Design and implementation of a Bluetooth signal strength based location sensing system

Bandara, Udana; Hasegawa, Makoto; Inoue, Masato; Morikawa, Hiroyuki; Aoyama, T.

doi:10.1109/rawcon.2004.1389140

Cited by 70 publications

(44 citation statements)

References 2 publications

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“…In addition to outdoor localization services being well served by a Global Positioning System (GPS), many localization techniques and sophisticated schemes have been developed utilizing deployed sensors [19], Radio Frequency Identification (RFID) [20], Bluetooth [21], infrared transceivers [22], etc. However, considering the deploying and maintenance costs, these methods are inferior to the Local Area Networks (LAN) positioning approaches for the faking of large pedestrian detection.…”

Section: Lan Mobile Localizationmentioning

confidence: 99%

Identification and Prediction of Large Pedestrian Flow in Urban Areas Based on a Hybrid Detection Approach

et al. 2016

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Abstract:Recently, population density has grown quickly with the increasing acceleration of urbanization. At the same time, overcrowded situations are more likely to occur in populous urban areas, increasing the risk of accidents. This paper proposes a synthetic approach to recognize and identify the large pedestrian flow. In particular, a hybrid pedestrian flow detection model was constructed by analyzing real data from major mobile phone operators in China, including information from smartphones and base stations (BS). With the hybrid model, the Log Distance Path Loss (LDPL) model was used to estimate the pedestrian density from raw network data, and retrieve information with the Gaussian Progress (GP) through supervised learning. Temporal-spatial prediction of the pedestrian data was carried out with Machine Learning (ML) approaches. Finally, a case study of a real Central Business District (CBD) scenario in Shanghai, China using records of millions of cell phone users was conducted. The results showed that the new approach significantly increases the utility and capacity of the mobile network. A more reasonable overcrowding detection and alert system can be developed to improve safety in subway lines and other hotspot landmark areas, such as the Bundle, People's Square or Disneyland, where a large passenger flow generally exists.

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Section: Lan Mobile Localizationmentioning

confidence: 99%

Identification and Prediction of Large Pedestrian Flow in Urban Areas Based on a Hybrid Detection Approach

et al. 2016

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“…This is a relatively low cost compared to mTag. However, the signal strengths vary depending on environmental factors such as obstacles and therefore can only provide an approximation of distance [22].…”

Section: Review Of Existing Near Lbs (Nlbs) Technologies For Indmentioning

confidence: 99%

Passive Indoor Positioning System (PIPS) Using Near Field Communication (NFC) Technology

Tang¹,

Tok²,

Hanneghan³

2015

2015 International Conference on Developments of E-Systems Engineering (DeSE)

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Abstract-Travel can be an enjoyable experience but it can also be stressful when one is unable to get to the destination in timely manner. Satellite navigation systems (satnav) such as the ubiquitous Global Positioning System (GPS) provide an aid to locating unfamiliar places without hassle. However, the effectiveness of satnav stops at the doorstep of the building due to its requirement for line of sight with orbiting satellites. Within a large complex building, navigation typically relies on building signage, information from kiosks and getting assistance from information desks. The advancement of mobile devices and wireless technology offer an interesting proposition for the development of indoor positioning systems. In this paper, we propose a passive indoor positioning system to provide navigational aid and discuss findings from our pilot experiment using NFC technology.

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“…Indoor localization techniques have employed proximity sensors such as RF-tag [8], existing RF network infrastructures such as Bluetooth [9], [10], ultrasonic and sound techniques [11]- [16], visible light [17], [18], motion sensors [19], [20], magnetic field exploitation [21], [22] …”

Section: Introductionmentioning

confidence: 99%

A Joint Indoor WLAN Localization and Outlier Detection Scheme Using LASSO and Elastic-Net Optimization Techniques

Khalajmehrabadi

Gatsis

Pack

et al. 2017

IEEE Trans. on Mobile Comput.

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Abstract-In this paper, we introduce two indoor Wireless Local Area Network (WLAN) positioning methods using augmented sparse recovery algorithms. These schemes render a sparse user's position vector, and in parallel, minimize the distance between the online measurement and radio map. The overall localization scheme for both methods consists of three steps: 1) coarse localization, obtained from comparing the online measurements with clustered radio map. A novel graph-based method is proposed to cluster the offline fingerprints. In the online phase, a Region Of Interest (ROI) is selected within which we search for the user's location; 2) Access Point (AP) selection; and 3) fine localization through the novel sparse recovery algorithms. Since the online measurements are subject to inordinate measurement readings, called outliers, the sparse recovery methods are modified in order to jointly estimate the outliers and user's position vector. The outlier detection procedure identifies the APs whose readings are either not available or erroneous. The proposed localization methods have been tested with Received Signal Strength (RSS) measurements in a typical office environment and the results show that they can localize the user with significantly high accuracy and resolution which is superior to the results from competing WLAN fingerprinting localization methods.

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Design and implementation of a Bluetooth signal strength based location sensing system

Cited by 70 publications

References 2 publications

Identification and Prediction of Large Pedestrian Flow in Urban Areas Based on a Hybrid Detection Approach

Identification and Prediction of Large Pedestrian Flow in Urban Areas Based on a Hybrid Detection Approach

Passive Indoor Positioning System (PIPS) Using Near Field Communication (NFC) Technology

A Joint Indoor WLAN Localization and Outlier Detection Scheme Using LASSO and Elastic-Net Optimization Techniques

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