaction Systems Based on Internet of Things and Augmented Reality: A Systematic Literature Review

2021

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Technology developments have expanded the diversity of interaction modalities that can be used by an agent (either a human or machine) to interact with a computer system. This expansion has created the need for more natural and user-friendly interfaces in order to achieve effective user experience and usability. More than one modality can be provided to an agent for interaction with a system to accomplish this goal, which is referred to as a multimodal interaction (MI) system. The Internet of Things (IoT) and augmented reality (AR) are popular technologies that allow interaction systems to combine the real-world context of the agent and immersive AR content. However, although MI systems have been extensively studied, there are only several studies that reviewed MI systems that used IoT and AR. Therefore, this paper presents an in-depth review of studies that proposed various MI systems utilizing IoT and AR. A total of 23 studies were identified and analyzed through a rigorous systematic literature review protocol. The results of our analysis of MI system architectures, the relationship between system components, input/output interaction modalities, and open research challenges are presented and discussed to summarize the findings and identify future research and development avenues for researchers and MI developers.

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“…To solve this problem, the idea of using another interesting approach in non-model-based techniques, which is based on a learning-based descriptor in simultaneous localization and mapping (SLAM) is proposed. This descriptor can be trained using bootstrapping training method [96]. SLAM is conceptualized in robotics based on the idea that it is possible for a mobile robot to move in a completely unknown environment while mapping and localizing simultaneously [97,123].…”

Section: No-model-based Trackingmentioning

confidence: 99%

A Survey of Marker-Less Tracking and Registration Techniques for Health & Environmental Applications to Augmented Reality and Ubiquitous Geospatial Information Systems

Sadeghi‐Niaraki

Choi

2020

Sensors

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Most existing augmented reality (AR) applications are suitable for cases in which only a small number of real world entities are involved, such as superimposing a character on a single surface. In this case, we only need to calculate pose of the camera relative to that surface. However, when an AR health or environmental application involves a one-to-one relationship between an entity in the real-world and the corresponding object in the computer model (geo-referenced object), we need to estimate the pose of the camera in reference to a common coordinate system for better geo-referenced object registration in the real-world. New innovations in developing cheap sensors, computer vision techniques, machine learning, and computing power have helped to develop applications with more precise matching between a real world and a virtual content. AR Tracking techniques can be divided into two subcategories: marker-based and marker-less approaches. This paper provides a comprehensive overview of marker-less registration and tracking techniques and reviews their most important categories in the context of ubiquitous Geospatial Information Systems (GIS) and AR focusing to health and environmental applications. Basic ideas, advantages, and disadvantages, as well as challenges, are discussed for each subcategory of tracking and registration techniques. We need precise enough virtual models of the environment for both calibrations of tracking and visualization. Ubiquitous GISs can play an important role in developing AR in terms of providing seamless and precise spatial data for outdoor (e.g., environmental applications) and indoor (e.g., health applications) environments.

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AR Enabled IoT for a Smart and Interactive Environment: A Survey and Future Directions

Cited by 61 publications

References 39 publications

Internet of Things for Smart Spaces: A University Campus Case Study

Internet of Things for Smart Spaces: A University Campus Case Study

Multimodal Interaction Systems Based on Internet of Things and Augmented Reality: A Systematic Literature Review

A Survey of Marker-Less Tracking and Registration Techniques for Health & Environmental Applications to Augmented Reality and Ubiquitous Geospatial Information Systems

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