Multi-Level Indoor Navigation Ontology for High Assurance Location-Based Services

Khruahong, Sanya; Kong, Xiaoying; Sandrasegaran, Kumbesan; Liu, Li

doi:10.1109/hase.2017.9

Cited by 14 publications

(4 citation statements)

References 14 publications

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“…There are three models of ontologies in computer systems: top-level ontologies, lexical ontologies, and domain ontologies. In a different situation, we will applicate different kinds of ontologies to deal with information [16,17,18].…”

Section: Software Development Industry Uses Unified Modelingmentioning

confidence: 99%

Development of Semantic Model of Multi-Level-Building Navigation Using Indoor Ontology and Dijkstra's Algorithm

Yuan

Kong

Fang

et al. 2019

2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)

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Location based services (LBS) can be separated into a number of layers: technology layer, application layer, standard layer, and social-ethical layer. This paper presents an ontology development at standard layer. We developed an ontology to identify and classify indoor semantic information to guide the development of LBS applications for multi-level building navigation. This ontology proposed models of multilevel building properties as classes of building, level, zone, link, node, and coordinate. To apply this ontology, we develop an indoor navigation algorithm using the ontology classes and Dijkstra's algorithm for shortest path in user navigation. A prototype and experiments are implemented to validate this ontology.

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Section: Software Development Industry Uses Unified Modelingmentioning

confidence: 99%

Development of Semantic Model of Multi-Level-Building Navigation Using Indoor Ontology and Dijkstra's Algorithm

Yuan

Kong

Fang

et al. 2019

2019 20th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)

Self Cite

View full text Add to dashboard Cite

show abstract

“…These systems have diverse applications across various domains, including factory logistics, healthcare, asset tracking in office environments [ 1 , 2 ], medical equipment [ 3 ], and patients [ 4 ], as well as monitoring indoor activities and optimizing processes and resource allocations [ 5 ]. They also enable the delivery of location-based services, such as providing recommendations to tourists [ 6 ], assisting customers in navigating in indoor environments [ 7 ], and offering directions in shopping malls [ 8 ]. To meet these demands, extensive research has been conducted, utilizing a range of technologies such as visual [ 9 ], geometric [ 10 , 11 ], ultrasonic [ 12 ], and RF-based sensors [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Cyber-WISE: A Cyber-Physical Deep Wireless Indoor Positioning System and Digital Twin Approach

Karakusak,

Kivrak,

Watson

et al. 2023

Sensors

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In recent decades, there have been significant research efforts focusing on wireless indoor localization systems, with fingerprinting techniques based on received signal strength leading the way. The majority of the suggested approaches require challenging and laborious Wi-Fi site surveys to construct a radio map, which is then utilized to match radio signatures with particular locations. In this paper, a novel next-generation cyber-physical wireless indoor positioning system is presented that addresses the challenges of fingerprinting techniques associated with data collection. The proposed approach not only facilitates an interactive digital representation that fosters informed decision-making through a digital twin interface but also ensures adaptability to new scenarios, scalability, and suitability for large environments and evolving conditions during the process of constructing the radio map. Additionally, it reduces the labor cost and laborious data collection process while helping to increase the efficiency of fingerprint-based positioning methods through accurate ground-truth data collection. This is also convenient for working in remote environments to improve human safety in locations where human access is limited or hazardous and to address issues related to radio map obsolescence. The feasibility of the cyber-physical system design is successfully verified and evaluated with real-world experiments in which a ground robot is utilized to obtain a radio map autonomously in real-time in a challenging environment through an informed decision process. With the proposed setup, the results demonstrate the success of RSSI-based indoor positioning using deep learning models, including MLP, LSTM Model 1, and LSTM Model 2, achieving an average localization error of ≤2.16 m in individual areas. Specifically, LSTM Model 2 achieves an average localization error as low as 1.55 m and 1.97 m with 83.33% and 81.05% of the errors within 2 m for individual and combined areas, respectively. These outcomes demonstrate that the proposed cyber-physical wireless indoor positioning approach, which is based on the application of dynamic Wi-Fi RSS surveying through human feedback using autonomous mobile robots, effectively leverages the precision of deep learning models, resulting in localization performance comparable to the literature. Furthermore, they highlight its potential for suitability for deployment in real-world scenarios and practical applicability.

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“…Serving as a requisite part for the pedestrian of this era, Location-Based Service (LBS) has been widely provided by portable devices [1], due to the miniaturization, low energy, and low cost of Micro-Electro-Mechanical System (MEMS) sensors. Localization is the basic precondition to realize LBS in both outdoor and indoor environments.…”

Section: Introductionmentioning

confidence: 99%

A Step Length Estimation Model of Coefficient Self-Determined Based on Peak-Valley Detection

Zhu

et al. 2020

Journal of Sensors

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Without any preinstalled infrastructure, pedestrian dead reckoning (PDR) is a promising indoor positioning technology for pedestrians carrying portable devices to navigate. Step detection and step length estimation (SLE) are two essential components for the pedestrian navigation based on PDR. To solve the overcounting problem, this study proposes a peak-valley detection method, which can remove the abnormal values effectively. The current step length models mostly depend on individual parameters that need to be predetermined for different users. Based on fuzzy logic (FL), we establish a rule base that can adjust the coefficient in the Weinberg model adaptively for every detected step of various human shapes walking. Specifically, to determine the FL rule base, we collect user acceleration data from 10 volunteers walking under the combination of diverse step length and stride frequency, and each one walks 49 times at all. The experimental results demonstrate that our proposed method adapts to different kinds of persons walking at various step velocities. Peak-valley detection can achieve an average accuracy of 99.77% during 500 steps of free walking. Besides, the average errors of 5 testers are all less than 4 m per 100 m and the smallest one is 1.74 m per 100 m using our coefficient self-determined step length estimation model.

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Multi-Level Indoor Navigation Ontology for High Assurance Location-Based Services

Cited by 14 publications

References 14 publications

Development of Semantic Model of Multi-Level-Building Navigation Using Indoor Ontology and Dijkstra's Algorithm

Development of Semantic Model of Multi-Level-Building Navigation Using Indoor Ontology and Dijkstra's Algorithm

Cyber-WISE: A Cyber-Physical Deep Wireless Indoor Positioning System and Digital Twin Approach

A Step Length Estimation Model of Coefficient Self-Determined Based on Peak-Valley Detection

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