Self-Organizing Distributed Architecture Supporting Dynamic Space Expanding and Reducing in Indoor LBS Environment

Jeong, Seonhyang; Jo, Hyeong Gon; Kang, Soon Ju

doi:10.3390/s150612156

Cited by 6 publications

(5 citation statements)

References 20 publications

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“…Indoor space has been studied for years [4][5][6][7]. In general, there are three kinds of indoor entities in indoor space, including indoor elements, sensor deployment, and moving object data.…”

Section: Preliminariesmentioning

confidence: 99%

Discovering Influential Positions in RFID-Based Indoor Tracking Data

Cui

2020

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The rapid development of indoor localization techniques such as Wi-Fi and RFID makes it possible to obtain users’ position-tracking data in indoor space. Indoor position-tracking data, also known as indoor moving trajectories, offer many new opportunities to mine decision-making knowledge. In this paper, we study the detection of highly influential positions from indoor position-tracking data, e.g., to detect highly influential positions in a business center, or to detect the hottest shops in a shopping mall according to users’ indoor position-tracking data. We first describe three baseline solutions to this problem, which are count-based, density-based, and duration-based algorithms. Then, motivated by the H-index for evaluating the influence of an author or a journal in academia, we propose a new algorithm called H-Count, which evaluates the influence of an indoor position similarly to the H-index. We further present an improvement of the H-Count by taking a filtering step to remove unqualified position-tracking records. This is based on the observation that many visits to a position such as a gate are meaningless for the detection of influential indoor positions. Finally, we simulate 100 moving objects in a real building deployed with 94 RFID readers over 30 days to generate 223,564 indoor moving trajectories, and conduct experiments to compare our proposed H-Count and H-Count* with three baseline algorithms. The results show that H-Count outperforms all baselines and H-Count* can further improve the F-measure of the H-Count by 113% on average.

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Section: Preliminariesmentioning

confidence: 99%

Discovering Influential Positions in RFID-Based Indoor Tracking Data

Cui

2020

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“…The LAH is an indoor location-based service hub with an overlay network. It is autonomously serviced without direct manipulation [ 13 , 14 , 15 ], and it recognizes the surrounding environment, providing services via autonomous collaboration among hubs without a central server. For example, in Figure 1 b, the doctor requests location-based services, such as finding or scheduling a hospital equipment near the current location.…”

Section: Related Researchmentioning

confidence: 99%

Opportunistic and Location-Based Collaboration Architecture among Mobile Assets and Fixed Manufacturing Processes

Kwon

Park

et al. 2018

Sensors

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Research into integrating the concept of the internet of things (IoT) into smart factories has accelerated, leading to the emergence of various smart factory solutions. Most ideas, however, focus on the automation and integration of processes in factory, rather than organic cooperation among mobile assets (e.g., the workers and manufactured products) and fixed manufacturing equipment (e.g., press molds, computer numerical controls, painting). Additionally, it is difficult to apply smart factory and IoT designs to analog factories, because such a factory would require the integration of mobile assets and smart manufacturing processes. Thus, existing analog factories remain intact and smart factories are newly constructed. To overcome this disparity and to make analog factories compatible with smart technologies and IoT, we propose the opportunistic and location-based collaboration architecture (OLCA) platform, which allows for smart devices to be attached to workers, products, and facilities to enable the collaboration of location and event information in devices. Using this system, we can monitor workers’ positions and production processes in real-time to help prevent dangerous situations and better understand product movement. We evaluate the proposed OLCA platform’s performance while using a simple smart factory scenario, thus confirming its suitability.

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“…A self-organizing middleware platform [ 4 , 5 ] is an indoor location-based service platform based on an overlay network and is autonomously serviced without direct manipulation, such as the initial settings required by the users. In addition, it is a platform that recognizes the surrounding environment and provides optimal indoor location-based services through autonomous collaboration between nodes without a central server.…”

Section: Related Researchmentioning

confidence: 99%

“…To solve these problems, we have previously proposed a fully distributed self-organizing middleware platform [ 4 , 5 ] without a central server. As shown in Figure 1 b, this study autonomously groups the mobile nodes based on their location and shows neighboring communication through the representative nodes of the group without a central server.…”

Section: Introductionmentioning

confidence: 99%

Self-Organizing Peer-To-Peer Middleware for Healthcare Monitoring in Real-Time

Kim

Kang

2017

Sensors

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As the number of elderly persons with chronic illnesses increases, a new public infrastructure for their care is becoming increasingly necessary. In particular, technologies that can monitoring bio-signals in real-time have been receiving significant attention. Currently, most healthcare monitoring services are implemented by wireless carrier through centralized servers. These services are vulnerable to data concentration because all data are sent to a remote server. To solve these problems, we propose self-organizing P2P middleware for healthcare monitoring that enables a real-time multi bio-signal streaming without any central server by connecting the caregiver and care recipient. To verify the performance of the proposed middleware, we evaluated the monitoring service matching time based on a monitoring request. We also confirmed that it is possible to provide an effective monitoring service by evaluating the connectivity between Peer-to-Peer and average jitter.

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Self-Organizing Distributed Architecture Supporting Dynamic Space Expanding and Reducing in Indoor LBS Environment

Cited by 6 publications

References 20 publications

Discovering Influential Positions in RFID-Based Indoor Tracking Data

Discovering Influential Positions in RFID-Based Indoor Tracking Data

Opportunistic and Location-Based Collaboration Architecture among Mobile Assets and Fixed Manufacturing Processes

Self-Organizing Peer-To-Peer Middleware for Healthcare Monitoring in Real-Time

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